fix: Fixed an issue where quotes in messages could not be displayed #2677 (#2683)

### What problem does this PR solve?

fix: Fixed an issue where quotes in messages could not be displayed
#2677

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):

CONTRIBUTING.md

@@ -1,16 +1,10 @@
----
-sidebar_position: 0
-slug: /contribution_guidelines
----
-
 # Contribution guidelines
 
-Thanks for wanting to contribute to RAGFlow. This document offers guidlines and major considerations for submitting your contributions. 
+This document offers guidlines and major considerations for submitting your contributions to RAGFlow.
 
 - To report a bug, file a [GitHub issue](https://github.com/infiniflow/ragflow/issues/new/choose) with us.
 - For further questions, you can explore existing discussions or initiate a new one in [Discussions](https://github.com/orgs/infiniflow/discussions).
 
-
 ## What you can contribute
 
 The list below mentions some contributions you can make, but it is not a complete list.
@@ -27,7 +21,7 @@ The list below mentions some contributions you can make, but it is not a complet
 ### General workflow
 
 1. Fork our GitHub repository.
-2. Clone your fork to your local machine: 
+2. Clone your fork to your local machine:
 `git clone git@github.com:<yourname>/ragflow.git`
 3. Create a local branch: 
 `git checkout -b my-branch`
@@ -39,14 +33,16 @@ The list below mentions some contributions you can make, but it is not a complet
 
 ### Before filing a PR
 
-- Consider splitting a large PR into multiple smaller, standalone PRs to keep a traceable development history. 
+- Consider splitting a large PR into multiple smaller, standalone PRs to keep a traceable development history.
 - Ensure that your PR addresses just one issue, or keep any unrelated changes small.
 - Add test cases when contributing new features. They demonstrate that your code functions correctly and protect against potential issues from future changes.
-### Describing your PR 
+
+### Describing your PR
 
 - Ensure that your PR title is concise and clear, providing all the required information.
-- Refer to a corresponding GitHub issue in your PR description if applicable. 
+- Refer to a corresponding GitHub issue in your PR description if applicable.
 - Include sufficient design details for *breaking changes* or *API changes* in your description.
 
 ### Reviewing & merging a PR
-- Ensure that your PR passes all Continuous Integration (CI) tests before merging it.
+
+Ensure that your PR passes all Continuous Integration (CI) tests before merging it.

Dockerfile

@@ -1,23 +1,108 @@
-FROM infiniflow/ragflow-base:v2.0
-USER  root
+# base stage
+FROM ubuntu:24.04 AS base
+USER root
+
+ENV LIGHTEN=0
 
 WORKDIR /ragflow
 
-ADD ./web ./web
-RUN cd ./web && npm i --force && npm run build
+RUN rm -f /etc/apt/apt.conf.d/docker-clean \
+    && echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
 
-ADD ./api ./api
-ADD ./conf ./conf
-ADD ./deepdoc ./deepdoc
-ADD ./rag ./rag
-ADD ./agent ./agent
-ADD ./graphrag ./graphrag
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+    apt update && apt-get --no-install-recommends install -y ca-certificates
+
+# if you located in China, you can use tsinghua mirror to speed up apt
+RUN  sed -i 's|http://archive.ubuntu.com|https://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list.d/ubuntu.sources
+
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+    apt update && apt install -y curl libpython3-dev nginx libglib2.0-0 libglx-mesa0 pkg-config libicu-dev libgdiplus \
+    && rm -rf /var/lib/apt/lists/* \
+    && curl -sSL https://install.python-poetry.org | python3 -
+
+RUN curl -o libssl1.deb http://archive.ubuntu.com/ubuntu/pool/main/o/openssl1.0/libssl1.0.0_1.0.2n-1ubuntu5_amd64.deb && dpkg -i libssl1.deb && rm -f libssl1.deb
+
+ENV PYTHONDONTWRITEBYTECODE=1 DOTNET_SYSTEM_GLOBALIZATION_INVARIANT=1
+
+# Configure Poetry
+ENV POETRY_NO_INTERACTION=1
+ENV POETRY_VIRTUALENVS_IN_PROJECT=true
+ENV POETRY_VIRTUALENVS_CREATE=true
+ENV POETRY_REQUESTS_TIMEOUT=15
+
+# builder stage
+FROM base AS builder
+USER root
+
+WORKDIR /ragflow
+
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+    apt update && apt install -y nodejs npm cargo && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY web web
+RUN cd web && npm i --force && npm run build
+
+# install dependencies from poetry.lock file
+COPY pyproject.toml poetry.toml poetry.lock ./
+
+RUN --mount=type=cache,target=/root/.cache/pypoetry,sharing=locked \
+    if [ "$LIGHTEN" -eq 0 ]; then \
+        /root/.local/bin/poetry install --sync --no-cache --no-root --with=full; \
+    else \
+        /root/.local/bin/poetry install --sync --no-cache --no-root; \
+    fi
+
+# production stage
+FROM base AS production
+USER root
+
+WORKDIR /ragflow
+
+# Install python packages' dependencies
+# cv2 requires libGL.so.1
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+    apt update && apt install -y --no-install-recommends nginx libgl1 vim less && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY web web
+COPY api api
+COPY conf conf
+COPY deepdoc deepdoc
+COPY rag rag
+COPY agent agent
+COPY graphrag graphrag
+COPY pyproject.toml poetry.toml poetry.lock ./
+
+# Copy models downloaded via download_deps.py
+RUN mkdir -p /ragflow/rag/res/deepdoc /root/.ragflow
+RUN --mount=type=bind,source=huggingface.co,target=/huggingface.co \
+    tar --exclude='.*' -cf - \
+        /huggingface.co/InfiniFlow/text_concat_xgb_v1.0 \
+        /huggingface.co/InfiniFlow/deepdoc \
+        | tar -xf - --strip-components=3 -C /ragflow/rag/res/deepdoc
+RUN --mount=type=bind,source=huggingface.co,target=/huggingface.co \
+    tar -cf - \
+        /huggingface.co/BAAI/bge-large-zh-v1.5 \
+        /huggingface.co/BAAI/bge-reranker-v2-m3 \
+        /huggingface.co/maidalun1020/bce-embedding-base_v1 \
+        /huggingface.co/maidalun1020/bce-reranker-base_v1 \
+        | tar -xf - --strip-components=2 -C /root/.ragflow
+
+# Copy compiled web pages
+COPY --from=builder /ragflow/web/dist /ragflow/web/dist
+
+# Copy Python environment and packages
+ENV VIRTUAL_ENV=/ragflow/.venv
+COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
+ENV PATH="${VIRTUAL_ENV}/bin:/root/.local/bin:${PATH}"
+
+# Download nltk data
+RUN python3 -m nltk.downloader wordnet punkt punkt_tab
 
 ENV PYTHONPATH=/ragflow/
-ENV HF_ENDPOINT=https://hf-mirror.com
 
-ADD docker/entrypoint.sh ./entrypoint.sh
-ADD docker/.env ./
+COPY docker/entrypoint.sh ./entrypoint.sh
 RUN chmod +x ./entrypoint.sh
 
-ENTRYPOINT ["./entrypoint.sh"]
+ENTRYPOINT ["./entrypoint.sh"]

Dockerfile.arm

@@ -1,34 +0,0 @@
-FROM python:3.11
-USER  root
-
-WORKDIR /ragflow
-
-COPY requirements_arm.txt /ragflow/requirements.txt
-RUN pip install -i https://mirrors.aliyun.com/pypi/simple/ --default-timeout=1000 -r requirements.txt &&\
-python -c "import nltk;nltk.download('punkt');nltk.download('wordnet')"
-
-RUN apt-get update && \
-    apt-get install -y curl gnupg && \
-    rm -rf /var/lib/apt/lists/*
-
-RUN curl -sL https://deb.nodesource.com/setup_20.x | bash - && \
-    apt-get install -y --fix-missing nodejs nginx ffmpeg libsm6 libxext6 libgl1
-
-ADD ./web ./web
-RUN cd ./web && npm i --force && npm run build
-
-ADD ./api ./api
-ADD ./conf ./conf
-ADD ./deepdoc ./deepdoc
-ADD ./rag ./rag
-ADD ./agent ./agent
-ADD ./graphrag ./graphrag
-
-ENV PYTHONPATH=/ragflow/
-ENV HF_ENDPOINT=https://hf-mirror.com
-
-ADD docker/entrypoint.sh ./entrypoint.sh
-ADD docker/.env ./
-RUN chmod +x ./entrypoint.sh
-
-ENTRYPOINT ["./entrypoint.sh"]

Dockerfile.cuda

@@ -1,27 +0,0 @@
-FROM infiniflow/ragflow-base:v2.0
-USER  root
-
-WORKDIR /ragflow
-
-## for cuda > 12.0
-RUN pip uninstall -y onnxruntime-gpu
-RUN pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
-
-
-ADD ./web ./web
-RUN cd ./web && npm i --force && npm run build
-
-ADD ./api ./api
-ADD ./conf ./conf
-ADD ./deepdoc ./deepdoc
-ADD ./rag ./rag
-ADD ./agent ./agent
-ADD ./graphrag ./graphrag
-
-ENV PYTHONPATH=/ragflow/
-ENV HF_ENDPOINT=https://hf-mirror.com
-
-ADD docker/entrypoint.sh ./entrypoint.sh
-RUN chmod +x ./entrypoint.sh
-
-ENTRYPOINT ["./entrypoint.sh"]

Dockerfile.scratch

@@ -1,56 +0,0 @@
-FROM ubuntu:22.04
-USER root
-
-WORKDIR /ragflow
-
-RUN apt-get update && apt-get install -y wget curl build-essential libopenmpi-dev
-
-RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \
-    bash ~/miniconda.sh -b -p /root/miniconda3 && \
-    rm ~/miniconda.sh && ln -s /root/miniconda3/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
-    echo ". /root/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrc && \
-    echo "conda activate base" >> ~/.bashrc
-
-ENV PATH /root/miniconda3/bin:$PATH
-
-RUN conda create -y --name py11 python=3.11
-
-ENV CONDA_DEFAULT_ENV py11
-ENV CONDA_PREFIX /root/miniconda3/envs/py11
-ENV PATH $CONDA_PREFIX/bin:$PATH
-
-RUN curl -sL https://deb.nodesource.com/setup_14.x | bash -
-RUN apt-get install -y nodejs
-
-RUN apt-get install -y nginx
-
-ADD ./web ./web
-ADD ./api ./api
-ADD ./conf ./conf
-ADD ./deepdoc ./deepdoc
-ADD ./rag ./rag
-ADD ./requirements.txt ./requirements.txt
-ADD ./agent ./agent
-ADD ./graphrag ./graphrag
-
-RUN apt install openmpi-bin openmpi-common libopenmpi-dev
-ENV LD_LIBRARY_PATH /usr/lib/x86_64-linux-gnu/openmpi/lib:$LD_LIBRARY_PATH
-RUN rm /root/miniconda3/envs/py11/compiler_compat/ld
-RUN cd ./web && npm i --force && npm run build
-RUN conda run -n py11 pip install -i https://mirrors.aliyun.com/pypi/simple/ -r ./requirements.txt
-
-RUN apt-get update && \
-    apt-get install -y libglib2.0-0 libgl1-mesa-glx && \
-    rm -rf /var/lib/apt/lists/*
-
-RUN conda run -n py11 pip install -i https://mirrors.aliyun.com/pypi/simple/ ollama
-RUN conda run -n py11 python -m nltk.downloader punkt
-RUN conda run -n py11 python -m nltk.downloader wordnet
-
-ENV PYTHONPATH=/ragflow/
-ENV HF_ENDPOINT=https://hf-mirror.com
-
-ADD docker/entrypoint.sh ./entrypoint.sh
-RUN chmod +x ./entrypoint.sh
-
-ENTRYPOINT ["./entrypoint.sh"]

Dockerfile.scratch.oc9

@@ -1,58 +1,58 @@
-FROM opencloudos/opencloudos:9.0
-USER root
-
-WORKDIR /ragflow
-
-RUN dnf update -y && dnf install -y wget curl gcc-c++ openmpi-devel
-
-RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \
-    bash ~/miniconda.sh -b -p /root/miniconda3 && \
-    rm ~/miniconda.sh && ln -s /root/miniconda3/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
-    echo ". /root/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrc && \
-    echo "conda activate base" >> ~/.bashrc
-
-ENV PATH /root/miniconda3/bin:$PATH
-
-RUN conda create -y --name py11 python=3.11
-
-ENV CONDA_DEFAULT_ENV py11
-ENV CONDA_PREFIX /root/miniconda3/envs/py11
-ENV PATH $CONDA_PREFIX/bin:$PATH
-
-# RUN curl -sL https://rpm.nodesource.com/setup_14.x | bash -
-RUN dnf install -y nodejs
-
-RUN dnf install -y nginx
-
-ADD ./web ./web
-ADD ./api ./api
-ADD ./conf ./conf
-ADD ./deepdoc ./deepdoc
-ADD ./rag ./rag
-ADD ./requirements.txt ./requirements.txt
-ADD ./agent ./agent
-ADD ./graphrag ./graphrag
-
-RUN dnf install -y openmpi openmpi-devel python3-openmpi
-ENV C_INCLUDE_PATH /usr/include/openmpi-x86_64:$C_INCLUDE_PATH
-ENV LD_LIBRARY_PATH /usr/lib64/openmpi/lib:$LD_LIBRARY_PATH
-RUN rm /root/miniconda3/envs/py11/compiler_compat/ld
-RUN cd ./web && npm i --force && npm run build
-RUN conda run -n py11 pip install $(grep -ivE "mpi4py" ./requirements.txt) # without mpi4py==3.1.5
-RUN conda run -n py11 pip install redis
-
-RUN dnf update -y && \
-    dnf install -y glib2 mesa-libGL && \
-    dnf clean all
-
-RUN conda run -n py11 pip install ollama
-RUN conda run -n py11 python -m nltk.downloader punkt
-RUN conda run -n py11 python -m nltk.downloader wordnet
-
-ENV PYTHONPATH=/ragflow/
-ENV HF_ENDPOINT=https://hf-mirror.com
-
-ADD docker/entrypoint.sh ./entrypoint.sh
-RUN chmod +x ./entrypoint.sh
-
-ENTRYPOINT ["./entrypoint.sh"]
+FROM opencloudos/opencloudos:9.0
+USER root
+
+WORKDIR /ragflow
+
+RUN dnf update -y && dnf install -y wget curl gcc-c++ openmpi-devel
+
+RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \
+    bash ~/miniconda.sh -b -p /root/miniconda3 && \
+    rm ~/miniconda.sh && ln -s /root/miniconda3/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
+    echo ". /root/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrc && \
+    echo "conda activate base" >> ~/.bashrc
+
+ENV PATH /root/miniconda3/bin:$PATH
+
+RUN conda create -y --name py11 python=3.11
+
+ENV CONDA_DEFAULT_ENV py11
+ENV CONDA_PREFIX /root/miniconda3/envs/py11
+ENV PATH $CONDA_PREFIX/bin:$PATH
+
+# RUN curl -sL https://rpm.nodesource.com/setup_14.x | bash -
+RUN dnf install -y nodejs
+
+RUN dnf install -y nginx
+
+ADD ./web ./web
+ADD ./api ./api
+ADD ./conf ./conf
+ADD ./deepdoc ./deepdoc
+ADD ./rag ./rag
+ADD ./requirements.txt ./requirements.txt
+ADD ./agent ./agent
+ADD ./graphrag ./graphrag
+
+RUN dnf install -y openmpi openmpi-devel python3-openmpi
+ENV C_INCLUDE_PATH /usr/include/openmpi-x86_64:$C_INCLUDE_PATH
+ENV LD_LIBRARY_PATH /usr/lib64/openmpi/lib:$LD_LIBRARY_PATH
+RUN rm /root/miniconda3/envs/py11/compiler_compat/ld
+RUN cd ./web && npm i --force && npm run build
+RUN conda run -n py11 pip install $(grep -ivE "mpi4py" ./requirements.txt) # without mpi4py==3.1.5
+RUN conda run -n py11 pip install redis
+
+RUN dnf update -y && \
+    dnf install -y glib2 mesa-libGL && \
+    dnf clean all
+
+RUN conda run -n py11 pip install ollama
+RUN conda run -n py11 python -m nltk.downloader punkt
+RUN conda run -n py11 python -m nltk.downloader wordnet
+
+ENV PYTHONPATH=/ragflow/
+ENV HF_ENDPOINT=https://hf-mirror.com
+
+ADD docker/entrypoint.sh ./entrypoint.sh
+RUN chmod +x ./entrypoint.sh
+
+ENTRYPOINT ["./entrypoint.sh"]

Dockerfile.slim

@@ -0,0 +1,101 @@
+# base stage
+FROM ubuntu:24.04 AS base
+USER root
+
+ENV LIGHTEN=1
+
+WORKDIR /ragflow
+
+RUN rm -f /etc/apt/apt.conf.d/docker-clean \
+    && echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
+
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+    apt update && apt-get --no-install-recommends install -y ca-certificates
+
+# if you located in China, you can use tsinghua mirror to speed up apt
+RUN  sed -i 's|http://archive.ubuntu.com|https://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list.d/ubuntu.sources
+
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+    apt update && apt install -y curl libpython3-dev nginx libglib2.0-0 libglx-mesa0 pkg-config libicu-dev libgdiplus \
+    && rm -rf /var/lib/apt/lists/* \
+    && curl -sSL https://install.python-poetry.org | python3 -
+
+RUN curl -o libssl1.deb http://archive.ubuntu.com/ubuntu/pool/main/o/openssl1.0/libssl1.0.0_1.0.2n-1ubuntu5_amd64.deb && dpkg -i libssl1.deb && rm -f libssl1.deb
+
+ENV PYTHONDONTWRITEBYTECODE=1 DOTNET_SYSTEM_GLOBALIZATION_INVARIANT=1
+
+# Configure Poetry
+ENV POETRY_NO_INTERACTION=1
+ENV POETRY_VIRTUALENVS_IN_PROJECT=true
+ENV POETRY_VIRTUALENVS_CREATE=true
+ENV POETRY_REQUESTS_TIMEOUT=15
+
+# builder stage
+FROM base AS builder
+USER root
+
+WORKDIR /ragflow
+
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+    apt update && apt install -y nodejs npm cargo && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY web web
+RUN cd web && npm i --force && npm run build
+
+# install dependencies from poetry.lock file
+COPY pyproject.toml poetry.toml poetry.lock ./
+
+RUN --mount=type=cache,target=/root/.cache/pypoetry,sharing=locked \
+    if [ "$LIGHTEN" -eq 0 ]; then \
+        /root/.local/bin/poetry install --sync --no-cache --no-root --with=full; \
+    else \
+        /root/.local/bin/poetry install --sync --no-cache --no-root; \
+    fi
+
+# production stage
+FROM base AS production
+USER root
+
+WORKDIR /ragflow
+
+# Install python packages' dependencies
+# cv2 requires libGL.so.1
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+    apt update && apt install -y --no-install-recommends nginx libgl1 vim less && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY web web
+COPY api api
+COPY conf conf
+COPY deepdoc deepdoc
+COPY rag rag
+COPY agent agent
+COPY graphrag graphrag
+COPY pyproject.toml poetry.toml poetry.lock ./
+
+# Copy models downloaded via download_deps.py
+RUN mkdir -p /ragflow/rag/res/deepdoc /root/.ragflow
+RUN --mount=type=bind,source=huggingface.co,target=/huggingface.co \
+    tar --exclude='.*' -cf - \
+        /huggingface.co/InfiniFlow/text_concat_xgb_v1.0 \
+        /huggingface.co/InfiniFlow/deepdoc \
+        | tar -xf - --strip-components=3 -C /ragflow/rag/res/deepdoc
+
+# Copy compiled web pages
+COPY --from=builder /ragflow/web/dist /ragflow/web/dist
+
+# Copy Python environment and packages
+ENV VIRTUAL_ENV=/ragflow/.venv
+COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
+ENV PATH="${VIRTUAL_ENV}/bin:/root/.local/bin:${PATH}"
+
+# Download nltk data
+RUN python3 -m nltk.downloader wordnet punkt punkt_tab
+
+ENV PYTHONPATH=/ragflow/
+
+COPY docker/entrypoint.sh ./entrypoint.sh
+RUN chmod +x ./entrypoint.sh
+
+ENTRYPOINT ["./entrypoint.sh"]

README.md

@@ -1,348 +1,312 @@
-<div align="center">
-<a href="https://demo.ragflow.io/">
-<img src="web/src/assets/logo-with-text.png" width="520" alt="ragflow logo">
-</a>
-</div>
-
-<p align="center">
-  <a href="./README.md">English</a> |
-  <a href="./README_zh.md">简体中文</a> |
-  <a href="./README_ja.md">日本語</a>
-</p>
-
-<p align="center">
-    <a href="https://github.com/infiniflow/ragflow/releases/latest">
-        <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
-    </a>
-    <a href="https://demo.ragflow.io" target="_blank">
-        <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99"></a>
-    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.9.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.9.0"></a>
-    <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
-    <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license">
-  </a>
-</p>
-
-<h4 align="center">
-  <a href="https://ragflow.io/docs/dev/">Document</a> |
-  <a href="https://github.com/infiniflow/ragflow/issues/162">Roadmap</a> |
-  <a href="https://twitter.com/infiniflowai">Twitter</a> |
-  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
-  <a href="https://demo.ragflow.io">Demo</a>
-</h4>
-
-<details open>
-<summary></b>📕 Table of Contents</b></summary>
-  
-- 💡 [What is RAGFlow?](#-what-is-ragflow)
-- 🎮 [Demo](#-demo)
-- 📌 [Latest Updates](#-latest-updates)
-- 🌟 [Key Features](#-key-features)
-- 🔎 [System Architecture](#-system-architecture)
-- 🎬 [Get Started](#-get-started)
-- 🔧 [Configurations](#-configurations)
-- 🛠️ [Build from source](#-build-from-source)
-- 🛠️ [Launch service from source](#-launch-service-from-source)
-- 📚 [Documentation](#-documentation)
-- 📜 [Roadmap](#-roadmap)
-- 🏄 [Community](#-community)
-- 🙌 [Contributing](#-contributing)
-
-</details>
-
-## 💡 What is RAGFlow?
-
-[RAGFlow](https://ragflow.io/) is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data.
-
-## 🎮 Demo
-
-Try our demo at [https://demo.ragflow.io](https://demo.ragflow.io).
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/7248/2f6baa3e-1092-4f11-866d-36f6a9d075e5" width="1200"/>
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/b083d173-dadc-4ea9-bdeb-180d7df514eb" width="1200"/>
-</div>
-
-
-## 🔥 Latest Updates
-
-- 2024-08-02 Supports GraphRAG inspired by [graphrag](https://github.com/microsoft/graphrag) , and mind map.
-  
-- 2024-07-23 Supports audio file parsing.
-
-- 2024-07-21 Supports more LLMs (LocalAI, OpenRouter, StepFun, and Nvidia).
-
-- 2024-07-18 Adds more components (Wikipedia, PubMed, Baidu, and Duckduckgo) to the graph.
-
-- 2024-07-08 Supports workflow based on [Graph](./graph/README.md).
-- 2024-06-27 Supports Markdown and Docx in the Q&A parsing method. 
-- 2024-06-27 Supports extracting images from Docx files. 
-- 2024-06-27 Supports extracting tables from Markdown files.
-- 2024-06-06 Supports [Self-RAG](https://huggingface.co/papers/2310.11511), which is enabled by default in dialog settings.
-- 2024-05-30 Integrates [BCE](https://github.com/netease-youdao/BCEmbedding) and [BGE](https://github.com/FlagOpen/FlagEmbedding) reranker models.
-- 2024-05-23 Supports [RAPTOR](https://arxiv.org/html/2401.18059v1) for better text retrieval.
-- 2024-05-15 Integrates OpenAI GPT-4o.
-
-## 🌟 Key Features
-
-### 🍭 **"Quality in, quality out"**
-
-- [Deep document understanding](./deepdoc/README.md)-based knowledge extraction from unstructured data with complicated formats.
-- Finds "needle in a data haystack" of literally unlimited tokens.
-
-### 🍱 **Template-based chunking**
-
-- Intelligent and explainable.
-- Plenty of template options to choose from.
-
-### 🌱 **Grounded citations with reduced hallucinations**
-
-- Visualization of text chunking to allow human intervention.
-- Quick view of the key references and traceable citations to support grounded answers.
-
-### 🍔 **Compatibility with heterogeneous data sources**
-
-- Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.
-
-### 🛀 **Automated and effortless RAG workflow**
-
-- Streamlined RAG orchestration catered to both personal and large businesses.
-- Configurable LLMs as well as embedding models.
-- Multiple recall paired with fused re-ranking.
-- Intuitive APIs for seamless integration with business.
-
-## 🔎 System Architecture
-
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
-</div>
-
-## 🎬 Get Started
-
-### 📝 Prerequisites
-
-- CPU >= 4 cores
-- RAM >= 16 GB
-- Disk >= 50 GB
-- Docker >= 24.0.0 & Docker Compose >= v2.26.1
-  > If you have not installed Docker on your local machine (Windows, Mac, or Linux), see [Install Docker Engine](https://docs.docker.com/engine/install/).
-
-### 🚀 Start up the server
-
-1. Ensure `vm.max_map_count` >= 262144:
-
-   > To check the value of `vm.max_map_count`:
-   >
-   > ```bash
-   > $ sysctl vm.max_map_count
-   > ```
-   >
-   > Reset `vm.max_map_count` to a value at least 262144 if it is not.
-   >
-   > ```bash
-   > # In this case, we set it to 262144:
-   > $ sudo sysctl -w vm.max_map_count=262144
-   > ```
-   >
-   > This change will be reset after a system reboot. To ensure your change remains permanent, add or update the `vm.max_map_count` value in **/etc/sysctl.conf** accordingly:
-   >
-   > ```bash
-   > vm.max_map_count=262144
-   > ```
-
-2. Clone the repo:
-
-   ```bash
-   $ git clone https://github.com/infiniflow/ragflow.git
-   ```
-
-3. Build the pre-built Docker images and start up the server:
-
-   > Running the following commands automatically downloads the *dev* version RAGFlow Docker image. To download and run a specified Docker version, update `RAGFLOW_VERSION` in **docker/.env** to the intended version, for example `RAGFLOW_VERSION=v0.8.0`, before running the following commands.
-
-   ```bash
-   $ cd ragflow/docker
-   $ chmod +x ./entrypoint.sh
-   $ docker compose up -d
-   ```
-   
-
-   > The core image is about 9 GB in size and may take a while to load.
-
-4. Check the server status after having the server up and running:
-
-   ```bash
-   $ docker logs -f ragflow-server
-   ```
-
-   _The following output confirms a successful launch of the system:_
-
-   ```bash
-       ____                 ______ __
-      / __ \ ____ _ ____ _ / ____// /____  _      __
-     / /_/ // __ `// __ `// /_   / // __ \| | /| / /
-    / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ /
-   /_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/
-                 /____/
-
-    * Running on all addresses (0.0.0.0)
-    * Running on http://127.0.0.1:9380
-    * Running on http://x.x.x.x:9380
-    INFO:werkzeug:Press CTRL+C to quit
-   ```
-   > If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a `network anomaly` error because, at that moment, your RAGFlow may not be fully initialized.  
-
-5. In your web browser, enter the IP address of your server and log in to RAGFlow.
-   > With the default settings, you only need to enter `http://IP_OF_YOUR_MACHINE` (**sans** port number) as the default HTTP serving port `80` can be omitted when using the default configurations.
-6. In [service_conf.yaml](./docker/service_conf.yaml), select the desired LLM factory in `user_default_llm` and update the `API_KEY` field with the corresponding API key.
-
-   > See [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) for more information.
-
-   _The show is now on!_
-
-## 🔧 Configurations
-
-When it comes to system configurations, you will need to manage the following files:
-
-- [.env](./docker/.env): Keeps the fundamental setups for the system, such as `SVR_HTTP_PORT`, `MYSQL_PASSWORD`, and `MINIO_PASSWORD`.
-- [service_conf.yaml](./docker/service_conf.yaml): Configures the back-end services.
-- [docker-compose.yml](./docker/docker-compose.yml): The system relies on [docker-compose.yml](./docker/docker-compose.yml) to start up.
-
-You must ensure that changes to the [.env](./docker/.env) file are in line with what are in the [service_conf.yaml](./docker/service_conf.yaml) file.
-
-> The [./docker/README](./docker/README.md) file provides a detailed description of the environment settings and service configurations, and you are REQUIRED to ensure that all environment settings listed in the [./docker/README](./docker/README.md) file are aligned with the corresponding configurations in the [service_conf.yaml](./docker/service_conf.yaml) file.
-
-To update the default HTTP serving port (80), go to [docker-compose.yml](./docker/docker-compose.yml) and change `80:80` to `<YOUR_SERVING_PORT>:80`.
-
-> Updates to all system configurations require a system reboot to take effect:
->
-> ```bash
-> $ docker-compose up -d
-> ```
-
-## 🛠️ Build from source
-
-To build the Docker images from source:
-
-```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
-$ docker build -t infiniflow/ragflow:dev .
-$ cd ragflow/docker
-$ chmod +x ./entrypoint.sh
-$ docker compose up -d
-```
-
-## 🛠️ Launch service from source
-
-To launch the service from source:
-
-1. Clone the repository: 
-
-   ```bash
-   $ git clone https://github.com/infiniflow/ragflow.git
-   $ cd ragflow/
-   ```
-
-2. Create a virtual environment, ensuring that Anaconda or Miniconda is installed:
-
-   ```bash
-   $ conda create -n ragflow python=3.11.0
-   $ conda activate ragflow
-   $ pip install -r requirements.txt
-   ```
-   
-   ```bash
-   # If your CUDA version is higher than 12.0, run the following additional commands:
-   $ pip uninstall -y onnxruntime-gpu
-   $ pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
-   ```
-
-3. Copy the entry script and configure environment variables:
-
-   ```bash
-   # Get the Python path:
-   $ which python
-   # Get the ragflow project path:
-   $ pwd
-   ```
-   
-   ```bash
-   $ cp docker/entrypoint.sh .
-   $ vi entrypoint.sh
-   ```
-
-   ```bash
-   # Adjust configurations according to your actual situation (the following two export commands are newly added):
-   # - Assign the result of `which python` to `PY`.
-   # - Assign the result of `pwd` to `PYTHONPATH`.
-   # - Comment out `LD_LIBRARY_PATH`, if it is configured.
-   # - Optional: Add Hugging Face mirror.
-   PY=${PY}
-   export PYTHONPATH=${PYTHONPATH}
-   export HF_ENDPOINT=https://hf-mirror.com
-   ```
-
-4. Launch the third-party services (MinIO, Elasticsearch, Redis, and MySQL):
-
-   ```bash
-   $ cd docker
-   $ docker compose -f docker-compose-base.yml up -d 
-   ```
-
-5. Check the configuration files, ensuring that:
-
-   - The settings in **docker/.env** match those in **conf/service_conf.yaml**. 
-   - The IP addresses and ports for related services in **service_conf.yaml** match the local machine IP and ports exposed by the container.
-
-6. Launch the RAGFlow backend service:
-
-   ```bash
-   $ chmod +x ./entrypoint.sh
-   $ bash ./entrypoint.sh
-   ```
-
-7. Launch the frontend service:
-
-   ```bash
-   $ cd web
-   $ npm install --registry=https://registry.npmmirror.com --force
-   $ vim .umirc.ts
-   # Update proxy.target to http://127.0.0.1:9380
-   $ npm run dev 
-   ```
-
-8. Deploy the frontend service:
-
-   ```bash
-   $ cd web
-   $ npm install --registry=https://registry.npmmirror.com --force
-   $ umi build
-   $ mkdir -p /ragflow/web
-   $ cp -r dist /ragflow/web
-   $ apt install nginx -y
-   $ cp ../docker/nginx/proxy.conf /etc/nginx
-   $ cp ../docker/nginx/nginx.conf /etc/nginx
-   $ cp ../docker/nginx/ragflow.conf /etc/nginx/conf.d
-   $ systemctl start nginx
-   ```
-
-## 📚 Documentation
-
-- [Quickstart](https://ragflow.io/docs/dev/)
-- [User guide](https://ragflow.io/docs/dev/category/user-guides)
-- [References](https://ragflow.io/docs/dev/category/references)
-- [FAQ](https://ragflow.io/docs/dev/faq)
-
-## 📜 Roadmap
-
-See the [RAGFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162)
-
-## 🏄 Community
-
-- [Discord](https://discord.gg/4XxujFgUN7)
-- [Twitter](https://twitter.com/infiniflowai)
-- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
-
-## 🙌 Contributing
-
-RAGFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community. If you would like to be a part, review our [Contribution Guidelines](./docs/references/CONTRIBUTING.md) first.
+<div align="center">
+<a href="https://demo.ragflow.io/">
+<img src="web/src/assets/logo-with-text.png" width="520" alt="ragflow logo">
+</a>
+</div>
+
+<p align="center">
+  <a href="./README.md">English</a> |
+  <a href="./README_zh.md">简体中文</a> |
+  <a href="./README_ja.md">日本語</a> |
+  <a href="./README_ko.md">한국어</a>
+</p>
+
+<p align="center">
+    <a href="https://github.com/infiniflow/ragflow/releases/latest">
+        <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
+    </a>
+    <a href="https://demo.ragflow.io" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99"></a>
+    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.12.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.12.0"></a>
+    <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
+    <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license">
+  </a>
+</p>
+
+<h4 align="center">
+  <a href="https://ragflow.io/docs/dev/">Document</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/162">Roadmap</a> |
+  <a href="https://twitter.com/infiniflowai">Twitter</a> |
+  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
+  <a href="https://demo.ragflow.io">Demo</a>
+</h4>
+
+<details open>
+<summary></b>📕 Table of Contents</b></summary>
+  
+- 💡 [What is RAGFlow?](#-what-is-ragflow)
+- 🎮 [Demo](#-demo)
+- 📌 [Latest Updates](#-latest-updates)
+- 🌟 [Key Features](#-key-features)
+- 🔎 [System Architecture](#-system-architecture)
+- 🎬 [Get Started](#-get-started)
+- 🔧 [Configurations](#-configurations)
+- 🪛 [Build the docker image without embedding models](#-build-the-docker-image-without-embedding-models)
+- 🪚 [Build the docker image including embedding models](#-build-the-docker-image-including-embedding-models)
+- 🔨 [Launch service from source for development](#-launch-service-from-source-for-development)
+- 📚 [Documentation](#-documentation)
+- 📜 [Roadmap](#-roadmap)
+- 🏄 [Community](#-community)
+- 🙌 [Contributing](#-contributing)
+
+</details>
+
+## 💡 What is RAGFlow?
+
+[RAGFlow](https://ragflow.io/) is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data.
+
+## 🎮 Demo
+
+Try our demo at [https://demo.ragflow.io](https://demo.ragflow.io).
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/infiniflow/ragflow/assets/7248/2f6baa3e-1092-4f11-866d-36f6a9d075e5" width="1200"/>
+<img src="https://github.com/infiniflow/ragflow/assets/12318111/b083d173-dadc-4ea9-bdeb-180d7df514eb" width="1200"/>
+</div>
+
+
+## 🔥 Latest Updates
+
+- 2024-09-29 Optimizes multi-round conversations.
+- 2024-09-13 Adds search mode for knowledge base Q&A.
+- 2024-09-09 Adds a medical consultant agent template.
+- 2024-08-22 Support text to SQL statements through RAG.
+- 2024-08-02 Supports GraphRAG inspired by [graphrag](https://github.com/microsoft/graphrag) and mind map.
+- 2024-07-23 Supports audio file parsing.
+- 2024-07-08 Supports workflow based on [Graph](./agent/README.md).
+- 2024-06-27 Supports Markdown and Docx in the Q&A parsing method, extracting images from Docx files, extracting tables from Markdown files.
+- 2024-05-23 Supports [RAPTOR](https://arxiv.org/html/2401.18059v1) for better text retrieval.
+
+
+## 🌟 Key Features
+
+### 🍭 **"Quality in, quality out"**
+
+- [Deep document understanding](./deepdoc/README.md)-based knowledge extraction from unstructured data with complicated formats.
+- Finds "needle in a data haystack" of literally unlimited tokens.
+
+### 🍱 **Template-based chunking**
+
+- Intelligent and explainable.
+- Plenty of template options to choose from.
+
+### 🌱 **Grounded citations with reduced hallucinations**
+
+- Visualization of text chunking to allow human intervention.
+- Quick view of the key references and traceable citations to support grounded answers.
+
+### 🍔 **Compatibility with heterogeneous data sources**
+
+- Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.
+
+### 🛀 **Automated and effortless RAG workflow**
+
+- Streamlined RAG orchestration catered to both personal and large businesses.
+- Configurable LLMs as well as embedding models.
+- Multiple recall paired with fused re-ranking.
+- Intuitive APIs for seamless integration with business.
+
+## 🔎 System Architecture
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
+</div>
+
+## 🎬 Get Started
+
+### 📝 Prerequisites
+
+- CPU >= 4 cores
+- RAM >= 16 GB
+- Disk >= 50 GB
+- Docker >= 24.0.0 & Docker Compose >= v2.26.1
+  > If you have not installed Docker on your local machine (Windows, Mac, or Linux), see [Install Docker Engine](https://docs.docker.com/engine/install/).
+
+### 🚀 Start up the server
+
+1. Ensure `vm.max_map_count` >= 262144:
+
+   > To check the value of `vm.max_map_count`:
+   >
+   > ```bash
+   > $ sysctl vm.max_map_count
+   > ```
+   >
+   > Reset `vm.max_map_count` to a value at least 262144 if it is not.
+   >
+   > ```bash
+   > # In this case, we set it to 262144:
+   > $ sudo sysctl -w vm.max_map_count=262144
+   > ```
+   >
+   > This change will be reset after a system reboot. To ensure your change remains permanent, add or update the `vm.max_map_count` value in **/etc/sysctl.conf** accordingly:
+   >
+   > ```bash
+   > vm.max_map_count=262144
+   > ```
+
+2. Clone the repo:
+
+   ```bash
+   $ git clone https://github.com/infiniflow/ragflow.git
+   ```
+
+3. Build the pre-built Docker images and start up the server:
+   > Running the following commands automatically downloads the *dev* version RAGFlow Docker image. To download and run a specified Docker version, update `RAGFLOW_IMAGE` in **docker/.env** to the intended version, for example `RAGFLOW_IMAGE=infiniflow/ragflow:v0.12.0`, before running the following commands.
+
+   ```bash
+   $ cd ragflow/docker
+   $ docker compose up -d
+   ```
+
+   > The core image is about 9 GB in size and may take a while to load.
+
+4. Check the server status after having the server up and running:
+
+   ```bash
+   $ docker logs -f ragflow-server
+   ```
+
+   _The following output confirms a successful launch of the system:_
+
+   ```bash
+        ____   ___    ______ ______ __               
+       / __ \ /   |  / ____// ____// /____  _      __
+      / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+     / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ / 
+    /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/  
+ 
+
+    * Running on all addresses (0.0.0.0)
+    * Running on http://127.0.0.1:9380
+    * Running on http://x.x.x.x:9380
+    INFO:werkzeug:Press CTRL+C to quit
+   ```
+   > If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a `network abnormal` error because, at that moment, your RAGFlow may not be fully initialized.  
+
+5. In your web browser, enter the IP address of your server and log in to RAGFlow.
+   > With the default settings, you only need to enter `http://IP_OF_YOUR_MACHINE` (**sans** port number) as the default HTTP serving port `80` can be omitted when using the default configurations.
+6. In [service_conf.yaml](./docker/service_conf.yaml), select the desired LLM factory in `user_default_llm` and update the `API_KEY` field with the corresponding API key.
+
+   > See [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) for more information.
+
+   _The show is on!_
+
+## 🔧 Configurations
+
+When it comes to system configurations, you will need to manage the following files:
+
+- [.env](./docker/.env): Keeps the fundamental setups for the system, such as `SVR_HTTP_PORT`, `MYSQL_PASSWORD`, and `MINIO_PASSWORD`.
+- [service_conf.yaml](./docker/service_conf.yaml): Configures the back-end services.
+- [docker-compose.yml](./docker/docker-compose.yml): The system relies on [docker-compose.yml](./docker/docker-compose.yml) to start up.
+
+You must ensure that changes to the [.env](./docker/.env) file are in line with what are in the [service_conf.yaml](./docker/service_conf.yaml) file.
+
+> The [./docker/README](./docker/README.md) file provides a detailed description of the environment settings and service configurations, and you are REQUIRED to ensure that all environment settings listed in the [./docker/README](./docker/README.md) file are aligned with the corresponding configurations in the [service_conf.yaml](./docker/service_conf.yaml) file.
+
+To update the default HTTP serving port (80), go to [docker-compose.yml](./docker/docker-compose.yml) and change `80:80` to `<YOUR_SERVING_PORT>:80`.
+
+Updates to the above configurations require a reboot of all containers to take effect:
+
+> ```bash
+> $ docker-compose -f docker/docker-compose.yml up -d
+> ```
+
+## 🪛 Build the Docker image without embedding models
+
+This image is approximately 1 GB in size and relies on external LLM and embedding services.
+
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+pip3 install huggingface-hub
+python3 download_deps.py
+docker build -f Dockerfile.slim -t infiniflow/ragflow:dev-slim .
+```
+
+## 🪚 Build the Docker image including embedding models
+
+This image is approximately 9 GB in size. As it includes embedding models, it relies on external LLM services only.  
+
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+pip3 install huggingface-hub
+python3 download_deps.py
+docker build -f Dockerfile -t infiniflow/ragflow:dev .
+```
+
+## 🔨 Launch service from source for development
+
+1. Install Poetry, or skip this step if it is already installed:  
+   ```bash
+   curl -sSL https://install.python-poetry.org | python3 -
+   ```
+
+2. Clone the source code and install Python dependencies:  
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   export POETRY_VIRTUALENVS_CREATE=true POETRY_VIRTUALENVS_IN_PROJECT=true
+   ~/.local/bin/poetry install --sync --no-root # install RAGFlow dependent python modules
+   ```
+
+3. Launch the dependent services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:  
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
+   ```
+
+   Add the following line to `/etc/hosts` to resolve all hosts specified in **docker/service_conf.yaml** to `127.0.0.1`:  
+   ```
+   127.0.0.1       es01 mysql minio redis
+   ```  
+   In **docker/service_conf.yaml**, update mysql port to `5455` and es port to `1200`, as specified in **docker/.env**.
+
+4. If you cannot access HuggingFace, set the `HF_ENDPOINT` environment variable to use a mirror site:  
+ 
+   ```bash
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
+
+5. Launch backend service:  
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+
+6. Install frontend dependencies:  
+   ```bash
+   cd web
+   npm install --force
+   ```  
+7. Configure frontend to update `proxy.target` in **.umirc.ts** to `http://127.0.0.1:9380`:
+8. Launch frontend service:  
+   ```bash
+   npm run dev 
+   ```  
+
+   _The following output confirms a successful launch of the system:_  
+
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
+
+## 📚 Documentation
+
+- [Quickstart](https://ragflow.io/docs/dev/)
+- [User guide](https://ragflow.io/docs/dev/category/user-guides)
+- [References](https://ragflow.io/docs/dev/category/references)
+- [FAQ](https://ragflow.io/docs/dev/faq)
+
+## 📜 Roadmap
+
+See the [RAGFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162)
+
+## 🏄 Community
+
+- [Discord](https://discord.gg/4XxujFgUN7)
+- [Twitter](https://twitter.com/infiniflowai)
+- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
+
+## 🙌 Contributing
+
+RAGFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community. If you would like to be a part, review our [Contribution Guidelines](./CONTRIBUTING.md) first.

README_ja.md

@@ -1,291 +1,294 @@
-<div align="center">
-<a href="https://demo.ragflow.io/">
-<img src="web/src/assets/logo-with-text.png" width="350" alt="ragflow logo">
-</a>
-</div>
-
-<p align="center">
-  <a href="./README.md">English</a> |
-  <a href="./README_zh.md">简体中文</a> |
-  <a href="./README_ja.md">日本語</a>
-</p>
-
-<p align="center">
-    <a href="https://github.com/infiniflow/ragflow/releases/latest">
-        <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
-    </a>
-    <a href="https://demo.ragflow.io" target="_blank">
-        <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99"></a>
-    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.9.0-brightgreen"
-            alt="docker pull infiniflow/ragflow:v0.9.0"></a>
-      <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
-    <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license">
-  </a>
-</p>
-
-<h4 align="center">
-  <a href="https://ragflow.io/docs/dev/">Document</a> |
-  <a href="https://github.com/infiniflow/ragflow/issues/162">Roadmap</a> |
-  <a href="https://twitter.com/infiniflowai">Twitter</a> |
-  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
-  <a href="https://demo.ragflow.io">Demo</a>
-</h4>
-
-## 💡 RAGFlow とは？
-
-[RAGFlow](https://ragflow.io/) は、深い文書理解に基づいたオープンソースの RAG (Retrieval-Augmented Generation) エンジンである。LLM（大規模言語モデル）を組み合わせることで、様々な複雑なフォーマットのデータから根拠のある引用に裏打ちされた、信頼できる質問応答機能を実現し、あらゆる規模のビジネスに適した RAG ワークフローを提供します。
-
-## 🎮 Demo
-
-デモをお試しください：[https://demo.ragflow.io](https://demo.ragflow.io)。
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/7248/2f6baa3e-1092-4f11-866d-36f6a9d075e5" width="1200"/>
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/b083d173-dadc-4ea9-bdeb-180d7df514eb" width="1200"/>
-</div>
-
-
-## 🔥 最新情報
-
-- 2024-08-02 [graphrag](https://github.com/microsoft/graphrag) からインスピレーションを得た GraphRAG とマインド マップをサポートします。
-- 2024-07-23 音声ファイルの解析をサポートしました。
-- 2024-07-21 より多くの LLM サプライヤー (LocalAI/OpenRouter/StepFun/Nvidia) をサポートします。
-- 2024-07-18 グラフにコンポーネント(Wikipedia/PubMed/Baidu/Duckduckgo)を追加しました。
-- 2024-07-08 [Graph](./graph/README.md) ベースのワークフローをサポート
-- 2024-06-27 Q&A解析方式はMarkdownファイルとDocxファイルをサポートしています。
-- 2024-06-27 Docxファイルからの画像の抽出をサポートします。
-- 2024-06-27 Markdownファイルからテーブルを抽出することをサポートします。
-- 2024-06-06 会話設定でデフォルトでチェックされている [Self-RAG](https://huggingface.co/papers/2310.11511) をサポートします。
-- 2024-05-30 [BCE](https://github.com/netease-youdao/BCEmbedding) 、[BGE](https://github.com/FlagOpen/FlagEmbedding) reranker を統合。
-- 2024-05-23 より良いテキスト検索のために [RAPTOR](https://arxiv.org/html/2401.18059v1) をサポート。
-- 2024-05-15 OpenAI GPT-4oを統合しました。
-
-## 🌟 主な特徴
-
-### 🍭 **"Quality in, quality out"**
-
-- 複雑な形式の非構造化データからの[深い文書理解](./deepdoc/README.md)ベースの知識抽出。
-- 無限のトークンから"干し草の山の中の針"を見つける。
-
-### 🍱 **テンプレートベースのチャンク化**
-
-- 知的で解釈しやすい。
-- テンプレートオプションが豊富。
-
-### 🌱 **ハルシネーションが軽減された根拠のある引用**
-
-- 可視化されたテキストチャンキング（text chunking）で人間の介入を可能にする。
-- 重要な参考文献のクイックビューと、追跡可能な引用によって根拠ある答えをサポートする。
-
-### 🍔 **多様なデータソースとの互換性**
-
-- Word、スライド、Excel、txt、画像、スキャンコピー、構造化データ、Web ページなどをサポート。
-
-### 🛀 **自動化された楽な RAG ワークフロー**
-
-- 個人から大企業まで対応できる RAG オーケストレーション（orchestration）。
-- カスタマイズ可能な LLM とエンベッディングモデル。
-- 複数の想起と融合された再ランク付け。
-- 直感的な API によってビジネスとの統合がシームレスに。
-
-## 🔎 システム構成
-
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
-</div>
-
-## 🎬 初期設定
-
-### 📝 必要条件
-
-- CPU >= 4 cores
-- RAM >= 16 GB
-- Disk >= 50 GB
-- Docker >= 24.0.0 & Docker Compose >= v2.26.1
-  > ローカルマシン（Windows、Mac、または Linux）に Docker をインストールしていない場合は、[Docker Engine のインストール](https://docs.docker.com/engine/install/) を参照してください。
-
-### 🚀 サーバーを起動
-
-1. `vm.max_map_count` >= 262144 であることを確認する:
-
-   > `vm.max_map_count` の値をチェックするには:
-   >
-   > ```bash
-   > $ sysctl vm.max_map_count
-   > ```
-   >
-   > `vm.max_map_count` が 262144 より大きい値でなければリセットする。
-   >
-   > ```bash
-   > # In this case, we set it to 262144:
-   > $ sudo sysctl -w vm.max_map_count=262144
-   > ```
-   >
-   > この変更はシステム再起動後にリセットされる。変更を恒久的なものにするには、**/etc/sysctl.conf** の `vm.max_map_count` 値を適宜追加または更新する:
-   >
-   > ```bash
-   > vm.max_map_count=262144
-   > ```
-
-2. リポジトリをクローンする:
-
-   ```bash
-   $ git clone https://github.com/infiniflow/ragflow.git
-   ```
-
-3. ビルド済みの Docker イメージをビルドし、サーバーを起動する:
-
-   ```bash
-   $ cd ragflow/docker
-   $ chmod +x ./entrypoint.sh
-   $ docker compose up -d
-   ```
-
-   > 上記のコマンドを実行すると、RAGFlowの開発版dockerイメージが自動的にダウンロードされます。 特定のバージョンのDockerイメージをダウンロードして実行したい場合は、docker/.envファイルのRAGFLOW_VERSION変数を見つけて、対応するバージョンに変更してください。 例えば、RAGFLOW_VERSION=v0.9.0として、上記のコマンドを実行してください。
-
-   > コアイメージのサイズは約 9 GB で、ロードに時間がかかる場合があります。
-
-4. サーバーを立ち上げた後、サーバーの状態を確認する:
-
-   ```bash
-   $ docker logs -f ragflow-server
-   ```
-
-   _以下の出力は、システムが正常に起動したことを確認するものです:_
-
-   ```bash
-       ____                 ______ __
-      / __ \ ____ _ ____ _ / ____// /____  _      __
-     / /_/ // __ `// __ `// /_   / // __ \| | /| / /
-    / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ /
-   /_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/
-                 /____/
-
-    * Running on all addresses (0.0.0.0)
-    * Running on http://127.0.0.1:9380
-    * Running on http://x.x.x.x:9380
-    INFO:werkzeug:Press CTRL+C to quit
-   ```
-   > もし確認ステップをスキップして直接 RAGFlow にログインした場合、その時点で RAGFlow が完全に初期化されていない可能性があるため、ブラウザーがネットワーク異常エラーを表示するかもしれません。
-
-5. ウェブブラウザで、プロンプトに従ってサーバーの IP アドレスを入力し、RAGFlow にログインします。
-   > デフォルトの設定を使用する場合、デフォルトの HTTP サービングポート `80` は省略できるので、与えられたシナリオでは、`http://IP_OF_YOUR_MACHINE`（ポート番号は省略）だけを入力すればよい。
-6. [service_conf.yaml](./docker/service_conf.yaml) で、`user_default_llm` で希望の LLM ファクトリを選択し、`API_KEY` フィールドを対応する API キーで更新する。
-
-   > 詳しくは [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) を参照してください。
-
-   _これで初期設定完了！ショーの開幕です！_
-
-## 🔧 コンフィグ
-
-システムコンフィグに関しては、以下のファイルを管理する必要がある:
-
-- [.env](./docker/.env): `SVR_HTTP_PORT`、`MYSQL_PASSWORD`、`MINIO_PASSWORD` などのシステムの基本設定を保持する。
-- [service_conf.yaml](./docker/service_conf.yaml): バックエンドのサービスを設定します。
-- [docker-compose.yml](./docker/docker-compose.yml): システムの起動は [docker-compose.yml](./docker/docker-compose.yml) に依存している。
-
-[.env](./docker/.env) ファイルの変更が [service_conf.yaml](./docker/service_conf.yaml) ファイルの内容と一致していることを確認する必要があります。
-
-> [./docker/README](./docker/README.md) ファイルは環境設定とサービスコンフィグの詳細な説明を提供し、[./docker/README](./docker/README.md) ファイルに記載されている全ての環境設定が [service_conf.yaml](./docker/service_conf.yaml) ファイルの対応するコンフィグと一致していることを確認することが義務付けられています。
-
-デフォルトの HTTP サービングポート(80)を更新するには、[docker-compose.yml](./docker/docker-compose.yml) にアクセスして、`80:80` を `<YOUR_SERVING_PORT>:80` に変更します。
-
-> すべてのシステム設定のアップデートを有効にするには、システムの再起動が必要です:
->
-> ```bash
-> $ docker-compose up -d
-> ```
-
-## 🛠️ ソースからビルドする
-
-ソースからDockerイメージをビルドするには:
-
-```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
-$ docker build -t infiniflow/ragflow:v0.8.0 .
-$ cd ragflow/docker
-$ chmod +x ./entrypoint.sh
-$ docker compose up -d
-```
-
-## 🛠️ ソースコードからサービスを起動する方法
-
-ソースコードからサービスを起動する場合は、以下の手順に従ってください:
-
-1. リポジトリをクローンします
-```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
-```
-
-2. 仮想環境を作成します（AnacondaまたはMinicondaがインストールされていることを確認してください）
-```bash
-$ conda create -n ragflow python=3.11.0
-$ conda activate ragflow
-$ pip install -r requirements.txt
-```
-CUDAのバージョンが12.0以上の場合、以下の追加コマンドを実行してください：
-```bash
-$ pip uninstall -y onnxruntime-gpu
-$ pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
-```
-
-3. エントリースクリプトをコピーし、環境変数を設定します
-```bash
-$ cp docker/entrypoint.sh .
-$ vi entrypoint.sh
-```
-以下のコマンドでPythonのパスとragflowプロジェクトのパスを取得します：
-```bash
-$ which python
-$ pwd
-```
-
-`which python`の出力を`PY`の値として、`pwd`の出力を`PYTHONPATH`の値として設定します。
-
-`LD_LIBRARY_PATH`が既に設定されている場合は、コメントアウトできます。
-
-```bash
-# 実際の状況に応じて設定を調整してください。以下の二つのexportは新たに追加された設定です
-PY=${PY}
-export PYTHONPATH=${PYTHONPATH}
-# オプション：Hugging Faceミラーを追加
-export HF_ENDPOINT=https://hf-mirror.com
-```
-
-4. 基本サービスを起動します
-```bash
-$ cd docker
-$ docker compose -f docker-compose-base.yml up -d 
-```
-
-5. 設定ファイルを確認します
-**docker/.env**内の設定が**conf/service_conf.yaml**内の設定と一致していることを確認してください。**service_conf.yaml**内の関連サービスのIPアドレスとポートは、ローカルマシンのIPアドレスとコンテナが公開するポートに変更する必要があります。
-
-6. サービスを起動します
-```bash
-$ chmod +x ./entrypoint.sh
-$ bash ./entrypoint.sh
-```
-
-## 📚 ドキュメンテーション
-
-- [Quickstart](https://ragflow.io/docs/dev/)
-- [User guide](https://ragflow.io/docs/dev/category/user-guides)
-- [References](https://ragflow.io/docs/dev/category/references)
-- [FAQ](https://ragflow.io/docs/dev/faq)
-
-## 📜 ロードマップ
-
-[RAGFlow ロードマップ 2024](https://github.com/infiniflow/ragflow/issues/162) を参照
-
-## 🏄 コミュニティ
-
-- [Discord](https://discord.gg/4XxujFgUN7)
-- [Twitter](https://twitter.com/infiniflowai)
-- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
-
-## 🙌 コントリビュート
-
-RAGFlow はオープンソースのコラボレーションによって発展してきました。この精神に基づき、私たちはコミュニティからの多様なコントリビュートを受け入れています。 参加を希望される方は、まず[コントリビューションガイド](./docs/references/CONTRIBUTING.md)をご覧ください。
+<div align="center">
+<a href="https://demo.ragflow.io/">
+<img src="web/src/assets/logo-with-text.png" width="350" alt="ragflow logo">
+</a>
+</div>
+
+<p align="center">
+  <a href="./README.md">English</a> |
+  <a href="./README_zh.md">简体中文</a> |
+  <a href="./README_ja.md">日本語</a> |
+  <a href="./README_ko.md">한국어</a>
+</p>
+
+<p align="center">
+    <a href="https://github.com/infiniflow/ragflow/releases/latest">
+        <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
+    </a>
+    <a href="https://demo.ragflow.io" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99"></a>
+    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.12.0-brightgreen"
+            alt="docker pull infiniflow/ragflow:v0.12.0"></a>
+      <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
+    <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license">
+  </a>
+</p>
+
+<h4 align="center">
+  <a href="https://ragflow.io/docs/dev/">Document</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/162">Roadmap</a> |
+  <a href="https://twitter.com/infiniflowai">Twitter</a> |
+  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
+  <a href="https://demo.ragflow.io">Demo</a>
+</h4>
+
+## 💡 RAGFlow とは？
+
+[RAGFlow](https://ragflow.io/) は、深い文書理解に基づいたオープンソースの RAG (Retrieval-Augmented Generation) エンジンである。LLM（大規模言語モデル）を組み合わせることで、様々な複雑なフォーマットのデータから根拠のある引用に裏打ちされた、信頼できる質問応答機能を実現し、あらゆる規模のビジネスに適した RAG ワークフローを提供します。
+
+## 🎮 Demo
+
+デモをお試しください：[https://demo.ragflow.io](https://demo.ragflow.io)。
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/infiniflow/ragflow/assets/7248/2f6baa3e-1092-4f11-866d-36f6a9d075e5" width="1200"/>
+<img src="https://github.com/infiniflow/ragflow/assets/12318111/b083d173-dadc-4ea9-bdeb-180d7df514eb" width="1200"/>
+</div>
+
+
+## 🔥 最新情報
+
+- 2024-09-29 マルチラウンドダイアログを最適化。
+- 2024-09-13 ナレッジベース Q&A の検索モードを追加しました。
+- 2024-09-09 エージェントに医療相談テンプレートを追加しました。
+- 2024-08-22 RAG を介して SQL ステートメントへのテキストをサポートします。
+- 2024-08-02 [graphrag](https://github.com/microsoft/graphrag) からインスピレーションを得た GraphRAG とマインド マップをサポートします。
+- 2024-07-23 音声ファイルの解析をサポートしました。
+- 2024-07-08 [Graph](./agent/README.md) ベースのワークフローをサポート
+- 2024-06-27 Q&A 解析メソッドで Markdown と Docx をサポートし、Docx ファイルから画像を抽出し、Markdown ファイルからテーブルを抽出します。
+- 2024-05-23 より良いテキスト検索のために [RAPTOR](https://arxiv.org/html/2401.18059v1) をサポート。
+
+
+## 🌟 主な特徴
+
+### 🍭 **"Quality in, quality out"**
+
+- 複雑な形式の非構造化データからの[深い文書理解](./deepdoc/README.md)ベースの知識抽出。
+- 無限のトークンから"干し草の山の中の針"を見つける。
+
+### 🍱 **テンプレートベースのチャンク化**
+
+- 知的で解釈しやすい。
+- テンプレートオプションが豊富。
+
+### 🌱 **ハルシネーションが軽減された根拠のある引用**
+
+- 可視化されたテキストチャンキング（text chunking）で人間の介入を可能にする。
+- 重要な参考文献のクイックビューと、追跡可能な引用によって根拠ある答えをサポートする。
+
+### 🍔 **多様なデータソースとの互換性**
+
+- Word、スライド、Excel、txt、画像、スキャンコピー、構造化データ、Web ページなどをサポート。
+
+### 🛀 **自動化された楽な RAG ワークフロー**
+
+- 個人から大企業まで対応できる RAG オーケストレーション（orchestration）。
+- カスタマイズ可能な LLM とエンベッディングモデル。
+- 複数の想起と融合された再ランク付け。
+- 直感的な API によってビジネスとの統合がシームレスに。
+
+## 🔎 システム構成
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
+</div>
+
+## 🎬 初期設定
+
+### 📝 必要条件
+
+- CPU >= 4 cores
+- RAM >= 16 GB
+- Disk >= 50 GB
+- Docker >= 24.0.0 & Docker Compose >= v2.26.1
+  > ローカルマシン（Windows、Mac、または Linux）に Docker をインストールしていない場合は、[Docker Engine のインストール](https://docs.docker.com/engine/install/) を参照してください。
+
+### 🚀 サーバーを起動
+
+1. `vm.max_map_count` >= 262144 であることを確認する:
+
+   > `vm.max_map_count` の値をチェックするには:
+   >
+   > ```bash
+   > $ sysctl vm.max_map_count
+   > ```
+   >
+   > `vm.max_map_count` が 262144 より大きい値でなければリセットする。
+   >
+   > ```bash
+   > # In this case, we set it to 262144:
+   > $ sudo sysctl -w vm.max_map_count=262144
+   > ```
+   >
+   > この変更はシステム再起動後にリセットされる。変更を恒久的なものにするには、**/etc/sysctl.conf** の `vm.max_map_count` 値を適宜追加または更新する:
+   >
+   > ```bash
+   > vm.max_map_count=262144
+   > ```
+
+2. リポジトリをクローンする:
+
+   ```bash
+   $ git clone https://github.com/infiniflow/ragflow.git
+   ```
+
+3. ビルド済みの Docker イメージをビルドし、サーバーを起動する:
+
+   ```bash
+   $ cd ragflow/docker
+   $ chmod +x ./entrypoint.sh
+   $ docker compose up -d
+   ```
+
+   > 上記のコマンドを実行すると、RAGFlowの開発版dockerイメージが自動的にダウンロードされます。 特定のバージョンのDockerイメージをダウンロードして実行したい場合は、docker/.envファイルのRAGFLOW_IMAGE変数を見つけて、対応するバージョンに変更してください。 例えば、`RAGFLOW_IMAGE=infiniflow/ragflow:v0.12.0`として、上記のコマンドを実行してください。
+
+   > コアイメージのサイズは約 9 GB で、ロードに時間がかかる場合があります。
+
+4. サーバーを立ち上げた後、サーバーの状態を確認する:
+
+   ```bash
+   $ docker logs -f ragflow-server
+   ```
+
+   _以下の出力は、システムが正常に起動したことを確認するものです:_
+
+   ```bash
+        ____   ___    ______ ______ __               
+       / __ \ /   |  / ____// ____// /____  _      __
+      / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+     / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ / 
+    /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/  
+
+    * Running on all addresses (0.0.0.0)
+    * Running on http://127.0.0.1:9380
+    * Running on http://x.x.x.x:9380
+    INFO:werkzeug:Press CTRL+C to quit
+   ```
+   > もし確認ステップをスキップして直接 RAGFlow にログインした場合、その時点で RAGFlow が完全に初期化されていない可能性があるため、ブラウザーがネットワーク異常エラーを表示するかもしれません。
+
+5. ウェブブラウザで、プロンプトに従ってサーバーの IP アドレスを入力し、RAGFlow にログインします。
+   > デフォルトの設定を使用する場合、デフォルトの HTTP サービングポート `80` は省略できるので、与えられたシナリオでは、`http://IP_OF_YOUR_MACHINE`（ポート番号は省略）だけを入力すればよい。
+6. [service_conf.yaml](./docker/service_conf.yaml) で、`user_default_llm` で希望の LLM ファクトリを選択し、`API_KEY` フィールドを対応する API キーで更新する。
+
+   > 詳しくは [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) を参照してください。
+
+   _これで初期設定完了！ショーの開幕です！_
+
+## 🔧 コンフィグ
+
+システムコンフィグに関しては、以下のファイルを管理する必要がある:
+
+- [.env](./docker/.env): `SVR_HTTP_PORT`、`MYSQL_PASSWORD`、`MINIO_PASSWORD` などのシステムの基本設定を保持する。
+- [service_conf.yaml](./docker/service_conf.yaml): バックエンドのサービスを設定します。
+- [docker-compose.yml](./docker/docker-compose.yml): システムの起動は [docker-compose.yml](./docker/docker-compose.yml) に依存している。
+
+[.env](./docker/.env) ファイルの変更が [service_conf.yaml](./docker/service_conf.yaml) ファイルの内容と一致していることを確認する必要があります。
+
+> [./docker/README](./docker/README.md) ファイルは環境設定とサービスコンフィグの詳細な説明を提供し、[./docker/README](./docker/README.md) ファイルに記載されている全ての環境設定が [service_conf.yaml](./docker/service_conf.yaml) ファイルの対応するコンフィグと一致していることを確認することが義務付けられています。
+
+デフォルトの HTTP サービングポート(80)を更新するには、[docker-compose.yml](./docker/docker-compose.yml) にアクセスして、`80:80` を `<YOUR_SERVING_PORT>:80` に変更します。
+
+> すべてのシステム設定のアップデートを有効にするには、システムの再起動が必要です:
+>
+> ```bash
+> $ docker-compose up -d
+> ```
+
+## 🪛 ソースコードでDockerイメージを作成（埋め込みモデルなし）
+
+この Docker イメージのサイズは約 1GB で、外部の大モデルと埋め込みサービスに依存しています。
+
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+pip3 install huggingface-hub
+python3 download_deps.py
+docker build -f Dockerfile.slim -t infiniflow/ragflow:dev-slim .
+```
+
+## 🪚 ソースコードをコンパイルしたDockerイメージ（埋め込みモデルを含む）
+
+この Docker のサイズは約 9GB で、埋め込みモデルを含むため、外部の大モデルサービスのみが必要です。
+
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+pip3 install huggingface-hub
+python3 download_deps.py
+docker build -f Dockerfile -t infiniflow/ragflow:dev .
+```
+
+## 🔨 ソースコードからサービスを起動する方法
+
+1. Poetry をインストールする。すでにインストールされている場合は、このステップをスキップしてください:
+   ```bash
+   curl -sSL https://install.python-poetry.org | python3 -
+   ```
+
+2. ソースコードをクローンし、Python の依存関係をインストールする:
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   export POETRY_VIRTUALENVS_CREATE=true POETRY_VIRTUALENVS_IN_PROJECT=true
+   ~/.local/bin/poetry install --sync --no-root # install RAGFlow dependent python modules
+   ```
+
+3. Docker Compose を使用して依存サービス（MinIO、Elasticsearch、Redis、MySQL）を起動する:
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
+   ```
+
+   `/etc/hosts` に以下の行を追加して、**docker/service_conf.yaml** に指定されたすべてのホストを `127.0.0.1` に解決します:  
+   ```
+   127.0.0.1       es01 mysql minio redis
+   ```  
+   **docker/service_conf.yaml** で mysql のポートを `5455` に、es のポートを `1200` に更新します（**docker/.env** に指定された通り）.
+
+4. HuggingFace にアクセスできない場合は、`HF_ENDPOINT` 環境変数を設定してミラーサイトを使用してください:
+ 
+   ```bash
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
+
+5. バックエンドサービスを起動する:
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+
+6. フロントエンドの依存関係をインストールする:  
+   ```bash
+   cd web
+   npm install --force
+   ```  
+7. フロントエンドを設定し、**.umirc.ts** の `proxy.target` を `http://127.0.0.1:9380` に更新します:
+8. フロントエンドサービスを起動する:  
+   ```bash
+   npm run dev 
+   ```
+
+   _以下の画面で、システムが正常に起動したことを示します:_  
+
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
+
+## 📚 ドキュメンテーション
+
+- [Quickstart](https://ragflow.io/docs/dev/)
+- [User guide](https://ragflow.io/docs/dev/category/user-guides)
+- [References](https://ragflow.io/docs/dev/category/references)
+- [FAQ](https://ragflow.io/docs/dev/faq)
+
+## 📜 ロードマップ
+
+[RAGFlow ロードマップ 2024](https://github.com/infiniflow/ragflow/issues/162) を参照
+
+## 🏄 コミュニティ
+
+- [Discord](https://discord.gg/4XxujFgUN7)
+- [Twitter](https://twitter.com/infiniflowai)
+- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
+
+## 🙌 コントリビュート
+
+RAGFlow はオープンソースのコラボレーションによって発展してきました。この精神に基づき、私たちはコミュニティからの多様なコントリビュートを受け入れています。 参加を希望される方は、まず [コントリビューションガイド](./CONTRIBUTING.md)をご覧ください。

README_ko.md

@@ -0,0 +1,299 @@
+<div align="center">
+<a href="https://demo.ragflow.io/">
+<img src="web/src/assets/logo-with-text.png" width="520" alt="ragflow logo">
+</a>
+</div>
+
+<p align="center">
+  <a href="./README.md">English</a> |
+  <a href="./README_zh.md">简体中文</a> |
+  <a href="./README_ja.md">日本語</a> |
+  <a href="./README_ko.md">한국어</a> |
+</p>
+
+<p align="center">
+    <a href="https://github.com/infiniflow/ragflow/releases/latest">
+        <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
+    </a>
+    <a href="https://demo.ragflow.io" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99"></a>
+    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.12.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.12.0"></a>
+    <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
+    <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license">
+  </a>
+</p>
+
+<h4 align="center">
+  <a href="https://ragflow.io/docs/dev/">Document</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/162">Roadmap</a> |
+  <a href="https://twitter.com/infiniflowai">Twitter</a> |
+  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
+  <a href="https://demo.ragflow.io">Demo</a>
+</h4>
+
+
+## 💡 RAGFlow란?
+
+[RAGFlow](https://ragflow.io/)는 심층 문서 이해에 기반한 오픈소스 RAG (Retrieval-Augmented Generation) 엔진입니다. 이 엔진은 대규모 언어 모델(LLM)과 결합하여 정확한 질문 응답 기능을 제공하며, 다양한 복잡한 형식의 데이터에서 신뢰할 수 있는 출처를 바탕으로 한 인용을 통해 이를 뒷받침합니다. RAGFlow는 규모에 상관없이 모든 기업에 최적화된 RAG 워크플로우를 제공합니다.
+
+
+
+## 🎮 데모
+데모를 [https://demo.ragflow.io](https://demo.ragflow.io)에서 실행해 보세요.
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/infiniflow/ragflow/assets/7248/2f6baa3e-1092-4f11-866d-36f6a9d075e5" width="1200"/>
+<img src="https://github.com/infiniflow/ragflow/assets/12318111/b083d173-dadc-4ea9-bdeb-180d7df514eb" width="1200"/>
+</div>
+
+
+## 🔥 업데이트
+
+- 2024-09-29 다단계 대화를 최적화합니다.
+  
+- 2024-09-13 지식베이스 Q&A 검색 모드를 추가합니다.
+  
+- 2024-09-09 Agent에 의료상담 템플릿을 추가하였습니다.
+  
+- 2024-08-22 RAG를 통해 SQL 문에 텍스트를 지원합니다.
+  
+- 2024-08-02: [graphrag](https://github.com/microsoft/graphrag)와 마인드맵에서 영감을 받은 GraphRAG를 지원합니다.
+
+- 2024-07-23: 오디오 파일 분석을 지원합니다.
+
+- 2024-07-08: [Graph](./agent/README.md)를 기반으로 한 워크플로우를 지원합니다.
+
+- 2024-06-27 Q&A 구문 분석 방식에서 Markdown 및 Docx를 지원하고, Docx 파일에서 이미지 추출, Markdown 파일에서 테이블 추출을 지원합니다.
+
+- 2024-05-23: 더 나은 텍스트 검색을 위해 [RAPTOR](https://arxiv.org/html/2401.18059v1)를 지원합니다.
+
+
+
+## 🌟 주요 기능
+
+### 🍭 **"Quality in, quality out"**
+- [심층 문서 이해](./deepdoc/README.md)를 기반으로 복잡한 형식의 비정형 데이터에서 지식을 추출합니다.
+- 문자 그대로 무한한 토큰에서 "데이터 속의 바늘"을 찾아냅니다.
+
+### 🍱 **템플릿 기반의 chunking**
+- 똑똑하고 설명 가능한 방식.
+- 다양한 템플릿 옵션을 제공합니다.
+
+
+### 🌱 **할루시네이션을 줄인 신뢰할 수 있는 인용**
+- 텍스트 청킹을 시각화하여 사용자가 개입할 수 있도록 합니다.
+- 중요한 참고 자료와 추적 가능한 인용을 빠르게 확인하여 신뢰할 수 있는 답변을 지원합니다.
+
+
+### 🍔 **다른 종류의 데이터 소스와의 호환성**
+- 워드, 슬라이드, 엑셀, 텍스트 파일, 이미지, 스캔본, 구조화된 데이터, 웹 페이지 등을 지원합니다.
+
+### 🛀 **자동화되고 손쉬운 RAG 워크플로우**
+- 개인 및 대규모 비즈니스에 맞춘 효율적인 RAG 오케스트레이션.
+- 구성 가능한 LLM 및 임베딩 모델.
+- 다중 검색과 결합된 re-ranking.
+- 비즈니스와 원활하게 통합할 수 있는 직관적인 API.
+
+
+## 🔎 시스템 아키텍처
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
+</div>
+
+## 🎬 시작하기
+### 📝 사전 준비 사항
+- CPU >= 4 cores
+- RAM >= 16 GB
+- Disk >= 50 GB
+- Docker >= 24.0.0 & Docker Compose >= v2.26.1
+  > 로컬 머신(Windows, Mac, Linux)에 Docker가 설치되지 않은 경우, [Docker 엔진 설치]((https://docs.docker.com/engine/install/))를 참조하세요.
+
+
+### 🚀 서버 시작하기
+
+1. `vm.max_map_count`가 262144 이상인지 확인하세요:
+   > `vm.max_map_count`의 값을 아래 명령어를 통해 확인하세요:
+   >
+   > ```bash
+   > $ sysctl vm.max_map_count
+   > ```
+   >
+   > 만약 `vm.max_map_count` 이 262144 보다 작다면 값을 쟈설정하세요.
+   >
+   > ```bash
+   > # 이 경우에 262144로 설정했습니다.:
+   > $ sudo sysctl -w vm.max_map_count=262144
+   > ```
+   >
+   > 이 변경 사항은 시스템 재부팅 후에 초기화됩니다. 변경 사항을 영구적으로 적용하려면 /etc/sysctl.conf 파일에 vm.max_map_count 값을 추가하거나 업데이트하세요:
+   >
+   > ```bash
+   > vm.max_map_count=262144
+   > ```
+
+2. 레포지토리를 클론하세요:
+
+   ```bash
+   $ git clone https://github.com/infiniflow/ragflow.git
+   ```
+
+3. 미리 빌드된 Docker 이미지를 생성하고 서버를 시작하세요:
+
+   > 다음 명령어를 실행하면 *dev* 버전의 RAGFlow Docker 이미지가 자동으로 다운로드됩니다. 특정 Docker 버전을 다운로드하고 실행하려면, **docker/.env** 파일에서 `RAGFLOW_IMAGE`을 원하는 버전으로 업데이트한 후, 예를 들어 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.12.0`로 업데이트 한 뒤, 다음 명령어를 실행하세요.
+   ```bash
+   $ cd ragflow/docker
+   $ chmod +x ./entrypoint.sh
+   $ docker compose up -d
+   ```
+   
+   > 기본 이미지는 약 9GB 크기이며 로드하는 데 시간이 걸릴 수 있습니다.
+
+
+4. 서버가 시작된 후 서버 상태를 확인하세요:
+
+   ```bash
+   $ docker logs -f ragflow-server
+   ```
+
+   _다음 출력 결과로 시스템이 성공적으로 시작되었음을 확인합니다:_
+
+   ```bash
+        ____   ___    ______ ______ __               
+       / __ \ /   |  / ____// ____// /____  _      __
+      / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+     / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ / 
+    /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/  
+
+    * Running on all addresses (0.0.0.0)
+    * Running on http://127.0.0.1:9380
+    * Running on http://x.x.x.x:9380
+    INFO:werkzeug:Press CTRL+C to quit
+   ```
+   > 만약 확인 단계를 건너뛰고 바로 RAGFlow에 로그인하면, RAGFlow가 완전히 초기화되지 않았기 때문에 브라우저에서 `network abnormal` 오류가 발생할 수 있습니다.
+
+5. 웹 브라우저에 서버의 IP 주소를 입력하고 RAGFlow에 로그인하세요.
+   > 기본 설정을 사용할 경우, `http://IP_OF_YOUR_MACHINE`만 입력하면 됩니다 (포트 번호는 제외). 기본 HTTP 서비스 포트 `80`은 기본 구성으로 사용할 때 생략할 수 있습니다.
+6. [service_conf.yaml](./docker/service_conf.yaml) 파일에서 원하는 LLM 팩토리를 `user_default_llm`에 선택하고, `API_KEY` 필드를 해당 API 키로 업데이트하세요.
+   > 자세한 내용은 [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup)를 참조하세요.
+
+   _이제 쇼가 시작됩니다!_
+
+## 🔧 설정
+
+시스템 설정과 관련하여 다음 파일들을 관리해야 합니다:
+
+- [.env](./docker/.env): `SVR_HTTP_PORT`, `MYSQL_PASSWORD`, `MINIO_PASSWORD`와 같은 시스템의 기본 설정을 포함합니다.
+- [service_conf.yaml](./docker/service_conf.yaml): 백엔드 서비스를 구성합니다.
+- [docker-compose.yml](./docker/docker-compose.yml): 시스템은 [docker-compose.yml](./docker/docker-compose.yml)을 사용하여 시작됩니다.
+
+[.env](./docker/.env) 파일의 변경 사항이 [service_conf.yaml](./docker/service_conf.yaml) 파일의 내용과 일치하도록 해야 합니다.
+
+> [./docker/README](./docker/README.md) 파일에는 환경 설정과 서비스 구성에 대한 자세한 설명이 있으며, [./docker/README](./docker/README.md) 파일에 나열된 모든 환경 설정이 [service_conf.yaml](./docker/service_conf.yaml) 파일의 해당 구성과 일치하도록 해야 합니다.
+
+기본 HTTP 서비스 포트(80)를 업데이트하려면 [docker-compose.yml](./docker/docker-compose.yml) 파일에서 `80:80`을 `<YOUR_SERVING_PORT>:80`으로 변경하세요.
+
+> 모든 시스템 구성 업데이트는 적용되기 위해 시스템 재부팅이 필요합니다.
+>
+> ```bash
+> $ docker-compose up -d
+> ```
+
+## 🪛 소스 코드로 Docker 이미지를 컴파일합니다(임베딩 모델 포함하지 않음)
+
+이 Docker 이미지의 크기는 약 1GB이며, 외부 대형 모델과 임베딩 서비스에 의존합니다.
+
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+pip3 install huggingface-hub
+python3 download_deps.py
+docker build -f Dockerfile.slim -t infiniflow/ragflow:dev-slim .
+```
+
+## 🪚 소스 코드로 Docker 이미지를 컴파일합니다(임베딩 모델 포함)
+
+이 Docker의 크기는 약 9GB이며, 이미 임베딩 모델을 포함하고 있으므로 외부 대형 모델 서비스에만 의존하면 됩니다.
+
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+pip3 install huggingface-hub
+python3 download_deps.py
+docker build -f Dockerfile -t infiniflow/ragflow:dev .
+```
+
+## 🔨 소스 코드로 서비스를 시작합니다.
+
+1. Poetry를 설치하거나 이미 설치된 경우 이 단계를 건너뜁니다:
+   ```bash
+   curl -sSL https://install.python-poetry.org | python3 -
+   ```
+
+2. 소스 코드를 클론하고 Python 의존성을 설치합니다:
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   export POETRY_VIRTUALENVS_CREATE=true POETRY_VIRTUALENVS_IN_PROJECT=true
+   ~/.local/bin/poetry install --sync --no-root # install RAGFlow dependent python modules
+   ```
+
+3. Docker Compose를 사용하여 의존 서비스(MinIO, Elasticsearch, Redis 및 MySQL)를 시작합니다:
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
+   ```
+
+   `/etc/hosts` 에 다음 줄을 추가하여 **docker/service_conf.yaml** 에 지정된 모든 호스트를 `127.0.0.1` 로 해결합니다:  
+   ```
+   127.0.0.1       es01 mysql minio redis
+   ```  
+   **docker/service_conf.yaml** 에서 mysql 포트를 `5455` 로, es 포트를 `1200` 으로 업데이트합니다( **docker/.env** 에 지정된 대로).
+
+4. HuggingFace에 접근할 수 없는 경우, `HF_ENDPOINT` 환경 변수를 설정하여 미러 사이트를 사용하세요:
+ 
+   ```bash
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
+
+5. 백엔드 서비스를 시작합니다:
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+
+6. 프론트엔드 의존성을 설치합니다:  
+   ```bash
+   cd web
+   npm install --force
+   ```  
+7. **.umirc.ts** 에서 `proxy.target` 을 `http://127.0.0.1:9380` 으로 업데이트합니다:
+8. 프론트엔드 서비스를 시작합니다:  
+   ```bash
+   npm run dev 
+   ```
+
+   _다음 인터페이스는 시스템이 성공적으로 시작되었음을 나타냅니다:_  
+
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
+
+## 📚 문서
+
+- [Quickstart](https://ragflow.io/docs/dev/)
+- [User guide](https://ragflow.io/docs/dev/category/user-guides)
+- [References](https://ragflow.io/docs/dev/category/references)
+- [FAQ](https://ragflow.io/docs/dev/faq)
+
+## 📜 로드맵
+
+[RAGFlow 로드맵 2024](https://github.com/infiniflow/ragflow/issues/162)을 확인하세요.
+
+## 🏄 커뮤니티
+
+- [Discord](https://discord.gg/4XxujFgUN7)
+- [Twitter](https://twitter.com/infiniflowai)
+- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
+
+## 🙌 컨트리뷰션
+
+RAGFlow는 오픈소스 협업을 통해 발전합니다. 이러한 정신을 바탕으로, 우리는 커뮤니티의 다양한 기여를 환영합니다. 참여하고 싶으시다면, 먼저 [가이드라인](./CONTRIBUTING.md)을 검토해 주세요.

README_zh.md

@@ -7,7 +7,8 @@
 <p align="center">
   <a href="./README.md">English</a> |
   <a href="./README_zh.md">简体中文</a> |
-  <a href="./README_ja.md">日本語</a>
+  <a href="./README_ja.md">日本語</a> |
+  <a href="./README_ko.md">한국어</a>
 </p>
 
 <p align="center">
@@ -17,7 +18,7 @@
     <a href="https://demo.ragflow.io" target="_blank">
         <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99"></a>
     <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.9.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.9.0"></a>
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.12.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.12.0"></a>
     <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
     <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license">
   </a>
@@ -46,18 +47,15 @@
 
 ## 🔥 近期更新
 
+- 2024-09-29 优化多轮对话.
+- 2024-09-13 增加知识库问答搜索模式。
+- 2024-09-09 在 Agent 中加入医疗问诊模板。
+- 2024-08-22 支持用 RAG 技术实现从自然语言到 SQL 语句的转换。
 - 2024-08-02 支持 GraphRAG 启发于 [graphrag](https://github.com/microsoft/graphrag) 和思维导图。
 - 2024-07-23 支持解析音频文件。
-- 2024-07-21 支持更多的大模型供应商(LocalAI/OpenRouter/StepFun/Nvidia)。
-- 2024-07-18 在Graph中支持算子：Wikipedia、PubMed、Baidu和Duckduckgo。
-- 2024-07-08 支持 Agentic RAG: 基于 [Graph](./graph/README.md) 的工作流。
-- 2024-06-27 Q&A 解析方式支持 Markdown 文件和 Docx 文件。 
-- 2024-06-27 支持提取出 Docx 文件中的图片。
-- 2024-06-27 支持提取出 Markdown 文件中的表格。
-- 2024-06-06 支持 [Self-RAG](https://huggingface.co/papers/2310.11511) ，在对话设置里面默认勾选。
-- 2024-05-30 集成 [BCE](https://github.com/netease-youdao/BCEmbedding) 和 [BGE](https://github.com/FlagOpen/FlagEmbedding) 重排序模型。
+- 2024-07-08 支持 Agentic RAG: 基于 [Graph](./agent/README.md) 的工作流。
+- 2024-06-27 Q&A 解析方式支持 Markdown 文件和 Docx 文件，支持提取出 Docx 文件中的图片和 Markdown 文件中的表格。
 - 2024-05-23 实现 [RAPTOR](https://arxiv.org/html/2401.18059v1) 提供更好的文本检索。
-- 2024-05-15 集成大模型 OpenAI GPT-4o。
 
 ## 🌟 主要功能
 
@@ -137,12 +135,12 @@
    ```bash
    $ cd ragflow/docker
    $ chmod +x ./entrypoint.sh
-   $ docker compose -f docker-compose-CN.yml up -d
+   $ docker compose -f docker-compose.yml up -d
    ```
 
-   > 请注意，运行上述命令会自动下载 RAGFlow 的开发版本 docker 镜像。如果你想下载并运行特定版本的 docker 镜像，请在 docker/.env 文件中找到 RAGFLOW_VERSION 变量，将其改为对应版本。例如 RAGFLOW_VERSION=v0.9.0，然后运行上述命令。
+   > 请注意，运行上述命令会自动下载 RAGFlow 的开发版本 docker 镜像。如果你想下载并运行特定版本的 docker 镜像，请在 docker/.env 文件中找到 RAGFLOW_IMAGE 变量，将其改为对应版本。例如 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.12.0`，然后运行上述命令。
 
-   > 核心镜像文件大约 9 GB，可能需要一定时间拉取。请耐心等待。
+   > 核心镜像下载大小为 9 GB，可能需要一定时间拉取。请耐心等待。
 
 4. 服务器启动成功后再次确认服务器状态：
 
@@ -153,19 +151,18 @@
    _出现以下界面提示说明服务器启动成功：_
 
    ```bash
-       ____                 ______ __
-      / __ \ ____ _ ____ _ / ____// /____  _      __
-     / /_/ // __ `// __ `// /_   / // __ \| | /| / /
-    / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ /
-   /_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/
-                 /____/
+        ____   ___    ______ ______ __               
+       / __ \ /   |  / ____// ____// /____  _      __
+      / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+     / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ / 
+    /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/  
 
     * Running on all addresses (0.0.0.0)
     * Running on http://127.0.0.1:9380
     * Running on http://x.x.x.x:9380
     INFO:werkzeug:Press CTRL+C to quit
    ```
-   > 如果您跳过这一步系统确认步骤就登录 RAGFlow，你的浏览器有可能会提示 `network anomaly` 或 `网络异常`，因为 RAGFlow 可能并未完全启动成功。
+   > 如果您跳过这一步系统确认步骤就登录 RAGFlow，你的浏览器有可能会提示 `network abnormal` 或 `网络异常`，因为 RAGFlow 可能并未完全启动成功。
 
 5. 在你的浏览器中输入你的服务器对应的 IP 地址并登录 RAGFlow。
    > 上面这个例子中，您只需输入 http://IP_OF_YOUR_MACHINE 即可：未改动过配置则无需输入端口（默认的 HTTP 服务端口 80）。
@@ -181,114 +178,100 @@
 
 - [.env](./docker/.env)：存放一些基本的系统环境变量，比如 `SVR_HTTP_PORT`、`MYSQL_PASSWORD`、`MINIO_PASSWORD` 等。
 - [service_conf.yaml](./docker/service_conf.yaml)：配置各类后台服务。
-- [docker-compose-CN.yml](./docker/docker-compose-CN.yml): 系统依赖该文件完成启动。
+- [docker-compose.yml](./docker/docker-compose.yml): 系统依赖该文件完成启动。
 
 请务必确保 [.env](./docker/.env) 文件中的变量设置与 [service_conf.yaml](./docker/service_conf.yaml) 文件中的配置保持一致！
 
+如果不能访问镜像站点hub.docker.com或者模型站点huggingface.co，请按照[.env](./docker/.env)注释修改`RAGFLOW_IMAGE`和`HF_ENDPOINT`。
+
 > [./docker/README](./docker/README.md) 文件提供了环境变量设置和服务配置的详细信息。请**一定要**确保 [./docker/README](./docker/README.md) 文件当中列出来的环境变量的值与 [service_conf.yaml](./docker/service_conf.yaml) 文件当中的系统配置保持一致。
 
-如需更新默认的 HTTP 服务端口(80), 可以在 [docker-compose-CN.yml](./docker/docker-compose-CN.yml) 文件中将配置 `80:80` 改为 `<YOUR_SERVING_PORT>:80`。
+如需更新默认的 HTTP 服务端口(80), 可以在 [docker-compose.yml](./docker/docker-compose.yml) 文件中将配置 `80:80` 改为 `<YOUR_SERVING_PORT>:80`。
 
 > 所有系统配置都需要通过系统重启生效：
 >
 > ```bash
-> $ docker compose -f docker-compose-CN.yml up -d
+> $ docker compose -f docker-compose.yml up -d
 > ```
 
-## 🛠️ 源码编译、安装 Docker 镜像
+## 🪛 源码编译 Docker 镜像（不含 embedding 模型）
 
-如需从源码安装 Docker 镜像：
+本 Docker 镜像大小约 1 GB 左右并且依赖外部的大模型和 embedding 服务。
 
 ```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
-$ docker build -t infiniflow/ragflow:v0.9.0 .
-$ cd ragflow/docker
-$ chmod +x ./entrypoint.sh
-$ docker compose up -d
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+pip3 install huggingface-hub
+python3 download_deps.py
+docker build -f Dockerfile.slim -t infiniflow/ragflow:dev-slim .
 ```
 
-## 🛠️ 源码启动服务
+## 🪚 源码编译 Docker 镜像（包含 embedding 模型）
 
-如需从源码启动服务，请参考以下步骤：
-
-1. 克隆仓库
-```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
-```
-
-2. 创建虚拟环境（确保已安装 Anaconda 或 Miniconda）
-```bash
-$ conda create -n ragflow python=3.11.0
-$ conda activate ragflow
-$ pip install -r requirements.txt
-```
-如果cuda > 12.0，需额外执行以下命令：
-```bash
-$ pip uninstall -y onnxruntime-gpu
-$ pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
-```
-
-3. 拷贝入口脚本并配置环境变量
-```bash
-$ cp docker/entrypoint.sh .
-$ vi entrypoint.sh
-```
-使用以下命令获取python路径及ragflow项目路径：
-```bash
-$ which python
-$ pwd
-```
-
-将上述`which python`的输出作为`PY`的值，将`pwd`的输出作为`PYTHONPATH`的值。
-
-`LD_LIBRARY_PATH`如果环境已经配置好，可以注释掉。
+本 Docker 大小约 9 GB 左右。由于已包含 embedding 模型，所以只需依赖外部的大模型服务即可。
 
 ```bash
-# 此处配置需要按照实际情况调整，两个export为新增配置
-PY=${PY}
-export PYTHONPATH=${PYTHONPATH}
-# 可选：添加Hugging Face镜像
-export HF_ENDPOINT=https://hf-mirror.com
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+pip3 install huggingface-hub
+python3 download_deps.py
+docker build -f Dockerfile -t infiniflow/ragflow:dev .
 ```
 
-4. 启动基础服务
-```bash
-$ cd docker
-$ docker compose -f docker-compose-base.yml up -d 
-```
+## 🔨 以源代码启动服务
 
-5. 检查配置文件
-确保**docker/.env**中的配置与**conf/service_conf.yaml**中配置一致， **service_conf.yaml**中相关服务的IP地址与端口应该改成本机IP地址及容器映射出来的端口。
+1. 安装 Poetry。如已经安装，可跳过本步骤：  
+   ```bash
+   curl -sSL https://install.python-poetry.org | python3 -
+   ```
 
-6. 启动服务
-```bash
-$ chmod +x ./entrypoint.sh
-$ bash ./entrypoint.sh
-```
-7. 启动WebUI服务
-```bash
-$ cd web
-$ npm install --registry=https://registry.npmmirror.com --force
-$ vim .umirc.ts
-# 修改proxy.target为http://127.0.0.1:9380
-$ npm run dev 
-```
+2. 下载源代码并安装 Python 依赖：  
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   export POETRY_VIRTUALENVS_CREATE=true POETRY_VIRTUALENVS_IN_PROJECT=true
+   ~/.local/bin/poetry install --sync --no-root # install RAGFlow dependent python modules
+   ```
+
+3. 通过 Docker Compose 启动依赖的服务（MinIO, Elasticsearch, Redis, and MySQL）：  
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
+   ```
+
+   在 `/etc/hosts` 中添加以下代码，将 **docker/service_conf.yaml** 文件中的所有 host 地址都解析为 `127.0.0.1`：  
+   ```
+   127.0.0.1       es01 mysql minio redis
+   ```  
+   在文件 **docker/service_conf.yaml** 中，对照 **docker/.env** 的配置将 mysql 端口更新为 `5455`，es 端口更新为 `1200`。
+
+4. 如果无法访问 HuggingFace，可以把环境变量 `HF_ENDPOINT` 设成相应的镜像站点：  
+ 
+   ```bash
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
+
+5. 启动后端服务：  
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+
+6. 安装前端依赖：  
+   ```bash
+   cd web
+   npm install --force
+   ```  
+7. 配置前端，将 **.umirc.ts** 的 `proxy.target` 更新为 `http://127.0.0.1:9380`：  
+8. 启动前端服务：  
+   ```bash
+   npm run dev 
+   ```  
+
+   _以下界面说明系统已经成功启动：_  
+
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
 
-8. 部署WebUI服务
-```bash
-$ cd web
-$ npm install --registry=https://registry.npmmirror.com --force
-$ umi build
-$ mkdir -p /ragflow/web
-$ cp -r dist /ragflow/web
-$ apt install nginx -y
-$ cp ../docker/nginx/proxy.conf /etc/nginx
-$ cp ../docker/nginx/nginx.conf /etc/nginx
-$ cp ../docker/nginx/ragflow.conf /etc/nginx/conf.d
-$ systemctl start nginx
-```
 ## 📚 技术文档
 
 - [Quickstart](https://ragflow.io/docs/dev/)
@@ -308,7 +291,7 @@ $ systemctl start nginx
 
 ## 🙌 贡献指南
 
-RAGFlow 只有通过开源协作才能蓬勃发展。秉持这一精神,我们欢迎来自社区的各种贡献。如果您有意参与其中,请查阅我们的[贡献者指南](./docs/references/CONTRIBUTING.md) 。
+RAGFlow 只有通过开源协作才能蓬勃发展。秉持这一精神,我们欢迎来自社区的各种贡献。如果您有意参与其中,请查阅我们的 [贡献者指南](./CONTRIBUTING.md) 。
 
 ## 🤝 商务合作
 

SECURITY.md

@@ -18,7 +18,7 @@ main
 ### Actual behavior
 
 The restricted_loads function at [api/utils/__init__.py#L215](https://github.com/infiniflow/ragflow/blob/main/api/utils/__init__.py#L215) is still vulnerable leading via code execution.
-The main reson is that numpy module has a numpy.f2py.diagnose.run_command function directly execute commands, but the restricted_loads function allows users import functions in module numpy.
+The main reason is that numpy module has a numpy.f2py.diagnose.run_command function directly execute commands, but the restricted_loads function allows users import functions in module numpy.
 
 
 ### Steps to reproduce

agent/canvas.py

@@ -260,7 +260,7 @@ class Canvas(ABC):
 
     def get_history(self, window_size):
         convs = []
-        for role, obj in self.history[window_size * -2:]:
+        for role, obj in self.history[(window_size + 1) * -1:]:
             convs.append({"role": role, "content": (obj if role == "user" else
                                                     '\n'.join(pd.DataFrame(obj)['content']))})
         return convs
@@ -274,7 +274,7 @@ class Canvas(ABC):
     def get_embedding_model(self):
         return self._embed_id
 
-    def _find_loop(self, max_loops=2):
+    def _find_loop(self, max_loops=6):
         path = self.path[-1][::-1]
         if len(path) < 2: return False
 
@@ -300,3 +300,6 @@ class Canvas(ABC):
                 return pat + " => " + pat
 
         return False
+
+    def get_prologue(self):
+        return self.components["begin"]["obj"]._param.prologue

agent/component/__init__.py

@@ -9,6 +9,7 @@ from .relevant import Relevant, RelevantParam
 from .message import Message, MessageParam
 from .rewrite import RewriteQuestion, RewriteQuestionParam
 from .keyword import KeywordExtract, KeywordExtractParam
+from .concentrator import Concentrator, ConcentratorParam
 from .baidu import Baidu, BaiduParam
 from .duckduckgo import DuckDuckGo, DuckDuckGoParam
 from .wikipedia import Wikipedia, WikipediaParam
@@ -17,9 +18,19 @@ from .arxiv import ArXiv, ArXivParam
 from .google import Google, GoogleParam
 from .bing import Bing, BingParam
 from .googlescholar import GoogleScholar, GoogleScholarParam
+from .deepl import DeepL, DeepLParam
+from .github import GitHub, GitHubParam
+from .baidufanyi import BaiduFanyi, BaiduFanyiParam
+from .qweather import QWeather, QWeatherParam
+from .exesql import ExeSQL, ExeSQLParam
+from .yahoofinance import YahooFinance, YahooFinanceParam
+from .wencai import WenCai, WenCaiParam
+from .jin10 import Jin10, Jin10Param
+from .tushare import TuShare, TuShareParam
+from .akshare import AkShare, AkShareParam
 
 
 def component_class(class_name):
-    m = importlib.import_module("graph.component")
+    m = importlib.import_module("agent.component")
     c = getattr(m, class_name)
     return c

agent/component/akshare.py

@@ -0,0 +1,56 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+import pandas as pd
+import akshare as ak
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class AkShareParam(ComponentParamBase):
+    """
+    Define the AkShare component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+
+
+class AkShare(ComponentBase, ABC):
+    component_name = "AkShare"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = ",".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return AkShare.be_output("")
+
+        try:
+            ak_res = []
+            stock_news_em_df = ak.stock_news_em(symbol=ans)
+            stock_news_em_df = stock_news_em_df.head(self._param.top_n)
+            ak_res = [{"content": '<a href="' + i["新闻链接"] + '">' + i["新闻标题"] + '</a>\n 新闻内容: ' + i[
+                "新闻内容"] + " \n发布时间:" + i["发布时间"] + " \n文章来源: " + i["文章来源"]} for index, i in stock_news_em_df.iterrows()]
+        except Exception as e:
+            return AkShare.be_output("**ERROR**: " + str(e))
+
+        if not ak_res:
+            return AkShare.be_output("")
+
+        return pd.DataFrame(ak_res)

agent/component/baidu.py

@@ -1,69 +1,69 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import random
-from abc import ABC
-from functools import partial
-import pandas as pd
-import requests
-import re
-from agent.settings import DEBUG
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class BaiduParam(ComponentParamBase):
-    """
-    Define the Baidu component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.top_n = 10
-
-    def check(self):
-        self.check_positive_integer(self.top_n, "Top N")
-
-
-class Baidu(ComponentBase, ABC):
-    component_name = "Baidu"
-
-    def _run(self, history, **kwargs):
-        ans = self.get_input()
-        ans = " - ".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return Baidu.be_output("")
-
-        try:
-            url = 'https://www.baidu.com/s?wd=' + ans + '&rn=' + str(self._param.top_n)
-            headers = {
-                'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.104 Safari/537.36'}
-            response = requests.get(url=url, headers=headers)
-
-            url_res = re.findall(r"'url': \\\"(.*?)\\\"}", response.text)
-            title_res = re.findall(r"'title': \\\"(.*?)\\\",\\n", response.text)
-            body_res = re.findall(r"\"contentText\":\"(.*?)\"", response.text)
-            baidu_res = [{"content": re.sub('<em>|</em>', '', '<a href="' + url + '">' + title + '</a>    ' + body)} for
-                         url, title, body in zip(url_res, title_res, body_res)]
-            del body_res, url_res, title_res
-        except Exception as e:
-            return Baidu.be_output("**ERROR**: " + str(e))
-
-        if not baidu_res:
-            return Baidu.be_output("")
-
-        df = pd.DataFrame(baidu_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
-        return df
-
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import random
+from abc import ABC
+from functools import partial
+import pandas as pd
+import requests
+import re
+from agent.settings import DEBUG
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class BaiduParam(ComponentParamBase):
+    """
+    Define the Baidu component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+
+
+class Baidu(ComponentBase, ABC):
+    component_name = "Baidu"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return Baidu.be_output("")
+
+        try:
+            url = 'https://www.baidu.com/s?wd=' + ans + '&rn=' + str(self._param.top_n)
+            headers = {
+                'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.104 Safari/537.36'}
+            response = requests.get(url=url, headers=headers)
+
+            url_res = re.findall(r"'url': \\\"(.*?)\\\"}", response.text)
+            title_res = re.findall(r"'title': \\\"(.*?)\\\",\\n", response.text)
+            body_res = re.findall(r"\"contentText\":\"(.*?)\"", response.text)
+            baidu_res = [{"content": re.sub('<em>|</em>', '', '<a href="' + url + '">' + title + '</a>    ' + body)} for
+                         url, title, body in zip(url_res, title_res, body_res)]
+            del body_res, url_res, title_res
+        except Exception as e:
+            return Baidu.be_output("**ERROR**: " + str(e))
+
+        if not baidu_res:
+            return Baidu.be_output("")
+
+        df = pd.DataFrame(baidu_res)
+        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        return df
+

agent/component/baidufanyi.py

@@ -0,0 +1,99 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import random
+from abc import ABC
+import requests
+from agent.component.base import ComponentBase, ComponentParamBase
+from hashlib import md5
+
+
+class BaiduFanyiParam(ComponentParamBase):
+    """
+    Define the BaiduFanyi component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.appid = "xxx"
+        self.secret_key = "xxx"
+        self.trans_type = 'translate'
+        self.parameters = []
+        self.source_lang = 'auto'
+        self.target_lang = 'auto'
+        self.domain = 'finance'
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_empty(self.appid, "BaiduFanyi APPID")
+        self.check_empty(self.secret_key, "BaiduFanyi Secret Key")
+        self.check_valid_value(self.trans_type, "Translate type", ['translate', 'fieldtranslate'])
+        self.check_valid_value(self.trans_type, "Translate domain",
+                               ['it', 'finance', 'machinery', 'senimed', 'novel', 'academic', 'aerospace', 'wiki',
+                                'news', 'law', 'contract'])
+        self.check_valid_value(self.source_lang, "Source language",
+                               ['auto', 'zh', 'en', 'yue', 'wyw', 'jp', 'kor', 'fra', 'spa', 'th', 'ara', 'ru', 'pt',
+                                'de', 'it', 'el', 'nl', 'pl', 'bul', 'est', 'dan', 'fin', 'cs', 'rom', 'slo', 'swe',
+                                'hu', 'cht', 'vie'])
+        self.check_valid_value(self.target_lang, "Target language",
+                               ['auto', 'zh', 'en', 'yue', 'wyw', 'jp', 'kor', 'fra', 'spa', 'th', 'ara', 'ru', 'pt',
+                                'de', 'it', 'el', 'nl', 'pl', 'bul', 'est', 'dan', 'fin', 'cs', 'rom', 'slo', 'swe',
+                                'hu', 'cht', 'vie'])
+        self.check_valid_value(self.domain, "Translate field",
+                               ['it', 'finance', 'machinery', 'senimed', 'novel', 'academic', 'aerospace', 'wiki',
+                                'news', 'law', 'contract'])
+
+
+class BaiduFanyi(ComponentBase, ABC):
+    component_name = "BaiduFanyi"
+
+    def _run(self, history, **kwargs):
+
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return BaiduFanyi.be_output("")
+
+        try:
+            source_lang = self._param.source_lang
+            target_lang = self._param.target_lang
+            appid = self._param.appid
+            salt = random.randint(32768, 65536)
+            secret_key = self._param.secret_key
+
+            if self._param.trans_type == 'translate':
+                sign = md5((appid + ans + salt + secret_key).encode('utf-8')).hexdigest()
+                url = 'http://api.fanyi.baidu.com/api/trans/vip/translate?' + 'q=' + ans + '&from=' + source_lang + '&to=' + target_lang + '&appid=' + appid + '&salt=' + salt + '&sign=' + sign
+                headers = {"Content-Type": "application/x-www-form-urlencoded"}
+                response = requests.post(url=url, headers=headers).json()
+
+                if response.get('error_code'):
+                    BaiduFanyi.be_output("**Error**:" + response['error_msg'])
+
+                return BaiduFanyi.be_output(response['trans_result'][0]['dst'])
+            elif self._param.trans_type == 'fieldtranslate':
+                domain = self._param.domain
+                sign = md5((appid + ans + salt + domain + secret_key).encode('utf-8')).hexdigest()
+                url = 'http://api.fanyi.baidu.com/api/trans/vip/fieldtranslate?' + 'q=' + ans + '&from=' + source_lang + '&to=' + target_lang + '&appid=' + appid + '&salt=' + salt + '&domain=' + domain + '&sign=' + sign
+                headers = {"Content-Type": "application/x-www-form-urlencoded"}
+                response = requests.post(url=url, headers=headers).json()
+
+                if response.get('error_code'):
+                    BaiduFanyi.be_output("**Error**:" + response['error_msg'])
+
+                return BaiduFanyi.be_output(response['trans_result'][0]['dst'])
+
+        except Exception as e:
+            BaiduFanyi.be_output("**Error**:" + str(e))

agent/component/base.py

@@ -444,7 +444,7 @@ class ComponentBase(ABC):
 
         if DEBUG: print(self.component_name, reversed_cpnts[::-1])
         for u in reversed_cpnts[::-1]:
-            if self.get_component_name(u) in ["switch"]: continue
+            if self.get_component_name(u) in ["switch", "concentrator"]: continue
             if self.component_name.lower() == "generate" and self.get_component_name(u) == "retrieval":
                 o = self._canvas.get_component(u)["obj"].output(allow_partial=False)[1]
                 if o is not None:
@@ -460,13 +460,11 @@ class ComponentBase(ABC):
                         upstream_outs.append(pd.DataFrame([{"content": c}]))
                         break
                 break
-            if self.component_name.lower().find("answer") >= 0:
-                if self.get_component_name(u) in ["relevant"]:
-                    continue
-            else:
-                o = self._canvas.get_component(u)["obj"].output(allow_partial=False)[1]
-                if o is not None:
-                    upstream_outs.append(o)
+            if self.component_name.lower().find("answer") >= 0 and self.get_component_name(u) in ["relevant"]:
+                continue
+            o = self._canvas.get_component(u)["obj"].output(allow_partial=False)[1]
+            if o is not None:
+                upstream_outs.append(o)
             break
 
         if upstream_outs:
@@ -474,7 +472,7 @@ class ComponentBase(ABC):
             if "content" in df:
                 df = df.drop_duplicates(subset=['content']).reset_index(drop=True)
             return df
-        return pd.DataFrame()
+        return pd.DataFrame(self._canvas.get_history(3)[-1:])
 
     def get_stream_input(self):
         reversed_cpnts = []

agent/component/bing.py

@@ -1,85 +1,85 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-import requests
-import pandas as pd
-from agent.settings import DEBUG
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class BingParam(ComponentParamBase):
-    """
-    Define the Bing component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.top_n = 10
-        self.channel = "Webpages"
-        self.api_key = "YOUR_ACCESS_KEY"
-        self.country = "CN"
-        self.language = "en"
-
-    def check(self):
-        self.check_positive_integer(self.top_n, "Top N")
-        self.check_valid_value(self.channel, "Bing Web Search or Bing News", ["Webpages", "News"])
-        self.check_empty(self.api_key, "Bing subscription key")
-        self.check_valid_value(self.country, "Bing Country",
-                               ['AR', 'AU', 'AT', 'BE', 'BR', 'CA', 'CL', 'DK', 'FI', 'FR', 'DE', 'HK', 'IN', 'ID',
-                                'IT', 'JP', 'KR', 'MY', 'MX', 'NL', 'NZ', 'NO', 'CN', 'PL', 'PT', 'PH', 'RU', 'SA',
-                                'ZA', 'ES', 'SE', 'CH', 'TW', 'TR', 'GB', 'US'])
-        self.check_valid_value(self.language, "Bing Languages",
-                               ['ar', 'eu', 'bn', 'bg', 'ca', 'ns', 'nt', 'hr', 'cs', 'da', 'nl', 'en', 'gb', 'et',
-                                'fi', 'fr', 'gl', 'de', 'gu', 'he', 'hi', 'hu', 'is', 'it', 'jp', 'kn', 'ko', 'lv',
-                                'lt', 'ms', 'ml', 'mr', 'nb', 'pl', 'br', 'pt', 'pa', 'ro', 'ru', 'sr', 'sk', 'sl',
-                                'es', 'sv', 'ta', 'te', 'th', 'tr', 'uk', 'vi'])
-
-
-class Bing(ComponentBase, ABC):
-    component_name = "Bing"
-
-    def _run(self, history, **kwargs):
-        ans = self.get_input()
-        ans = " - ".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return Bing.be_output("")
-
-        try:
-            headers = {"Ocp-Apim-Subscription-Key": self._param.api_key, 'Accept-Language': self._param.language}
-            params = {"q": ans, "textDecorations": True, "textFormat": "HTML", "cc": self._param.country,
-                      "answerCount": 1, "promote": self._param.channel}
-            if self._param.channel == "Webpages":
-                response = requests.get("https://api.bing.microsoft.com/v7.0/search", headers=headers, params=params)
-                response.raise_for_status()
-                search_results = response.json()
-                bing_res = [{"content": '<a href="' + i["url"] + '">' + i["name"] + '</a>    ' + i["snippet"]} for i in
-                            search_results["webPages"]["value"]]
-            elif self._param.channel == "News":
-                response = requests.get("https://api.bing.microsoft.com/v7.0/news/search", headers=headers,
-                                        params=params)
-                response.raise_for_status()
-                search_results = response.json()
-                bing_res = [{"content": '<a href="' + i["url"] + '">' + i["name"] + '</a>    ' + i["description"]} for i
-                            in search_results['news']['value']]
-        except Exception as e:
-            return Bing.be_output("**ERROR**: " + str(e))
-
-        if not bing_res:
-            return Bing.be_output("")
-
-        df = pd.DataFrame(bing_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
-        return df
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+import requests
+import pandas as pd
+from agent.settings import DEBUG
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class BingParam(ComponentParamBase):
+    """
+    Define the Bing component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+        self.channel = "Webpages"
+        self.api_key = "YOUR_ACCESS_KEY"
+        self.country = "CN"
+        self.language = "en"
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_valid_value(self.channel, "Bing Web Search or Bing News", ["Webpages", "News"])
+        self.check_empty(self.api_key, "Bing subscription key")
+        self.check_valid_value(self.country, "Bing Country",
+                               ['AR', 'AU', 'AT', 'BE', 'BR', 'CA', 'CL', 'DK', 'FI', 'FR', 'DE', 'HK', 'IN', 'ID',
+                                'IT', 'JP', 'KR', 'MY', 'MX', 'NL', 'NZ', 'NO', 'CN', 'PL', 'PT', 'PH', 'RU', 'SA',
+                                'ZA', 'ES', 'SE', 'CH', 'TW', 'TR', 'GB', 'US'])
+        self.check_valid_value(self.language, "Bing Languages",
+                               ['ar', 'eu', 'bn', 'bg', 'ca', 'ns', 'nt', 'hr', 'cs', 'da', 'nl', 'en', 'gb', 'et',
+                                'fi', 'fr', 'gl', 'de', 'gu', 'he', 'hi', 'hu', 'is', 'it', 'jp', 'kn', 'ko', 'lv',
+                                'lt', 'ms', 'ml', 'mr', 'nb', 'pl', 'br', 'pt', 'pa', 'ro', 'ru', 'sr', 'sk', 'sl',
+                                'es', 'sv', 'ta', 'te', 'th', 'tr', 'uk', 'vi'])
+
+
+class Bing(ComponentBase, ABC):
+    component_name = "Bing"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return Bing.be_output("")
+
+        try:
+            headers = {"Ocp-Apim-Subscription-Key": self._param.api_key, 'Accept-Language': self._param.language}
+            params = {"q": ans, "textDecorations": True, "textFormat": "HTML", "cc": self._param.country,
+                      "answerCount": 1, "promote": self._param.channel}
+            if self._param.channel == "Webpages":
+                response = requests.get("https://api.bing.microsoft.com/v7.0/search", headers=headers, params=params)
+                response.raise_for_status()
+                search_results = response.json()
+                bing_res = [{"content": '<a href="' + i["url"] + '">' + i["name"] + '</a>    ' + i["snippet"]} for i in
+                            search_results["webPages"]["value"]]
+            elif self._param.channel == "News":
+                response = requests.get("https://api.bing.microsoft.com/v7.0/news/search", headers=headers,
+                                        params=params)
+                response.raise_for_status()
+                search_results = response.json()
+                bing_res = [{"content": '<a href="' + i["url"] + '">' + i["name"] + '</a>    ' + i["description"]} for i
+                            in search_results['news']['value']]
+        except Exception as e:
+            return Bing.be_output("**ERROR**: " + str(e))
+
+        if not bing_res:
+            return Bing.be_output("")
+
+        df = pd.DataFrame(bing_res)
+        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        return df

agent/component/categorize.py

@@ -82,6 +82,6 @@ class Categorize(Generate, ABC):
             if ans.lower().find(c.lower()) >= 0:
                 return Categorize.be_output(self._param.category_description[c]["to"])
 
-        return Categorize.be_output(self._param.category_description.items()[-1][1]["to"])
+        return Categorize.be_output(list(self._param.category_description.items())[-1][1]["to"])
 
 

agent/component/concentrator.py

@@ -0,0 +1,36 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class ConcentratorParam(ComponentParamBase):
+    """
+    Define the Concentrator component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    def check(self):
+        return True
+
+
+class Concentrator(ComponentBase, ABC):
+    component_name = "Concentrator"
+
+    def _run(self, history, **kwargs):
+        return Concentrator.be_output("")

agent/component/deepl.py

@@ -0,0 +1,62 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+import re
+from agent.component.base import ComponentBase, ComponentParamBase
+import deepl
+
+
+class DeepLParam(ComponentParamBase):
+    """
+    Define the DeepL component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.auth_key = "xxx"
+        self.parameters = []
+        self.source_lang = 'ZH'
+        self.target_lang = 'EN-GB'
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_valid_value(self.source_lang, "Source language",
+                               ['AR', 'BG', 'CS', 'DA', 'DE', 'EL', 'EN', 'ES', 'ET', 'FI', 'FR', 'HU', 'ID', 'IT',
+                                'JA', 'KO', 'LT', 'LV', 'NB', 'NL', 'PL', 'PT', 'RO', 'RU', 'SK', 'SL', 'SV', 'TR',
+                                'UK', 'ZH'])
+        self.check_valid_value(self.target_lang, "Target language",
+                               ['AR', 'BG', 'CS', 'DA', 'DE', 'EL', 'EN-GB', 'EN-US', 'ES', 'ET', 'FI', 'FR', 'HU',
+                                'ID', 'IT', 'JA', 'KO', 'LT', 'LV', 'NB', 'NL', 'PL', 'PT-BR', 'PT-PT', 'RO', 'RU',
+                                'SK', 'SL', 'SV', 'TR', 'UK', 'ZH'])
+
+
+class DeepL(ComponentBase, ABC):
+    component_name = "GitHub"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return DeepL.be_output("")
+
+        try:
+            translator = deepl.Translator(self._param.auth_key)
+            result = translator.translate_text(ans, source_lang=self._param.source_lang,
+                                               target_lang=self._param.target_lang)
+
+            return DeepL.be_output(result.text)
+        except Exception as e:
+            DeepL.be_output("**Error**:" + str(e))

agent/component/exesql.py

@@ -0,0 +1,99 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+import re
+import pandas as pd
+from peewee import MySQLDatabase, PostgresqlDatabase
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class ExeSQLParam(ComponentParamBase):
+    """
+    Define the ExeSQL component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.db_type = "mysql"
+        self.database = ""
+        self.username = ""
+        self.host = ""
+        self.port = 3306
+        self.password = ""
+        self.loop = 3
+        self.top_n = 30
+
+    def check(self):
+        self.check_valid_value(self.db_type, "Choose DB type", ['mysql', 'postgresql', 'mariadb'])
+        self.check_empty(self.database, "Database name")
+        self.check_empty(self.username, "database username")
+        self.check_empty(self.host, "IP Address")
+        self.check_positive_integer(self.port, "IP Port")
+        self.check_empty(self.password, "Database password")
+        self.check_positive_integer(self.top_n, "Number of records")
+
+
+class ExeSQL(ComponentBase, ABC):
+    component_name = "ExeSQL"
+
+    def _run(self, history, **kwargs):
+        if not hasattr(self, "_loop"):
+            setattr(self, "_loop", 0)
+        if self._loop >= self._param.loop:
+            self._loop = 0
+            raise Exception("Maximum loop time exceeds. Can't query the correct data via SQL statement.")
+        self._loop += 1
+
+        ans = self.get_input()
+        ans = "".join(ans["content"]) if "content" in ans else ""
+        ans = re.sub(r'^.*?SELECT ', 'SELECT ', repr(ans), flags=re.IGNORECASE)
+        ans = re.sub(r';.*?SELECT ', '; SELECT ', ans, flags=re.IGNORECASE)
+        ans = re.sub(r';[^;]*$', r';', ans)
+        if not ans:
+            raise Exception("SQL statement not found!")
+
+        if self._param.db_type in ["mysql", "mariadb"]:
+            db = MySQLDatabase(self._param.database, user=self._param.username, host=self._param.host,
+                               port=self._param.port, password=self._param.password)
+        elif self._param.db_type == 'postgresql':
+            db = PostgresqlDatabase(self._param.database, user=self._param.username, host=self._param.host,
+                                    port=self._param.port, password=self._param.password)
+
+        try:
+            db.connect()
+        except Exception as e:
+            raise Exception("Database Connection Failed! \n" + str(e))
+        sql_res = []
+        for single_sql in re.split(r';', ans.replace(r"\n", " ")):
+            if not single_sql:
+                continue
+            try:
+                query = db.execute_sql(single_sql)
+                if query.rowcount == 0:
+                    sql_res.append({"content": "\nTotal: " + str(query.rowcount) + "\n No record in the database!"})
+                    continue
+                single_res = pd.DataFrame([i for i in query.fetchmany(size=self._param.top_n)])
+                single_res.columns = [i[0] for i in query.description]
+                sql_res.append({"content": "\nTotal: " + str(query.rowcount) + "\n" + single_res.to_markdown()})
+            except Exception as e:
+                sql_res.append({"content": "**Error**:" + str(e) + "\nError SQL Statement:" + single_sql})
+                pass
+        db.close()
+
+        if not sql_res:
+            return ExeSQL.be_output("")
+
+        return pd.DataFrame(sql_res)

agent/component/generate.py

@@ -66,6 +66,9 @@ class Generate(ComponentBase):
         return cpnts
 
     def set_cite(self, retrieval_res, answer):
+        retrieval_res = retrieval_res.dropna(subset=["vector", "content_ltks"]).reset_index(drop=True)
+        if "empty_response" in retrieval_res.columns:
+            retrieval_res["empty_response"].fillna("", inplace=True)
         answer, idx = retrievaler.insert_citations(answer, [ck["content_ltks"] for _, ck in retrieval_res.iterrows()],
                                                    [ck["vector"] for _, ck in retrieval_res.iterrows()],
                                                    LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING,
@@ -109,29 +112,31 @@ class Generate(ComponentBase):
 
         kwargs["input"] = input
         for n, v in kwargs.items():
-            # prompt = re.sub(r"\{%s\}"%n, re.escape(str(v)), prompt)
-            prompt = re.sub(r"\{%s\}" % n, str(v), prompt)
+            prompt = re.sub(r"\{%s\}" % n, re.escape(str(v)), prompt)
 
         downstreams = self._canvas.get_component(self._id)["downstream"]
         if kwargs.get("stream") and len(downstreams) == 1 and self._canvas.get_component(downstreams[0])[
             "obj"].component_name.lower() == "answer":
             return partial(self.stream_output, chat_mdl, prompt, retrieval_res)
 
-        if "empty_response" in retrieval_res.columns:
-            return Generate.be_output(input)
+        if "empty_response" in retrieval_res.columns and not "".join(retrieval_res["content"]):
+            res = {"content": "\n- ".join(retrieval_res["empty_response"]) if "\n- ".join(
+                retrieval_res["empty_response"]) else "Nothing found in knowledgebase!", "reference": []}
+            return pd.DataFrame([res])
 
         ans = chat_mdl.chat(prompt, self._canvas.get_history(self._param.message_history_window_size),
                             self._param.gen_conf())
         if self._param.cite and "content_ltks" in retrieval_res.columns and "vector" in retrieval_res.columns:
-            df = self.set_cite(retrieval_res, ans)
-            return pd.DataFrame(df)
+            res = self.set_cite(retrieval_res, ans)
+            return pd.DataFrame([res])
 
         return Generate.be_output(ans)
 
     def stream_output(self, chat_mdl, prompt, retrieval_res):
         res = None
-        if "empty_response" in retrieval_res.columns and "\n- ".join(retrieval_res["content"]):
-            res = {"content": "\n- ".join(retrieval_res["content"]), "reference": []}
+        if "empty_response" in retrieval_res.columns and not "".join(retrieval_res["content"]):
+            res = {"content": "\n- ".join(retrieval_res["empty_response"]) if "\n- ".join(
+                retrieval_res["empty_response"]) else "Nothing found in knowledgebase!", "reference": []}
             yield res
             self.set_output(res)
             return

agent/component/github.py

@@ -0,0 +1,61 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+import pandas as pd
+import requests
+from agent.settings import DEBUG
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class GitHubParam(ComponentParamBase):
+    """
+    Define the GitHub component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+
+
+class GitHub(ComponentBase, ABC):
+    component_name = "GitHub"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return GitHub.be_output("")
+
+        try:
+            url = 'https://api.github.com/search/repositories?q=' + ans + '&sort=stars&order=desc&per_page=' + str(
+                self._param.top_n)
+            headers = {"Content-Type": "application/vnd.github+json", "X-GitHub-Api-Version": '2022-11-28'}
+            response = requests.get(url=url, headers=headers).json()
+
+            github_res = [{"content": '<a href="' + i["html_url"] + '">' + i["name"] + '</a>' + str(
+                i["description"]) + '\n stars:' + str(i['watchers'])} for i in response['items']]
+        except Exception as e:
+            return GitHub.be_output("**ERROR**: " + str(e))
+
+        if not github_res:
+            return GitHub.be_output("")
+
+        df = pd.DataFrame(github_res)
+        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        return df

agent/component/google.py

@@ -1,96 +1,96 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-from serpapi import GoogleSearch
-import pandas as pd
-from agent.settings import DEBUG
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class GoogleParam(ComponentParamBase):
-    """
-    Define the Google component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.top_n = 10
-        self.api_key = "xxx"
-        self.country = "cn"
-        self.language = "en"
-
-    def check(self):
-        self.check_positive_integer(self.top_n, "Top N")
-        self.check_empty(self.api_key, "SerpApi API key")
-        self.check_valid_value(self.country, "Google Country",
-                               ['af', 'al', 'dz', 'as', 'ad', 'ao', 'ai', 'aq', 'ag', 'ar', 'am', 'aw', 'au', 'at',
-                                'az', 'bs', 'bh', 'bd', 'bb', 'by', 'be', 'bz', 'bj', 'bm', 'bt', 'bo', 'ba', 'bw',
-                                'bv', 'br', 'io', 'bn', 'bg', 'bf', 'bi', 'kh', 'cm', 'ca', 'cv', 'ky', 'cf', 'td',
-                                'cl', 'cn', 'cx', 'cc', 'co', 'km', 'cg', 'cd', 'ck', 'cr', 'ci', 'hr', 'cu', 'cy',
-                                'cz', 'dk', 'dj', 'dm', 'do', 'ec', 'eg', 'sv', 'gq', 'er', 'ee', 'et', 'fk', 'fo',
-                                'fj', 'fi', 'fr', 'gf', 'pf', 'tf', 'ga', 'gm', 'ge', 'de', 'gh', 'gi', 'gr', 'gl',
-                                'gd', 'gp', 'gu', 'gt', 'gn', 'gw', 'gy', 'ht', 'hm', 'va', 'hn', 'hk', 'hu', 'is',
-                                'in', 'id', 'ir', 'iq', 'ie', 'il', 'it', 'jm', 'jp', 'jo', 'kz', 'ke', 'ki', 'kp',
-                                'kr', 'kw', 'kg', 'la', 'lv', 'lb', 'ls', 'lr', 'ly', 'li', 'lt', 'lu', 'mo', 'mk',
-                                'mg', 'mw', 'my', 'mv', 'ml', 'mt', 'mh', 'mq', 'mr', 'mu', 'yt', 'mx', 'fm', 'md',
-                                'mc', 'mn', 'ms', 'ma', 'mz', 'mm', 'na', 'nr', 'np', 'nl', 'an', 'nc', 'nz', 'ni',
-                                'ne', 'ng', 'nu', 'nf', 'mp', 'no', 'om', 'pk', 'pw', 'ps', 'pa', 'pg', 'py', 'pe',
-                                'ph', 'pn', 'pl', 'pt', 'pr', 'qa', 're', 'ro', 'ru', 'rw', 'sh', 'kn', 'lc', 'pm',
-                                'vc', 'ws', 'sm', 'st', 'sa', 'sn', 'rs', 'sc', 'sl', 'sg', 'sk', 'si', 'sb', 'so',
-                                'za', 'gs', 'es', 'lk', 'sd', 'sr', 'sj', 'sz', 'se', 'ch', 'sy', 'tw', 'tj', 'tz',
-                                'th', 'tl', 'tg', 'tk', 'to', 'tt', 'tn', 'tr', 'tm', 'tc', 'tv', 'ug', 'ua', 'ae',
-                                'uk', 'gb', 'us', 'um', 'uy', 'uz', 'vu', 've', 'vn', 'vg', 'vi', 'wf', 'eh', 'ye',
-                                'zm', 'zw'])
-        self.check_valid_value(self.language, "Google languages",
-                               ['af', 'ak', 'sq', 'ws', 'am', 'ar', 'hy', 'az', 'eu', 'be', 'bem', 'bn', 'bh',
-                                'xx-bork', 'bs', 'br', 'bg', 'bt', 'km', 'ca', 'chr', 'ny', 'zh-cn', 'zh-tw', 'co',
-                                'hr', 'cs', 'da', 'nl', 'xx-elmer', 'en', 'eo', 'et', 'ee', 'fo', 'tl', 'fi', 'fr',
-                                'fy', 'gaa', 'gl', 'ka', 'de', 'el', 'kl', 'gn', 'gu', 'xx-hacker', 'ht', 'ha', 'haw',
-                                'iw', 'hi', 'hu', 'is', 'ig', 'id', 'ia', 'ga', 'it', 'ja', 'jw', 'kn', 'kk', 'rw',
-                                'rn', 'xx-klingon', 'kg', 'ko', 'kri', 'ku', 'ckb', 'ky', 'lo', 'la', 'lv', 'ln', 'lt',
-                                'loz', 'lg', 'ach', 'mk', 'mg', 'ms', 'ml', 'mt', 'mv', 'mi', 'mr', 'mfe', 'mo', 'mn',
-                                'sr-me', 'my', 'ne', 'pcm', 'nso', 'no', 'nn', 'oc', 'or', 'om', 'ps', 'fa',
-                                'xx-pirate', 'pl', 'pt', 'pt-br', 'pt-pt', 'pa', 'qu', 'ro', 'rm', 'nyn', 'ru', 'gd',
-                                'sr', 'sh', 'st', 'tn', 'crs', 'sn', 'sd', 'si', 'sk', 'sl', 'so', 'es', 'es-419', 'su',
-                                'sw', 'sv', 'tg', 'ta', 'tt', 'te', 'th', 'ti', 'to', 'lua', 'tum', 'tr', 'tk', 'tw',
-                                'ug', 'uk', 'ur', 'uz', 'vu', 'vi', 'cy', 'wo', 'xh', 'yi', 'yo', 'zu']
-                               )
-
-
-class Google(ComponentBase, ABC):
-    component_name = "Google"
-
-    def _run(self, history, **kwargs):
-        ans = self.get_input()
-        ans = " - ".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return Google.be_output("")
-
-        try:
-            client = GoogleSearch(
-                {"engine": "google", "q": ans, "api_key": self._param.api_key, "gl": self._param.country,
-                 "hl": self._param.language, "num": self._param.top_n})
-            google_res = [{"content": '<a href="' + i["link"] + '">' + i["title"] + '</a>    ' + i["snippet"]} for i in
-                          client.get_dict()["organic_results"]]
-        except Exception as e:
-            return Google.be_output("**ERROR**: Existing Unavailable Parameters!")
-
-        if not google_res:
-            return Google.be_output("")
-
-        df = pd.DataFrame(google_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
-        return df
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+from serpapi import GoogleSearch
+import pandas as pd
+from agent.settings import DEBUG
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class GoogleParam(ComponentParamBase):
+    """
+    Define the Google component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+        self.api_key = "xxx"
+        self.country = "cn"
+        self.language = "en"
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_empty(self.api_key, "SerpApi API key")
+        self.check_valid_value(self.country, "Google Country",
+                               ['af', 'al', 'dz', 'as', 'ad', 'ao', 'ai', 'aq', 'ag', 'ar', 'am', 'aw', 'au', 'at',
+                                'az', 'bs', 'bh', 'bd', 'bb', 'by', 'be', 'bz', 'bj', 'bm', 'bt', 'bo', 'ba', 'bw',
+                                'bv', 'br', 'io', 'bn', 'bg', 'bf', 'bi', 'kh', 'cm', 'ca', 'cv', 'ky', 'cf', 'td',
+                                'cl', 'cn', 'cx', 'cc', 'co', 'km', 'cg', 'cd', 'ck', 'cr', 'ci', 'hr', 'cu', 'cy',
+                                'cz', 'dk', 'dj', 'dm', 'do', 'ec', 'eg', 'sv', 'gq', 'er', 'ee', 'et', 'fk', 'fo',
+                                'fj', 'fi', 'fr', 'gf', 'pf', 'tf', 'ga', 'gm', 'ge', 'de', 'gh', 'gi', 'gr', 'gl',
+                                'gd', 'gp', 'gu', 'gt', 'gn', 'gw', 'gy', 'ht', 'hm', 'va', 'hn', 'hk', 'hu', 'is',
+                                'in', 'id', 'ir', 'iq', 'ie', 'il', 'it', 'jm', 'jp', 'jo', 'kz', 'ke', 'ki', 'kp',
+                                'kr', 'kw', 'kg', 'la', 'lv', 'lb', 'ls', 'lr', 'ly', 'li', 'lt', 'lu', 'mo', 'mk',
+                                'mg', 'mw', 'my', 'mv', 'ml', 'mt', 'mh', 'mq', 'mr', 'mu', 'yt', 'mx', 'fm', 'md',
+                                'mc', 'mn', 'ms', 'ma', 'mz', 'mm', 'na', 'nr', 'np', 'nl', 'an', 'nc', 'nz', 'ni',
+                                'ne', 'ng', 'nu', 'nf', 'mp', 'no', 'om', 'pk', 'pw', 'ps', 'pa', 'pg', 'py', 'pe',
+                                'ph', 'pn', 'pl', 'pt', 'pr', 'qa', 're', 'ro', 'ru', 'rw', 'sh', 'kn', 'lc', 'pm',
+                                'vc', 'ws', 'sm', 'st', 'sa', 'sn', 'rs', 'sc', 'sl', 'sg', 'sk', 'si', 'sb', 'so',
+                                'za', 'gs', 'es', 'lk', 'sd', 'sr', 'sj', 'sz', 'se', 'ch', 'sy', 'tw', 'tj', 'tz',
+                                'th', 'tl', 'tg', 'tk', 'to', 'tt', 'tn', 'tr', 'tm', 'tc', 'tv', 'ug', 'ua', 'ae',
+                                'uk', 'gb', 'us', 'um', 'uy', 'uz', 'vu', 've', 'vn', 'vg', 'vi', 'wf', 'eh', 'ye',
+                                'zm', 'zw'])
+        self.check_valid_value(self.language, "Google languages",
+                               ['af', 'ak', 'sq', 'ws', 'am', 'ar', 'hy', 'az', 'eu', 'be', 'bem', 'bn', 'bh',
+                                'xx-bork', 'bs', 'br', 'bg', 'bt', 'km', 'ca', 'chr', 'ny', 'zh-cn', 'zh-tw', 'co',
+                                'hr', 'cs', 'da', 'nl', 'xx-elmer', 'en', 'eo', 'et', 'ee', 'fo', 'tl', 'fi', 'fr',
+                                'fy', 'gaa', 'gl', 'ka', 'de', 'el', 'kl', 'gn', 'gu', 'xx-hacker', 'ht', 'ha', 'haw',
+                                'iw', 'hi', 'hu', 'is', 'ig', 'id', 'ia', 'ga', 'it', 'ja', 'jw', 'kn', 'kk', 'rw',
+                                'rn', 'xx-klingon', 'kg', 'ko', 'kri', 'ku', 'ckb', 'ky', 'lo', 'la', 'lv', 'ln', 'lt',
+                                'loz', 'lg', 'ach', 'mk', 'mg', 'ms', 'ml', 'mt', 'mv', 'mi', 'mr', 'mfe', 'mo', 'mn',
+                                'sr-me', 'my', 'ne', 'pcm', 'nso', 'no', 'nn', 'oc', 'or', 'om', 'ps', 'fa',
+                                'xx-pirate', 'pl', 'pt', 'pt-br', 'pt-pt', 'pa', 'qu', 'ro', 'rm', 'nyn', 'ru', 'gd',
+                                'sr', 'sh', 'st', 'tn', 'crs', 'sn', 'sd', 'si', 'sk', 'sl', 'so', 'es', 'es-419', 'su',
+                                'sw', 'sv', 'tg', 'ta', 'tt', 'te', 'th', 'ti', 'to', 'lua', 'tum', 'tr', 'tk', 'tw',
+                                'ug', 'uk', 'ur', 'uz', 'vu', 'vi', 'cy', 'wo', 'xh', 'yi', 'yo', 'zu']
+                               )
+
+
+class Google(ComponentBase, ABC):
+    component_name = "Google"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return Google.be_output("")
+
+        try:
+            client = GoogleSearch(
+                {"engine": "google", "q": ans, "api_key": self._param.api_key, "gl": self._param.country,
+                 "hl": self._param.language, "num": self._param.top_n})
+            google_res = [{"content": '<a href="' + i["link"] + '">' + i["title"] + '</a>    ' + i["snippet"]} for i in
+                          client.get_dict()["organic_results"]]
+        except Exception as e:
+            return Google.be_output("**ERROR**: Existing Unavailable Parameters!")
+
+        if not google_res:
+            return Google.be_output("")
+
+        df = pd.DataFrame(google_res)
+        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        return df

agent/component/googlescholar.py

@@ -1,70 +1,70 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-import pandas as pd
-from agent.settings import DEBUG
-from agent.component.base import ComponentBase, ComponentParamBase
-from scholarly import scholarly
-
-
-class GoogleScholarParam(ComponentParamBase):
-    """
-    Define the GoogleScholar component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.top_n = 6
-        self.sort_by = 'relevance'
-        self.year_low = None
-        self.year_high = None
-        self.patents = True
-
-    def check(self):
-        self.check_positive_integer(self.top_n, "Top N")
-        self.check_valid_value(self.sort_by, "GoogleScholar Sort_by", ['date', 'relevance'])
-        self.check_boolean(self.patents, "Whether or not to include patents, defaults to True")
-
-
-class GoogleScholar(ComponentBase, ABC):
-    component_name = "GoogleScholar"
-
-    def _run(self, history, **kwargs):
-        ans = self.get_input()
-        ans = " - ".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return GoogleScholar.be_output("")
-
-        scholar_client = scholarly.search_pubs(ans, patents=self._param.patents, year_low=self._param.year_low,
-                                               year_high=self._param.year_high, sort_by=self._param.sort_by)
-        scholar_res = []
-        for i in range(self._param.top_n):
-            try:
-                pub = next(scholar_client)
-                scholar_res.append({"content": 'Title: ' + pub['bib']['title'] + '\n_Url: <a href="' + pub[
-                    'pub_url'] + '"></a> ' + "\n author: " + ",".join(pub['bib']['author']) + '\n Abstract: ' + pub[
-                                                   'bib'].get('abstract', 'no abstract')})
-
-            except StopIteration or Exception as e:
-                print("**ERROR** " + str(e))
-                break
-
-        if not scholar_res:
-            return GoogleScholar.be_output("")
-
-        df = pd.DataFrame(scholar_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
-        return df
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+import pandas as pd
+from agent.settings import DEBUG
+from agent.component.base import ComponentBase, ComponentParamBase
+from scholarly import scholarly
+
+
+class GoogleScholarParam(ComponentParamBase):
+    """
+    Define the GoogleScholar component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 6
+        self.sort_by = 'relevance'
+        self.year_low = None
+        self.year_high = None
+        self.patents = True
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_valid_value(self.sort_by, "GoogleScholar Sort_by", ['date', 'relevance'])
+        self.check_boolean(self.patents, "Whether or not to include patents, defaults to True")
+
+
+class GoogleScholar(ComponentBase, ABC):
+    component_name = "GoogleScholar"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return GoogleScholar.be_output("")
+
+        scholar_client = scholarly.search_pubs(ans, patents=self._param.patents, year_low=self._param.year_low,
+                                               year_high=self._param.year_high, sort_by=self._param.sort_by)
+        scholar_res = []
+        for i in range(self._param.top_n):
+            try:
+                pub = next(scholar_client)
+                scholar_res.append({"content": 'Title: ' + pub['bib']['title'] + '\n_Url: <a href="' + pub[
+                    'pub_url'] + '"></a> ' + "\n author: " + ",".join(pub['bib']['author']) + '\n Abstract: ' + pub[
+                                                   'bib'].get('abstract', 'no abstract')})
+
+            except StopIteration or Exception as e:
+                print("**ERROR** " + str(e))
+                break
+
+        if not scholar_res:
+            return GoogleScholar.be_output("")
+
+        df = pd.DataFrame(scholar_res)
+        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        return df

agent/component/jin10.py

@@ -0,0 +1,130 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import json
+from abc import ABC
+import pandas as pd
+import requests
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class Jin10Param(ComponentParamBase):
+    """
+    Define the Jin10 component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.type = "flash"
+        self.secret_key = "xxx"
+        self.flash_type = '1'
+        self.calendar_type = 'cj'
+        self.calendar_datatype = 'data'
+        self.symbols_type = 'GOODS'
+        self.symbols_datatype = 'symbols'
+        self.contain = ""
+        self.filter = ""
+
+    def check(self):
+        self.check_valid_value(self.type, "Type", ['flash', 'calendar', 'symbols', 'news'])
+        self.check_valid_value(self.flash_type, "Flash Type", ['1', '2', '3', '4', '5'])
+        self.check_valid_value(self.calendar_type, "Calendar Type", ['cj', 'qh', 'hk', 'us'])
+        self.check_valid_value(self.calendar_datatype, "Calendar DataType", ['data', 'event', 'holiday'])
+        self.check_valid_value(self.symbols_type, "Symbols Type", ['GOODS', 'FOREX', 'FUTURE', 'CRYPTO'])
+        self.check_valid_value(self.symbols_datatype, 'Symbols DataType', ['symbols', 'quotes'])
+
+
+class Jin10(ComponentBase, ABC):
+    component_name = "Jin10"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return Jin10.be_output("")
+
+        jin10_res = []
+        headers = {'secret-key': self._param.secret_key}
+        try:
+            if self._param.type == "flash":
+                params = {
+                    'category': self._param.flash_type,
+                    'contain': self._param.contain,
+                    'filter': self._param.filter
+                }
+                response = requests.get(
+                    url='https://open-data-api.jin10.com/data-api/flash?category=' + self._param.flash_type,
+                    headers=headers, data=json.dumps(params))
+                response = response.json()
+                for i in response['data']:
+                    jin10_res.append({"content": i['data']['content']})
+            if self._param.type == "calendar":
+                params = {
+                    'category': self._param.calendar_type
+                }
+                response = requests.get(
+                    url='https://open-data-api.jin10.com/data-api/calendar/' + self._param.calendar_datatype + '?category=' + self._param.calendar_type,
+                    headers=headers, data=json.dumps(params))
+
+                response = response.json()
+                jin10_res.append({"content": pd.DataFrame(response['data']).to_markdown()})
+            if self._param.type == "symbols":
+                params = {
+                    'type': self._param.symbols_type
+                }
+                if self._param.symbols_datatype == "quotes":
+                    params['codes'] = 'BTCUSD'
+                response = requests.get(
+                    url='https://open-data-api.jin10.com/data-api/' + self._param.symbols_datatype + '?type=' + self._param.symbols_type,
+                    headers=headers, data=json.dumps(params))
+                response = response.json()
+                if self._param.symbols_datatype == "symbols":
+                    for i in response['data']:
+                        i['Commodity Code'] = i['c']
+                        i['Stock Exchange'] = i['e']
+                        i['Commodity Name'] = i['n']
+                        i['Commodity Type'] = i['t']
+                        del i['c'], i['e'], i['n'], i['t']
+                if self._param.symbols_datatype == "quotes":
+                    for i in response['data']:
+                        i['Selling Price'] = i['a']
+                        i['Buying Price'] = i['b']
+                        i['Commodity Code'] = i['c']
+                        i['Stock Exchange'] = i['e']
+                        i['Highest Price'] = i['h']
+                        i['Yesterday’s Closing Price'] = i['hc']
+                        i['Lowest Price'] = i['l']
+                        i['Opening Price'] = i['o']
+                        i['Latest Price'] = i['p']
+                        i['Market Quote Time'] = i['t']
+                        del i['a'], i['b'], i['c'], i['e'], i['h'], i['hc'], i['l'], i['o'], i['p'], i['t']
+                jin10_res.append({"content": pd.DataFrame(response['data']).to_markdown()})
+            if self._param.type == "news":
+                params = {
+                    'contain': self._param.contain,
+                    'filter': self._param.filter
+                }
+                response = requests.get(
+                    url='https://open-data-api.jin10.com/data-api/news',
+                    headers=headers, data=json.dumps(params))
+                response = response.json()
+                jin10_res.append({"content": pd.DataFrame(response['data']).to_markdown()})
+        except Exception as e:
+            return Jin10.be_output("**ERROR**: " + str(e))
+
+        if not jin10_res:
+            return Jin10.be_output("")
+
+        return pd.DataFrame(jin10_res)

agent/component/pubmed.py

@@ -15,6 +15,7 @@
 #
 from abc import ABC
 from Bio import Entrez
+import re
 import pandas as pd
 import xml.etree.ElementTree as ET
 from agent.settings import DEBUG
@@ -47,12 +48,15 @@ class PubMed(ComponentBase, ABC):
         try:
             Entrez.email = self._param.email
             pubmedids = Entrez.read(Entrez.esearch(db='pubmed', retmax=self._param.top_n, term=ans))['IdList']
-            pubmedcnt = ET.fromstring(
-                Entrez.efetch(db='pubmed', id=",".join(pubmedids), retmode="xml").read().decode("utf-8"))
+            pubmedcnt = ET.fromstring(re.sub(r'<(/?)b>|<(/?)i>', '', Entrez.efetch(db='pubmed', id=",".join(pubmedids),
+                                                                                   retmode="xml").read().decode(
+                "utf-8")))
             pubmed_res = [{"content": 'Title:' + child.find("MedlineCitation").find("Article").find(
                 "ArticleTitle").text + '\nUrl:<a href=" https://pubmed.ncbi.nlm.nih.gov/' + child.find(
-                "MedlineCitation").find("PMID").text + '">' + '</a>\n' + 'Abstract:' + child.find(
-                "MedlineCitation").find("Article").find("Abstract").find("AbstractText").text} for child in
+                "MedlineCitation").find("PMID").text + '">' + '</a>\n' + 'Abstract:' + (
+                                          child.find("MedlineCitation").find("Article").find("Abstract").find(
+                                              "AbstractText").text if child.find("MedlineCitation").find(
+                                              "Article").find("Abstract") else "No abstract available")} for child in
                           pubmedcnt.findall("PubmedArticle")]
         except Exception as e:
             return PubMed.be_output("**ERROR**: " + str(e))

agent/component/qweather.py

@@ -0,0 +1,111 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+import pandas as pd
+import requests
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class QWeatherParam(ComponentParamBase):
+    """
+    Define the QWeather component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.web_apikey = "xxx"
+        self.lang = "zh"
+        self.type = "weather"
+        self.user_type = 'free'
+        self.error_code = {
+            "204": "The request was successful, but the region you are querying does not have the data you need at this time.",
+            "400": "Request error, may contain incorrect request parameters or missing mandatory request parameters.",
+            "401": "Authentication fails, possibly using the wrong KEY, wrong digital signature, wrong type of KEY (e.g. using the SDK's KEY to access the Web API).",
+            "402": "Exceeded the number of accesses or the balance is not enough to support continued access to the service, you can recharge, upgrade the accesses or wait for the accesses to be reset.",
+            "403": "No access, may be the binding PackageName, BundleID, domain IP address is inconsistent, or the data that requires additional payment.",
+            "404": "The queried data or region does not exist.",
+            "429": "Exceeded the limited QPM (number of accesses per minute), please refer to the QPM description",
+            "500": "No response or timeout, interface service abnormality please contact us"
+            }
+        # Weather
+        self.time_period = 'now'
+
+    def check(self):
+        self.check_empty(self.web_apikey, "BaiduFanyi APPID")
+        self.check_valid_value(self.type, "Type", ["weather", "indices", "airquality"])
+        self.check_valid_value(self.user_type, "Free subscription or paid subscription", ["free", "paid"])
+        self.check_valid_value(self.lang, "Use language",
+                               ['zh', 'zh-hant', 'en', 'de', 'es', 'fr', 'it', 'ja', 'ko', 'ru', 'hi', 'th', 'ar', 'pt',
+                                'bn', 'ms', 'nl', 'el', 'la', 'sv', 'id', 'pl', 'tr', 'cs', 'et', 'vi', 'fil', 'fi',
+                                'he', 'is', 'nb'])
+        self.check_valid_value(self.time_period, "Time period", ['now', '3d', '7d', '10d', '15d', '30d'])
+
+
+class QWeather(ComponentBase, ABC):
+    component_name = "QWeather"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = "".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return QWeather.be_output("")
+
+        try:
+            response = requests.get(
+                url="https://geoapi.qweather.com/v2/city/lookup?location=" + ans + "&key=" + self._param.web_apikey).json()
+            if response["code"] == "200":
+                location_id = response["location"][0]["id"]
+            else:
+                return QWeather.be_output("**Error**" + self._param.error_code[response["code"]])
+
+            base_url = "https://api.qweather.com/v7/" if self._param.user_type == 'paid' else "https://devapi.qweather.com/v7/"
+
+            if self._param.type == "weather":
+                url = base_url + "weather/" + self._param.time_period + "?location=" + location_id + "&key=" + self._param.web_apikey + "&lang=" + self._param.lang
+                response = requests.get(url=url).json()
+                if response["code"] == "200":
+                    if self._param.time_period == "now":
+                        return QWeather.be_output(str(response["now"]))
+                    else:
+                        qweather_res = [{"content": str(i) + "\n"} for i in response["daily"]]
+                        if not qweather_res:
+                            return QWeather.be_output("")
+
+                        df = pd.DataFrame(qweather_res)
+                        return df
+                else:
+                    return QWeather.be_output("**Error**" + self._param.error_code[response["code"]])
+
+            elif self._param.type == "indices":
+                url = base_url + "indices/1d?type=0&location=" + location_id + "&key=" + self._param.web_apikey + "&lang=" + self._param.lang
+                response = requests.get(url=url).json()
+                if response["code"] == "200":
+                    indices_res = response["daily"][0]["date"] + "\n" + "\n".join(
+                        [i["name"] + ": " + i["category"] + ", " + i["text"] for i in response["daily"]])
+                    return QWeather.be_output(indices_res)
+
+                else:
+                    return QWeather.be_output("**Error**" + self._param.error_code[response["code"]])
+
+            elif self._param.type == "airquality":
+                url = base_url + "air/now?location=" + location_id + "&key=" + self._param.web_apikey + "&lang=" + self._param.lang
+                response = requests.get(url=url).json()
+                if response["code"] == "200":
+                    return QWeather.be_output(str(response["now"]))
+                else:
+                    return QWeather.be_output("**Error**" + self._param.error_code[response["code"]])
+        except Exception as e:
+            return QWeather.be_output("**Error**" + str(e))

agent/component/retrieval.py

@@ -54,8 +54,8 @@ class Retrieval(ComponentBase, ABC):
         for role, cnt in history[::-1][:self._param.message_history_window_size]:
             if role != "user":continue
             query.append(cnt)
-        query = "\n".join(query)
-
+        # query = "\n".join(query)
+        query = query[0]
         kbs = KnowledgebaseService.get_by_ids(self._param.kb_ids)
         if not kbs:
             raise ValueError("Can't find knowledgebases by {}".format(self._param.kb_ids))
@@ -75,8 +75,9 @@ class Retrieval(ComponentBase, ABC):
                                         aggs=False, rerank_mdl=rerank_mdl)
 
         if not kbinfos["chunks"]:
-            df = Retrieval.be_output(self._param.empty_response)
-            df["empty_response"] = True
+            df = Retrieval.be_output("")
+            if self._param.empty_response and self._param.empty_response.strip():
+                df["empty_response"] = self._param.empty_response
             return df
 
         df = pd.DataFrame(kbinfos["chunks"])

agent/component/rewrite.py

@@ -54,7 +54,7 @@ class RewriteQuestion(Generate, ABC):
             setattr(self, "_loop", 0)
         if self._loop >= self._param.loop:
             self._loop = 0
-            raise Exception("Maximum loop time exceeds. Can't find relevant information.")
+            raise Exception("Sorry! Nothing relevant found.")
         self._loop += 1
         q = "Question: "
         for r, c in self._canvas.history[::-1]:
@@ -65,6 +65,8 @@ class RewriteQuestion(Generate, ABC):
         chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
         ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": q}],
                             self._param.gen_conf())
+        self._canvas.history.pop()
+        self._canvas.history.append(("user", ans))
 
         print(ans, ":::::::::::::::::::::::::::::::::")
         return RewriteQuestion.be_output(ans)

agent/component/switch.py

@@ -14,64 +14,93 @@
 #  limitations under the License.
 #
 from abc import ABC
-
-import pandas as pd
 from agent.component.base import ComponentBase, ComponentParamBase
 
 
 class SwitchParam(ComponentParamBase):
-
     """
     Define the Switch component parameters.
     """
+
     def __init__(self):
         super().__init__()
         """
         {
-            "cpn_id": "categorize:0",
-            "not": False,
-            "operator": "gt/gte/lt/lte/eq/in",
-            "value": "",
+            "logical_operator" : "and | or"
+            "items" : [
+                            {"cpn_id": "categorize:0", "operator": "contains", "value": ""},
+                            {"cpn_id": "categorize:0", "operator": "contains", "value": ""},...],
             "to": ""
         }
         """
         self.conditions = []
-        self.default = ""
+        self.end_cpn_id = "answer:0"
+        self.operators = ['contains', 'not contains', 'start with', 'end with', 'empty', 'not empty', '=', '≠', '>',
+                          '<', '≥', '≤']
 
     def check(self):
         self.check_empty(self.conditions, "[Switch] conditions")
-        self.check_empty(self.default, "[Switch] Default path")
         for cond in self.conditions:
             if not cond["to"]: raise ValueError(f"[Switch] 'To' can not be empty!")
 
-    def operators(self, field, op, value):
-        if op == "gt":
-            return float(field) > float(value)
-        if op == "gte":
-            return float(field) >= float(value)
-        if op == "lt":
-            return float(field) < float(value)
-        if op == "lte":
-            return float(field) <= float(value)
-        if op == "eq":
-            return str(field) == str(value)
-        if op == "in":
-            return str(field).find(str(value)) >= 0
-        return False
-
 
 class Switch(ComponentBase, ABC):
     component_name = "Switch"
 
     def _run(self, history, **kwargs):
         for cond in self._param.conditions:
-            input = self._canvas.get_component(cond["cpn_id"])["obj"].output()[1]
-            if self._param.operators(input.iloc[0, 0], cond["operator"], cond["value"]):
-                if not cond["not"]:
-                    return pd.DataFrame([{"content": cond["to"]}])
+            res = []
+            for item in cond["items"]:
+                out = self._canvas.get_component(item["cpn_id"])["obj"].output()[1]
+                cpn_input = "" if "content" not in out.columns else " ".join(out["content"])
+                res.append(self.process_operator(cpn_input, item["operator"], item["value"]))
+                if cond["logical_operator"] != "and" and any(res):
+                    return Switch.be_output(cond["to"])
 
-        return pd.DataFrame([{"content": self._param.default}])
+            if all(res):
+                return Switch.be_output(cond["to"])
 
+        return Switch.be_output(self._param.end_cpn_id)
 
+    def process_operator(self, input: str, operator: str, value: str) -> bool:
+        if not isinstance(input, str) or not isinstance(value, str):
+            raise ValueError('Invalid input or value type: string')
 
+        if operator == "contains":
+            return True if value.lower() in input.lower() else False
+        elif operator == "not contains":
+            return True if value.lower() not in input.lower() else False
+        elif operator == "start with":
+            return True if input.lower().startswith(value.lower()) else False
+        elif operator == "end with":
+            return True if input.lower().endswith(value.lower()) else False
+        elif operator == "empty":
+            return True if not input else False
+        elif operator == "not empty":
+            return True if input else False
+        elif operator == "=":
+            return True if input == value else False
+        elif operator == "≠":
+            return True if input != value else False
+        elif operator == ">":
+            try:
+                return True if float(input) > float(value) else False
+            except Exception as e:
+                return True if input > value else False
+        elif operator == "<":
+            try:
+                return True if float(input) < float(value) else False
+            except Exception as e:
+                return True if input < value else False
+        elif operator == "≥":
+            try:
+                return True if float(input) >= float(value) else False
+            except Exception as e:
+                return True if input >= value else False
+        elif operator == "≤":
+            try:
+                return True if float(input) <= float(value) else False
+            except Exception as e:
+                return True if input <= value else False
 
+        raise ValueError('Not supported operator' + operator)

agent/component/tushare.py

@@ -0,0 +1,72 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import json
+from abc import ABC
+import pandas as pd
+import time
+import requests
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class TuShareParam(ComponentParamBase):
+    """
+    Define the TuShare component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.token = "xxx"
+        self.src = "eastmoney"
+        self.start_date = "2024-01-01 09:00:00"
+        self.end_date = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+        self.keyword = ""
+
+    def check(self):
+        self.check_valid_value(self.src, "Quick News Source",
+                               ["sina", "wallstreetcn", "10jqka", "eastmoney", "yuncaijing", "fenghuang", "jinrongjie"])
+
+
+class TuShare(ComponentBase, ABC):
+    component_name = "TuShare"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = ",".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return TuShare.be_output("")
+
+        try:
+            tus_res = []
+            params = {
+                "api_name": "news",
+                "token": self._param.token,
+                "params": {"src": self._param.src, "start_date": self._param.start_date,
+                           "end_date": self._param.end_date}
+            }
+            response = requests.post(url="http://api.tushare.pro", data=json.dumps(params).encode('utf-8'))
+            response = response.json()
+            if response['code'] != 0:
+                return TuShare.be_output(response['msg'])
+            df = pd.DataFrame(response['data']['items'])
+            df.columns = response['data']['fields']
+            tus_res.append({"content": (df[df['content'].str.contains(self._param.keyword, case=False)]).to_markdown()})
+        except Exception as e:
+            return TuShare.be_output("**ERROR**: " + str(e))
+
+        if not tus_res:
+            return TuShare.be_output("")
+
+        return pd.DataFrame(tus_res)

agent/component/wencai.py

@@ -0,0 +1,80 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+import pandas as pd
+import pywencai
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class WenCaiParam(ComponentParamBase):
+    """
+    Define the WenCai component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+        self.query_type = "stock"
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_valid_value(self.query_type, "Query type",
+                               ['stock', 'zhishu', 'fund', 'hkstock', 'usstock', 'threeboard', 'conbond', 'insurance',
+                                'futures', 'lccp',
+                                'foreign_exchange'])
+
+
+class WenCai(ComponentBase, ABC):
+    component_name = "WenCai"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = ",".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return WenCai.be_output("")
+
+        try:
+            wencai_res = []
+            res = pywencai.get(query=ans, query_type=self._param.query_type, perpage=self._param.top_n)
+            if isinstance(res, pd.DataFrame):
+                wencai_res.append({"content": res.to_markdown()})
+            if isinstance(res, dict):
+                for item in res.items():
+                    if isinstance(item[1], list):
+                        wencai_res.append({"content": item[0] + "\n" + pd.DataFrame(item[1]).to_markdown()})
+                        continue
+                    if isinstance(item[1], str):
+                        wencai_res.append({"content": item[0] + "\n" + item[1]})
+                        continue
+                    if isinstance(item[1], dict):
+                        if "meta" in item[1].keys():
+                            continue
+                        wencai_res.append({"content": pd.DataFrame.from_dict(item[1], orient='index').to_markdown()})
+                        continue
+                    if isinstance(item[1], pd.DataFrame):
+                        if "image_url" in item[1].columns:
+                            continue
+                        wencai_res.append({"content": item[1].to_markdown()})
+                        continue
+                        
+                    wencai_res.append({"content": item[0] + "\n" + str(item[1])})
+        except Exception as e:
+            return WenCai.be_output("**ERROR**: " + str(e))
+
+        if not wencai_res:
+            return WenCai.be_output("")
+
+        return pd.DataFrame(wencai_res)

agent/component/yahoofinance.py

@@ -0,0 +1,83 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+import pandas as pd
+from agent.component.base import ComponentBase, ComponentParamBase
+import yfinance as yf
+
+
+class YahooFinanceParam(ComponentParamBase):
+    """
+    Define the YahooFinance component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.info = True
+        self.history = False
+        self.count = False
+        self.financials = False
+        self.income_stmt = False
+        self.balance_sheet = False
+        self.cash_flow_statement = False
+        self.news = True
+
+    def check(self):
+        self.check_boolean(self.info, "get all stock info")
+        self.check_boolean(self.history, "get historical market data")
+        self.check_boolean(self.count, "show share count")
+        self.check_boolean(self.financials, "show financials")
+        self.check_boolean(self.income_stmt, "income statement")
+        self.check_boolean(self.balance_sheet, "balance sheet")
+        self.check_boolean(self.cash_flow_statement, "cash flow statement")
+        self.check_boolean(self.news, "show news")
+
+
+class YahooFinance(ComponentBase, ABC):
+    component_name = "YahooFinance"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = "".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return YahooFinance.be_output("")
+
+        yohoo_res = []
+        try:
+            msft = yf.Ticker(ans)
+            if self._param.info:
+                yohoo_res.append({"content": "info:\n" + pd.Series(msft.info).to_markdown() + "\n"})
+            if self._param.history:
+                yohoo_res.append({"content": "history:\n" + msft.history().to_markdown() + "\n"})
+            if self._param.financials:
+                yohoo_res.append({"content": "calendar:\n" + pd.DataFrame(msft.calendar).to_markdown() + "\n"})
+            if self._param.balance_sheet:
+                yohoo_res.append({"content": "balance sheet:\n" + msft.balance_sheet.to_markdown() + "\n"})
+                yohoo_res.append(
+                    {"content": "quarterly balance sheet:\n" + msft.quarterly_balance_sheet.to_markdown() + "\n"})
+            if self._param.cash_flow_statement:
+                yohoo_res.append({"content": "cash flow statement:\n" + msft.cashflow.to_markdown() + "\n"})
+                yohoo_res.append(
+                    {"content": "quarterly cash flow statement:\n" + msft.quarterly_cashflow.to_markdown() + "\n"})
+            if self._param.news:
+                yohoo_res.append({"content": "news:\n" + pd.DataFrame(msft.news).to_markdown() + "\n"})
+        except Exception as e:
+            print("**ERROR** " + str(e))
+
+        if not yohoo_res:
+            return YahooFinance.be_output("")
+
+        return pd.DataFrame(yohoo_res)

agent/test/dsl_examples/baidu_generate_and_switch.json

@@ -0,0 +1,129 @@
+{
+  "components": {
+            "begin": {
+                "obj":{
+                    "component_name": "Begin",
+                    "params": {
+                      "prologue": "Hi there!"
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": []
+            },
+            "answer:0": {
+                "obj": {
+                    "component_name": "Answer",
+                    "params": {}
+                },
+                "downstream": ["baidu:0"],
+                "upstream": ["begin", "message:0","message:1"]
+            },
+            "baidu:0": {
+                "obj": {
+                    "component_name": "Baidu",
+                    "params": {}
+                },
+                "downstream": ["generate:0"],
+                "upstream": ["answer:0"]
+            },
+            "generate:0": {
+                "obj": {
+                    "component_name": "Generate",
+                    "params": {
+                      "llm_id": "deepseek-chat",
+		              "prompt": "You are an intelligent assistant. Please answer the user's question based on what Baidu searched. First, please output the user's question and the content searched by Baidu, and then answer yes, no, or i don't know.Here is the user's question:{user_input}The above is the user's question.Here is what Baidu searched for:{baidu}The above is the content searched by Baidu.",
+                      "temperature": 0.2
+                    },
+                    "parameters": [
+                                      {
+                                          "component_id": "answer:0",
+                                          "id": "69415446-49bf-4d4b-8ec9-ac86066f7709",
+                                          "key": "user_input"
+                                      },
+                                      {
+                                          "component_id": "baidu:0",
+                                          "id": "83363c2a-00a8-402f-a45c-ddc4097d7d8b",
+                                          "key": "baidu"
+                                      }
+                                  ]
+                        },
+                "downstream": ["switch:0"],
+                "upstream": ["baidu:0"]
+            },
+            "switch:0": {
+                "obj": {
+                    "component_name": "Switch",
+                    "params": {
+                      "conditions": [
+                                        {
+                                            "logical_operator" : "or",
+                                            "items" : [
+                                                          {"cpn_id": "generate:0", "operator": "contains", "value": "yes"},
+                                                          {"cpn_id": "generate:0", "operator": "contains", "value": "yeah"}
+                                                      ],
+                                             "to": "message:0"
+                                        },
+                                        {
+                                            "logical_operator" : "and",
+                                            "items" : [
+                                                          {"cpn_id": "generate:0", "operator": "contains", "value": "no"},
+                                                          {"cpn_id": "generate:0", "operator": "not contains", "value": "yes"},
+							  {"cpn_id": "generate:0", "operator": "not contains", "value": "know"}
+                                                      ],
+                                            "to": "message:1"
+                                        },
+                                        {
+                                            "logical_operator" : "",
+                                            "items" : [
+                                                          {"cpn_id": "generate:0", "operator": "contains", "value": "know"}
+                                                      ],
+                                             "to": "message:2"
+                                        }
+                                    ],
+                      "end_cpn_id": "answer:0"
+
+                    }
+                },
+                "downstream": ["message:0","message:1"],
+                "upstream": ["generate:0"]
+            },
+            "message:0": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                        "messages": ["YES YES YES YES YES YES YES YES YES YES YES YES"]
+                    }
+                },
+
+                "upstream": ["switch:0"],
+                "downstream": ["answer:0"]
+            },
+            "message:1": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                        "messages": ["NO NO NO NO NO NO NO NO NO NO NO NO NO NO"]
+                    }
+                },
+
+                "upstream": ["switch:0"],
+                "downstream": ["answer:0"]
+            },
+            "message:2": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                        "messages": ["I DON'T KNOW---------------------------"]
+                    }
+                },
+
+                "upstream": ["switch:0"],
+                "downstream": ["answer:0"]
+            }
+  },
+  "history": [],
+  "messages": [],
+  "reference": {},
+  "path": [],
+  "answer": []
+}

agent/test/dsl_examples/categorize.json

@@ -26,20 +26,48 @@
                       "category_description": {
                         "product_related": {
                           "description": "The question is about the product usage, appearance and how it works.",
-                          "examples": "Why it always beaming?\nHow to install it onto the wall?\nIt leaks, what to do?"
+                          "examples": "Why it always beaming?\nHow to install it onto the wall?\nIt leaks, what to do?",
+                          "to": "message:0"
                         },
                         "others": {
                           "description": "The question is not about the product usage, appearance and how it works.",
-                          "examples": "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?"
+                          "examples": "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?",
+                          "to": "message:1"
                         }
                       }
                     }
                 },
-                "downstream": [],
+                "downstream": ["message:0","message:1"],
                 "upstream": ["answer:0"]
+            },
+            "message:0": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                      "messages": [
+                        "Message 0!!!!!!!"
+                      ]
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["categorize:0"]
+            },
+            "message:1": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                      "messages": [
+                        "Message 1!!!!!!!"
+                      ]
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["categorize:0"]
             }
   },
   "history": [],
+  "messages": [],
   "path": [],
+  "reference": [],
   "answer": []
-}
+}

agent/test/dsl_examples/concentrator_message.json

@@ -0,0 +1,113 @@
+{
+  "components": {
+            "begin": {
+                "obj":{
+                    "component_name": "Begin",
+                    "params": {
+                      "prologue": "Hi there!"
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": []
+            },
+            "answer:0": {
+                "obj": {
+                    "component_name": "Answer",
+                    "params": {}
+                },
+                "downstream": ["categorize:0"],
+                "upstream": ["begin"]
+            },
+            "categorize:0": {
+                "obj": {
+                    "component_name": "Categorize",
+                    "params": {
+                      "llm_id": "deepseek-chat",
+                      "category_description": {
+                        "product_related": {
+                          "description": "The question is about the product usage, appearance and how it works.",
+                          "examples": "Why it always beaming?\nHow to install it onto the wall?\nIt leaks, what to do?",
+                          "to": "concentrator:0"
+                        },
+                        "others": {
+                          "description": "The question is not about the product usage, appearance and how it works.",
+                          "examples": "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?",
+                          "to": "concentrator:1"
+                        }
+                      }
+                    }
+                },
+                "downstream": ["concentrator:0","concentrator:1"],
+                "upstream": ["answer:0"]
+            },
+            "concentrator:0": {
+                "obj": {
+                    "component_name": "Concentrator",
+                    "params": {}
+                },
+                "downstream": ["message:0"],
+                "upstream": ["categorize:0"]
+            },
+            "concentrator:1": {
+                "obj": {
+                    "component_name": "Concentrator",
+                    "params": {}
+                },
+                "downstream": ["message:1_0","message:1_1","message:1_2"],
+                "upstream": ["categorize:0"]
+            },
+            "message:0": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                      "messages": [
+                        "Message 0_0!!!!!!!"
+                      ]
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["concentrator:0"]
+            },
+            "message:1_0": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                      "messages": [
+                        "Message 1_0!!!!!!!"
+                      ]
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["concentrator:1"]
+            },
+            "message:1_1": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                      "messages": [
+                        "Message 1_1!!!!!!!"
+                      ]
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["concentrator:1"]
+            },
+            "message:1_2": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                      "messages": [
+                        "Message 1_2!!!!!!!"
+                      ]
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["concentrator:1"]
+            }
+  },
+  "history": [],
+  "messages": [],
+  "path": [],
+  "reference": [],
+  "answer": []
+}

agent/test/dsl_examples/exesql.json

@@ -0,0 +1,43 @@
+{
+  "components": {
+            "begin": {
+                "obj":{
+                    "component_name": "Begin",
+                    "params": {
+                      "prologue": "Hi there!"
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": []
+            },
+            "answer:0": {
+                "obj": {
+                    "component_name": "Answer",
+                    "params": {}
+                },
+                "downstream": ["exesql:0"],
+                "upstream": ["begin", "exesql:0"]
+            },
+            "exesql:0": {
+                "obj": {
+                    "component_name": "ExeSQL",
+                    "params": {
+                                    "database": "rag_flow",
+                                    "username": "root",
+                                    "host": "mysql",
+                                    "port": 3306,
+                                    "password": "infini_rag_flow",
+				    "top_n": 3
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["answer:0"]
+            }
+  },
+  "history": [],
+  "messages": [],
+  "reference": {},
+  "path": [],
+  "answer": []
+}
+

agent/test/dsl_examples/keyword_wikipedia_and_generate.json

@@ -1,62 +1,62 @@
-{
-  "components": {
-            "begin": {
-                "obj":{
-                    "component_name": "Begin",
-                    "params": {
-                      "prologue": "Hi there!"
-                    }
-                },
-                "downstream": ["answer:0"],
-                "upstream": []
-            },
-            "answer:0": {
-                "obj": {
-                    "component_name": "Answer",
-                    "params": {}
-                },
-                "downstream": ["keyword:0"],
-                "upstream": ["begin"]
-            },
-            "keyword:0": {
-                "obj": {
-                    "component_name": "KeywordExtract",
-                    "params": {
-                      "llm_id": "deepseek-chat",
-                      "prompt": "- Role: You're a question analyzer.\n    - Requirements:\n     - Summarize user's question, and give top %s important keyword/phrase.\n    - Use comma as a delimiter to separate keywords/phrases.\n    - Answer format: (in language of user's question)\n    - keyword: ",
-                      "temperature": 0.2,
-                      "top_n": 1
-                    }
-                },
-                "downstream": ["wikipedia:0"],
-                "upstream": ["answer:0"]
-            },
-            "wikipedia:0": {
-                "obj":{
-                    "component_name": "Wikipedia",
-                    "params": {
-                      "top_n": 10
-                    }
-                },
-                "downstream": ["generate:0"],
-                "upstream": ["keyword:0"]
-            },
-            "generate:1": {
-                "obj": {
-                    "component_name": "Generate",
-                    "params": {
-                      "llm_id": "deepseek-chat",
-                      "prompt": "You are an intelligent assistant. Please answer the question based on content from Wikipedia. When the answer from Wikipedia is incomplete, you need to output the URL link of the corresponding content as well. When all the content searched from Wikipedia is irrelevant to the question, your answer must include the sentence, \"The answer you are looking for is not found in the Wikipedia!\". Answers need to consider chat history.\n       The content of Wikipedia is as follows:\n    {input}\n     The above is the content of Wikipedia.",
-                      "temperature": 0.2
-                    }
-                },
-                "downstream": ["answer:0"],
-                "upstream": ["wikipedia:0"]
-            }
-  },
-  "history": [],
-  "path": [],
-  "messages": [],
-  "reference": {},
-  "answer": []
-}
+{
+  "components": {
+            "begin": {
+                "obj":{
+                    "component_name": "Begin",
+                    "params": {
+                      "prologue": "Hi there!"
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": []
+            },
+            "answer:0": {
+                "obj": {
+                    "component_name": "Answer",
+                    "params": {}
+                },
+                "downstream": ["keyword:0"],
+                "upstream": ["begin"]
+            },
+            "keyword:0": {
+                "obj": {
+                    "component_name": "KeywordExtract",
+                    "params": {
+                      "llm_id": "deepseek-chat",
+                      "prompt": "- Role: You're a question analyzer.\n    - Requirements:\n     - Summarize user's question, and give top %s important keyword/phrase.\n    - Use comma as a delimiter to separate keywords/phrases.\n    - Answer format: (in language of user's question)\n    - keyword: ",
+                      "temperature": 0.2,
+                      "top_n": 1
+                    }
+                },
+                "downstream": ["wikipedia:0"],
+                "upstream": ["answer:0"]
+            },
+            "wikipedia:0": {
+                "obj":{
+                    "component_name": "Wikipedia",
+                    "params": {
+                      "top_n": 10
+                    }
+                },
+                "downstream": ["generate:0"],
+                "upstream": ["keyword:0"]
+            },
+            "generate:1": {
+                "obj": {
+                    "component_name": "Generate",
+                    "params": {
+                      "llm_id": "deepseek-chat",
+                      "prompt": "You are an intelligent assistant. Please answer the question based on content from Wikipedia. When the answer from Wikipedia is incomplete, you need to output the URL link of the corresponding content as well. When all the content searched from Wikipedia is irrelevant to the question, your answer must include the sentence, \"The answer you are looking for is not found in the Wikipedia!\". Answers need to consider chat history.\n       The content of Wikipedia is as follows:\n    {input}\n     The above is the content of Wikipedia.",
+                      "temperature": 0.2
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["wikipedia:0"]
+            }
+  },
+  "history": [],
+  "path": [],
+  "messages": [],
+  "reference": {},
+  "answer": []
+}

api/apps/__init__.py

@@ -1,125 +1,126 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import logging
-import os
-import sys
-from importlib.util import module_from_spec, spec_from_file_location
-from pathlib import Path
-from flask import Blueprint, Flask
-from werkzeug.wrappers.request import Request
-from flask_cors import CORS
-
-from api.db import StatusEnum
-from api.db.db_models import close_connection
-from api.db.services import UserService
-from api.utils import CustomJSONEncoder, commands
-
-from flask_session import Session
-from flask_login import LoginManager
-from api.settings import SECRET_KEY, stat_logger
-from api.settings import API_VERSION, access_logger
-from api.utils.api_utils import server_error_response
-from itsdangerous.url_safe import URLSafeTimedSerializer as Serializer
-
-__all__ = ['app']
-
-
-logger = logging.getLogger('flask.app')
-for h in access_logger.handlers:
-    logger.addHandler(h)
-
-Request.json = property(lambda self: self.get_json(force=True, silent=True))
-
-app = Flask(__name__)
-CORS(app, supports_credentials=True,max_age=2592000)
-app.url_map.strict_slashes = False
-app.json_encoder = CustomJSONEncoder
-app.errorhandler(Exception)(server_error_response)
-
-
-## convince for dev and debug
-#app.config["LOGIN_DISABLED"] = True
-app.config["SESSION_PERMANENT"] = False
-app.config["SESSION_TYPE"] = "filesystem"
-app.config['MAX_CONTENT_LENGTH'] = int(os.environ.get("MAX_CONTENT_LENGTH", 128 * 1024 * 1024))
-
-Session(app)
-login_manager = LoginManager()
-login_manager.init_app(app)
-
-commands.register_commands(app)
-
-
-def search_pages_path(pages_dir):
-    app_path_list = [path for path in pages_dir.glob('*_app.py') if not path.name.startswith('.')]
-    api_path_list = [path for path in pages_dir.glob('*_api.py') if not path.name.startswith('.')]
-    app_path_list.extend(api_path_list)
-    return app_path_list
-
-
-def register_page(page_path):
-    path = f'{page_path}'
-
-    page_name = page_path.stem.rstrip('_api') if "_api" in path else page_path.stem.rstrip('_app')
-    module_name = '.'.join(page_path.parts[page_path.parts.index('api'):-1] + (page_name,))
-
-    spec = spec_from_file_location(module_name, page_path)
-    page = module_from_spec(spec)
-    page.app = app
-    page.manager = Blueprint(page_name, module_name)
-    sys.modules[module_name] = page
-    spec.loader.exec_module(page)
-    page_name = getattr(page, 'page_name', page_name)
-    url_prefix = f'/api/{API_VERSION}/{page_name}' if "_api" in path else f'/{API_VERSION}/{page_name}'
-
-    app.register_blueprint(page.manager, url_prefix=url_prefix)
-    return url_prefix
-
-
-pages_dir = [
-    Path(__file__).parent,
-    Path(__file__).parent.parent / 'api' / 'apps', # FIXME: ragflow/api/api/apps, can be remove?
-]
-
-client_urls_prefix = [
-    register_page(path)
-    for dir in pages_dir
-    for path in search_pages_path(dir)
-]
-
-
-@login_manager.request_loader
-def load_user(web_request):
-    jwt = Serializer(secret_key=SECRET_KEY)
-    authorization = web_request.headers.get("Authorization")
-    if authorization:
-        try:
-            access_token = str(jwt.loads(authorization))
-            user = UserService.query(access_token=access_token, status=StatusEnum.VALID.value)
-            if user:
-                return user[0]
-            else:
-                return None
-        except Exception as e:
-            stat_logger.exception(e)
-            return None
-    else:
-        return None
-
-
-@app.teardown_request
-def _db_close(exc):
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import logging
+import os
+import sys
+from importlib.util import module_from_spec, spec_from_file_location
+from pathlib import Path
+from flask import Blueprint, Flask
+from werkzeug.wrappers.request import Request
+from flask_cors import CORS
+
+from api.db import StatusEnum
+from api.db.db_models import close_connection
+from api.db.services import UserService
+from api.utils import CustomJSONEncoder, commands
+
+from flask_session import Session
+from flask_login import LoginManager
+from api.settings import SECRET_KEY, stat_logger
+from api.settings import API_VERSION, access_logger
+from api.utils.api_utils import server_error_response
+from itsdangerous.url_safe import URLSafeTimedSerializer as Serializer
+
+__all__ = ['app']
+
+
+logger = logging.getLogger('flask.app')
+for h in access_logger.handlers:
+    logger.addHandler(h)
+
+Request.json = property(lambda self: self.get_json(force=True, silent=True))
+
+app = Flask(__name__)
+CORS(app, supports_credentials=True,max_age=2592000)
+app.url_map.strict_slashes = False
+app.json_encoder = CustomJSONEncoder
+app.errorhandler(Exception)(server_error_response)
+
+
+## convince for dev and debug
+#app.config["LOGIN_DISABLED"] = True
+app.config["SESSION_PERMANENT"] = False
+app.config["SESSION_TYPE"] = "filesystem"
+app.config['MAX_CONTENT_LENGTH'] = int(os.environ.get("MAX_CONTENT_LENGTH", 128 * 1024 * 1024))
+
+Session(app)
+login_manager = LoginManager()
+login_manager.init_app(app)
+
+commands.register_commands(app)
+
+
+def search_pages_path(pages_dir):
+    app_path_list = [path for path in pages_dir.glob('*_app.py') if not path.name.startswith('.')]
+    api_path_list = [path for path in pages_dir.glob('*sdk/*.py') if not path.name.startswith('.')]
+    app_path_list.extend(api_path_list)
+    return app_path_list
+
+
+def register_page(page_path):
+    path = f'{page_path}'
+
+    page_name = page_path.stem.rstrip('_app')
+    module_name = '.'.join(page_path.parts[page_path.parts.index('api'):-1] + (page_name,))
+
+    spec = spec_from_file_location(module_name, page_path)
+    page = module_from_spec(spec)
+    page.app = app
+    page.manager = Blueprint(page_name, module_name)
+    sys.modules[module_name] = page
+    spec.loader.exec_module(page)
+    page_name = getattr(page, 'page_name', page_name)
+    url_prefix = f'/api/{API_VERSION}/{page_name}' if "/sdk/" in path else f'/{API_VERSION}/{page_name}'
+
+    app.register_blueprint(page.manager, url_prefix=url_prefix)
+    return url_prefix
+
+
+pages_dir = [
+    Path(__file__).parent,
+    Path(__file__).parent.parent / 'api' / 'apps',
+    Path(__file__).parent.parent / 'api' / 'apps' / 'sdk',
+]
+
+client_urls_prefix = [
+    register_page(path)
+    for dir in pages_dir
+    for path in search_pages_path(dir)
+]
+
+
+@login_manager.request_loader
+def load_user(web_request):
+    jwt = Serializer(secret_key=SECRET_KEY)
+    authorization = web_request.headers.get("Authorization")
+    if authorization:
+        try:
+            access_token = str(jwt.loads(authorization))
+            user = UserService.query(access_token=access_token, status=StatusEnum.VALID.value)
+            if user:
+                return user[0]
+            else:
+                return None
+        except Exception as e:
+            stat_logger.exception(e)
+            return None
+    else:
+        return None
+
+
+@app.teardown_request
+def _db_close(exc):
     close_connection()

api/apps/canvas_app.py

@@ -18,9 +18,13 @@ from functools import partial
 from flask import request, Response
 from flask_login import login_required, current_user
 from api.db.services.canvas_service import CanvasTemplateService, UserCanvasService
+from api.db.services.dialog_service import full_question
+from api.db.services.user_service import TenantService
+from api.settings import RetCode
 from api.utils import get_uuid
-from api.utils.api_utils import get_json_result, server_error_response, validate_request
+from api.utils.api_utils import get_json_result, server_error_response, validate_request, get_data_error_result
 from agent.canvas import Canvas
+from peewee import MySQLDatabase, PostgresqlDatabase
 
 
 @manager.route('/templates', methods=['GET'])
@@ -42,6 +46,10 @@ def canvas_list():
 @login_required
 def rm():
     for i in request.json["canvas_ids"]:
+        if not UserCanvasService.query(user_id=current_user.id,id=i):
+            return get_json_result(
+                data=False, retmsg=f'Only owner of canvas authorized for this operation.',
+                retcode=RetCode.OPERATING_ERROR)
         UserCanvasService.delete_by_id(i)
     return get_json_result(data=True)
 
@@ -60,10 +68,13 @@ def save():
             return server_error_response(ValueError("Duplicated title."))
         req["id"] = get_uuid()
         if not UserCanvasService.save(**req):
-            return server_error_response("Fail to save canvas.")
+            return get_data_error_result(retmsg="Fail to save canvas.")
     else:
+        if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
+            return get_json_result(
+                data=False, retmsg=f'Only owner of canvas authorized for this operation.',
+                retcode=RetCode.OPERATING_ERROR)
         UserCanvasService.update_by_id(req["id"], req)
-
     return get_json_result(data=req)
 
 
@@ -72,7 +83,7 @@ def save():
 def get(canvas_id):
     e, c = UserCanvasService.get_by_id(canvas_id)
     if not e:
-        return server_error_response("canvas not found.")
+        return get_data_error_result(retmsg="canvas not found.")
     return get_json_result(data=c.to_dict())
 
 
@@ -84,16 +95,24 @@ def run():
     stream = req.get("stream", True)
     e, cvs = UserCanvasService.get_by_id(req["id"])
     if not e:
-        return server_error_response("canvas not found.")
+        return get_data_error_result(retmsg="canvas not found.")
+    if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
+        return get_json_result(
+            data=False, retmsg=f'Only owner of canvas authorized for this operation.',
+            retcode=RetCode.OPERATING_ERROR)
 
     if not isinstance(cvs.dsl, str):
         cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
 
     final_ans = {"reference": [], "content": ""}
+    message_id = req.get("message_id", get_uuid())
     try:
         canvas = Canvas(cvs.dsl, current_user.id)
         if "message" in req:
-            canvas.messages.append({"role": "user", "content": req["message"]})
+            canvas.messages.append({"role": "user", "content": req["message"], "id": message_id})
+            if len([m for m in canvas.messages if m["role"] == "user"]) > 1:
+                ten = TenantService.get_by_user_id(current_user.id)[0]
+                req["message"] = full_question(ten["tenant_id"], ten["llm_id"], canvas.messages)
             canvas.add_user_input(req["message"])
         answer = canvas.run(stream=stream)
         print(canvas)
@@ -114,7 +133,7 @@ def run():
                     ans = {"answer": ans["content"], "reference": ans.get("reference", [])}
                     yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": ans}, ensure_ascii=False) + "\n\n"
 
-                canvas.messages.append({"role": "assistant", "content": final_ans["content"]})
+                canvas.messages.append({"role": "assistant", "content": final_ans["content"], "id": message_id})
                 if final_ans.get("reference"):
                     canvas.reference.append(final_ans["reference"])
                 cvs.dsl = json.loads(str(canvas))
@@ -133,7 +152,7 @@ def run():
         return resp
 
     final_ans["content"] = "\n".join(answer["content"]) if "content" in answer else ""
-    canvas.messages.append({"role": "assistant", "content": final_ans["content"]})
+    canvas.messages.append({"role": "assistant", "content": final_ans["content"], "id": message_id})
     if final_ans.get("reference"):
         canvas.reference.append(final_ans["reference"])
     cvs.dsl = json.loads(str(canvas))
@@ -149,7 +168,11 @@ def reset():
     try:
         e, user_canvas = UserCanvasService.get_by_id(req["id"])
         if not e:
-            return server_error_response("canvas not found.")
+            return get_data_error_result(retmsg="canvas not found.")
+        if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
+            return get_json_result(
+                data=False, retmsg=f'Only owner of canvas authorized for this operation.',
+                retcode=RetCode.OPERATING_ERROR)
 
         canvas = Canvas(json.dumps(user_canvas.dsl), current_user.id)
         canvas.reset()
@@ -158,3 +181,22 @@ def reset():
         return get_json_result(data=req["dsl"])
     except Exception as e:
         return server_error_response(e)
+
+
+@manager.route('/test_db_connect', methods=['POST'])
+@validate_request("db_type", "database", "username", "host", "port", "password")
+@login_required
+def test_db_connect():
+    req = request.json
+    try:
+        if req["db_type"] in ["mysql", "mariadb"]:
+            db = MySQLDatabase(req["database"], user=req["username"], host=req["host"], port=req["port"],
+                               password=req["password"])
+        elif req["db_type"] == 'postgresql':
+            db = PostgresqlDatabase(req["database"], user=req["username"], host=req["host"], port=req["port"],
+                                    password=req["password"])
+        db.connect()
+        db.close()
+        return get_json_result(data="Database Connection Successful!")
+    except Exception as e:
+        return server_error_response(e)

api/apps/chunk_app.py

@@ -1,318 +1,327 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import datetime
-import json
-import traceback
-
-from flask import request
-from flask_login import login_required, current_user
-from elasticsearch_dsl import Q
-
-from rag.app.qa import rmPrefix, beAdoc
-from rag.nlp import search, rag_tokenizer, keyword_extraction
-from rag.utils.es_conn import ELASTICSEARCH
-from rag.utils import rmSpace
-from api.db import LLMType, ParserType
-from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.services.llm_service import TenantLLMService
-from api.db.services.user_service import UserTenantService
-from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
-from api.db.services.document_service import DocumentService
-from api.settings import RetCode, retrievaler, kg_retrievaler
-from api.utils.api_utils import get_json_result
-import hashlib
-import re
-
-
-@manager.route('/list', methods=['POST'])
-@login_required
-@validate_request("doc_id")
-def list_chunk():
-    req = request.json
-    doc_id = req["doc_id"]
-    page = int(req.get("page", 1))
-    size = int(req.get("size", 30))
-    question = req.get("keywords", "")
-    try:
-        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
-        if not tenant_id:
-            return get_data_error_result(retmsg="Tenant not found!")
-        e, doc = DocumentService.get_by_id(doc_id)
-        if not e:
-            return get_data_error_result(retmsg="Document not found!")
-        query = {
-            "doc_ids": [doc_id], "page": page, "size": size, "question": question, "sort": True
-        }
-        if "available_int" in req:
-            query["available_int"] = int(req["available_int"])
-        sres = retrievaler.search(query, search.index_name(tenant_id))
-        res = {"total": sres.total, "chunks": [], "doc": doc.to_dict()}
-        for id in sres.ids:
-            d = {
-                "chunk_id": id,
-                "content_with_weight": rmSpace(sres.highlight[id]) if question and id in sres.highlight else sres.field[
-                    id].get(
-                    "content_with_weight", ""),
-                "doc_id": sres.field[id]["doc_id"],
-                "docnm_kwd": sres.field[id]["docnm_kwd"],
-                "important_kwd": sres.field[id].get("important_kwd", []),
-                "img_id": sres.field[id].get("img_id", ""),
-                "available_int": sres.field[id].get("available_int", 1),
-                "positions": sres.field[id].get("position_int", "").split("\t")
-            }
-            if len(d["positions"]) % 5 == 0:
-                poss = []
-                for i in range(0, len(d["positions"]), 5):
-                    poss.append([float(d["positions"][i]), float(d["positions"][i + 1]), float(d["positions"][i + 2]),
-                                 float(d["positions"][i + 3]), float(d["positions"][i + 4])])
-                d["positions"] = poss
-            res["chunks"].append(d)
-        return get_json_result(data=res)
-    except Exception as e:
-        if str(e).find("not_found") > 0:
-            return get_json_result(data=False, retmsg=f'No chunk found!',
-                                   retcode=RetCode.DATA_ERROR)
-        return server_error_response(e)
-
-
-@manager.route('/get', methods=['GET'])
-@login_required
-def get():
-    chunk_id = request.args["chunk_id"]
-    try:
-        tenants = UserTenantService.query(user_id=current_user.id)
-        if not tenants:
-            return get_data_error_result(retmsg="Tenant not found!")
-        res = ELASTICSEARCH.get(
-            chunk_id, search.index_name(
-                tenants[0].tenant_id))
-        if not res.get("found"):
-            return server_error_response("Chunk not found")
-        id = res["_id"]
-        res = res["_source"]
-        res["chunk_id"] = id
-        k = []
-        for n in res.keys():
-            if re.search(r"(_vec$|_sm_|_tks|_ltks)", n):
-                k.append(n)
-        for n in k:
-            del res[n]
-
-        return get_json_result(data=res)
-    except Exception as e:
-        if str(e).find("NotFoundError") >= 0:
-            return get_json_result(data=False, retmsg=f'Chunk not found!',
-                                   retcode=RetCode.DATA_ERROR)
-        return server_error_response(e)
-
-
-@manager.route('/set', methods=['POST'])
-@login_required
-@validate_request("doc_id", "chunk_id", "content_with_weight",
-                  "important_kwd")
-def set():
-    req = request.json
-    d = {
-        "id": req["chunk_id"],
-        "content_with_weight": req["content_with_weight"]}
-    d["content_ltks"] = rag_tokenizer.tokenize(req["content_with_weight"])
-    d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
-    d["important_kwd"] = req["important_kwd"]
-    d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_kwd"]))
-    if "available_int" in req:
-        d["available_int"] = req["available_int"]
-
-    try:
-        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
-        if not tenant_id:
-            return get_data_error_result(retmsg="Tenant not found!")
-
-        embd_id = DocumentService.get_embd_id(req["doc_id"])
-        embd_mdl = TenantLLMService.model_instance(
-            tenant_id, LLMType.EMBEDDING.value, embd_id)
-
-        e, doc = DocumentService.get_by_id(req["doc_id"])
-        if not e:
-            return get_data_error_result(retmsg="Document not found!")
-
-        if doc.parser_id == ParserType.QA:
-            arr = [
-                t for t in re.split(
-                    r"[\n\t]",
-                    req["content_with_weight"]) if len(t) > 1]
-            if len(arr) != 2:
-                return get_data_error_result(
-                    retmsg="Q&A must be separated by TAB/ENTER key.")
-            q, a = rmPrefix(arr[0]), rmPrefix(arr[1])
-            d = beAdoc(d, arr[0], arr[1], not any(
-                [rag_tokenizer.is_chinese(t) for t in q + a]))
-
-        v, c = embd_mdl.encode([doc.name, req["content_with_weight"]])
-        v = 0.1 * v[0] + 0.9 * v[1] if doc.parser_id != ParserType.QA else v[1]
-        d["q_%d_vec" % len(v)] = v.tolist()
-        ELASTICSEARCH.upsert([d], search.index_name(tenant_id))
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/switch', methods=['POST'])
-@login_required
-@validate_request("chunk_ids", "available_int", "doc_id")
-def switch():
-    req = request.json
-    try:
-        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
-        if not tenant_id:
-            return get_data_error_result(retmsg="Tenant not found!")
-        if not ELASTICSEARCH.upsert([{"id": i, "available_int": int(req["available_int"])} for i in req["chunk_ids"]],
-                                    search.index_name(tenant_id)):
-            return get_data_error_result(retmsg="Index updating failure")
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/rm', methods=['POST'])
-@login_required
-@validate_request("chunk_ids", "doc_id")
-def rm():
-    req = request.json
-    try:
-        if not ELASTICSEARCH.deleteByQuery(
-                Q("ids", values=req["chunk_ids"]), search.index_name(current_user.id)):
-            return get_data_error_result(retmsg="Index updating failure")
-        e, doc = DocumentService.get_by_id(req["doc_id"])
-        if not e:
-            return get_data_error_result(retmsg="Document not found!")
-        deleted_chunk_ids = req["chunk_ids"]
-        chunk_number = len(deleted_chunk_ids)
-        DocumentService.decrement_chunk_num(doc.id, doc.kb_id, 1, chunk_number, 0)
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/create', methods=['POST'])
-@login_required
-@validate_request("doc_id", "content_with_weight")
-def create():
-    req = request.json
-    md5 = hashlib.md5()
-    md5.update((req["content_with_weight"] + req["doc_id"]).encode("utf-8"))
-    chunck_id = md5.hexdigest()
-    d = {"id": chunck_id, "content_ltks": rag_tokenizer.tokenize(req["content_with_weight"]),
-         "content_with_weight": req["content_with_weight"]}
-    d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
-    d["important_kwd"] = req.get("important_kwd", [])
-    d["important_tks"] = rag_tokenizer.tokenize(" ".join(req.get("important_kwd", [])))
-    d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
-    d["create_timestamp_flt"] = datetime.datetime.now().timestamp()
-
-    try:
-        e, doc = DocumentService.get_by_id(req["doc_id"])
-        if not e:
-            return get_data_error_result(retmsg="Document not found!")
-        d["kb_id"] = [doc.kb_id]
-        d["docnm_kwd"] = doc.name
-        d["doc_id"] = doc.id
-
-        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
-        if not tenant_id:
-            return get_data_error_result(retmsg="Tenant not found!")
-
-        embd_id = DocumentService.get_embd_id(req["doc_id"])
-        embd_mdl = TenantLLMService.model_instance(
-            tenant_id, LLMType.EMBEDDING.value, embd_id)
-
-        v, c = embd_mdl.encode([doc.name, req["content_with_weight"]])
-        v = 0.1 * v[0] + 0.9 * v[1]
-        d["q_%d_vec" % len(v)] = v.tolist()
-        ELASTICSEARCH.upsert([d], search.index_name(tenant_id))
-
-        DocumentService.increment_chunk_num(
-            doc.id, doc.kb_id, c, 1, 0)
-        return get_json_result(data={"chunk_id": chunck_id})
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/retrieval_test', methods=['POST'])
-@login_required
-@validate_request("kb_id", "question")
-def retrieval_test():
-    req = request.json
-    page = int(req.get("page", 1))
-    size = int(req.get("size", 30))
-    question = req["question"]
-    kb_id = req["kb_id"]
-    doc_ids = req.get("doc_ids", [])
-    similarity_threshold = float(req.get("similarity_threshold", 0.2))
-    vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3))
-    top = int(req.get("top_k", 1024))
-    try:
-        e, kb = KnowledgebaseService.get_by_id(kb_id)
-        if not e:
-            return get_data_error_result(retmsg="Knowledgebase not found!")
-
-        embd_mdl = TenantLLMService.model_instance(
-            kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
-
-        rerank_mdl = None
-        if req.get("rerank_id"):
-            rerank_mdl = TenantLLMService.model_instance(
-                kb.tenant_id, LLMType.RERANK.value, llm_name=req["rerank_id"])
-
-        if req.get("keyword", False):
-            chat_mdl = TenantLLMService.model_instance(kb.tenant_id, LLMType.CHAT)
-            question += keyword_extraction(chat_mdl, question)
-
-        retr = retrievaler if kb.parser_id != ParserType.KG else kg_retrievaler
-        ranks = retr.retrieval(question, embd_mdl, kb.tenant_id, [kb_id], page, size,
-                               similarity_threshold, vector_similarity_weight, top,
-                               doc_ids, rerank_mdl=rerank_mdl)
-        for c in ranks["chunks"]:
-            if "vector" in c:
-                del c["vector"]
-
-        return get_json_result(data=ranks)
-    except Exception as e:
-        if str(e).find("not_found") > 0:
-            return get_json_result(data=False, retmsg=f'No chunk found! Check the chunk status please!',
-                                   retcode=RetCode.DATA_ERROR)
-        return server_error_response(e)
-
-
-@manager.route('/knowledge_graph', methods=['GET'])
-@login_required
-def knowledge_graph():
-    doc_id = request.args["doc_id"]
-    req = {
-        "doc_ids":[doc_id],
-        "knowledge_graph_kwd": ["graph", "mind_map"]
-    }
-    tenant_id = DocumentService.get_tenant_id(doc_id)
-    sres = retrievaler.search(req, search.index_name(tenant_id))
-    obj = {"graph": {}, "mind_map": {}}
-    for id in sres.ids[:2]:
-        ty = sres.field[id]["knowledge_graph_kwd"]
-        try:
-            obj[ty] = json.loads(sres.field[id]["content_with_weight"])
-        except Exception as e:
-            print(traceback.format_exc(), flush=True)
-
-    return get_json_result(data=obj)
-
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import datetime
+import json
+import traceback
+
+from flask import request
+from flask_login import login_required, current_user
+from elasticsearch_dsl import Q
+
+from rag.app.qa import rmPrefix, beAdoc
+from rag.nlp import search, rag_tokenizer, keyword_extraction
+from rag.utils.es_conn import ELASTICSEARCH
+from rag.utils import rmSpace
+from api.db import LLMType, ParserType
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.services.llm_service import LLMBundle
+from api.db.services.user_service import UserTenantService
+from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+from api.db.services.document_service import DocumentService
+from api.settings import RetCode, retrievaler, kg_retrievaler
+from api.utils.api_utils import get_json_result
+import hashlib
+import re
+
+
+@manager.route('/list', methods=['POST'])
+@login_required
+@validate_request("doc_id")
+def list_chunk():
+    req = request.json
+    doc_id = req["doc_id"]
+    page = int(req.get("page", 1))
+    size = int(req.get("size", 30))
+    question = req.get("keywords", "")
+    try:
+        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
+        if not tenant_id:
+            return get_data_error_result(retmsg="Tenant not found!")
+        e, doc = DocumentService.get_by_id(doc_id)
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        query = {
+            "doc_ids": [doc_id], "page": page, "size": size, "question": question, "sort": True
+        }
+        if "available_int" in req:
+            query["available_int"] = int(req["available_int"])
+        sres = retrievaler.search(query, search.index_name(tenant_id), highlight=True)
+        res = {"total": sres.total, "chunks": [], "doc": doc.to_dict()}
+        for id in sres.ids:
+            d = {
+                "chunk_id": id,
+                "content_with_weight": rmSpace(sres.highlight[id]) if question and id in sres.highlight else sres.field[
+                    id].get(
+                    "content_with_weight", ""),
+                "doc_id": sres.field[id]["doc_id"],
+                "docnm_kwd": sres.field[id]["docnm_kwd"],
+                "important_kwd": sres.field[id].get("important_kwd", []),
+                "img_id": sres.field[id].get("img_id", ""),
+                "available_int": sres.field[id].get("available_int", 1),
+                "positions": sres.field[id].get("position_int", "").split("\t")
+            }
+            if len(d["positions"]) % 5 == 0:
+                poss = []
+                for i in range(0, len(d["positions"]), 5):
+                    poss.append([float(d["positions"][i]), float(d["positions"][i + 1]), float(d["positions"][i + 2]),
+                                 float(d["positions"][i + 3]), float(d["positions"][i + 4])])
+                d["positions"] = poss
+            res["chunks"].append(d)
+        return get_json_result(data=res)
+    except Exception as e:
+        if str(e).find("not_found") > 0:
+            return get_json_result(data=False, retmsg=f'No chunk found!',
+                                   retcode=RetCode.DATA_ERROR)
+        return server_error_response(e)
+
+
+@manager.route('/get', methods=['GET'])
+@login_required
+def get():
+    chunk_id = request.args["chunk_id"]
+    try:
+        tenants = UserTenantService.query(user_id=current_user.id)
+        if not tenants:
+            return get_data_error_result(retmsg="Tenant not found!")
+        res = ELASTICSEARCH.get(
+            chunk_id, search.index_name(
+                tenants[0].tenant_id))
+        if not res.get("found"):
+            return server_error_response("Chunk not found")
+        id = res["_id"]
+        res = res["_source"]
+        res["chunk_id"] = id
+        k = []
+        for n in res.keys():
+            if re.search(r"(_vec$|_sm_|_tks|_ltks)", n):
+                k.append(n)
+        for n in k:
+            del res[n]
+
+        return get_json_result(data=res)
+    except Exception as e:
+        if str(e).find("NotFoundError") >= 0:
+            return get_json_result(data=False, retmsg=f'Chunk not found!',
+                                   retcode=RetCode.DATA_ERROR)
+        return server_error_response(e)
+
+
+@manager.route('/set', methods=['POST'])
+@login_required
+@validate_request("doc_id", "chunk_id", "content_with_weight",
+                  "important_kwd")
+def set():
+    req = request.json
+    d = {
+        "id": req["chunk_id"],
+        "content_with_weight": req["content_with_weight"]}
+    d["content_ltks"] = rag_tokenizer.tokenize(req["content_with_weight"])
+    d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
+    d["important_kwd"] = req["important_kwd"]
+    d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_kwd"]))
+    if "available_int" in req:
+        d["available_int"] = req["available_int"]
+
+    try:
+        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
+        if not tenant_id:
+            return get_data_error_result(retmsg="Tenant not found!")
+
+        embd_id = DocumentService.get_embd_id(req["doc_id"])
+        embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embd_id)
+
+        e, doc = DocumentService.get_by_id(req["doc_id"])
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+
+        if doc.parser_id == ParserType.QA:
+            arr = [
+                t for t in re.split(
+                    r"[\n\t]",
+                    req["content_with_weight"]) if len(t) > 1]
+            if len(arr) != 2:
+                return get_data_error_result(
+                    retmsg="Q&A must be separated by TAB/ENTER key.")
+            q, a = rmPrefix(arr[0]), rmPrefix(arr[1])
+            d = beAdoc(d, arr[0], arr[1], not any(
+                [rag_tokenizer.is_chinese(t) for t in q + a]))
+
+        v, c = embd_mdl.encode([doc.name, req["content_with_weight"]])
+        v = 0.1 * v[0] + 0.9 * v[1] if doc.parser_id != ParserType.QA else v[1]
+        d["q_%d_vec" % len(v)] = v.tolist()
+        ELASTICSEARCH.upsert([d], search.index_name(tenant_id))
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/switch', methods=['POST'])
+@login_required
+@validate_request("chunk_ids", "available_int", "doc_id")
+def switch():
+    req = request.json
+    try:
+        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
+        if not tenant_id:
+            return get_data_error_result(retmsg="Tenant not found!")
+        if not ELASTICSEARCH.upsert([{"id": i, "available_int": int(req["available_int"])} for i in req["chunk_ids"]],
+                                    search.index_name(tenant_id)):
+            return get_data_error_result(retmsg="Index updating failure")
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/rm', methods=['POST'])
+@login_required
+@validate_request("chunk_ids", "doc_id")
+def rm():
+    req = request.json
+    try:
+        if not ELASTICSEARCH.deleteByQuery(
+                Q("ids", values=req["chunk_ids"]), search.index_name(current_user.id)):
+            return get_data_error_result(retmsg="Index updating failure")
+        e, doc = DocumentService.get_by_id(req["doc_id"])
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        deleted_chunk_ids = req["chunk_ids"]
+        chunk_number = len(deleted_chunk_ids)
+        DocumentService.decrement_chunk_num(doc.id, doc.kb_id, 1, chunk_number, 0)
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/create', methods=['POST'])
+@login_required
+@validate_request("doc_id", "content_with_weight")
+def create():
+    req = request.json
+    md5 = hashlib.md5()
+    md5.update((req["content_with_weight"] + req["doc_id"]).encode("utf-8"))
+    chunck_id = md5.hexdigest()
+    d = {"id": chunck_id, "content_ltks": rag_tokenizer.tokenize(req["content_with_weight"]),
+         "content_with_weight": req["content_with_weight"]}
+    d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
+    d["important_kwd"] = req.get("important_kwd", [])
+    d["important_tks"] = rag_tokenizer.tokenize(" ".join(req.get("important_kwd", [])))
+    d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
+    d["create_timestamp_flt"] = datetime.datetime.now().timestamp()
+
+    try:
+        e, doc = DocumentService.get_by_id(req["doc_id"])
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        d["kb_id"] = [doc.kb_id]
+        d["docnm_kwd"] = doc.name
+        d["doc_id"] = doc.id
+
+        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
+        if not tenant_id:
+            return get_data_error_result(retmsg="Tenant not found!")
+
+        embd_id = DocumentService.get_embd_id(req["doc_id"])
+        embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING.value, embd_id)
+
+        v, c = embd_mdl.encode([doc.name, req["content_with_weight"]])
+        v = 0.1 * v[0] + 0.9 * v[1]
+        d["q_%d_vec" % len(v)] = v.tolist()
+        ELASTICSEARCH.upsert([d], search.index_name(tenant_id))
+
+        DocumentService.increment_chunk_num(
+            doc.id, doc.kb_id, c, 1, 0)
+        return get_json_result(data={"chunk_id": chunck_id})
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/retrieval_test', methods=['POST'])
+@login_required
+@validate_request("kb_id", "question")
+def retrieval_test():
+    req = request.json
+    page = int(req.get("page", 1))
+    size = int(req.get("size", 30))
+    question = req["question"]
+    kb_id = req["kb_id"]
+    if isinstance(kb_id, str): kb_id = [kb_id]
+    doc_ids = req.get("doc_ids", [])
+    similarity_threshold = float(req.get("similarity_threshold", 0.0))
+    vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3))
+    top = int(req.get("top_k", 1024))
+
+    try:
+        tenants = UserTenantService.query(user_id=current_user.id)
+        for kid in kb_id:
+            for tenant in tenants:
+                if KnowledgebaseService.query(
+                        tenant_id=tenant.tenant_id, id=kid):
+                    break
+            else:
+                return get_json_result(
+                    data=False, retmsg=f'Only owner of knowledgebase authorized for this operation.',
+                    retcode=RetCode.OPERATING_ERROR)
+
+        e, kb = KnowledgebaseService.get_by_id(kb_id[0])
+        if not e:
+            return get_data_error_result(retmsg="Knowledgebase not found!")
+
+        embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
+
+        rerank_mdl = None
+        if req.get("rerank_id"):
+            rerank_mdl = LLMBundle(kb.tenant_id, LLMType.RERANK.value, llm_name=req["rerank_id"])
+
+        if req.get("keyword", False):
+            chat_mdl = LLMBundle(kb.tenant_id, LLMType.CHAT)
+            question += keyword_extraction(chat_mdl, question)
+
+        retr = retrievaler if kb.parser_id != ParserType.KG else kg_retrievaler
+        ranks = retr.retrieval(question, embd_mdl, kb.tenant_id, kb_id, page, size,
+                               similarity_threshold, vector_similarity_weight, top,
+                               doc_ids, rerank_mdl=rerank_mdl, highlight=req.get("highlight"))
+        for c in ranks["chunks"]:
+            if "vector" in c:
+                del c["vector"]
+
+        return get_json_result(data=ranks)
+    except Exception as e:
+        if str(e).find("not_found") > 0:
+            return get_json_result(data=False, retmsg=f'No chunk found! Check the chunk status please!',
+                                   retcode=RetCode.DATA_ERROR)
+        return server_error_response(e)
+
+
+@manager.route('/knowledge_graph', methods=['GET'])
+@login_required
+def knowledge_graph():
+    doc_id = request.args["doc_id"]
+    req = {
+        "doc_ids":[doc_id],
+        "knowledge_graph_kwd": ["graph", "mind_map"]
+    }
+    tenant_id = DocumentService.get_tenant_id(doc_id)
+    sres = retrievaler.search(req, search.index_name(tenant_id))
+    obj = {"graph": {}, "mind_map": {}}
+    for id in sres.ids[:2]:
+        ty = sres.field[id]["knowledge_graph_kwd"]
+        try:
+            obj[ty] = json.loads(sres.field[id]["content_with_weight"])
+        except Exception as e:
+            print(traceback.format_exc(), flush=True)
+
+    return get_json_result(data=obj)
+

api/apps/conversation_app.py

@@ -1,175 +1,379 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from copy import deepcopy
-from flask import request, Response
-from flask_login import login_required
-from api.db.services.dialog_service import DialogService, ConversationService, chat
-from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
-from api.utils import get_uuid
-from api.utils.api_utils import get_json_result
-import json
-
-
-@manager.route('/set', methods=['POST'])
-@login_required
-def set_conversation():
-    req = request.json
-    conv_id = req.get("conversation_id")
-    if conv_id:
-        del req["conversation_id"]
-        try:
-            if not ConversationService.update_by_id(conv_id, req):
-                return get_data_error_result(retmsg="Conversation not found!")
-            e, conv = ConversationService.get_by_id(conv_id)
-            if not e:
-                return get_data_error_result(
-                    retmsg="Fail to update a conversation!")
-            conv = conv.to_dict()
-            return get_json_result(data=conv)
-        except Exception as e:
-            return server_error_response(e)
-
-    try:
-        e, dia = DialogService.get_by_id(req["dialog_id"])
-        if not e:
-            return get_data_error_result(retmsg="Dialog not found")
-        conv = {
-            "id": get_uuid(),
-            "dialog_id": req["dialog_id"],
-            "name": req.get("name", "New conversation"),
-            "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}]
-        }
-        ConversationService.save(**conv)
-        e, conv = ConversationService.get_by_id(conv["id"])
-        if not e:
-            return get_data_error_result(retmsg="Fail to new a conversation!")
-        conv = conv.to_dict()
-        return get_json_result(data=conv)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/get', methods=['GET'])
-@login_required
-def get():
-    conv_id = request.args["conversation_id"]
-    try:
-        e, conv = ConversationService.get_by_id(conv_id)
-        if not e:
-            return get_data_error_result(retmsg="Conversation not found!")
-        conv = conv.to_dict()
-        return get_json_result(data=conv)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/rm', methods=['POST'])
-@login_required
-def rm():
-    conv_ids = request.json["conversation_ids"]
-    try:
-        for cid in conv_ids:
-            ConversationService.delete_by_id(cid)
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/list', methods=['GET'])
-@login_required
-def list_convsersation():
-    dialog_id = request.args["dialog_id"]
-    try:
-        convs = ConversationService.query(
-            dialog_id=dialog_id,
-            order_by=ConversationService.model.create_time,
-            reverse=True)
-        convs = [d.to_dict() for d in convs]
-        return get_json_result(data=convs)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/completion', methods=['POST'])
-@login_required
-#@validate_request("conversation_id", "messages")
-def completion():
-    req = request.json
-    #req = {"conversation_id": "9aaaca4c11d311efa461fa163e197198", "messages": [
-    #    {"role": "user", "content": "上海有吗？"}
-    #]}
-    msg = []
-    for m in req["messages"]:
-        if m["role"] == "system":
-            continue
-        if m["role"] == "assistant" and not msg:
-            continue
-        msg.append({"role": m["role"], "content": m["content"]})
-    try:
-        e, conv = ConversationService.get_by_id(req["conversation_id"])
-        if not e:
-            return get_data_error_result(retmsg="Conversation not found!")
-        conv.message.append(deepcopy(msg[-1]))
-        e, dia = DialogService.get_by_id(conv.dialog_id)
-        if not e:
-            return get_data_error_result(retmsg="Dialog not found!")
-        del req["conversation_id"]
-        del req["messages"]
-
-        if not conv.reference:
-            conv.reference = []
-        conv.message.append({"role": "assistant", "content": ""})
-        conv.reference.append({"chunks": [], "doc_aggs": []})
-
-        def fillin_conv(ans):
-            nonlocal conv
-            if not conv.reference:
-                conv.reference.append(ans["reference"])
-            else: conv.reference[-1] = ans["reference"]
-            conv.message[-1] = {"role": "assistant", "content": ans["answer"]}
-
-        def stream():
-            nonlocal dia, msg, req, conv
-            try:
-                for ans in chat(dia, msg, True, **req):
-                    fillin_conv(ans)
-                    yield "data:"+json.dumps({"retcode": 0, "retmsg": "", "data": ans}, ensure_ascii=False) + "\n\n"
-                ConversationService.update_by_id(conv.id, conv.to_dict())
-            except Exception as e:
-                yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e),
-                                            "data": {"answer": "**ERROR**: "+str(e), "reference": []}},
-                                           ensure_ascii=False) + "\n\n"
-            yield "data:"+json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n"
-
-        if req.get("stream", True):
-            resp = Response(stream(), mimetype="text/event-stream")
-            resp.headers.add_header("Cache-control", "no-cache")
-            resp.headers.add_header("Connection", "keep-alive")
-            resp.headers.add_header("X-Accel-Buffering", "no")
-            resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
-            return resp
-
-        else:
-            answer = None
-            for ans in chat(dia, msg, **req):
-                answer = ans
-                fillin_conv(ans)
-                ConversationService.update_by_id(conv.id, conv.to_dict())
-                break
-            return get_json_result(data=answer)
-    except Exception as e:
-        return server_error_response(e)
-
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import json
+import re
+import traceback
+from copy import deepcopy
+from api.db.services.user_service import UserTenantService
+from flask import request, Response
+from flask_login import login_required, current_user
+
+from api.db import LLMType
+from api.db.services.dialog_service import DialogService, ConversationService, chat, ask
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.services.llm_service import LLMBundle, TenantService, TenantLLMService
+from api.settings import RetCode, retrievaler
+from api.utils import get_uuid
+from api.utils.api_utils import get_json_result
+from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+from graphrag.mind_map_extractor import MindMapExtractor
+
+
+@manager.route('/set', methods=['POST'])
+@login_required
+def set_conversation():
+    req = request.json
+    conv_id = req.get("conversation_id")
+    is_new = req.get("is_new")
+    del req["is_new"]
+    if not is_new:
+        del req["conversation_id"]
+        try:
+            if not ConversationService.update_by_id(conv_id, req):
+                return get_data_error_result(retmsg="Conversation not found!")
+            e, conv = ConversationService.get_by_id(conv_id)
+            if not e:
+                return get_data_error_result(
+                    retmsg="Fail to update a conversation!")
+            conv = conv.to_dict()
+            return get_json_result(data=conv)
+        except Exception as e:
+            return server_error_response(e)
+
+    try:
+        e, dia = DialogService.get_by_id(req["dialog_id"])
+        if not e:
+            return get_data_error_result(retmsg="Dialog not found")
+        conv = {
+            "id": conv_id,
+            "dialog_id": req["dialog_id"],
+            "name": req.get("name", "New conversation"),
+            "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}]
+        }
+        ConversationService.save(**conv)
+        e, conv = ConversationService.get_by_id(conv["id"])
+        if not e:
+            return get_data_error_result(retmsg="Fail to new a conversation!")
+        conv = conv.to_dict()
+        return get_json_result(data=conv)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/get', methods=['GET'])
+@login_required
+def get():
+    conv_id = request.args["conversation_id"]
+    try:
+        e, conv = ConversationService.get_by_id(conv_id)
+        if not e:
+            return get_data_error_result(retmsg="Conversation not found!")
+        tenants = UserTenantService.query(user_id=current_user.id)
+        for tenant in tenants:
+            if DialogService.query(tenant_id=tenant.tenant_id, id=conv.dialog_id):
+                break
+        else:
+            return get_json_result(
+                data=False, retmsg=f'Only owner of conversation authorized for this operation.',
+                retcode=RetCode.OPERATING_ERROR)
+        conv = conv.to_dict()
+        return get_json_result(data=conv)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/rm', methods=['POST'])
+@login_required
+def rm():
+    conv_ids = request.json["conversation_ids"]
+    try:
+        for cid in conv_ids:
+            exist, conv = ConversationService.get_by_id(cid)
+            if not exist:
+                return get_data_error_result(retmsg="Conversation not found!")
+            tenants = UserTenantService.query(user_id=current_user.id)
+            for tenant in tenants:
+                if DialogService.query(tenant_id=tenant.tenant_id, id=conv.dialog_id):
+                    break
+            else:
+                return get_json_result(
+                    data=False, retmsg=f'Only owner of conversation authorized for this operation.',
+                    retcode=RetCode.OPERATING_ERROR)
+            ConversationService.delete_by_id(cid)
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/list', methods=['GET'])
+@login_required
+def list_convsersation():
+    dialog_id = request.args["dialog_id"]
+    try:
+        if not DialogService.query(tenant_id=current_user.id, id=dialog_id):
+            return get_json_result(
+                data=False, retmsg=f'Only owner of dialog authorized for this operation.',
+                retcode=RetCode.OPERATING_ERROR)
+        convs = ConversationService.query(
+            dialog_id=dialog_id,
+            order_by=ConversationService.model.create_time,
+            reverse=True)
+        convs = [d.to_dict() for d in convs]
+        return get_json_result(data=convs)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/completion', methods=['POST'])
+@login_required
+@validate_request("conversation_id", "messages")
+def completion():
+    req = request.json
+    # req = {"conversation_id": "9aaaca4c11d311efa461fa163e197198", "messages": [
+    #    {"role": "user", "content": "上海有吗？"}
+    # ]}
+    msg = []
+    for m in req["messages"]:
+        if m["role"] == "system":
+            continue
+        if m["role"] == "assistant" and not msg:
+            continue
+        msg.append(m)
+    message_id = msg[-1].get("id")
+    try:
+        e, conv = ConversationService.get_by_id(req["conversation_id"])
+        if not e:
+            return get_data_error_result(retmsg="Conversation not found!")
+        conv.message = deepcopy(req["messages"])
+        e, dia = DialogService.get_by_id(conv.dialog_id)
+        if not e:
+            return get_data_error_result(retmsg="Dialog not found!")
+        del req["conversation_id"]
+        del req["messages"]
+
+        if not conv.reference:
+            conv.reference = []
+        conv.message.append({"role": "assistant", "content": "", "id": message_id})
+        conv.reference.append({"chunks": [], "doc_aggs": []})
+
+        def fillin_conv(ans):
+            nonlocal conv, message_id
+            if not conv.reference:
+                conv.reference.append(ans["reference"])
+            else:
+                conv.reference[-1] = ans["reference"]
+            conv.message[-1] = {"role": "assistant", "content": ans["answer"],
+                                "id": message_id, "prompt": ans.get("prompt", "")}
+            ans["id"] = message_id
+
+        def stream():
+            nonlocal dia, msg, req, conv
+            try:
+                for ans in chat(dia, msg, True, **req):
+                    fillin_conv(ans)
+                    yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": ans}, ensure_ascii=False) + "\n\n"
+                ConversationService.update_by_id(conv.id, conv.to_dict())
+            except Exception as e:
+                yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e),
+                                            "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
+                                           ensure_ascii=False) + "\n\n"
+            yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n"
+
+        if req.get("stream", True):
+            resp = Response(stream(), mimetype="text/event-stream")
+            resp.headers.add_header("Cache-control", "no-cache")
+            resp.headers.add_header("Connection", "keep-alive")
+            resp.headers.add_header("X-Accel-Buffering", "no")
+            resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
+            return resp
+
+        else:
+            answer = None
+            for ans in chat(dia, msg, **req):
+                answer = ans
+                fillin_conv(ans)
+                ConversationService.update_by_id(conv.id, conv.to_dict())
+                break
+            return get_json_result(data=answer)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/tts', methods=['POST'])
+@login_required
+def tts():
+    req = request.json
+    text = req["text"]
+
+    tenants = TenantService.get_by_user_id(current_user.id)
+    if not tenants:
+        return get_data_error_result(retmsg="Tenant not found!")
+
+    tts_id = tenants[0]["tts_id"]
+    if not tts_id:
+        return get_data_error_result(retmsg="No default TTS model is set")
+
+    tts_mdl = LLMBundle(tenants[0]["tenant_id"], LLMType.TTS, tts_id)
+
+    def stream_audio():
+        try:
+            for txt in re.split(r"[，。/《》？；：！\n\r:;]+", text):
+                for chunk in tts_mdl.tts(txt):
+                    yield chunk
+        except Exception as e:
+            yield ("data:" + json.dumps({"retcode": 500, "retmsg": str(e),
+                                         "data": {"answer": "**ERROR**: " + str(e)}},
+                                        ensure_ascii=False)).encode('utf-8')
+
+    resp = Response(stream_audio(), mimetype="audio/mpeg")
+    resp.headers.add_header("Cache-Control", "no-cache")
+    resp.headers.add_header("Connection", "keep-alive")
+    resp.headers.add_header("X-Accel-Buffering", "no")
+
+    return resp
+
+
+@manager.route('/delete_msg', methods=['POST'])
+@login_required
+@validate_request("conversation_id", "message_id")
+def delete_msg():
+    req = request.json
+    e, conv = ConversationService.get_by_id(req["conversation_id"])
+    if not e:
+        return get_data_error_result(retmsg="Conversation not found!")
+
+    conv = conv.to_dict()
+    for i, msg in enumerate(conv["message"]):
+        if req["message_id"] != msg.get("id", ""):
+            continue
+        assert conv["message"][i + 1]["id"] == req["message_id"]
+        conv["message"].pop(i)
+        conv["message"].pop(i)
+        conv["reference"].pop(max(0, i // 2 - 1))
+        break
+
+    ConversationService.update_by_id(conv["id"], conv)
+    return get_json_result(data=conv)
+
+
+@manager.route('/thumbup', methods=['POST'])
+@login_required
+@validate_request("conversation_id", "message_id")
+def thumbup():
+    req = request.json
+    e, conv = ConversationService.get_by_id(req["conversation_id"])
+    if not e:
+        return get_data_error_result(retmsg="Conversation not found!")
+    up_down = req.get("set")
+    feedback = req.get("feedback", "")
+    conv = conv.to_dict()
+    for i, msg in enumerate(conv["message"]):
+        if req["message_id"] == msg.get("id", "") and msg.get("role", "") == "assistant":
+            if up_down:
+                msg["thumbup"] = True
+                if "feedback" in msg: del msg["feedback"]
+            else:
+                msg["thumbup"] = False
+                if feedback: msg["feedback"] = feedback
+            break
+
+    ConversationService.update_by_id(conv["id"], conv)
+    return get_json_result(data=conv)
+
+
+@manager.route('/ask', methods=['POST'])
+@login_required
+@validate_request("question", "kb_ids")
+def ask_about():
+    req = request.json
+    uid = current_user.id
+    def stream():
+        nonlocal req, uid
+        try:
+            for ans in ask(req["question"], req["kb_ids"], uid):
+                yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": ans}, ensure_ascii=False) + "\n\n"
+        except Exception as e:
+            yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e),
+                                        "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
+                                       ensure_ascii=False) + "\n\n"
+        yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n"
+
+    resp = Response(stream(), mimetype="text/event-stream")
+    resp.headers.add_header("Cache-control", "no-cache")
+    resp.headers.add_header("Connection", "keep-alive")
+    resp.headers.add_header("X-Accel-Buffering", "no")
+    resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
+    return resp
+
+
+@manager.route('/mindmap', methods=['POST'])
+@login_required
+@validate_request("question", "kb_ids")
+def mindmap():
+    req = request.json
+    kb_ids = req["kb_ids"]
+    e, kb = KnowledgebaseService.get_by_id(kb_ids[0])
+    if not e:
+        return get_data_error_result(retmsg="Knowledgebase not found!")
+
+    embd_mdl = TenantLLMService.model_instance(
+        kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
+    chat_mdl = LLMBundle(current_user.id, LLMType.CHAT)
+    ranks = retrievaler.retrieval(req["question"], embd_mdl, kb.tenant_id, kb_ids, 1, 12,
+                           0.3, 0.3, aggs=False)
+    mindmap = MindMapExtractor(chat_mdl)
+    mind_map = mindmap([c["content_with_weight"] for c in ranks["chunks"]]).output
+    if "error" in mind_map:
+        return server_error_response(Exception(mind_map["error"]))
+    return get_json_result(data=mind_map)
+
+
+@manager.route('/related_questions', methods=['POST'])
+@login_required
+@validate_request("question")
+def related_questions():
+    req = request.json
+    question = req["question"]
+    chat_mdl = LLMBundle(current_user.id, LLMType.CHAT)
+    prompt = """
+Objective: To generate search terms related to the user's search keywords, helping users find more valuable information.
+Instructions:
+ - Based on the keywords provided by the user, generate 5-10 related search terms.
+ - Each search term should be directly or indirectly related to the keyword, guiding the user to find more valuable information.
+ - Use common, general terms as much as possible, avoiding obscure words or technical jargon.
+ - Keep the term length between 2-4 words, concise and clear.
+ - DO NOT translate, use the language of the original keywords.
+
+### Example:
+Keywords: Chinese football
+Related search terms:
+1. Current status of Chinese football
+2. Reform of Chinese football
+3. Youth training of Chinese football
+4. Chinese football in the Asian Cup
+5. Chinese football in the World Cup
+
+Reason:
+ - When searching, users often only use one or two keywords, making it difficult to fully express their information needs.
+ - Generating related search terms can help users dig deeper into relevant information and improve search efficiency. 
+ - At the same time, related terms can also help search engines better understand user needs and return more accurate search results.
+ 
+"""
+    ans = chat_mdl.chat(prompt, [{"role": "user", "content": f"""
+Keywords: {question}
+Related search terms:
+    """}], {"temperature": 0.9})
+    return get_json_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)])

api/apps/dataset_api.py

@@ -39,10 +39,10 @@ from api.utils import get_uuid
 from api.utils.api_utils import construct_json_result, construct_error_response
 from api.utils.api_utils import construct_result, validate_request
 from api.utils.file_utils import filename_type, thumbnail
-from rag.app import book, laws, manual, naive, one, paper, presentation, qa, resume, table, picture, audio
+from rag.app import book, laws, manual, naive, one, paper, presentation, qa, resume, table, picture, audio, email
 from rag.nlp import search
 from rag.utils.es_conn import ELASTICSEARCH
-from rag.utils.minio_conn import MINIO
+from rag.utils.storage_factory import STORAGE_IMPL
 
 MAXIMUM_OF_UPLOADING_FILES = 256
 
@@ -352,7 +352,7 @@ def upload_documents(dataset_id):
 
             # upload to the minio
             location = filename
-            while MINIO.obj_exist(dataset_id, location):
+            while STORAGE_IMPL.obj_exist(dataset_id, location):
                 location += "_"
 
             blob = file.read()
@@ -361,7 +361,7 @@ def upload_documents(dataset_id):
             if blob == b'':
                 warnings.warn(f"[WARNING]: The content of the file {filename} is empty.")
 
-            MINIO.put(dataset_id, location, blob)
+            STORAGE_IMPL.put(dataset_id, location, blob)
 
             doc = {
                 "id": get_uuid(),
@@ -381,6 +381,8 @@ def upload_documents(dataset_id):
                 doc["parser_id"] = ParserType.AUDIO.value
             if re.search(r"\.(ppt|pptx|pages)$", filename):
                 doc["parser_id"] = ParserType.PRESENTATION.value
+            if re.search(r"\.(eml)$", filename):
+                doc["parser_id"] = ParserType.EMAIL.value
             DocumentService.insert(doc)
 
             FileService.add_file_from_kb(doc, kb_folder["id"], dataset.tenant_id)
@@ -420,7 +422,7 @@ def delete_document(document_id, dataset_id):  # string
                         f" reason!", code=RetCode.AUTHENTICATION_ERROR)
 
         # get the doc's id and location
-        real_dataset_id, location = File2DocumentService.get_minio_address(doc_id=document_id)
+        real_dataset_id, location = File2DocumentService.get_storage_address(doc_id=document_id)
 
         if real_dataset_id != dataset_id:
             return construct_json_result(message=f"The document {document_id} is not in the dataset: {dataset_id}, "
@@ -441,7 +443,7 @@ def delete_document(document_id, dataset_id):  # string
         File2DocumentService.delete_by_document_id(document_id)
 
         # delete it from minio
-        MINIO.rm(dataset_id, location)
+        STORAGE_IMPL.rm(dataset_id, location)
     except Exception as e:
         errors += str(e)
     if errors:
@@ -595,8 +597,8 @@ def download_document(dataset_id, document_id):
                                          code=RetCode.ARGUMENT_ERROR)
 
         # The process of downloading
-        doc_id, doc_location = File2DocumentService.get_minio_address(doc_id=document_id)  # minio address
-        file_stream = MINIO.get(doc_id, doc_location)
+        doc_id, doc_location = File2DocumentService.get_storage_address(doc_id=document_id)  # minio address
+        file_stream = STORAGE_IMPL.get(doc_id, doc_location)
         if not file_stream:
             return construct_json_result(message="This file is empty.", code=RetCode.DATA_ERROR)
 
@@ -652,6 +654,8 @@ def doc_parse(binary, doc_name, parser_name, tenant_id, doc_id):
             table.chunk(doc_name, binary=binary, callback=partial(doc_parse_callback, doc_id))
         case "audio":
             audio.chunk(doc_name, binary=binary, callback=partial(doc_parse_callback, doc_id))
+        case "email":
+            email.chunk(doc_name, binary=binary, callback=partial(doc_parse_callback, doc_id))
         case _:
             return False
 
@@ -734,8 +738,8 @@ def parsing_document_internal(id):
         doc_attributes = doc_attributes.to_dict()
         doc_id = doc_attributes["id"]
 
-        bucket, doc_name = File2DocumentService.get_minio_address(doc_id=doc_id)
-        binary = MINIO.get(bucket, doc_name)
+        bucket, doc_name = File2DocumentService.get_storage_address(doc_id=doc_id)
+        binary = STORAGE_IMPL.get(bucket, doc_name)
         parser_name = doc_attributes["parser_id"]
         if binary:
             res = doc_parse(binary, doc_name, parser_name, tenant_id, doc_id)

api/apps/dialog_app.py

@@ -1,172 +1,183 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-
-from flask import request
-from flask_login import login_required, current_user
-from api.db.services.dialog_service import DialogService
-from api.db import StatusEnum
-from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.services.user_service import TenantService
-from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
-from api.utils import get_uuid
-from api.utils.api_utils import get_json_result
-
-
-@manager.route('/set', methods=['POST'])
-@login_required
-def set_dialog():
-    req = request.json
-    dialog_id = req.get("dialog_id")
-    name = req.get("name", "New Dialog")
-    description = req.get("description", "A helpful Dialog")
-    icon = req.get("icon", "")
-    top_n = req.get("top_n", 6)
-    top_k = req.get("top_k", 1024)
-    rerank_id = req.get("rerank_id", "")
-    if not rerank_id: req["rerank_id"] = ""
-    similarity_threshold = req.get("similarity_threshold", 0.1)
-    vector_similarity_weight = req.get("vector_similarity_weight", 0.3)
-    if vector_similarity_weight is None: vector_similarity_weight = 0.3
-    llm_setting = req.get("llm_setting", {})
-    default_prompt = {
-        "system": """你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。
-以下是知识库：
-{knowledge}
-以上是知识库。""",
-        "prologue": "您好，我是您的助手小樱，长得可爱又善良，can I help you?",
-        "parameters": [
-            {"key": "knowledge", "optional": False}
-        ],
-        "empty_response": "Sorry! 知识库中未找到相关内容！"
-    }
-    prompt_config = req.get("prompt_config", default_prompt)
-
-    if not prompt_config["system"]:
-        prompt_config["system"] = default_prompt["system"]
-    # if len(prompt_config["parameters"]) < 1:
-    #     prompt_config["parameters"] = default_prompt["parameters"]
-    # for p in prompt_config["parameters"]:
-    #     if p["key"] == "knowledge":break
-    # else: prompt_config["parameters"].append(default_prompt["parameters"][0])
-
-    for p in prompt_config["parameters"]:
-        if p["optional"]:
-            continue
-        if prompt_config["system"].find("{%s}" % p["key"]) < 0:
-            return get_data_error_result(
-                retmsg="Parameter '{}' is not used".format(p["key"]))
-
-    try:
-        e, tenant = TenantService.get_by_id(current_user.id)
-        if not e:
-            return get_data_error_result(retmsg="Tenant not found!")
-        llm_id = req.get("llm_id", tenant.llm_id)
-        if not dialog_id:
-            if not req.get("kb_ids"):
-                return get_data_error_result(
-                    retmsg="Fail! Please select knowledgebase!")
-            dia = {
-                "id": get_uuid(),
-                "tenant_id": current_user.id,
-                "name": name,
-                "kb_ids": req["kb_ids"],
-                "description": description,
-                "llm_id": llm_id,
-                "llm_setting": llm_setting,
-                "prompt_config": prompt_config,
-                "top_n": top_n,
-                "top_k": top_k,
-                "rerank_id": rerank_id,
-                "similarity_threshold": similarity_threshold,
-                "vector_similarity_weight": vector_similarity_weight,
-                "icon": icon
-            }
-            if not DialogService.save(**dia):
-                return get_data_error_result(retmsg="Fail to new a dialog!")
-            e, dia = DialogService.get_by_id(dia["id"])
-            if not e:
-                return get_data_error_result(retmsg="Fail to new a dialog!")
-            return get_json_result(data=dia.to_json())
-        else:
-            del req["dialog_id"]
-            if "kb_names" in req:
-                del req["kb_names"]
-            if not DialogService.update_by_id(dialog_id, req):
-                return get_data_error_result(retmsg="Dialog not found!")
-            e, dia = DialogService.get_by_id(dialog_id)
-            if not e:
-                return get_data_error_result(retmsg="Fail to update a dialog!")
-            dia = dia.to_dict()
-            dia["kb_ids"], dia["kb_names"] = get_kb_names(dia["kb_ids"])
-            return get_json_result(data=dia)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/get', methods=['GET'])
-@login_required
-def get():
-    dialog_id = request.args["dialog_id"]
-    try:
-        e, dia = DialogService.get_by_id(dialog_id)
-        if not e:
-            return get_data_error_result(retmsg="Dialog not found!")
-        dia = dia.to_dict()
-        dia["kb_ids"], dia["kb_names"] = get_kb_names(dia["kb_ids"])
-        return get_json_result(data=dia)
-    except Exception as e:
-        return server_error_response(e)
-
-
-def get_kb_names(kb_ids):
-    ids, nms = [], []
-    for kid in kb_ids:
-        e, kb = KnowledgebaseService.get_by_id(kid)
-        if not e or kb.status != StatusEnum.VALID.value:
-            continue
-        ids.append(kid)
-        nms.append(kb.name)
-    return ids, nms
-
-
-@manager.route('/list', methods=['GET'])
-@login_required
-def list_dialogs():
-    try:
-        diags = DialogService.query(
-            tenant_id=current_user.id,
-            status=StatusEnum.VALID.value,
-            reverse=True,
-            order_by=DialogService.model.create_time)
-        diags = [d.to_dict() for d in diags]
-        for d in diags:
-            d["kb_ids"], d["kb_names"] = get_kb_names(d["kb_ids"])
-        return get_json_result(data=diags)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/rm', methods=['POST'])
-@login_required
-@validate_request("dialog_ids")
-def rm():
-    req = request.json
-    try:
-        DialogService.update_many_by_id(
-            [{"id": id, "status": StatusEnum.INVALID.value} for id in req["dialog_ids"]])
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+from flask import request
+from flask_login import login_required, current_user
+from api.db.services.dialog_service import DialogService
+from api.db import StatusEnum
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.services.user_service import TenantService, UserTenantService
+from api.settings import RetCode
+from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+from api.utils import get_uuid
+from api.utils.api_utils import get_json_result
+
+
+@manager.route('/set', methods=['POST'])
+@login_required
+def set_dialog():
+    req = request.json
+    dialog_id = req.get("dialog_id")
+    name = req.get("name", "New Dialog")
+    description = req.get("description", "A helpful Dialog")
+    icon = req.get("icon", "")
+    top_n = req.get("top_n", 6)
+    top_k = req.get("top_k", 1024)
+    rerank_id = req.get("rerank_id", "")
+    if not rerank_id: req["rerank_id"] = ""
+    similarity_threshold = req.get("similarity_threshold", 0.1)
+    vector_similarity_weight = req.get("vector_similarity_weight", 0.3)
+    if vector_similarity_weight is None: vector_similarity_weight = 0.3
+    llm_setting = req.get("llm_setting", {})
+    default_prompt = {
+        "system": """你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。
+以下是知识库：
+{knowledge}
+以上是知识库。""",
+        "prologue": "您好，我是您的助手小樱，长得可爱又善良，can I help you?",
+        "parameters": [
+            {"key": "knowledge", "optional": False}
+        ],
+        "empty_response": "Sorry! 知识库中未找到相关内容！"
+    }
+    prompt_config = req.get("prompt_config", default_prompt)
+
+    if not prompt_config["system"]:
+        prompt_config["system"] = default_prompt["system"]
+    # if len(prompt_config["parameters"]) < 1:
+    #     prompt_config["parameters"] = default_prompt["parameters"]
+    # for p in prompt_config["parameters"]:
+    #     if p["key"] == "knowledge":break
+    # else: prompt_config["parameters"].append(default_prompt["parameters"][0])
+
+    for p in prompt_config["parameters"]:
+        if p["optional"]:
+            continue
+        if prompt_config["system"].find("{%s}" % p["key"]) < 0:
+            return get_data_error_result(
+                retmsg="Parameter '{}' is not used".format(p["key"]))
+
+    try:
+        e, tenant = TenantService.get_by_id(current_user.id)
+        if not e:
+            return get_data_error_result(retmsg="Tenant not found!")
+        llm_id = req.get("llm_id", tenant.llm_id)
+        if not dialog_id:
+            if not req.get("kb_ids"):
+                return get_data_error_result(
+                    retmsg="Fail! Please select knowledgebase!")
+            dia = {
+                "id": get_uuid(),
+                "tenant_id": current_user.id,
+                "name": name,
+                "kb_ids": req["kb_ids"],
+                "description": description,
+                "llm_id": llm_id,
+                "llm_setting": llm_setting,
+                "prompt_config": prompt_config,
+                "top_n": top_n,
+                "top_k": top_k,
+                "rerank_id": rerank_id,
+                "similarity_threshold": similarity_threshold,
+                "vector_similarity_weight": vector_similarity_weight,
+                "icon": icon
+            }
+            if not DialogService.save(**dia):
+                return get_data_error_result(retmsg="Fail to new a dialog!")
+            e, dia = DialogService.get_by_id(dia["id"])
+            if not e:
+                return get_data_error_result(retmsg="Fail to new a dialog!")
+            return get_json_result(data=dia.to_json())
+        else:
+            del req["dialog_id"]
+            if "kb_names" in req:
+                del req["kb_names"]
+            if not DialogService.update_by_id(dialog_id, req):
+                return get_data_error_result(retmsg="Dialog not found!")
+            e, dia = DialogService.get_by_id(dialog_id)
+            if not e:
+                return get_data_error_result(retmsg="Fail to update a dialog!")
+            dia = dia.to_dict()
+            dia["kb_ids"], dia["kb_names"] = get_kb_names(dia["kb_ids"])
+            return get_json_result(data=dia)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/get', methods=['GET'])
+@login_required
+def get():
+    dialog_id = request.args["dialog_id"]
+    try:
+        e, dia = DialogService.get_by_id(dialog_id)
+        if not e:
+            return get_data_error_result(retmsg="Dialog not found!")
+        dia = dia.to_dict()
+        dia["kb_ids"], dia["kb_names"] = get_kb_names(dia["kb_ids"])
+        return get_json_result(data=dia)
+    except Exception as e:
+        return server_error_response(e)
+
+
+def get_kb_names(kb_ids):
+    ids, nms = [], []
+    for kid in kb_ids:
+        e, kb = KnowledgebaseService.get_by_id(kid)
+        if not e or kb.status != StatusEnum.VALID.value:
+            continue
+        ids.append(kid)
+        nms.append(kb.name)
+    return ids, nms
+
+
+@manager.route('/list', methods=['GET'])
+@login_required
+def list_dialogs():
+    try:
+        diags = DialogService.query(
+            tenant_id=current_user.id,
+            status=StatusEnum.VALID.value,
+            reverse=True,
+            order_by=DialogService.model.create_time)
+        diags = [d.to_dict() for d in diags]
+        for d in diags:
+            d["kb_ids"], d["kb_names"] = get_kb_names(d["kb_ids"])
+        return get_json_result(data=diags)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/rm', methods=['POST'])
+@login_required
+@validate_request("dialog_ids")
+def rm():
+    req = request.json
+    dialog_list=[]
+    tenants = UserTenantService.query(user_id=current_user.id)
+    try:
+        for id in req["dialog_ids"]:
+            for tenant in tenants:
+                if DialogService.query(tenant_id=tenant.tenant_id, id=id):
+                    break
+            else:
+                return get_json_result(
+                    data=False, retmsg=f'Only owner of dialog authorized for this operation.',
+                    retcode=RetCode.OPERATING_ERROR)
+            dialog_list.append({"id": id,"status":StatusEnum.INVALID.value})
+        DialogService.update_many_by_id(dialog_list)
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)

api/apps/document_app.py

@@ -1,486 +1,486 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License
-#
-
-import os
-import pathlib
-import re
-
-import flask
-from elasticsearch_dsl import Q
-from flask import request
-from flask_login import login_required, current_user
-
-from api.db.db_models import Task, File
-from api.db.services.file2document_service import File2DocumentService
-from api.db.services.file_service import FileService
-from api.db.services.task_service import TaskService, queue_tasks
-from rag.nlp import search
-from rag.utils.es_conn import ELASTICSEARCH
-from api.db.services import duplicate_name
-from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
-from api.utils import get_uuid
-from api.db import FileType, TaskStatus, ParserType, FileSource
-from api.db.services.document_service import DocumentService
-from api.settings import RetCode
-from api.utils.api_utils import get_json_result
-from rag.utils.minio_conn import MINIO
-from api.utils.file_utils import filename_type, thumbnail
-from api.utils.web_utils import html2pdf, is_valid_url
-from api.utils.web_utils import html2pdf, is_valid_url
-
-
-@manager.route('/upload', methods=['POST'])
-@login_required
-@validate_request("kb_id")
-def upload():
-    kb_id = request.form.get("kb_id")
-    if not kb_id:
-        return get_json_result(
-            data=False, retmsg='Lack of "KB ID"', retcode=RetCode.ARGUMENT_ERROR)
-    if 'file' not in request.files:
-        return get_json_result(
-            data=False, retmsg='No file part!', retcode=RetCode.ARGUMENT_ERROR)
-
-    file_objs = request.files.getlist('file')
-    for file_obj in file_objs:
-        if file_obj.filename == '':
-            return get_json_result(
-                data=False, retmsg='No file selected!', retcode=RetCode.ARGUMENT_ERROR)
-
-    e, kb = KnowledgebaseService.get_by_id(kb_id)
-    if not e:
-        raise LookupError("Can't find this knowledgebase!")
-
-    root_folder = FileService.get_root_folder(current_user.id)
-    pf_id = root_folder["id"]
-    FileService.init_knowledgebase_docs(pf_id, current_user.id)
-    kb_root_folder = FileService.get_kb_folder(current_user.id)
-    kb_folder = FileService.new_a_file_from_kb(kb.tenant_id, kb.name, kb_root_folder["id"])
-
-    err = []
-    for file in file_objs:
-        try:
-            MAX_FILE_NUM_PER_USER = int(os.environ.get('MAX_FILE_NUM_PER_USER', 0))
-            if MAX_FILE_NUM_PER_USER > 0 and DocumentService.get_doc_count(kb.tenant_id) >= MAX_FILE_NUM_PER_USER:
-                raise RuntimeError("Exceed the maximum file number of a free user!")
-
-            filename = duplicate_name(
-                DocumentService.query,
-                name=file.filename,
-                kb_id=kb.id)
-            filetype = filename_type(filename)
-            if filetype == FileType.OTHER.value:
-                raise RuntimeError("This type of file has not been supported yet!")
-
-            location = filename
-            while MINIO.obj_exist(kb_id, location):
-                location += "_"
-            blob = file.read()
-            MINIO.put(kb_id, location, blob)
-            doc = {
-                "id": get_uuid(),
-                "kb_id": kb.id,
-                "parser_id": kb.parser_id,
-                "parser_config": kb.parser_config,
-                "created_by": current_user.id,
-                "type": filetype,
-                "name": filename,
-                "location": location,
-                "size": len(blob),
-                "thumbnail": thumbnail(filename, blob)
-            }
-            if doc["type"] == FileType.VISUAL:
-                doc["parser_id"] = ParserType.PICTURE.value
-            if doc["type"] == FileType.AURAL:
-                doc["parser_id"] = ParserType.AUDIO.value
-            if re.search(r"\.(ppt|pptx|pages)$", filename):
-                doc["parser_id"] = ParserType.PRESENTATION.value
-            DocumentService.insert(doc)
-
-            FileService.add_file_from_kb(doc, kb_folder["id"], kb.tenant_id)
-        except Exception as e:
-            err.append(file.filename + ": " + str(e))
-    if err:
-        return get_json_result(
-            data=False, retmsg="\n".join(err), retcode=RetCode.SERVER_ERROR)
-    return get_json_result(data=True)
-
-
-@manager.route('/web_crawl', methods=['POST'])
-@login_required
-@validate_request("kb_id", "name", "url")
-def web_crawl():
-    kb_id = request.form.get("kb_id")
-    if not kb_id:
-        return get_json_result(
-            data=False, retmsg='Lack of "KB ID"', retcode=RetCode.ARGUMENT_ERROR)
-    name = request.form.get("name")
-    url = request.form.get("url")
-    if not is_valid_url(url):
-        return get_json_result(
-            data=False, retmsg='The URL format is invalid', retcode=RetCode.ARGUMENT_ERROR)
-    e, kb = KnowledgebaseService.get_by_id(kb_id)
-    if not e:
-        raise LookupError("Can't find this knowledgebase!")
-
-    blob = html2pdf(url)
-    if not blob: return server_error_response(ValueError("Download failure."))
-
-    root_folder = FileService.get_root_folder(current_user.id)
-    pf_id = root_folder["id"]
-    FileService.init_knowledgebase_docs(pf_id, current_user.id)
-    kb_root_folder = FileService.get_kb_folder(current_user.id)
-    kb_folder = FileService.new_a_file_from_kb(kb.tenant_id, kb.name, kb_root_folder["id"])
-
-    try:
-        filename = duplicate_name(
-            DocumentService.query,
-            name=name+".pdf",
-            kb_id=kb.id)
-        filetype = filename_type(filename)
-        if filetype == FileType.OTHER.value:
-            raise RuntimeError("This type of file has not been supported yet!")
-
-        location = filename
-        while MINIO.obj_exist(kb_id, location):
-            location += "_"
-        MINIO.put(kb_id, location, blob)
-        doc = {
-            "id": get_uuid(),
-            "kb_id": kb.id,
-            "parser_id": kb.parser_id,
-            "parser_config": kb.parser_config,
-            "created_by": current_user.id,
-            "type": filetype,
-            "name": filename,
-            "location": location,
-            "size": len(blob),
-            "thumbnail": thumbnail(filename, blob)
-        }
-        if doc["type"] == FileType.VISUAL:
-            doc["parser_id"] = ParserType.PICTURE.value
-        if doc["type"] == FileType.AURAL:
-            doc["parser_id"] = ParserType.AUDIO.value
-        if re.search(r"\.(ppt|pptx|pages)$", filename):
-            doc["parser_id"] = ParserType.PRESENTATION.value
-        DocumentService.insert(doc)
-        FileService.add_file_from_kb(doc, kb_folder["id"], kb.tenant_id)
-    except Exception as e:
-        return server_error_response(e)
-    return get_json_result(data=True)
-
-
-@manager.route('/create', methods=['POST'])
-@login_required
-@validate_request("name", "kb_id")
-def create():
-    req = request.json
-    kb_id = req["kb_id"]
-    if not kb_id:
-        return get_json_result(
-            data=False, retmsg='Lack of "KB ID"', retcode=RetCode.ARGUMENT_ERROR)
-
-    try:
-        e, kb = KnowledgebaseService.get_by_id(kb_id)
-        if not e:
-            return get_data_error_result(
-                retmsg="Can't find this knowledgebase!")
-
-        if DocumentService.query(name=req["name"], kb_id=kb_id):
-            return get_data_error_result(
-                retmsg="Duplicated document name in the same knowledgebase.")
-
-        doc = DocumentService.insert({
-            "id": get_uuid(),
-            "kb_id": kb.id,
-            "parser_id": kb.parser_id,
-            "parser_config": kb.parser_config,
-            "created_by": current_user.id,
-            "type": FileType.VIRTUAL,
-            "name": req["name"],
-            "location": "",
-            "size": 0
-        })
-        return get_json_result(data=doc.to_json())
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/list', methods=['GET'])
-@login_required
-def list_docs():
-    kb_id = request.args.get("kb_id")
-    if not kb_id:
-        return get_json_result(
-            data=False, retmsg='Lack of "KB ID"', retcode=RetCode.ARGUMENT_ERROR)
-    keywords = request.args.get("keywords", "")
-
-    page_number = int(request.args.get("page", 1))
-    items_per_page = int(request.args.get("page_size", 15))
-    orderby = request.args.get("orderby", "create_time")
-    desc = request.args.get("desc", True)
-    try:
-        docs, tol = DocumentService.get_by_kb_id(
-            kb_id, page_number, items_per_page, orderby, desc, keywords)
-        return get_json_result(data={"total": tol, "docs": docs})
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/thumbnails', methods=['GET'])
-@login_required
-def thumbnails():
-    doc_ids = request.args.get("doc_ids").split(",")
-    if not doc_ids:
-        return get_json_result(
-            data=False, retmsg='Lack of "Document ID"', retcode=RetCode.ARGUMENT_ERROR)
-
-    try:
-        docs = DocumentService.get_thumbnails(doc_ids)
-        return get_json_result(data={d["id"]: d["thumbnail"] for d in docs})
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/change_status', methods=['POST'])
-@login_required
-@validate_request("doc_id", "status")
-def change_status():
-    req = request.json
-    if str(req["status"]) not in ["0", "1"]:
-        get_json_result(
-            data=False,
-            retmsg='"Status" must be either 0 or 1!',
-            retcode=RetCode.ARGUMENT_ERROR)
-
-    try:
-        e, doc = DocumentService.get_by_id(req["doc_id"])
-        if not e:
-            return get_data_error_result(retmsg="Document not found!")
-        e, kb = KnowledgebaseService.get_by_id(doc.kb_id)
-        if not e:
-            return get_data_error_result(
-                retmsg="Can't find this knowledgebase!")
-
-        if not DocumentService.update_by_id(
-                req["doc_id"], {"status": str(req["status"])}):
-            return get_data_error_result(
-                retmsg="Database error (Document update)!")
-
-        if str(req["status"]) == "0":
-            ELASTICSEARCH.updateScriptByQuery(Q("term", doc_id=req["doc_id"]),
-                                              scripts="ctx._source.available_int=0;",
-                                              idxnm=search.index_name(
-                                                  kb.tenant_id)
-                                              )
-        else:
-            ELASTICSEARCH.updateScriptByQuery(Q("term", doc_id=req["doc_id"]),
-                                              scripts="ctx._source.available_int=1;",
-                                              idxnm=search.index_name(
-                                                  kb.tenant_id)
-                                              )
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/rm', methods=['POST'])
-@login_required
-@validate_request("doc_id")
-def rm():
-    req = request.json
-    doc_ids = req["doc_id"]
-    if isinstance(doc_ids, str): doc_ids = [doc_ids]
-    root_folder = FileService.get_root_folder(current_user.id)
-    pf_id = root_folder["id"]
-    FileService.init_knowledgebase_docs(pf_id, current_user.id)
-    errors = ""
-    for doc_id in doc_ids:
-        try:
-            e, doc = DocumentService.get_by_id(doc_id)
-            if not e:
-                return get_data_error_result(retmsg="Document not found!")
-            tenant_id = DocumentService.get_tenant_id(doc_id)
-            if not tenant_id:
-                return get_data_error_result(retmsg="Tenant not found!")
-
-            b, n = File2DocumentService.get_minio_address(doc_id=doc_id)
-
-            if not DocumentService.remove_document(doc, tenant_id):
-                return get_data_error_result(
-                    retmsg="Database error (Document removal)!")
-
-            f2d = File2DocumentService.get_by_document_id(doc_id)
-            FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id])
-            File2DocumentService.delete_by_document_id(doc_id)
-
-            MINIO.rm(b, n)
-        except Exception as e:
-            errors += str(e)
-
-    if errors:
-        return get_json_result(data=False, retmsg=errors, retcode=RetCode.SERVER_ERROR)
-
-    return get_json_result(data=True)
-
-
-@manager.route('/run', methods=['POST'])
-@login_required
-@validate_request("doc_ids", "run")
-def run():
-    req = request.json
-    try:
-        for id in req["doc_ids"]:
-            info = {"run": str(req["run"]), "progress": 0}
-            if str(req["run"]) == TaskStatus.RUNNING.value:
-                info["progress_msg"] = ""
-                info["chunk_num"] = 0
-                info["token_num"] = 0
-            DocumentService.update_by_id(id, info)
-            # if str(req["run"]) == TaskStatus.CANCEL.value:
-            tenant_id = DocumentService.get_tenant_id(id)
-            if not tenant_id:
-                return get_data_error_result(retmsg="Tenant not found!")
-            ELASTICSEARCH.deleteByQuery(
-                Q("match", doc_id=id), idxnm=search.index_name(tenant_id))
-
-            if str(req["run"]) == TaskStatus.RUNNING.value:
-                TaskService.filter_delete([Task.doc_id == id])
-                e, doc = DocumentService.get_by_id(id)
-                doc = doc.to_dict()
-                doc["tenant_id"] = tenant_id
-                bucket, name = File2DocumentService.get_minio_address(doc_id=doc["id"])
-                queue_tasks(doc, bucket, name)
-
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/rename', methods=['POST'])
-@login_required
-@validate_request("doc_id", "name")
-def rename():
-    req = request.json
-    try:
-        e, doc = DocumentService.get_by_id(req["doc_id"])
-        if not e:
-            return get_data_error_result(retmsg="Document not found!")
-        if pathlib.Path(req["name"].lower()).suffix != pathlib.Path(
-                doc.name.lower()).suffix:
-            return get_json_result(
-                data=False,
-                retmsg="The extension of file can't be changed",
-                retcode=RetCode.ARGUMENT_ERROR)
-        for d in DocumentService.query(name=req["name"], kb_id=doc.kb_id):
-            if d.name == req["name"]:
-                return get_data_error_result(
-                    retmsg="Duplicated document name in the same knowledgebase.")
-
-        if not DocumentService.update_by_id(
-                req["doc_id"], {"name": req["name"]}):
-            return get_data_error_result(
-                retmsg="Database error (Document rename)!")
-
-        informs = File2DocumentService.get_by_document_id(req["doc_id"])
-        if informs:
-            e, file = FileService.get_by_id(informs[0].file_id)
-            FileService.update_by_id(file.id, {"name": req["name"]})
-
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/get/<doc_id>', methods=['GET'])
-# @login_required
-def get(doc_id):
-    try:
-        e, doc = DocumentService.get_by_id(doc_id)
-        if not e:
-            return get_data_error_result(retmsg="Document not found!")
-
-        b,n = File2DocumentService.get_minio_address(doc_id=doc_id)
-        response = flask.make_response(MINIO.get(b, n))
-
-        ext = re.search(r"\.([^.]+)$", doc.name)
-        if ext:
-            if doc.type == FileType.VISUAL.value:
-                response.headers.set('Content-Type', 'image/%s' % ext.group(1))
-            else:
-                response.headers.set(
-                    'Content-Type',
-                    'application/%s' %
-                    ext.group(1))
-        return response
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/change_parser', methods=['POST'])
-@login_required
-@validate_request("doc_id", "parser_id")
-def change_parser():
-    req = request.json
-    try:
-        e, doc = DocumentService.get_by_id(req["doc_id"])
-        if not e:
-            return get_data_error_result(retmsg="Document not found!")
-        if doc.parser_id.lower() == req["parser_id"].lower():
-            if "parser_config" in req:
-                if req["parser_config"] == doc.parser_config:
-                    return get_json_result(data=True)
-            else:
-                return get_json_result(data=True)
-
-        if doc.type == FileType.VISUAL or re.search(
-                r"\.(ppt|pptx|pages)$", doc.name):
-            return get_data_error_result(retmsg="Not supported yet!")
-
-        e = DocumentService.update_by_id(doc.id,
-                                         {"parser_id": req["parser_id"], "progress": 0, "progress_msg": "",
-                                          "run": TaskStatus.UNSTART.value})
-        if not e:
-            return get_data_error_result(retmsg="Document not found!")
-        if "parser_config" in req:
-            DocumentService.update_parser_config(doc.id, req["parser_config"])
-        if doc.token_num > 0:
-            e = DocumentService.increment_chunk_num(doc.id, doc.kb_id, doc.token_num * -1, doc.chunk_num * -1,
-                                                    doc.process_duation * -1)
-            if not e:
-                return get_data_error_result(retmsg="Document not found!")
-            tenant_id = DocumentService.get_tenant_id(req["doc_id"])
-            if not tenant_id:
-                return get_data_error_result(retmsg="Tenant not found!")
-            ELASTICSEARCH.deleteByQuery(
-                Q("match", doc_id=doc.id), idxnm=search.index_name(tenant_id))
-
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/image/<image_id>', methods=['GET'])
-# @login_required
-def get_image(image_id):
-    try:
-        bkt, nm = image_id.split("-")
-        response = flask.make_response(MINIO.get(bkt, nm))
-        response.headers.set('Content-Type', 'image/JPEG')
-        return response
-    except Exception as e:
-        return server_error_response(e)
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License
+#
+import datetime
+import hashlib
+import json
+import os
+import pathlib
+import re
+import traceback
+from concurrent.futures import ThreadPoolExecutor
+from copy import deepcopy
+from io import BytesIO
+
+import flask
+from elasticsearch_dsl import Q
+from flask import request
+from flask_login import login_required, current_user
+
+from api.db.db_models import Task, File
+from api.db.services.dialog_service import DialogService, ConversationService
+from api.db.services.file2document_service import File2DocumentService
+from api.db.services.file_service import FileService
+from api.db.services.llm_service import LLMBundle
+from api.db.services.task_service import TaskService, queue_tasks
+from api.db.services.user_service import TenantService, UserTenantService
+from graphrag.mind_map_extractor import MindMapExtractor
+from rag.app import naive
+from rag.nlp import search
+from rag.utils.es_conn import ELASTICSEARCH
+from api.db.services import duplicate_name
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+from api.utils import get_uuid
+from api.db import FileType, TaskStatus, ParserType, FileSource, LLMType
+from api.db.services.document_service import DocumentService, doc_upload_and_parse
+from api.settings import RetCode, stat_logger
+from api.utils.api_utils import get_json_result
+from rag.utils.storage_factory import STORAGE_IMPL
+from api.utils.file_utils import filename_type, thumbnail, get_project_base_directory
+from api.utils.web_utils import html2pdf, is_valid_url
+
+
+@manager.route('/upload', methods=['POST'])
+@login_required
+@validate_request("kb_id")
+def upload():
+    kb_id = request.form.get("kb_id")
+    if not kb_id:
+        return get_json_result(
+            data=False, retmsg='Lack of "KB ID"', retcode=RetCode.ARGUMENT_ERROR)
+    if 'file' not in request.files:
+        return get_json_result(
+            data=False, retmsg='No file part!', retcode=RetCode.ARGUMENT_ERROR)
+
+    file_objs = request.files.getlist('file')
+    for file_obj in file_objs:
+        if file_obj.filename == '':
+            return get_json_result(
+                data=False, retmsg='No file selected!', retcode=RetCode.ARGUMENT_ERROR)
+
+    e, kb = KnowledgebaseService.get_by_id(kb_id)
+    if not e:
+        raise LookupError("Can't find this knowledgebase!")
+
+    err, _ = FileService.upload_document(kb, file_objs, current_user.id)
+    if err:
+        return get_json_result(
+            data=False, retmsg="\n".join(err), retcode=RetCode.SERVER_ERROR)
+    return get_json_result(data=True)
+
+
+@manager.route('/web_crawl', methods=['POST'])
+@login_required
+@validate_request("kb_id", "name", "url")
+def web_crawl():
+    kb_id = request.form.get("kb_id")
+    if not kb_id:
+        return get_json_result(
+            data=False, retmsg='Lack of "KB ID"', retcode=RetCode.ARGUMENT_ERROR)
+    name = request.form.get("name")
+    url = request.form.get("url")
+    if not is_valid_url(url):
+        return get_json_result(
+            data=False, retmsg='The URL format is invalid', retcode=RetCode.ARGUMENT_ERROR)
+    e, kb = KnowledgebaseService.get_by_id(kb_id)
+    if not e:
+        raise LookupError("Can't find this knowledgebase!")
+
+    blob = html2pdf(url)
+    if not blob: return server_error_response(ValueError("Download failure."))
+
+    root_folder = FileService.get_root_folder(current_user.id)
+    pf_id = root_folder["id"]
+    FileService.init_knowledgebase_docs(pf_id, current_user.id)
+    kb_root_folder = FileService.get_kb_folder(current_user.id)
+    kb_folder = FileService.new_a_file_from_kb(kb.tenant_id, kb.name, kb_root_folder["id"])
+
+    try:
+        filename = duplicate_name(
+            DocumentService.query,
+            name=name + ".pdf",
+            kb_id=kb.id)
+        filetype = filename_type(filename)
+        if filetype == FileType.OTHER.value:
+            raise RuntimeError("This type of file has not been supported yet!")
+
+        location = filename
+        while STORAGE_IMPL.obj_exist(kb_id, location):
+            location += "_"
+        STORAGE_IMPL.put(kb_id, location, blob)
+        doc = {
+            "id": get_uuid(),
+            "kb_id": kb.id,
+            "parser_id": kb.parser_id,
+            "parser_config": kb.parser_config,
+            "created_by": current_user.id,
+            "type": filetype,
+            "name": filename,
+            "location": location,
+            "size": len(blob),
+            "thumbnail": thumbnail(filename, blob)
+        }
+        if doc["type"] == FileType.VISUAL:
+            doc["parser_id"] = ParserType.PICTURE.value
+        if doc["type"] == FileType.AURAL:
+            doc["parser_id"] = ParserType.AUDIO.value
+        if re.search(r"\.(ppt|pptx|pages)$", filename):
+            doc["parser_id"] = ParserType.PRESENTATION.value
+        if re.search(r"\.(eml)$", filename):
+            doc["parser_id"] = ParserType.EMAIL.value
+        DocumentService.insert(doc)
+        FileService.add_file_from_kb(doc, kb_folder["id"], kb.tenant_id)
+    except Exception as e:
+        return server_error_response(e)
+    return get_json_result(data=True)
+
+
+@manager.route('/create', methods=['POST'])
+@login_required
+@validate_request("name", "kb_id")
+def create():
+    req = request.json
+    kb_id = req["kb_id"]
+    if not kb_id:
+        return get_json_result(
+            data=False, retmsg='Lack of "KB ID"', retcode=RetCode.ARGUMENT_ERROR)
+
+    try:
+        e, kb = KnowledgebaseService.get_by_id(kb_id)
+        if not e:
+            return get_data_error_result(
+                retmsg="Can't find this knowledgebase!")
+
+        if DocumentService.query(name=req["name"], kb_id=kb_id):
+            return get_data_error_result(
+                retmsg="Duplicated document name in the same knowledgebase.")
+
+        doc = DocumentService.insert({
+            "id": get_uuid(),
+            "kb_id": kb.id,
+            "parser_id": kb.parser_id,
+            "parser_config": kb.parser_config,
+            "created_by": current_user.id,
+            "type": FileType.VIRTUAL,
+            "name": req["name"],
+            "location": "",
+            "size": 0
+        })
+        return get_json_result(data=doc.to_json())
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/list', methods=['GET'])
+@login_required
+def list_docs():
+    kb_id = request.args.get("kb_id")
+    if not kb_id:
+        return get_json_result(
+            data=False, retmsg='Lack of "KB ID"', retcode=RetCode.ARGUMENT_ERROR)
+    tenants = UserTenantService.query(user_id=current_user.id)
+    for tenant in tenants:
+        if KnowledgebaseService.query(
+                tenant_id=tenant.tenant_id, id=kb_id):
+            break
+    else:
+        return get_json_result(
+            data=False, retmsg=f'Only owner of knowledgebase authorized for this operation.',
+            retcode=RetCode.OPERATING_ERROR)
+    keywords = request.args.get("keywords", "")
+
+    page_number = int(request.args.get("page", 1))
+    items_per_page = int(request.args.get("page_size", 15))
+    orderby = request.args.get("orderby", "create_time")
+    desc = request.args.get("desc", True)
+    try:
+        docs, tol = DocumentService.get_by_kb_id(
+            kb_id, page_number, items_per_page, orderby, desc, keywords)
+        return get_json_result(data={"total": tol, "docs": docs})
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/infos', methods=['POST'])
+def docinfos():
+    req = request.json
+    doc_ids = req["doc_ids"]
+    docs = DocumentService.get_by_ids(doc_ids)
+    return get_json_result(data=list(docs.dicts()))
+
+
+@manager.route('/thumbnails', methods=['GET'])
+#@login_required
+def thumbnails():
+    doc_ids = request.args.get("doc_ids").split(",")
+    if not doc_ids:
+        return get_json_result(
+            data=False, retmsg='Lack of "Document ID"', retcode=RetCode.ARGUMENT_ERROR)
+
+    try:
+        docs = DocumentService.get_thumbnails(doc_ids)
+        return get_json_result(data={d["id"]: d["thumbnail"] for d in docs})
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/change_status', methods=['POST'])
+@login_required
+@validate_request("doc_id", "status")
+def change_status():
+    req = request.json
+    if str(req["status"]) not in ["0", "1"]:
+        get_json_result(
+            data=False,
+            retmsg='"Status" must be either 0 or 1!',
+            retcode=RetCode.ARGUMENT_ERROR)
+
+    try:
+        e, doc = DocumentService.get_by_id(req["doc_id"])
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        e, kb = KnowledgebaseService.get_by_id(doc.kb_id)
+        if not e:
+            return get_data_error_result(
+                retmsg="Can't find this knowledgebase!")
+
+        if not DocumentService.update_by_id(
+                req["doc_id"], {"status": str(req["status"])}):
+            return get_data_error_result(
+                retmsg="Database error (Document update)!")
+
+        if str(req["status"]) == "0":
+            ELASTICSEARCH.updateScriptByQuery(Q("term", doc_id=req["doc_id"]),
+                                              scripts="ctx._source.available_int=0;",
+                                              idxnm=search.index_name(
+                                                  kb.tenant_id)
+                                              )
+        else:
+            ELASTICSEARCH.updateScriptByQuery(Q("term", doc_id=req["doc_id"]),
+                                              scripts="ctx._source.available_int=1;",
+                                              idxnm=search.index_name(
+                                                  kb.tenant_id)
+                                              )
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/rm', methods=['POST'])
+@login_required
+@validate_request("doc_id")
+def rm():
+    req = request.json
+    doc_ids = req["doc_id"]
+    if isinstance(doc_ids, str): doc_ids = [doc_ids]
+    root_folder = FileService.get_root_folder(current_user.id)
+    pf_id = root_folder["id"]
+    FileService.init_knowledgebase_docs(pf_id, current_user.id)
+    errors = ""
+    for doc_id in doc_ids:
+        try:
+            e, doc = DocumentService.get_by_id(doc_id)
+            if not e:
+                return get_data_error_result(retmsg="Document not found!")
+            tenant_id = DocumentService.get_tenant_id(doc_id)
+            if not tenant_id:
+                return get_data_error_result(retmsg="Tenant not found!")
+
+            b, n = File2DocumentService.get_storage_address(doc_id=doc_id)
+
+            if not DocumentService.remove_document(doc, tenant_id):
+                return get_data_error_result(
+                    retmsg="Database error (Document removal)!")
+
+            f2d = File2DocumentService.get_by_document_id(doc_id)
+            FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id])
+            File2DocumentService.delete_by_document_id(doc_id)
+
+            STORAGE_IMPL.rm(b, n)
+        except Exception as e:
+            errors += str(e)
+
+    if errors:
+        return get_json_result(data=False, retmsg=errors, retcode=RetCode.SERVER_ERROR)
+
+    return get_json_result(data=True)
+
+
+@manager.route('/run', methods=['POST'])
+@login_required
+@validate_request("doc_ids", "run")
+def run():
+    req = request.json
+    try:
+        for id in req["doc_ids"]:
+            info = {"run": str(req["run"]), "progress": 0}
+            if str(req["run"]) == TaskStatus.RUNNING.value:
+                info["progress_msg"] = ""
+                info["chunk_num"] = 0
+                info["token_num"] = 0
+            DocumentService.update_by_id(id, info)
+            # if str(req["run"]) == TaskStatus.CANCEL.value:
+            tenant_id = DocumentService.get_tenant_id(id)
+            if not tenant_id:
+                return get_data_error_result(retmsg="Tenant not found!")
+            ELASTICSEARCH.deleteByQuery(
+                Q("match", doc_id=id), idxnm=search.index_name(tenant_id))
+
+            if str(req["run"]) == TaskStatus.RUNNING.value:
+                TaskService.filter_delete([Task.doc_id == id])
+                e, doc = DocumentService.get_by_id(id)
+                doc = doc.to_dict()
+                doc["tenant_id"] = tenant_id
+                bucket, name = File2DocumentService.get_storage_address(doc_id=doc["id"])
+                queue_tasks(doc, bucket, name)
+
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/rename', methods=['POST'])
+@login_required
+@validate_request("doc_id", "name")
+def rename():
+    req = request.json
+    try:
+        e, doc = DocumentService.get_by_id(req["doc_id"])
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        if pathlib.Path(req["name"].lower()).suffix != pathlib.Path(
+                doc.name.lower()).suffix:
+            return get_json_result(
+                data=False,
+                retmsg="The extension of file can't be changed",
+                retcode=RetCode.ARGUMENT_ERROR)
+        for d in DocumentService.query(name=req["name"], kb_id=doc.kb_id):
+            if d.name == req["name"]:
+                return get_data_error_result(
+                    retmsg="Duplicated document name in the same knowledgebase.")
+
+        if not DocumentService.update_by_id(
+                req["doc_id"], {"name": req["name"]}):
+            return get_data_error_result(
+                retmsg="Database error (Document rename)!")
+
+        informs = File2DocumentService.get_by_document_id(req["doc_id"])
+        if informs:
+            e, file = FileService.get_by_id(informs[0].file_id)
+            FileService.update_by_id(file.id, {"name": req["name"]})
+
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/get/<doc_id>', methods=['GET'])
+# @login_required
+def get(doc_id):
+    try:
+        e, doc = DocumentService.get_by_id(doc_id)
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+
+        b, n = File2DocumentService.get_storage_address(doc_id=doc_id)
+        response = flask.make_response(STORAGE_IMPL.get(b, n))
+
+        ext = re.search(r"\.([^.]+)$", doc.name)
+        if ext:
+            if doc.type == FileType.VISUAL.value:
+                response.headers.set('Content-Type', 'image/%s' % ext.group(1))
+            else:
+                response.headers.set(
+                    'Content-Type',
+                    'application/%s' %
+                    ext.group(1))
+        return response
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/change_parser', methods=['POST'])
+@login_required
+@validate_request("doc_id", "parser_id")
+def change_parser():
+    req = request.json
+    try:
+        e, doc = DocumentService.get_by_id(req["doc_id"])
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        if doc.parser_id.lower() == req["parser_id"].lower():
+            if "parser_config" in req:
+                if req["parser_config"] == doc.parser_config:
+                    return get_json_result(data=True)
+            else:
+                return get_json_result(data=True)
+
+        if doc.type == FileType.VISUAL or re.search(
+                r"\.(ppt|pptx|pages)$", doc.name):
+            return get_data_error_result(retmsg="Not supported yet!")
+
+        e = DocumentService.update_by_id(doc.id,
+                                         {"parser_id": req["parser_id"], "progress": 0, "progress_msg": "",
+                                          "run": TaskStatus.UNSTART.value})
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        if "parser_config" in req:
+            DocumentService.update_parser_config(doc.id, req["parser_config"])
+        if doc.token_num > 0:
+            e = DocumentService.increment_chunk_num(doc.id, doc.kb_id, doc.token_num * -1, doc.chunk_num * -1,
+                                                    doc.process_duation * -1)
+            if not e:
+                return get_data_error_result(retmsg="Document not found!")
+            tenant_id = DocumentService.get_tenant_id(req["doc_id"])
+            if not tenant_id:
+                return get_data_error_result(retmsg="Tenant not found!")
+            ELASTICSEARCH.deleteByQuery(
+                Q("match", doc_id=doc.id), idxnm=search.index_name(tenant_id))
+
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/image/<image_id>', methods=['GET'])
+# @login_required
+def get_image(image_id):
+    try:
+        bkt, nm = image_id.split("-")
+        response = flask.make_response(STORAGE_IMPL.get(bkt, nm))
+        response.headers.set('Content-Type', 'image/JPEG')
+        return response
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/upload_and_parse', methods=['POST'])
+@login_required
+@validate_request("conversation_id")
+def upload_and_parse():
+    if 'file' not in request.files:
+        return get_json_result(
+            data=False, retmsg='No file part!', retcode=RetCode.ARGUMENT_ERROR)
+
+    file_objs = request.files.getlist('file')
+    for file_obj in file_objs:
+        if file_obj.filename == '':
+            return get_json_result(
+                data=False, retmsg='No file selected!', retcode=RetCode.ARGUMENT_ERROR)
+
+    doc_ids = doc_upload_and_parse(request.form.get("conversation_id"), file_objs, current_user.id)
+
+    return get_json_result(data=doc_ids)

api/apps/file2document_app.py

@@ -77,7 +77,7 @@ def convert():
                     doc = DocumentService.insert({
                         "id": get_uuid(),
                         "kb_id": kb.id,
-                        "parser_id": kb.parser_id,
+                        "parser_id": FileService.get_parser(file.type, file.name, kb.parser_id),
                         "parser_config": kb.parser_config,
                         "created_by": current_user.id,
                         "type": file.type,

api/apps/file_app.py

@@ -34,7 +34,7 @@ from api.utils.api_utils import get_json_result
 from api.utils.file_utils import filename_type
 from rag.nlp import search
 from rag.utils.es_conn import ELASTICSEARCH
-from rag.utils.minio_conn import MINIO
+from rag.utils.storage_factory import STORAGE_IMPL
 
 
 @manager.route('/upload', methods=['POST'])
@@ -98,7 +98,7 @@ def upload():
             # file type
             filetype = filename_type(file_obj_names[file_len - 1])
             location = file_obj_names[file_len - 1]
-            while MINIO.obj_exist(last_folder.id, location):
+            while STORAGE_IMPL.obj_exist(last_folder.id, location):
                 location += "_"
             blob = file_obj.read()
             filename = duplicate_name(
@@ -116,7 +116,7 @@ def upload():
                 "size": len(blob),
             }
             file = FileService.insert(file)
-            MINIO.put(last_folder.id, location, blob)
+            STORAGE_IMPL.put(last_folder.id, location, blob)
             file_res.append(file.to_json())
         return get_json_result(data=file_res)
     except Exception as e:
@@ -260,7 +260,7 @@ def rm():
                     e, file = FileService.get_by_id(inner_file_id)
                     if not e:
                         return get_data_error_result(retmsg="File not found!")
-                    MINIO.rm(file.parent_id, file.location)
+                    STORAGE_IMPL.rm(file.parent_id, file.location)
                 FileService.delete_folder_by_pf_id(current_user.id, file_id)
             else:
                 if not FileService.delete(file):
@@ -296,7 +296,8 @@ def rename():
         e, file = FileService.get_by_id(req["file_id"])
         if not e:
             return get_data_error_result(retmsg="File not found!")
-        if pathlib.Path(req["name"].lower()).suffix != pathlib.Path(
+        if file.type != FileType.FOLDER.value \
+            and pathlib.Path(req["name"].lower()).suffix != pathlib.Path(
                 file.name.lower()).suffix:
             return get_json_result(
                 data=False,
@@ -331,8 +332,8 @@ def get(file_id):
         e, file = FileService.get_by_id(file_id)
         if not e:
             return get_data_error_result(retmsg="Document not found!")
-        b, n = File2DocumentService.get_minio_address(file_id=file_id)
-        response = flask.make_response(MINIO.get(b, n))
+        b, n = File2DocumentService.get_storage_address(file_id=file_id)
+        response = flask.make_response(STORAGE_IMPL.get(b, n))
         ext = re.search(r"\.([^.]+)$", file.name)
         if ext:
             if file.type == FileType.VISUAL.value:

api/apps/kb_app.py

@@ -1,153 +1,162 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from elasticsearch_dsl import Q
-from flask import request
-from flask_login import login_required, current_user
-
-from api.db.services import duplicate_name
-from api.db.services.document_service import DocumentService
-from api.db.services.file2document_service import File2DocumentService
-from api.db.services.file_service import FileService
-from api.db.services.user_service import TenantService, UserTenantService
-from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
-from api.utils import get_uuid, get_format_time
-from api.db import StatusEnum, UserTenantRole, FileSource
-from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.db_models import Knowledgebase, File
-from api.settings import stat_logger, RetCode
-from api.utils.api_utils import get_json_result
-from rag.nlp import search
-from rag.utils.es_conn import ELASTICSEARCH
-
-
-@manager.route('/create', methods=['post'])
-@login_required
-@validate_request("name")
-def create():
-    req = request.json
-    req["name"] = req["name"].strip()
-    req["name"] = duplicate_name(
-        KnowledgebaseService.query,
-        name=req["name"],
-        tenant_id=current_user.id,
-        status=StatusEnum.VALID.value)
-    try:
-        req["id"] = get_uuid()
-        req["tenant_id"] = current_user.id
-        req["created_by"] = current_user.id
-        e, t = TenantService.get_by_id(current_user.id)
-        if not e:
-            return get_data_error_result(retmsg="Tenant not found.")
-        req["embd_id"] = t.embd_id
-        if not KnowledgebaseService.save(**req):
-            return get_data_error_result()
-        return get_json_result(data={"kb_id": req["id"]})
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/update', methods=['post'])
-@login_required
-@validate_request("kb_id", "name", "description", "permission", "parser_id")
-def update():
-    req = request.json
-    req["name"] = req["name"].strip()
-    try:
-        if not KnowledgebaseService.query(
-                created_by=current_user.id, id=req["kb_id"]):
-            return get_json_result(
-                data=False, retmsg=f'Only owner of knowledgebase authorized for this operation.', retcode=RetCode.OPERATING_ERROR)
-
-        e, kb = KnowledgebaseService.get_by_id(req["kb_id"])
-        if not e:
-            return get_data_error_result(
-                retmsg="Can't find this knowledgebase!")
-
-        if req["name"].lower() != kb.name.lower() \
-                and len(KnowledgebaseService.query(name=req["name"], tenant_id=current_user.id, status=StatusEnum.VALID.value)) > 1:
-            return get_data_error_result(
-                retmsg="Duplicated knowledgebase name.")
-
-        del req["kb_id"]
-        if not KnowledgebaseService.update_by_id(kb.id, req):
-            return get_data_error_result()
-
-        e, kb = KnowledgebaseService.get_by_id(kb.id)
-        if not e:
-            return get_data_error_result(
-                retmsg="Database error (Knowledgebase rename)!")
-
-        return get_json_result(data=kb.to_json())
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/detail', methods=['GET'])
-@login_required
-def detail():
-    kb_id = request.args["kb_id"]
-    try:
-        kb = KnowledgebaseService.get_detail(kb_id)
-        if not kb:
-            return get_data_error_result(
-                retmsg="Can't find this knowledgebase!")
-        return get_json_result(data=kb)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/list', methods=['GET'])
-@login_required
-def list_kbs():
-    page_number = request.args.get("page", 1)
-    items_per_page = request.args.get("page_size", 150)
-    orderby = request.args.get("orderby", "create_time")
-    desc = request.args.get("desc", True)
-    try:
-        tenants = TenantService.get_joined_tenants_by_user_id(current_user.id)
-        kbs = KnowledgebaseService.get_by_tenant_ids(
-            [m["tenant_id"] for m in tenants], current_user.id, page_number, items_per_page, orderby, desc)
-        return get_json_result(data=kbs)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/rm', methods=['post'])
-@login_required
-@validate_request("kb_id")
-def rm():
-    req = request.json
-    try:
-        kbs = KnowledgebaseService.query(
-                created_by=current_user.id, id=req["kb_id"])
-        if not kbs:
-            return get_json_result(
-                data=False, retmsg=f'Only owner of knowledgebase authorized for this operation.', retcode=RetCode.OPERATING_ERROR)
-
-        for doc in DocumentService.query(kb_id=req["kb_id"]):
-            if not DocumentService.remove_document(doc, kbs[0].tenant_id):
-                return get_data_error_result(
-                    retmsg="Database error (Document removal)!")
-            f2d = File2DocumentService.get_by_document_id(doc.id)
-            FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id])
-            File2DocumentService.delete_by_document_id(doc.id)
-
-        if not KnowledgebaseService.delete_by_id(req["kb_id"]):
-            return get_data_error_result(
-                retmsg="Database error (Knowledgebase removal)!")
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from elasticsearch_dsl import Q
+from flask import request
+from flask_login import login_required, current_user
+
+from api.db.services import duplicate_name
+from api.db.services.document_service import DocumentService
+from api.db.services.file2document_service import File2DocumentService
+from api.db.services.file_service import FileService
+from api.db.services.user_service import TenantService, UserTenantService
+from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+from api.utils import get_uuid, get_format_time
+from api.db import StatusEnum, UserTenantRole, FileSource
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.db_models import Knowledgebase, File
+from api.settings import stat_logger, RetCode
+from api.utils.api_utils import get_json_result
+from rag.nlp import search
+from rag.utils.es_conn import ELASTICSEARCH
+
+
+@manager.route('/create', methods=['post'])
+@login_required
+@validate_request("name")
+def create():
+    req = request.json
+    req["name"] = req["name"].strip()
+    req["name"] = duplicate_name(
+        KnowledgebaseService.query,
+        name=req["name"],
+        tenant_id=current_user.id,
+        status=StatusEnum.VALID.value)
+    try:
+        req["id"] = get_uuid()
+        req["tenant_id"] = current_user.id
+        req["created_by"] = current_user.id
+        e, t = TenantService.get_by_id(current_user.id)
+        if not e:
+            return get_data_error_result(retmsg="Tenant not found.")
+        req["embd_id"] = t.embd_id
+        if not KnowledgebaseService.save(**req):
+            return get_data_error_result()
+        return get_json_result(data={"kb_id": req["id"]})
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/update', methods=['post'])
+@login_required
+@validate_request("kb_id", "name", "description", "permission", "parser_id")
+def update():
+    req = request.json
+    req["name"] = req["name"].strip()
+    try:
+        if not KnowledgebaseService.query(
+                created_by=current_user.id, id=req["kb_id"]):
+            return get_json_result(
+                data=False, retmsg=f'Only owner of knowledgebase authorized for this operation.', retcode=RetCode.OPERATING_ERROR)
+
+        e, kb = KnowledgebaseService.get_by_id(req["kb_id"])
+        if not e:
+            return get_data_error_result(
+                retmsg="Can't find this knowledgebase!")
+
+        if req["name"].lower() != kb.name.lower() \
+                and len(KnowledgebaseService.query(name=req["name"], tenant_id=current_user.id, status=StatusEnum.VALID.value)) > 1:
+            return get_data_error_result(
+                retmsg="Duplicated knowledgebase name.")
+
+        del req["kb_id"]
+        if not KnowledgebaseService.update_by_id(kb.id, req):
+            return get_data_error_result()
+
+        e, kb = KnowledgebaseService.get_by_id(kb.id)
+        if not e:
+            return get_data_error_result(
+                retmsg="Database error (Knowledgebase rename)!")
+
+        return get_json_result(data=kb.to_json())
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/detail', methods=['GET'])
+@login_required
+def detail():
+    kb_id = request.args["kb_id"]
+    try:
+        tenants = UserTenantService.query(user_id=current_user.id)
+        for tenant in tenants:
+            if KnowledgebaseService.query(
+                    tenant_id=tenant.tenant_id, id=kb_id):
+                break
+        else:
+            return get_json_result(
+                data=False, retmsg=f'Only owner of knowledgebase authorized for this operation.',
+                retcode=RetCode.OPERATING_ERROR)
+        kb = KnowledgebaseService.get_detail(kb_id)
+        if not kb:
+            return get_data_error_result(
+                retmsg="Can't find this knowledgebase!")
+        return get_json_result(data=kb)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/list', methods=['GET'])
+@login_required
+def list_kbs():
+    page_number = request.args.get("page", 1)
+    items_per_page = request.args.get("page_size", 150)
+    orderby = request.args.get("orderby", "create_time")
+    desc = request.args.get("desc", True)
+    try:
+        tenants = TenantService.get_joined_tenants_by_user_id(current_user.id)
+        kbs = KnowledgebaseService.get_by_tenant_ids(
+            [m["tenant_id"] for m in tenants], current_user.id, page_number, items_per_page, orderby, desc)
+        return get_json_result(data=kbs)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/rm', methods=['post'])
+@login_required
+@validate_request("kb_id")
+def rm():
+    req = request.json
+    try:
+        kbs = KnowledgebaseService.query(
+                created_by=current_user.id, id=req["kb_id"])
+        if not kbs:
+            return get_json_result(
+                data=False, retmsg=f'Only owner of knowledgebase authorized for this operation.', retcode=RetCode.OPERATING_ERROR)
+
+        for doc in DocumentService.query(kb_id=req["kb_id"]):
+            if not DocumentService.remove_document(doc, kbs[0].tenant_id):
+                return get_data_error_result(
+                    retmsg="Database error (Document removal)!")
+            f2d = File2DocumentService.get_by_document_id(doc.id)
+            FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id])
+            File2DocumentService.delete_by_document_id(doc.id)
+
+        if not KnowledgebaseService.delete_by_id(req["kb_id"]):
+            return get_data_error_result(
+                retmsg="Database error (Knowledgebase removal)!")
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)

api/apps/llm_app.py

@@ -1,275 +1,354 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from flask import request
-from flask_login import login_required, current_user
-from api.db.services.llm_service import LLMFactoriesService, TenantLLMService, LLMService
-from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
-from api.db import StatusEnum, LLMType
-from api.db.db_models import TenantLLM
-from api.utils.api_utils import get_json_result
-from rag.llm import EmbeddingModel, ChatModel, RerankModel,CvModel
-import requests
-
-@manager.route('/factories', methods=['GET'])
-@login_required
-def factories():
-    try:
-        fac = LLMFactoriesService.get_all()
-        return get_json_result(data=[f.to_dict() for f in fac if f.name not in ["Youdao", "FastEmbed", "BAAI"]])
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/set_api_key', methods=['POST'])
-@login_required
-@validate_request("llm_factory", "api_key")
-def set_api_key():
-    req = request.json
-    # test if api key works
-    chat_passed, embd_passed, rerank_passed = False, False, False
-    factory = req["llm_factory"]
-    msg = ""
-    for llm in LLMService.query(fid=factory):
-        if not embd_passed and llm.model_type == LLMType.EMBEDDING.value:
-            mdl = EmbeddingModel[factory](
-                req["api_key"], llm.llm_name, base_url=req.get("base_url"))
-            try:
-                arr, tc = mdl.encode(["Test if the api key is available"])
-                if len(arr[0]) == 0 or tc == 0:
-                    raise Exception("Fail")
-                embd_passed = True
-            except Exception as e:
-                msg += f"\nFail to access embedding model({llm.llm_name}) using this api key." + str(e)
-        elif not chat_passed and llm.model_type == LLMType.CHAT.value:
-            mdl = ChatModel[factory](
-                req["api_key"], llm.llm_name, base_url=req.get("base_url"))
-            try:
-                m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], 
-                                 {"temperature": 0.9,'max_tokens':50})
-                if not tc:
-                    raise Exception(m)
-            except Exception as e:
-                msg += f"\nFail to access model({llm.llm_name}) using this api key." + str(
-                    e)
-            chat_passed = True
-        elif not rerank_passed and llm.model_type == LLMType.RERANK:
-            mdl = RerankModel[factory](
-                req["api_key"], llm.llm_name, base_url=req.get("base_url"))
-            try:
-                arr, tc = mdl.similarity("What's the weather?", ["Is it sunny today?"])
-                if len(arr) == 0 or tc == 0:
-                    raise Exception("Fail")
-            except Exception as e:
-                msg += f"\nFail to access model({llm.llm_name}) using this api key." + str(
-                    e)
-            rerank_passed = True
-
-    if msg:
-        return get_data_error_result(retmsg=msg)
-
-    llm = {
-        "api_key": req["api_key"],
-        "api_base": req.get("base_url", "")
-    }
-    for n in ["model_type", "llm_name"]:
-        if n in req:
-            llm[n] = req[n]
-
-    if not TenantLLMService.filter_update(
-            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == factory], llm):
-        for llm in LLMService.query(fid=factory):
-            TenantLLMService.save(
-                tenant_id=current_user.id,
-                llm_factory=factory,
-                llm_name=llm.llm_name,
-                model_type=llm.model_type,
-                api_key=req["api_key"],
-                api_base=req.get("base_url", "")
-            )
-
-    return get_json_result(data=True)
-
-
-@manager.route('/add_llm', methods=['POST'])
-@login_required
-@validate_request("llm_factory", "llm_name", "model_type")
-def add_llm():
-    req = request.json
-    factory = req["llm_factory"]
-
-    if factory == "VolcEngine":
-        # For VolcEngine, due to its special authentication method
-        # Assemble volc_ak, volc_sk, endpoint_id into api_key
-        temp = list(eval(req["llm_name"]).items())[0]
-        llm_name = temp[0]
-        endpoint_id = temp[1]
-        api_key = '{' + f'"volc_ak": "{req.get("volc_ak", "")}", ' \
-                        f'"volc_sk": "{req.get("volc_sk", "")}", ' \
-                        f'"ep_id": "{endpoint_id}", ' + '}'
-    elif factory == "Bedrock":
-        # For Bedrock, due to its special authentication method
-        # Assemble bedrock_ak, bedrock_sk, bedrock_region
-        llm_name = req["llm_name"]
-        api_key = '{' + f'"bedrock_ak": "{req.get("bedrock_ak", "")}", ' \
-                        f'"bedrock_sk": "{req.get("bedrock_sk", "")}", ' \
-                        f'"bedrock_region": "{req.get("bedrock_region", "")}", ' + '}'
-    elif factory == "LocalAI":
-        llm_name = req["llm_name"]+"___LocalAI"
-        api_key = "xxxxxxxxxxxxxxx"
-    else:
-        llm_name = req["llm_name"]
-        api_key = "xxxxxxxxxxxxxxx"
-
-    llm = {
-        "tenant_id": current_user.id,
-        "llm_factory": factory,
-        "model_type": req["model_type"],
-        "llm_name": llm_name,
-        "api_base": req.get("api_base", ""),
-        "api_key": api_key
-    }
-
-    msg = ""
-    if llm["model_type"] == LLMType.EMBEDDING.value:
-        mdl = EmbeddingModel[factory](
-            key=llm['api_key'] if factory in ["VolcEngine", "Bedrock"] else None,
-            model_name=llm["llm_name"], 
-            base_url=llm["api_base"])
-        try:
-            arr, tc = mdl.encode(["Test if the api key is available"])
-            if len(arr[0]) == 0 or tc == 0:
-                raise Exception("Fail")
-        except Exception as e:
-            msg += f"\nFail to access embedding model({llm['llm_name']})." + str(e)
-    elif llm["model_type"] == LLMType.CHAT.value:
-        mdl = ChatModel[factory](
-            key=llm['api_key'] if factory in ["VolcEngine", "Bedrock"] else None,
-            model_name=llm["llm_name"],
-            base_url=llm["api_base"]
-        )
-        try:
-            m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], {
-                             "temperature": 0.9})
-            if not tc:
-                raise Exception(m)
-        except Exception as e:
-            msg += f"\nFail to access model({llm['llm_name']})." + str(
-                e)
-    elif llm["model_type"] == LLMType.RERANK:
-        mdl = RerankModel[factory](
-            key=None, model_name=llm["llm_name"], base_url=llm["api_base"]
-        )
-        try:
-            arr, tc = mdl.similarity("Hello~ Ragflower!", ["Hi, there!"])
-            if len(arr) == 0 or tc == 0:
-                raise Exception("Not known.")
-        except Exception as e:
-            msg += f"\nFail to access model({llm['llm_name']})." + str(
-                e)
-    elif llm["model_type"] == LLMType.IMAGE2TEXT.value:
-        mdl = CvModel[factory](
-            key=None, model_name=llm["llm_name"], base_url=llm["api_base"]
-        )
-        try:
-            img_url = (
-                "https://upload.wikimedia.org/wikipedia/comm"
-                "ons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/256"
-                "0px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
-            )
-            res = requests.get(img_url)
-            if res.status_code == 200:
-                m, tc = mdl.describe(res.content)
-                if not tc:
-                    raise Exception(m)
-            else:
-                pass
-        except Exception as e:
-            msg += f"\nFail to access model({llm['llm_name']})." + str(e)
-    else:
-        # TODO: check other type of models
-        pass
-
-    if msg:
-        return get_data_error_result(retmsg=msg)
-
-    if not TenantLLMService.filter_update(
-            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == factory, TenantLLM.llm_name == llm["llm_name"]], llm):
-        TenantLLMService.save(**llm)
-
-    return get_json_result(data=True)
-
-
-@manager.route('/delete_llm', methods=['POST'])
-@login_required
-@validate_request("llm_factory", "llm_name")
-def delete_llm():
-    req = request.json
-    TenantLLMService.filter_delete(
-            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"], TenantLLM.llm_name == req["llm_name"]])
-    return get_json_result(data=True)
-
-
-@manager.route('/my_llms', methods=['GET'])
-@login_required
-def my_llms():
-    try:
-        res = {}
-        for o in TenantLLMService.get_my_llms(current_user.id):
-            if o["llm_factory"] not in res:
-                res[o["llm_factory"]] = {
-                    "tags": o["tags"],
-                    "llm": []
-                }
-            res[o["llm_factory"]]["llm"].append({
-                "type": o["model_type"],
-                "name": o["llm_name"],
-                "used_token": o["used_tokens"]
-            })
-        return get_json_result(data=res)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/list', methods=['GET'])
-@login_required
-def list_app():
-    model_type = request.args.get("model_type")
-    try:
-        objs = TenantLLMService.query(tenant_id=current_user.id)
-        facts = set([o.to_dict()["llm_factory"] for o in objs if o.api_key])
-        llms = LLMService.get_all()
-        llms = [m.to_dict()
-                for m in llms if m.status == StatusEnum.VALID.value]
-        for m in llms:
-            m["available"] = m["fid"] in facts or m["llm_name"].lower() == "flag-embedding" or m["fid"] in ["Youdao","FastEmbed", "BAAI"]
-
-        llm_set = set([m["llm_name"] for m in llms])
-        for o in objs:
-            if not o.api_key:continue
-            if o.llm_name in llm_set:continue
-            llms.append({"llm_name": o.llm_name, "model_type": o.model_type, "fid": o.llm_factory, "available": True})
-
-        res = {}
-        for m in llms:
-            if model_type and m["model_type"].find(model_type)<0:
-                continue
-            if m["fid"] not in res:
-                res[m["fid"]] = []
-            res[m["fid"]].append(m)
-
-        return get_json_result(data=res)
-    except Exception as e:
-        return server_error_response(e)
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import json
+
+from flask import request
+from flask_login import login_required, current_user
+from api.db.services.llm_service import LLMFactoriesService, TenantLLMService, LLMService
+from api.settings import LIGHTEN
+from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+from api.db import StatusEnum, LLMType
+from api.db.db_models import TenantLLM
+from api.utils.api_utils import get_json_result
+from rag.llm import EmbeddingModel, ChatModel, RerankModel, CvModel, TTSModel
+import requests
+
+
+@manager.route('/factories', methods=['GET'])
+@login_required
+def factories():
+    try:
+        fac = LLMFactoriesService.get_all()
+        fac = [f.to_dict() for f in fac if f.name not in ["Youdao", "FastEmbed", "BAAI"]]
+        llms = LLMService.get_all()
+        mdl_types = {}
+        for m in llms:
+            if m.status != StatusEnum.VALID.value:
+                continue
+            if m.fid not in mdl_types:
+                mdl_types[m.fid] = set([])
+            mdl_types[m.fid].add(m.model_type)
+        for f in fac:
+            f["model_types"] = list(mdl_types.get(f["name"], [LLMType.CHAT, LLMType.EMBEDDING, LLMType.RERANK,
+                                                              LLMType.IMAGE2TEXT, LLMType.SPEECH2TEXT, LLMType.TTS]))
+        return get_json_result(data=fac)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/set_api_key', methods=['POST'])
+@login_required
+@validate_request("llm_factory", "api_key")
+def set_api_key():
+    req = request.json
+    # test if api key works
+    chat_passed, embd_passed, rerank_passed = False, False, False
+    factory = req["llm_factory"]
+    msg = ""
+    for llm in LLMService.query(fid=factory)[:3]:
+        if not embd_passed and llm.model_type == LLMType.EMBEDDING.value:
+            mdl = EmbeddingModel[factory](
+                req["api_key"], llm.llm_name, base_url=req.get("base_url"))
+            try:
+                arr, tc = mdl.encode(["Test if the api key is available"])
+                if len(arr[0]) == 0:
+                    raise Exception("Fail")
+                embd_passed = True
+            except Exception as e:
+                msg += f"\nFail to access embedding model({llm.llm_name}) using this api key." + str(e)
+        elif not chat_passed and llm.model_type == LLMType.CHAT.value:
+            mdl = ChatModel[factory](
+                req["api_key"], llm.llm_name, base_url=req.get("base_url"))
+            try:
+                m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], 
+                                 {"temperature": 0.9,'max_tokens':50})
+                if m.find("**ERROR**") >=0:
+                    raise Exception(m)
+            except Exception as e:
+                msg += f"\nFail to access model({llm.llm_name}) using this api key." + str(
+                    e)
+            chat_passed = True
+        elif not rerank_passed and llm.model_type == LLMType.RERANK:
+            mdl = RerankModel[factory](
+                req["api_key"], llm.llm_name, base_url=req.get("base_url"))
+            try:
+                arr, tc = mdl.similarity("What's the weather?", ["Is it sunny today?"])
+                if len(arr) == 0 or tc == 0:
+                    raise Exception("Fail")
+            except Exception as e:
+                msg += f"\nFail to access model({llm.llm_name}) using this api key." + str(
+                    e)
+            rerank_passed = True
+
+    if msg:
+        return get_data_error_result(retmsg=msg)
+
+    llm_config = {
+        "api_key": req["api_key"],
+        "api_base": req.get("base_url", "")
+    }
+    for n in ["model_type", "llm_name"]:
+        if n in req:
+            llm_config[n] = req[n]
+
+    for llm in LLMService.query(fid=factory):
+        if not TenantLLMService.filter_update(
+                [TenantLLM.tenant_id == current_user.id,
+                 TenantLLM.llm_factory == factory,
+                 TenantLLM.llm_name == llm.llm_name],
+                llm_config):
+            TenantLLMService.save(
+                tenant_id=current_user.id,
+                llm_factory=factory,
+                llm_name=llm.llm_name,
+                model_type=llm.model_type,
+                api_key=llm_config["api_key"],
+                api_base=llm_config["api_base"]
+            )
+
+    return get_json_result(data=True)
+
+
+@manager.route('/add_llm', methods=['POST'])
+@login_required
+@validate_request("llm_factory")
+def add_llm():
+    req = request.json
+    factory = req["llm_factory"]
+
+    def apikey_json(keys):
+        nonlocal req
+        return json.dumps({k: req.get(k, "") for k in keys})
+
+    if factory == "VolcEngine":
+        # For VolcEngine, due to its special authentication method
+        # Assemble ark_api_key endpoint_id into api_key
+        llm_name = req["llm_name"]
+        api_key = apikey_json(["ark_api_key", "endpoint_id"])
+
+    elif factory == "Tencent Hunyuan":
+        req["api_key"] = apikey_json(["hunyuan_sid", "hunyuan_sk"])
+        return set_api_key()
+
+    elif factory == "Tencent Cloud":
+        req["api_key"] = apikey_json(["tencent_cloud_sid", "tencent_cloud_sk"])
+
+    elif factory == "Bedrock":
+        # For Bedrock, due to its special authentication method
+        # Assemble bedrock_ak, bedrock_sk, bedrock_region
+        llm_name = req["llm_name"]
+        api_key = apikey_json(["bedrock_ak", "bedrock_sk", "bedrock_region"])
+
+    elif factory == "LocalAI":
+        llm_name = req["llm_name"]+"___LocalAI"
+        api_key = "xxxxxxxxxxxxxxx"
+        
+    elif factory == "HuggingFace":
+        llm_name = req["llm_name"]+"___HuggingFace"
+        api_key = "xxxxxxxxxxxxxxx"
+
+    elif factory == "OpenAI-API-Compatible":
+        llm_name = req["llm_name"]+"___OpenAI-API"
+        api_key = req.get("api_key","xxxxxxxxxxxxxxx")
+
+    elif factory =="XunFei Spark":
+        llm_name = req["llm_name"]
+        if req["model_type"] == "chat":
+            api_key = req.get("spark_api_password", "xxxxxxxxxxxxxxx")
+        elif req["model_type"] == "tts":
+            api_key = apikey_json(["spark_app_id", "spark_api_secret","spark_api_key"])
+
+    elif factory == "BaiduYiyan":
+        llm_name = req["llm_name"]
+        api_key = apikey_json(["yiyan_ak", "yiyan_sk"])
+
+    elif factory == "Fish Audio":
+        llm_name = req["llm_name"]
+        api_key = apikey_json(["fish_audio_ak", "fish_audio_refid"])
+
+    elif factory == "Google Cloud":
+        llm_name = req["llm_name"]
+        api_key = apikey_json(["google_project_id", "google_region", "google_service_account_key"])
+
+    else:
+        llm_name = req["llm_name"]
+        api_key = req.get("api_key", "xxxxxxxxxxxxxxx")
+
+    llm = {
+        "tenant_id": current_user.id,
+        "llm_factory": factory,
+        "model_type": req["model_type"],
+        "llm_name": llm_name,
+        "api_base": req.get("api_base", ""),
+        "api_key": api_key
+    }
+
+    msg = ""
+    if llm["model_type"] == LLMType.EMBEDDING.value:
+        mdl = EmbeddingModel[factory](
+            key=llm['api_key'],
+            model_name=llm["llm_name"], 
+            base_url=llm["api_base"])
+        try:
+            arr, tc = mdl.encode(["Test if the api key is available"])
+            if len(arr[0]) == 0 or tc == 0:
+                raise Exception("Fail")
+        except Exception as e:
+            msg += f"\nFail to access embedding model({llm['llm_name']})." + str(e)
+    elif llm["model_type"] == LLMType.CHAT.value:
+        mdl = ChatModel[factory](
+            key=llm['api_key'],
+            model_name=llm["llm_name"],
+            base_url=llm["api_base"]
+        )
+        try:
+            m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], {
+                             "temperature": 0.9})
+            if not tc:
+                raise Exception(m)
+        except Exception as e:
+            msg += f"\nFail to access model({llm['llm_name']})." + str(
+                e)
+    elif llm["model_type"] == LLMType.RERANK:
+        mdl = RerankModel[factory](
+            key=llm["api_key"], 
+            model_name=llm["llm_name"], 
+            base_url=llm["api_base"]
+        )
+        try:
+            arr, tc = mdl.similarity("Hello~ Ragflower!", ["Hi, there!"])
+            if len(arr) == 0 or tc == 0:
+                raise Exception("Not known.")
+        except Exception as e:
+            msg += f"\nFail to access model({llm['llm_name']})." + str(
+                e)
+    elif llm["model_type"] == LLMType.IMAGE2TEXT.value:
+        mdl = CvModel[factory](
+            key=llm["api_key"], 
+            model_name=llm["llm_name"], 
+            base_url=llm["api_base"]
+        )
+        try:
+            img_url = (
+                "https://upload.wikimedia.org/wikipedia/comm"
+                "ons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/256"
+                "0px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
+            )
+            res = requests.get(img_url)
+            if res.status_code == 200:
+                m, tc = mdl.describe(res.content)
+                if not tc:
+                    raise Exception(m)
+            else:
+                pass
+        except Exception as e:
+            msg += f"\nFail to access model({llm['llm_name']})." + str(e)
+    elif llm["model_type"] == LLMType.TTS:
+        mdl = TTSModel[factory](
+            key=llm["api_key"], model_name=llm["llm_name"], base_url=llm["api_base"]
+        )
+        try:
+            for resp in mdl.tts("Hello~ Ragflower!"):
+                pass
+        except RuntimeError as e:
+            msg += f"\nFail to access model({llm['llm_name']})." + str(e)
+    else:
+        # TODO: check other type of models
+        pass
+
+    if msg:
+        return get_data_error_result(retmsg=msg)
+
+    if not TenantLLMService.filter_update(
+            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == factory, TenantLLM.llm_name == llm["llm_name"]], llm):
+        TenantLLMService.save(**llm)
+
+    return get_json_result(data=True)
+
+
+@manager.route('/delete_llm', methods=['POST'])
+@login_required
+@validate_request("llm_factory", "llm_name")
+def delete_llm():
+    req = request.json
+    TenantLLMService.filter_delete(
+            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"], TenantLLM.llm_name == req["llm_name"]])
+    return get_json_result(data=True)
+
+
+@manager.route('/delete_factory', methods=['POST'])
+@login_required
+@validate_request("llm_factory")
+def delete_factory():
+    req = request.json
+    TenantLLMService.filter_delete(
+            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"]])
+    return get_json_result(data=True)
+
+
+@manager.route('/my_llms', methods=['GET'])
+@login_required
+def my_llms():
+    try:
+        res = {}
+        for o in TenantLLMService.get_my_llms(current_user.id):
+            if o["llm_factory"] not in res:
+                res[o["llm_factory"]] = {
+                    "tags": o["tags"],
+                    "llm": []
+                }
+            res[o["llm_factory"]]["llm"].append({
+                "type": o["model_type"],
+                "name": o["llm_name"],
+                "used_token": o["used_tokens"]
+            })
+        return get_json_result(data=res)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/list', methods=['GET'])
+@login_required
+def list_app():
+    self_deploied = ["Youdao","FastEmbed", "BAAI", "Ollama", "Xinference", "LocalAI", "LM-Studio"]
+    weighted = ["Youdao","FastEmbed", "BAAI"] if LIGHTEN else []
+    model_type = request.args.get("model_type")
+    try:
+        objs = TenantLLMService.query(tenant_id=current_user.id)
+        facts = set([o.to_dict()["llm_factory"] for o in objs if o.api_key])
+        llms = LLMService.get_all()
+        llms = [m.to_dict()
+                for m in llms if m.status == StatusEnum.VALID.value and m.fid not in weighted]
+        for m in llms:
+            m["available"] = m["fid"] in facts or m["llm_name"].lower() == "flag-embedding" or m["fid"] in self_deploied
+
+        llm_set = set([m["llm_name"] for m in llms])
+        for o in objs:
+            if not o.api_key:continue
+            if o.llm_name in llm_set:continue
+            llms.append({"llm_name": o.llm_name, "model_type": o.model_type, "fid": o.llm_factory, "available": True})
+
+        res = {}
+        for m in llms:
+            if model_type and m["model_type"].find(model_type)<0:
+                continue
+            if m["fid"] not in res:
+                res[m["fid"]] = []
+            res[m["fid"]].append(m)
+
+        return get_json_result(data=res)
+    except Exception as e:
+        return server_error_response(e)

api/apps/sdk/assistant.py

@@ -0,0 +1,304 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from flask import request
+
+from api.db import StatusEnum
+from api.db.db_models import TenantLLM
+from api.db.services.dialog_service import DialogService
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.services.llm_service import LLMService, TenantLLMService
+from api.db.services.user_service import TenantService
+from api.settings import RetCode
+from api.utils import get_uuid
+from api.utils.api_utils import get_data_error_result, token_required
+from api.utils.api_utils import get_json_result
+
+
+@manager.route('/save', methods=['POST'])
+@token_required
+def save(tenant_id):
+    req = request.json
+    # dataset
+    if req.get("knowledgebases") == []:
+        return get_data_error_result(retmsg="knowledgebases can not be empty list")
+    kb_list = []
+    if req.get("knowledgebases"):
+        for kb in req.get("knowledgebases"):
+            if not kb["id"]:
+                return get_data_error_result(retmsg="knowledgebase needs id")
+            if not KnowledgebaseService.query(id=kb["id"], tenant_id=tenant_id):
+                return get_data_error_result(retmsg="you do not own the knowledgebase")
+            # if not DocumentService.query(kb_id=kb["id"]):
+            #  return get_data_error_result(retmsg="There is a invalid knowledgebase")
+            kb_list.append(kb["id"])
+    req["kb_ids"] = kb_list
+    # llm
+    llm = req.get("llm")
+    if llm:
+        if "model_name" in llm:
+            req["llm_id"] = llm.pop("model_name")
+        req["llm_setting"] = req.pop("llm")
+    e, tenant = TenantService.get_by_id(tenant_id)
+    if not e:
+        return get_data_error_result(retmsg="Tenant not found!")
+    # prompt
+    prompt = req.get("prompt")
+    key_mapping = {"parameters": "variables",
+                   "prologue": "opener",
+                   "quote": "show_quote",
+                   "system": "prompt",
+                   "rerank_id": "rerank_model",
+                   "vector_similarity_weight": "keywords_similarity_weight"}
+    key_list = ["similarity_threshold", "vector_similarity_weight", "top_n", "rerank_id"]
+    if prompt:
+        for new_key, old_key in key_mapping.items():
+            if old_key in prompt:
+                prompt[new_key] = prompt.pop(old_key)
+        for key in key_list:
+            if key in prompt:
+                req[key] = prompt.pop(key)
+        req["prompt_config"] = req.pop("prompt")
+    # create
+    if "id" not in req:
+        # dataset
+        if not kb_list:
+            return get_data_error_result(retmsg="knowledgebases are required!")
+        # init
+        req["id"] = get_uuid()
+        req["description"] = req.get("description", "A helpful Assistant")
+        req["icon"] = req.get("avatar", "")
+        req["top_n"] = req.get("top_n", 6)
+        req["top_k"] = req.get("top_k", 1024)
+        req["rerank_id"] = req.get("rerank_id", "")
+        if req.get("llm_id"):
+            if not TenantLLMService.query(llm_name=req["llm_id"]):
+                return get_data_error_result(retmsg="the model_name does not exist.")
+        else:
+            req["llm_id"] = tenant.llm_id
+        if not req.get("name"):
+            return get_data_error_result(retmsg="name is required.")
+        if DialogService.query(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value):
+            return get_data_error_result(retmsg="Duplicated assistant name in creating dataset.")
+        # tenant_id
+        if req.get("tenant_id"):
+            return get_data_error_result(retmsg="tenant_id must not be provided.")
+        req["tenant_id"] = tenant_id
+        # prompt more parameter
+        default_prompt = {
+            "system": """你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。
+                以下是知识库：
+                {knowledge}
+                以上是知识库。""",
+            "prologue": "您好，我是您的助手小樱，长得可爱又善良，can I help you?",
+            "parameters": [
+                {"key": "knowledge", "optional": False}
+            ],
+            "empty_response": "Sorry! 知识库中未找到相关内容！"
+        }
+        key_list_2 = ["system", "prologue", "parameters", "empty_response"]
+        if "prompt_config" not in req:
+            req['prompt_config'] = {}
+        for key in key_list_2:
+            temp = req['prompt_config'].get(key)
+            if not temp:
+                req['prompt_config'][key] = default_prompt[key]
+        for p in req['prompt_config']["parameters"]:
+            if p["optional"]:
+                continue
+            if req['prompt_config']["system"].find("{%s}" % p["key"]) < 0:
+                return get_data_error_result(
+                    retmsg="Parameter '{}' is not used".format(p["key"]))
+        # save
+        if not DialogService.save(**req):
+            return get_data_error_result(retmsg="Fail to new an assistant!")
+        # response
+        e, res = DialogService.get_by_id(req["id"])
+        if not e:
+            return get_data_error_result(retmsg="Fail to new an assistant!")
+        res = res.to_json()
+        renamed_dict = {}
+        for key, value in res["prompt_config"].items():
+            new_key = key_mapping.get(key, key)
+            renamed_dict[new_key] = value
+        res["prompt"] = renamed_dict
+        del res["prompt_config"]
+        new_dict = {"similarity_threshold": res["similarity_threshold"],
+                    "keywords_similarity_weight": res["vector_similarity_weight"],
+                    "top_n": res["top_n"],
+                    "rerank_model": res['rerank_id']}
+        res["prompt"].update(new_dict)
+        for key in key_list:
+            del res[key]
+        res["llm"] = res.pop("llm_setting")
+        res["llm"]["model_name"] = res.pop("llm_id")
+        del res["kb_ids"]
+        res["knowledgebases"] = req["knowledgebases"]
+        res["avatar"] = res.pop("icon")
+        return get_json_result(data=res)
+    else:
+        # authorization
+        if not DialogService.query(tenant_id=tenant_id, id=req["id"], status=StatusEnum.VALID.value):
+            return get_json_result(data=False, retmsg='You do not own the assistant', retcode=RetCode.OPERATING_ERROR)
+        # prompt
+        if not req["id"]:
+            return get_data_error_result(retmsg="id can not be empty")
+        e, res = DialogService.get_by_id(req["id"])
+        res = res.to_json()
+        if "llm_id" in req:
+            if not TenantLLMService.query(llm_name=req["llm_id"]):
+                return get_data_error_result(retmsg="the model_name does not exist.")
+        if "name" in req:
+            if not req.get("name"):
+                return get_data_error_result(retmsg="name is not empty.")
+            if req["name"].lower() != res["name"].lower() \
+                    and len(
+                DialogService.query(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value)) > 0:
+                return get_data_error_result(retmsg="Duplicated assistant name in updating dataset.")
+        if "prompt_config" in req:
+            res["prompt_config"].update(req["prompt_config"])
+            for p in res["prompt_config"]["parameters"]:
+                if p["optional"]:
+                    continue
+                if res["prompt_config"]["system"].find("{%s}" % p["key"]) < 0:
+                    return get_data_error_result(retmsg="Parameter '{}' is not used".format(p["key"]))
+        if "llm_setting" in req:
+            res["llm_setting"].update(req["llm_setting"])
+        req["prompt_config"] = res["prompt_config"]
+        req["llm_setting"] = res["llm_setting"]
+        # avatar
+        if "avatar" in req:
+            req["icon"] = req.pop("avatar")
+        assistant_id = req.pop("id")
+        if "knowledgebases" in req:
+            req.pop("knowledgebases")
+        if not DialogService.update_by_id(assistant_id, req):
+            return get_data_error_result(retmsg="Assistant not found!")
+        return get_json_result(data=True)
+
+
+@manager.route('/delete', methods=['DELETE'])
+@token_required
+def delete(tenant_id):
+    req = request.args
+    if "id" not in req:
+        return get_data_error_result(retmsg="id is required")
+    id = req['id']
+    if not DialogService.query(tenant_id=tenant_id, id=id, status=StatusEnum.VALID.value):
+        return get_json_result(data=False, retmsg='you do not own the assistant.', retcode=RetCode.OPERATING_ERROR)
+
+    temp_dict = {"status": StatusEnum.INVALID.value}
+    DialogService.update_by_id(req["id"], temp_dict)
+    return get_json_result(data=True)
+
+
+@manager.route('/get', methods=['GET'])
+@token_required
+def get(tenant_id):
+    req = request.args
+    if "id" in req:
+        id = req["id"]
+        ass = DialogService.query(tenant_id=tenant_id, id=id, status=StatusEnum.VALID.value)
+        if not ass:
+            return get_json_result(data=False, retmsg='You do not own the assistant.', retcode=RetCode.OPERATING_ERROR)
+        if "name" in req:
+            name = req["name"]
+            if ass[0].name != name:
+                return get_json_result(data=False, retmsg='name does not match id.', retcode=RetCode.OPERATING_ERROR)
+        res = ass[0].to_json()
+    else:
+        if "name" in req:
+            name = req["name"]
+            ass = DialogService.query(name=name, tenant_id=tenant_id, status=StatusEnum.VALID.value)
+            if not ass:
+                return get_json_result(data=False, retmsg='You do not own the assistant.',
+                                       retcode=RetCode.OPERATING_ERROR)
+            res = ass[0].to_json()
+        else:
+            return get_data_error_result(retmsg="At least one of `id` or `name` must be provided.")
+    renamed_dict = {}
+    key_mapping = {"parameters": "variables",
+                   "prologue": "opener",
+                   "quote": "show_quote",
+                   "system": "prompt",
+                   "rerank_id": "rerank_model",
+                   "vector_similarity_weight": "keywords_similarity_weight"}
+    key_list = ["similarity_threshold", "vector_similarity_weight", "top_n", "rerank_id"]
+    for key, value in res["prompt_config"].items():
+        new_key = key_mapping.get(key, key)
+        renamed_dict[new_key] = value
+    res["prompt"] = renamed_dict
+    del res["prompt_config"]
+    new_dict = {"similarity_threshold": res["similarity_threshold"],
+                "keywords_similarity_weight": res["vector_similarity_weight"],
+                "top_n": res["top_n"],
+                "rerank_model": res['rerank_id']}
+    res["prompt"].update(new_dict)
+    for key in key_list:
+        del res[key]
+    res["llm"] = res.pop("llm_setting")
+    res["llm"]["model_name"] = res.pop("llm_id")
+    kb_list = []
+    for kb_id in res["kb_ids"]:
+        kb = KnowledgebaseService.query(id=kb_id)
+        kb_list.append(kb[0].to_json())
+    del res["kb_ids"]
+    res["knowledgebases"] = kb_list
+    res["avatar"] = res.pop("icon")
+    return get_json_result(data=res)
+
+
+@manager.route('/list', methods=['GET'])
+@token_required
+def list_assistants(tenant_id):
+    assts = DialogService.query(
+        tenant_id=tenant_id,
+        status=StatusEnum.VALID.value,
+        reverse=True,
+        order_by=DialogService.model.create_time)
+    assts = [d.to_dict() for d in assts]
+    list_assts = []
+    renamed_dict = {}
+    key_mapping = {"parameters": "variables",
+                   "prologue": "opener",
+                   "quote": "show_quote",
+                   "system": "prompt",
+                   "rerank_id": "rerank_model",
+                   "vector_similarity_weight": "keywords_similarity_weight"}
+    key_list = ["similarity_threshold", "vector_similarity_weight", "top_n", "rerank_id"]
+    for res in assts:
+        for key, value in res["prompt_config"].items():
+            new_key = key_mapping.get(key, key)
+            renamed_dict[new_key] = value
+        res["prompt"] = renamed_dict
+        del res["prompt_config"]
+        new_dict = {"similarity_threshold": res["similarity_threshold"],
+                    "keywords_similarity_weight": res["vector_similarity_weight"],
+                    "top_n": res["top_n"],
+                    "rerank_model": res['rerank_id']}
+        res["prompt"].update(new_dict)
+        for key in key_list:
+            del res[key]
+        res["llm"] = res.pop("llm_setting")
+        res["llm"]["model_name"] = res.pop("llm_id")
+        kb_list = []
+        for kb_id in res["kb_ids"]:
+            kb = KnowledgebaseService.query(id=kb_id)
+            kb_list.append(kb[0].to_json())
+        del res["kb_ids"]
+        res["knowledgebases"] = kb_list
+        res["avatar"] = res.pop("icon")
+        list_assts.append(res)
+    return get_json_result(data=list_assts)

api/apps/sdk/dataset.py

@@ -0,0 +1,224 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+from flask import request
+
+from api.db import StatusEnum, FileSource
+from api.db.db_models import File
+from api.db.services.document_service import DocumentService
+from api.db.services.file2document_service import File2DocumentService
+from api.db.services.file_service import FileService
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.services.user_service import TenantService
+from api.settings import RetCode
+from api.utils import get_uuid
+from api.utils.api_utils import get_json_result, token_required, get_data_error_result
+
+
+@manager.route('/save', methods=['POST'])
+@token_required
+def save(tenant_id):
+    req = request.json
+    e, t = TenantService.get_by_id(tenant_id)
+    if "id" not in req:
+        if "tenant_id" in req or "embedding_model" in req:
+            return get_data_error_result(
+                retmsg="Tenant_id or embedding_model must not be provided")
+        if "name" not in req:
+            return get_data_error_result(
+                retmsg="Name is not empty!")
+        req['id'] = get_uuid()
+        req["name"] = req["name"].strip()
+        if req["name"] == "":
+            return get_data_error_result(
+                retmsg="Name is not empty string!")
+        if KnowledgebaseService.query(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value):
+            return get_data_error_result(
+                retmsg="Duplicated knowledgebase name in creating dataset.")
+        req["tenant_id"] = req['created_by'] = tenant_id
+        req['embedding_model'] = t.embd_id
+        key_mapping = {
+            "chunk_num": "chunk_count",
+            "doc_num": "document_count",
+            "parser_id": "parse_method",
+            "embd_id": "embedding_model"
+        }
+        mapped_keys = {new_key: req[old_key] for new_key, old_key in key_mapping.items() if old_key in req}
+        req.update(mapped_keys)
+        if not KnowledgebaseService.save(**req):
+            return get_data_error_result(retmsg="Create dataset error.(Database error)")
+        renamed_data = {}
+        e, k = KnowledgebaseService.get_by_id(req["id"])
+        for key, value in k.to_dict().items():
+            new_key = key_mapping.get(key, key)
+            renamed_data[new_key] = value
+        return get_json_result(data=renamed_data)
+    else:
+        invalid_keys = {"embd_id", "chunk_num", "doc_num", "parser_id"}
+        if any(key in req for key in invalid_keys):
+            return get_data_error_result(retmsg="The input parameters are invalid.")
+
+        if "tenant_id" in req:
+            if req["tenant_id"] != tenant_id:
+                return get_data_error_result(
+                    retmsg="Can't change tenant_id.")
+
+        if "embedding_model" in req:
+            if req["embedding_model"] != t.embd_id:
+                return get_data_error_result(
+                    retmsg="Can't change embedding_model.")
+            req.pop("embedding_model")
+
+        if not KnowledgebaseService.query(
+                created_by=tenant_id, id=req["id"]):
+            return get_json_result(
+                data=False, retmsg='You do not own the dataset.',
+                retcode=RetCode.OPERATING_ERROR)
+
+        if not req["id"]:
+            return get_data_error_result(
+                retmsg="id can not be empty.")
+        e, kb = KnowledgebaseService.get_by_id(req["id"])
+
+        if "chunk_count" in req:
+            if req["chunk_count"] != kb.chunk_num:
+                return get_data_error_result(
+                    retmsg="Can't change chunk_count.")
+            req.pop("chunk_count")
+
+        if "document_count" in req:
+            if req['document_count'] != kb.doc_num:
+                return get_data_error_result(
+                    retmsg="Can't change document_count.")
+            req.pop("document_count")
+
+        if "parse_method" in req:
+            if kb.chunk_num != 0 and req['parse_method'] != kb.parser_id:
+                return get_data_error_result(
+                    retmsg="If chunk count is not 0, parse method is not changable.")
+            req['parser_id'] = req.pop('parse_method')
+        if "name" in req:
+            req["name"] = req["name"].strip()
+            if req["name"].lower() != kb.name.lower() \
+                    and len(KnowledgebaseService.query(name=req["name"], tenant_id=tenant_id,
+                                                       status=StatusEnum.VALID.value)) > 0:
+                return get_data_error_result(
+                    retmsg="Duplicated knowledgebase name in updating dataset.")
+
+        del req["id"]
+        if not KnowledgebaseService.update_by_id(kb.id, req):
+            return get_data_error_result(retmsg="Update dataset error.(Database error)")
+        return get_json_result(data=True)
+
+
+@manager.route('/delete', methods=['DELETE'])
+@token_required
+def delete(tenant_id):
+    req = request.args
+    if "id" not in req:
+        return get_data_error_result(
+            retmsg="id is required")
+    kbs = KnowledgebaseService.query(
+        created_by=tenant_id, id=req["id"])
+    if not kbs:
+        return get_json_result(
+            data=False, retmsg='You do not own the dataset',
+            retcode=RetCode.OPERATING_ERROR)
+
+    for doc in DocumentService.query(kb_id=req["id"]):
+        if not DocumentService.remove_document(doc, kbs[0].tenant_id):
+            return get_data_error_result(
+                retmsg="Remove document error.(Database error)")
+        f2d = File2DocumentService.get_by_document_id(doc.id)
+        FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id])
+        File2DocumentService.delete_by_document_id(doc.id)
+
+    if not KnowledgebaseService.delete_by_id(req["id"]):
+        return get_data_error_result(
+            retmsg="Delete dataset error.(Database serror)")
+    return get_json_result(data=True)
+
+
+@manager.route('/list', methods=['GET'])
+@token_required
+def list_datasets(tenant_id):
+    page_number = int(request.args.get("page", 1))
+    items_per_page = int(request.args.get("page_size", 1024))
+    orderby = request.args.get("orderby", "create_time")
+    desc = bool(request.args.get("desc", True))
+    tenants = TenantService.get_joined_tenants_by_user_id(tenant_id)
+    kbs = KnowledgebaseService.get_by_tenant_ids(
+        [m["tenant_id"] for m in tenants], tenant_id, page_number, items_per_page, orderby, desc)
+    renamed_list = []
+    for kb in kbs:
+        key_mapping = {
+            "chunk_num": "chunk_count",
+            "doc_num": "document_count",
+            "parser_id": "parse_method",
+            "embd_id": "embedding_model"
+        }
+        renamed_data = {}
+        for key, value in kb.items():
+            new_key = key_mapping.get(key, key)
+            renamed_data[new_key] = value
+        renamed_list.append(renamed_data)
+    return get_json_result(data=renamed_list)
+
+
+@manager.route('/detail', methods=['GET'])
+@token_required
+def detail(tenant_id):
+    req = request.args
+    key_mapping = {
+        "chunk_num": "chunk_count",
+        "doc_num": "document_count",
+        "parser_id": "parse_method",
+        "embd_id": "embedding_model"
+    }
+    renamed_data = {}
+    if "id" in req:
+        id = req["id"]
+        kb = KnowledgebaseService.query(created_by=tenant_id, id=req["id"])
+        if not kb:
+            return get_json_result(
+                data=False, retmsg='You do not own the dataset.',
+                retcode=RetCode.OPERATING_ERROR)
+        if "name" in req:
+            name = req["name"]
+            if kb[0].name != name:
+                return get_json_result(
+                    data=False, retmsg='You do not own the dataset.',
+                    retcode=RetCode.OPERATING_ERROR)
+        e, k = KnowledgebaseService.get_by_id(id)
+        for key, value in k.to_dict().items():
+            new_key = key_mapping.get(key, key)
+            renamed_data[new_key] = value
+        return get_json_result(data=renamed_data)
+    else:
+        if "name" in req:
+            name = req["name"]
+            e, k = KnowledgebaseService.get_by_name(kb_name=name, tenant_id=tenant_id)
+            if not e:
+                return get_json_result(
+                    data=False, retmsg='You do not own the dataset.',
+                    retcode=RetCode.OPERATING_ERROR)
+            for key, value in k.to_dict().items():
+                new_key = key_mapping.get(key, key)
+                renamed_data[new_key] = value
+            return get_json_result(data=renamed_data)
+        else:
+            return get_data_error_result(
+                retmsg="At least one of `id` or `name` must be provided.")

api/apps/sdk/doc.py

@@ -0,0 +1,720 @@
+import pathlib
+import re
+import datetime
+import json
+import traceback
+
+from flask import request
+from flask_login import login_required, current_user
+from elasticsearch_dsl import Q
+
+from rag.app.qa import rmPrefix, beAdoc
+from rag.nlp import search, rag_tokenizer, keyword_extraction
+from rag.utils.es_conn import ELASTICSEARCH
+from rag.utils import rmSpace
+from api.db import LLMType, ParserType
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.services.llm_service import TenantLLMService
+from api.db.services.user_service import UserTenantService
+from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+from api.db.services.document_service import DocumentService
+from api.settings import RetCode, retrievaler, kg_retrievaler
+from api.utils.api_utils import get_json_result
+import hashlib
+import re
+from api.utils.api_utils import get_json_result, token_required, get_data_error_result
+
+from api.db.db_models import Task, File
+
+from api.db.services.task_service import TaskService, queue_tasks
+from api.db.services.user_service import TenantService, UserTenantService
+
+from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+
+from api.utils.api_utils import get_json_result
+
+from functools import partial
+from io import BytesIO
+
+from elasticsearch_dsl import Q
+from flask import request, send_file
+from flask_login import login_required
+
+from api.db import FileSource, TaskStatus, FileType
+from api.db.db_models import File
+from api.db.services.document_service import DocumentService
+from api.db.services.file2document_service import File2DocumentService
+from api.db.services.file_service import FileService
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.settings import RetCode, retrievaler
+from api.utils.api_utils import construct_json_result, construct_error_response
+from rag.app import book, laws, manual, naive, one, paper, presentation, qa, resume, table, picture, audio, email
+from rag.nlp import search
+from rag.utils import rmSpace
+from rag.utils.es_conn import ELASTICSEARCH
+from rag.utils.storage_factory import STORAGE_IMPL
+
+MAXIMUM_OF_UPLOADING_FILES = 256
+
+MAXIMUM_OF_UPLOADING_FILES = 256
+
+
+@manager.route('/dataset/<dataset_id>/documents/upload', methods=['POST'])
+@token_required
+def upload(dataset_id, tenant_id):
+    if 'file' not in request.files:
+        return get_json_result(
+            data=False, retmsg='No file part!', retcode=RetCode.ARGUMENT_ERROR)
+    file_objs = request.files.getlist('file')
+    for file_obj in file_objs:
+        if file_obj.filename == '':
+            return get_json_result(
+                data=False, retmsg='No file selected!', retcode=RetCode.ARGUMENT_ERROR)
+    e, kb = KnowledgebaseService.get_by_id(dataset_id)
+    if not e:
+        raise LookupError(f"Can't find the knowledgebase with ID {dataset_id}!")
+    err, _ = FileService.upload_document(kb, file_objs, tenant_id)
+    if err:
+        return get_json_result(
+            data=False, retmsg="\n".join(err), retcode=RetCode.SERVER_ERROR)
+    return get_json_result(data=True)
+
+
+@manager.route('/infos', methods=['GET'])
+@token_required
+def docinfos(tenant_id):
+    req = request.args
+    if "id" not in req and "name" not in req:
+        return get_data_error_result(
+            retmsg="Id or name should be provided")
+    doc_id=None
+    if "id" in req:
+        doc_id = req["id"]
+    if "name" in req:
+        doc_name = req["name"]
+        doc_id = DocumentService.get_doc_id_by_doc_name(doc_name)
+    e, doc = DocumentService.get_by_id(doc_id)
+    #rename key's name
+    key_mapping = {
+        "chunk_num": "chunk_count",
+        "kb_id": "knowledgebase_id",
+        "token_num": "token_count",
+        "parser_id":"parser_method",
+    }
+    renamed_doc = {}
+    for key, value in doc.to_dict().items():
+        new_key = key_mapping.get(key, key)
+        renamed_doc[new_key] = value
+
+    return get_json_result(data=renamed_doc)
+
+
+@manager.route('/save', methods=['POST'])
+@token_required
+def save_doc(tenant_id):
+    req = request.json
+    #get doc by id or name
+    doc_id = None
+    if "id" in req:
+        doc_id = req["id"]
+    elif "name" in req:
+        doc_name = req["name"]
+        doc_id = DocumentService.get_doc_id_by_doc_name(doc_name)
+    if not doc_id:
+        return get_json_result(retcode=400, retmsg="Document ID or name is required")
+    e, doc = DocumentService.get_by_id(doc_id)
+    if not e:
+        return get_data_error_result(retmsg="Document not found!")
+    #other value can't be changed
+    if "chunk_count" in req:
+        if req["chunk_count"] != doc.chunk_num:
+            return get_data_error_result(
+                retmsg="Can't change chunk_count.")
+    if "token_count" in req:
+        if req["token_count"] != doc.token_num:
+            return get_data_error_result(
+                retmsg="Can't change token_count.")
+    if "progress" in req:
+        if req['progress'] != doc.progress:
+            return get_data_error_result(
+                retmsg="Can't change progress.")
+    #change name or parse_method
+    if "name" in req and req["name"] != doc.name:
+        try:
+            if pathlib.Path(req["name"].lower()).suffix != pathlib.Path(
+                    doc.name.lower()).suffix:
+                return get_json_result(
+                    data=False,
+                    retmsg="The extension of file can't be changed",
+                    retcode=RetCode.ARGUMENT_ERROR)
+            for d in DocumentService.query(name=req["name"], kb_id=doc.kb_id):
+                if d.name == req["name"]:
+                    return get_data_error_result(
+                        retmsg="Duplicated document name in the same knowledgebase.")
+
+            if not DocumentService.update_by_id(
+                    doc_id, {"name": req["name"]}):
+                return get_data_error_result(
+                    retmsg="Database error (Document rename)!")
+
+            informs = File2DocumentService.get_by_document_id(doc_id)
+            if informs:
+                e, file = FileService.get_by_id(informs[0].file_id)
+                FileService.update_by_id(file.id, {"name": req["name"]})
+        except Exception as e:
+            return server_error_response(e)
+    if "parser_method" in req:
+        try:
+            if doc.parser_id.lower() == req["parser_method"].lower():
+                if "parser_config" in req:
+                    if req["parser_config"] == doc.parser_config:
+                        return get_json_result(data=True)
+                else:
+                    return get_json_result(data=True)
+
+            if doc.type == FileType.VISUAL or re.search(
+                    r"\.(ppt|pptx|pages)$", doc.name):
+                return get_data_error_result(retmsg="Not supported yet!")
+
+            e = DocumentService.update_by_id(doc.id,
+                                             {"parser_id": req["parser_method"], "progress": 0, "progress_msg": "",
+                                              "run": TaskStatus.UNSTART.value})
+            if not e:
+                return get_data_error_result(retmsg="Document not found!")
+            if "parser_config" in req:
+                DocumentService.update_parser_config(doc.id, req["parser_config"])
+            if doc.token_num > 0:
+                e = DocumentService.increment_chunk_num(doc.id, doc.kb_id, doc.token_num * -1, doc.chunk_num * -1,
+                                                        doc.process_duation * -1)
+                if not e:
+                    return get_data_error_result(retmsg="Document not found!")
+                tenant_id = DocumentService.get_tenant_id(req["id"])
+                if not tenant_id:
+                    return get_data_error_result(retmsg="Tenant not found!")
+                ELASTICSEARCH.deleteByQuery(
+                    Q("match", doc_id=doc.id), idxnm=search.index_name(tenant_id))
+        except Exception as e:
+            return server_error_response(e)
+    return get_json_result(data=True)
+
+
+
+@manager.route('/change_parser', methods=['POST'])
+@token_required
+def change_parser(tenant_id):
+    req = request.json
+    try:
+        e, doc = DocumentService.get_by_id(req["doc_id"])
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        if doc.parser_id.lower() == req["parser_id"].lower():
+            if "parser_config" in req:
+                if req["parser_config"] == doc.parser_config:
+                    return get_json_result(data=True)
+            else:
+                return get_json_result(data=True)
+
+        if doc.type == FileType.VISUAL or re.search(
+                r"\.(ppt|pptx|pages)$", doc.name):
+            return get_data_error_result(retmsg="Not supported yet!")
+
+        e = DocumentService.update_by_id(doc.id,
+                                         {"parser_id": req["parser_id"], "progress": 0, "progress_msg": "",
+                                          "run": TaskStatus.UNSTART.value})
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        if "parser_config" in req:
+            DocumentService.update_parser_config(doc.id, req["parser_config"])
+        if doc.token_num > 0:
+            e = DocumentService.increment_chunk_num(doc.id, doc.kb_id, doc.token_num * -1, doc.chunk_num * -1,
+                                                    doc.process_duation * -1)
+            if not e:
+                return get_data_error_result(retmsg="Document not found!")
+            tenant_id = DocumentService.get_tenant_id(req["doc_id"])
+            if not tenant_id:
+                return get_data_error_result(retmsg="Tenant not found!")
+            ELASTICSEARCH.deleteByQuery(
+                Q("match", doc_id=doc.id), idxnm=search.index_name(tenant_id))
+
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+@manager.route('/rename', methods=['POST'])
+@login_required
+@validate_request("doc_id", "name")
+def rename():
+    req = request.json
+    try:
+        e, doc = DocumentService.get_by_id(req["doc_id"])
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        if pathlib.Path(req["name"].lower()).suffix != pathlib.Path(
+                doc.name.lower()).suffix:
+            return get_json_result(
+                data=False,
+                retmsg="The extension of file can't be changed",
+                retcode=RetCode.ARGUMENT_ERROR)
+        for d in DocumentService.query(name=req["name"], kb_id=doc.kb_id):
+            if d.name == req["name"]:
+                return get_data_error_result(
+                    retmsg="Duplicated document name in the same knowledgebase.")
+
+        if not DocumentService.update_by_id(
+                req["doc_id"], {"name": req["name"]}):
+            return get_data_error_result(
+                retmsg="Database error (Document rename)!")
+          
+        informs = File2DocumentService.get_by_document_id(req["doc_id"])
+        if informs:
+            e, file = FileService.get_by_id(informs[0].file_id)
+            FileService.update_by_id(file.id, {"name": req["name"]})
+
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route("/<document_id>", methods=["GET"])
+@token_required
+def download_document(document_id,tenant_id):
+    try:
+        # Check whether there is this document
+        exist, document = DocumentService.get_by_id(document_id)
+        if not exist:
+            return construct_json_result(message=f"This document '{document_id}' cannot be found!",
+                                         code=RetCode.ARGUMENT_ERROR)
+
+        # The process of downloading
+        doc_id, doc_location = File2DocumentService.get_storage_address(doc_id=document_id)  # minio address
+        file_stream = STORAGE_IMPL.get(doc_id, doc_location)
+        if not file_stream:
+            return construct_json_result(message="This file is empty.", code=RetCode.DATA_ERROR)
+
+        file = BytesIO(file_stream)
+
+        # Use send_file with a proper filename and MIME type
+        return send_file(
+            file,
+            as_attachment=True,
+            download_name=document.name,
+            mimetype='application/octet-stream'  # Set a default MIME type
+        )
+
+    # Error
+    except Exception as e:
+        return construct_error_response(e)
+
+
+@manager.route('/dataset/<dataset_id>/documents', methods=['GET'])
+@token_required
+def list_docs(dataset_id, tenant_id):
+    kb_id = request.args.get("knowledgebase_id")
+    if not kb_id:
+        return get_json_result(
+            data=False, retmsg='Lack of "KB ID"', retcode=RetCode.ARGUMENT_ERROR)
+    tenants = UserTenantService.query(user_id=tenant_id)
+    for tenant in tenants:
+        if KnowledgebaseService.query(
+                tenant_id=tenant.tenant_id, id=kb_id):
+            break
+    else:
+        return get_json_result(
+            data=False, retmsg=f'Only owner of knowledgebase authorized for this operation.',
+            retcode=RetCode.OPERATING_ERROR)
+    keywords = request.args.get("keywords", "")
+
+    page_number = int(request.args.get("page", 1))
+    items_per_page = int(request.args.get("page_size", 15))
+    orderby = request.args.get("orderby", "create_time")
+    desc = request.args.get("desc", True)
+    try:
+        docs, tol = DocumentService.get_by_kb_id(
+            kb_id, page_number, items_per_page, orderby, desc, keywords)
+
+        # rename key's name
+        renamed_doc_list = []
+        for doc in docs:
+            key_mapping = {
+                "chunk_num": "chunk_count",
+                "kb_id": "knowledgebase_id",
+                "token_num": "token_count",
+                "parser_id":"parser_method"
+            }
+            renamed_doc = {}
+            for key, value in doc.items():
+                new_key = key_mapping.get(key, key)
+                renamed_doc[new_key] = value
+            renamed_doc_list.append(renamed_doc)
+        return get_json_result(data={"total": tol, "docs": renamed_doc_list})
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/delete', methods=['DELETE'])
+@token_required
+def rm(tenant_id):
+    req = request.args
+    if "document_id" not in req:
+        return get_data_error_result(
+            retmsg="doc_id is required")
+    doc_ids = req["document_id"]
+    if isinstance(doc_ids, str): doc_ids = [doc_ids]
+    root_folder = FileService.get_root_folder(tenant_id)
+    pf_id = root_folder["id"]
+    FileService.init_knowledgebase_docs(pf_id, tenant_id)
+    errors = ""
+    for doc_id in doc_ids:
+        try:
+            e, doc = DocumentService.get_by_id(doc_id)
+            if not e:
+                return get_data_error_result(retmsg="Document not found!")
+            tenant_id = DocumentService.get_tenant_id(doc_id)
+            if not tenant_id:
+                return get_data_error_result(retmsg="Tenant not found!")
+
+            b, n = File2DocumentService.get_storage_address(doc_id=doc_id)
+
+            if not DocumentService.remove_document(doc, tenant_id):
+                return get_data_error_result(
+                    retmsg="Database error (Document removal)!")
+
+            f2d = File2DocumentService.get_by_document_id(doc_id)
+            FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id])
+            File2DocumentService.delete_by_document_id(doc_id)
+
+            STORAGE_IMPL.rm(b, n)
+        except Exception as e:
+            errors += str(e)
+
+    if errors:
+        return get_json_result(data=False, retmsg=errors, retcode=RetCode.SERVER_ERROR)
+
+    return get_json_result(data=True, retmsg="success")
+
+@manager.route("/<document_id>/status", methods=["GET"])
+@token_required
+def show_parsing_status(tenant_id, document_id):
+    try:
+        # valid document
+        exist, _ = DocumentService.get_by_id(document_id)
+        if not exist:
+            return construct_json_result(code=RetCode.DATA_ERROR,
+                                         message=f"This document: '{document_id}' is not a valid document.")
+
+        _, doc = DocumentService.get_by_id(document_id)  # get doc object
+        doc_attributes = doc.to_dict()
+
+        return construct_json_result(
+            data={"progress": doc_attributes["progress"], "status": TaskStatus(doc_attributes["status"]).name},
+            code=RetCode.SUCCESS
+        )
+    except Exception as e:
+        return construct_error_response(e)
+
+
+
+@manager.route('/run', methods=['POST'])
+@token_required
+def run(tenant_id):
+    req = request.json
+    try:
+        for id in req["document_ids"]:
+            info = {"run": str(req["run"]), "progress": 0}
+            if str(req["run"]) == TaskStatus.RUNNING.value:
+                info["progress_msg"] = ""
+                info["chunk_num"] = 0
+                info["token_num"] = 0
+            DocumentService.update_by_id(id, info)
+            # if str(req["run"]) == TaskStatus.CANCEL.value:
+            tenant_id = DocumentService.get_tenant_id(id)
+            if not tenant_id:
+                return get_data_error_result(retmsg="Tenant not found!")
+            ELASTICSEARCH.deleteByQuery(
+                Q("match", doc_id=id), idxnm=search.index_name(tenant_id))
+
+            if str(req["run"]) == TaskStatus.RUNNING.value:
+                TaskService.filter_delete([Task.doc_id == id])
+                e, doc = DocumentService.get_by_id(id)
+                doc = doc.to_dict()
+                doc["tenant_id"] = tenant_id
+                bucket, name = File2DocumentService.get_storage_address(doc_id=doc["id"])
+                queue_tasks(doc, bucket, name)
+
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/chunk/list', methods=['POST'])
+@token_required
+@validate_request("document_id")
+def list_chunk(tenant_id):
+    req = request.json
+    doc_id = req["document_id"]
+    page = int(req.get("page", 1))
+    size = int(req.get("size", 30))
+    question = req.get("keywords", "")
+    try:
+        tenant_id = DocumentService.get_tenant_id(req["document_id"])
+        if not tenant_id:
+            return get_data_error_result(retmsg="Tenant not found!")
+        e, doc = DocumentService.get_by_id(doc_id)
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        query = {
+            "doc_ids": [doc_id], "page": page, "size": size, "question": question, "sort": True
+        }
+        if "available_int" in req:
+            query["available_int"] = int(req["available_int"])
+        sres = retrievaler.search(query, search.index_name(tenant_id), highlight=True)
+        res = {"total": sres.total, "chunks": [], "doc": doc.to_dict()}
+
+        origin_chunks=[]
+        for id in sres.ids:
+            d = {
+                "chunk_id": id,
+                "content_with_weight": rmSpace(sres.highlight[id]) if question and id in sres.highlight else sres.field[
+                    id].get(
+                    "content_with_weight", ""),
+                "doc_id": sres.field[id]["doc_id"],
+                "docnm_kwd": sres.field[id]["docnm_kwd"],
+                "important_kwd": sres.field[id].get("important_kwd", []),
+                "img_id": sres.field[id].get("img_id", ""),
+                "available_int": sres.field[id].get("available_int", 1),
+                "positions": sres.field[id].get("position_int", "").split("\t")
+            }
+            if len(d["positions"]) % 5 == 0:
+                poss = []
+                for i in range(0, len(d["positions"]), 5):
+                    poss.append([float(d["positions"][i]), float(d["positions"][i + 1]), float(d["positions"][i + 2]),
+                                 float(d["positions"][i + 3]), float(d["positions"][i + 4])])
+                d["positions"] = poss
+                
+            origin_chunks.append(d)
+            ##rename keys
+            for chunk in origin_chunks:
+                key_mapping = {
+                    "chunk_id": "id",
+                    "content_with_weight": "content",
+                    "doc_id": "document_id",
+                    "important_kwd": "important_keywords",
+                    "img_id":"image_id",
+                }
+                renamed_chunk = {}
+                for key, value in chunk.items():
+                    new_key = key_mapping.get(key, key)
+                    renamed_chunk[new_key] = value
+                res["chunks"].append(renamed_chunk)
+        return get_json_result(data=res)
+    except Exception as e:
+        if str(e).find("not_found") > 0:
+            return get_json_result(data=False, retmsg=f'No chunk found!',
+                                   retcode=RetCode.DATA_ERROR)
+        return server_error_response(e)
+
+
+@manager.route('/chunk/create', methods=['POST'])
+@token_required
+@validate_request("document_id", "content")
+def create(tenant_id):
+    req = request.json
+    md5 = hashlib.md5()
+    md5.update((req["content"] + req["document_id"]).encode("utf-8"))
+
+    chunk_id = md5.hexdigest()
+    d = {"id": chunk_id, "content_ltks": rag_tokenizer.tokenize(req["content"]),
+         "content_with_weight": req["content"]}
+    d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
+    d["important_kwd"] = req.get("important_kwd", [])
+    d["important_tks"] = rag_tokenizer.tokenize(" ".join(req.get("important_kwd", [])))
+    d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
+    d["create_timestamp_flt"] = datetime.datetime.now().timestamp()
+
+    try:
+        e, doc = DocumentService.get_by_id(req["document_id"])
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        d["kb_id"] = [doc.kb_id]
+        d["docnm_kwd"] = doc.name
+        d["doc_id"] = doc.id
+
+        tenant_id = DocumentService.get_tenant_id(req["document_id"])
+        if not tenant_id:
+            return get_data_error_result(retmsg="Tenant not found!")
+
+        embd_id = DocumentService.get_embd_id(req["document_id"])
+        embd_mdl = TenantLLMService.model_instance(
+            tenant_id, LLMType.EMBEDDING.value, embd_id)
+
+        v, c = embd_mdl.encode([doc.name, req["content"]])
+        v = 0.1 * v[0] + 0.9 * v[1]
+        d["q_%d_vec" % len(v)] = v.tolist()
+        ELASTICSEARCH.upsert([d], search.index_name(tenant_id))
+
+        DocumentService.increment_chunk_num(
+            doc.id, doc.kb_id, c, 1, 0)
+        d["chunk_id"] = chunk_id
+        #rename keys
+        key_mapping = {
+            "chunk_id": "id",
+            "content_with_weight": "content",
+            "doc_id": "document_id",
+            "important_kwd": "important_keywords",
+            "kb_id":"dataset_id",
+            "create_timestamp_flt":"create_timestamp",
+            "create_time": "create_time",
+            "document_keyword":"document",
+        }
+        renamed_chunk = {}
+        for key, value in d.items():
+            if key in key_mapping:
+                new_key = key_mapping.get(key, key)
+                renamed_chunk[new_key] = value
+
+        return get_json_result(data={"chunk": renamed_chunk})
+        # return get_json_result(data={"chunk_id": chunk_id})
+    except Exception as e:
+        return server_error_response(e)
+
+@manager.route('/chunk/rm', methods=['POST'])
+@token_required
+@validate_request("chunk_ids", "document_id")
+def rm_chunk(tenant_id):
+    req = request.json
+    try:
+        if not ELASTICSEARCH.deleteByQuery(
+                Q("ids", values=req["chunk_ids"]), search.index_name(tenant_id)):
+            return get_data_error_result(retmsg="Index updating failure")
+        e, doc = DocumentService.get_by_id(req["document_id"])
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        deleted_chunk_ids = req["chunk_ids"]
+        chunk_number = len(deleted_chunk_ids)
+        DocumentService.decrement_chunk_num(doc.id, doc.kb_id, 1, chunk_number, 0)
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+@manager.route('/chunk/set', methods=['POST'])
+@token_required
+@validate_request("document_id", "chunk_id", "content",
+                  "important_keywords")
+def set(tenant_id):
+    req = request.json
+    d = {
+        "id": req["chunk_id"],
+        "content_with_weight": req["content"]}
+    d["content_ltks"] = rag_tokenizer.tokenize(req["content"])
+    d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
+    d["important_kwd"] = req["important_keywords"]
+    d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_keywords"]))
+    if "available" in req:
+        d["available_int"] = req["available"]
+
+    try:
+        tenant_id = DocumentService.get_tenant_id(req["document_id"])
+        if not tenant_id:
+            return get_data_error_result(retmsg="Tenant not found!")
+
+        embd_id = DocumentService.get_embd_id(req["document_id"])
+        embd_mdl = TenantLLMService.model_instance(
+            tenant_id, LLMType.EMBEDDING.value, embd_id)
+
+        e, doc = DocumentService.get_by_id(req["document_id"])
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+
+        if doc.parser_id == ParserType.QA:
+            arr = [
+                t for t in re.split(
+                    r"[\n\t]",
+                    req["content"]) if len(t) > 1]
+            if len(arr) != 2:
+                return get_data_error_result(
+                    retmsg="Q&A must be separated by TAB/ENTER key.")
+            q, a = rmPrefix(arr[0]), rmPrefix(arr[1])
+            d = beAdoc(d, arr[0], arr[1], not any(
+                [rag_tokenizer.is_chinese(t) for t in q + a]))
+
+        v, c = embd_mdl.encode([doc.name, req["content"]])
+        v = 0.1 * v[0] + 0.9 * v[1] if doc.parser_id != ParserType.QA else v[1]
+        d["q_%d_vec" % len(v)] = v.tolist()
+        ELASTICSEARCH.upsert([d], search.index_name(tenant_id))
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+@manager.route('/retrieval_test', methods=['POST'])
+@token_required
+@validate_request("knowledgebase_id", "question")
+def retrieval_test(tenant_id):
+    req = request.json
+    page = int(req.get("page", 1))
+    size = int(req.get("size", 30))
+    question = req["question"]
+    kb_id = req["knowledgebase_id"]
+    if isinstance(kb_id, str): kb_id = [kb_id]
+    doc_ids = req.get("doc_ids", [])
+    similarity_threshold = float(req.get("similarity_threshold", 0.2))
+    vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3))
+    top = int(req.get("top_k", 1024))
+
+    try:
+        tenants = UserTenantService.query(user_id=tenant_id)
+        for kid in kb_id:
+            for tenant in tenants:
+                if KnowledgebaseService.query(
+                        tenant_id=tenant.tenant_id, id=kid):
+                    break
+            else:
+                return get_json_result(
+                    data=False, retmsg=f'Only owner of knowledgebase authorized for this operation.',
+                    retcode=RetCode.OPERATING_ERROR)
+
+        e, kb = KnowledgebaseService.get_by_id(kb_id[0])
+        if not e:
+            return get_data_error_result(retmsg="Knowledgebase not found!")
+
+        embd_mdl = TenantLLMService.model_instance(
+            kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
+
+        rerank_mdl = None
+        if req.get("rerank_id"):
+            rerank_mdl = TenantLLMService.model_instance(
+                kb.tenant_id, LLMType.RERANK.value, llm_name=req["rerank_id"])
+
+        if req.get("keyword", False):
+            chat_mdl = TenantLLMService.model_instance(kb.tenant_id, LLMType.CHAT)
+            question += keyword_extraction(chat_mdl, question)
+
+        retr = retrievaler if kb.parser_id != ParserType.KG else kg_retrievaler
+        ranks = retr.retrieval(question, embd_mdl, kb.tenant_id, kb_id, page, size,
+                               similarity_threshold, vector_similarity_weight, top,
+                               doc_ids, rerank_mdl=rerank_mdl, highlight=req.get("highlight"))
+        for c in ranks["chunks"]:
+            if "vector" in c:
+                del c["vector"]
+
+        ##rename keys
+        renamed_chunks=[]
+        for chunk in ranks["chunks"]:
+            key_mapping = {
+                "chunk_id": "id",
+                "content_with_weight": "content",
+                "doc_id": "document_id",
+                "important_kwd": "important_keywords",
+                "docnm_kwd":"document_keyword"
+            }
+            rename_chunk={}
+            for key, value in chunk.items():
+                new_key = key_mapping.get(key, key)
+                rename_chunk[new_key] = value
+                renamed_chunks.append(rename_chunk)
+        ranks["chunks"] = renamed_chunks
+        return get_json_result(data=ranks)
+    except Exception as e:
+        if str(e).find("not_found") > 0:
+            return get_json_result(data=False, retmsg=f'No chunk found! Check the chunk status please!',
+                                   retcode=RetCode.DATA_ERROR)
+        return server_error_response(e)

api/apps/sdk/session.py

@@ -0,0 +1,266 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import json
+from uuid import uuid4
+
+from flask import request, Response
+
+from api.db import StatusEnum
+from api.db.services.dialog_service import DialogService, ConversationService, chat
+from api.settings import RetCode
+from api.utils import get_uuid
+from api.utils.api_utils import get_data_error_result
+from api.utils.api_utils import get_json_result, token_required
+
+
+@manager.route('/save', methods=['POST'])
+@token_required
+def set_conversation(tenant_id):
+    req = request.json
+    conv_id = req.get("id")
+    if "assistant_id" in req:
+        req["dialog_id"] = req.pop("assistant_id")
+    if "id" in req:
+        del req["id"]
+        conv = ConversationService.query(id=conv_id)
+        if not conv:
+            return get_data_error_result(retmsg="Session does not exist")
+        if not DialogService.query(id=conv[0].dialog_id, tenant_id=tenant_id, status=StatusEnum.VALID.value):
+            return get_data_error_result(retmsg="You do not own the session")
+        if req.get("dialog_id"):
+            dia = DialogService.query(tenant_id=tenant_id, id=req["dialog_id"], status=StatusEnum.VALID.value)
+            if not dia:
+                return get_data_error_result(retmsg="You do not own the assistant")
+        if "dialog_id" in req and not req.get("dialog_id"):
+            return get_data_error_result(retmsg="assistant_id can not be empty.")
+        if "message" in req:
+            return get_data_error_result(retmsg="message can not be change")
+        if "reference" in req:
+            return get_data_error_result(retmsg="reference can not be change")
+        if "name" in req and not req.get("name"):
+            return get_data_error_result(retmsg="name can not be empty.")
+        if not ConversationService.update_by_id(conv_id, req):
+            return get_data_error_result(retmsg="Session updates error")
+        return get_json_result(data=True)
+
+    if not req.get("dialog_id"):
+        return get_data_error_result(retmsg="assistant_id is required.")
+    dia = DialogService.query(tenant_id=tenant_id, id=req["dialog_id"], status=StatusEnum.VALID.value)
+    if not dia:
+        return get_data_error_result(retmsg="You do not own the assistant")
+    conv = {
+        "id": get_uuid(),
+        "dialog_id": req["dialog_id"],
+        "name": req.get("name", "New session"),
+        "message": [{"role": "assistant", "content": "Hi! I am your assistant，can I help you?"}]
+    }
+    if not conv.get("name"):
+        return get_data_error_result(retmsg="name can not be empty.")
+    ConversationService.save(**conv)
+    e, conv = ConversationService.get_by_id(conv["id"])
+    if not e:
+        return get_data_error_result(retmsg="Fail to new session!")
+    conv = conv.to_dict()
+    conv['messages'] = conv.pop("message")
+    conv["assistant_id"] = conv.pop("dialog_id")
+    del conv["reference"]
+    return get_json_result(data=conv)
+
+
+@manager.route('/completion', methods=['POST'])
+@token_required
+def completion(tenant_id):
+    req = request.json
+    # req = {"conversation_id": "9aaaca4c11d311efa461fa163e197198", "messages": [
+    #    {"role": "user", "content": "上海有吗？"}
+    # ]}
+    if "session_id" not in req:
+        return get_data_error_result(retmsg="session_id is required")
+    conv = ConversationService.query(id=req["session_id"])
+    if not conv:
+        return get_data_error_result(retmsg="Session does not exist")
+    conv = conv[0]
+    if not DialogService.query(id=conv.dialog_id, tenant_id=tenant_id, status=StatusEnum.VALID.value):
+        return get_data_error_result(retmsg="You do not own the session")
+    msg = []
+    question = {
+        "content": req.get("question"),
+        "role": "user",
+        "id": str(uuid4())
+    }
+    conv.message.append(question)
+    for m in conv.message:
+        if m["role"] == "system": continue
+        if m["role"] == "assistant" and not msg: continue
+        msg.append(m)
+    message_id = msg[-1].get("id")
+    e, dia = DialogService.get_by_id(conv.dialog_id)
+    del req["session_id"]
+
+    if not conv.reference:
+        conv.reference = []
+    conv.message.append({"role": "assistant", "content": "", "id": message_id})
+    conv.reference.append({"chunks": [], "doc_aggs": []})
+
+    def fillin_conv(ans):
+        nonlocal conv, message_id
+        if not conv.reference:
+            conv.reference.append(ans["reference"])
+        else:
+            conv.reference[-1] = ans["reference"]
+        conv.message[-1] = {"role": "assistant", "content": ans["answer"],
+                            "id": message_id, "prompt": ans.get("prompt", "")}
+        ans["id"] = message_id
+
+    def stream():
+        nonlocal dia, msg, req, conv
+        try:
+            for ans in chat(dia, msg, **req):
+                fillin_conv(ans)
+                yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": ans}, ensure_ascii=False) + "\n\n"
+            ConversationService.update_by_id(conv.id, conv.to_dict())
+        except Exception as e:
+            yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e),
+                                        "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
+                                       ensure_ascii=False) + "\n\n"
+        yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n"
+
+    if req.get("stream", True):
+        resp = Response(stream(), mimetype="text/event-stream")
+        resp.headers.add_header("Cache-control", "no-cache")
+        resp.headers.add_header("Connection", "keep-alive")
+        resp.headers.add_header("X-Accel-Buffering", "no")
+        resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
+        return resp
+
+    else:
+        answer = None
+        for ans in chat(dia, msg, **req):
+            answer = ans
+            fillin_conv(ans)
+            ConversationService.update_by_id(conv.id, conv.to_dict())
+            break
+        return get_json_result(data=answer)
+
+
+@manager.route('/get', methods=['GET'])
+@token_required
+def get(tenant_id):
+    req = request.args
+    if "id" not in req:
+        return get_data_error_result(retmsg="id is required")
+    conv_id = req["id"]
+    conv = ConversationService.query(id=conv_id)
+    if not conv:
+        return get_data_error_result(retmsg="Session does not exist")
+    if not DialogService.query(id=conv[0].dialog_id, tenant_id=tenant_id, status=StatusEnum.VALID.value):
+        return get_data_error_result(retmsg="You do not own the session")
+    if "assistant_id" in req:
+        if req["assistant_id"] != conv[0].dialog_id:
+            return get_data_error_result(retmsg="The session doesn't belong to the assistant")
+    conv = conv[0].to_dict()
+    conv['messages'] = conv.pop("message")
+    conv["assistant_id"] = conv.pop("dialog_id")
+    if conv["reference"]:
+        messages = conv["messages"]
+        message_num = 0
+        chunk_num = 0
+        while message_num < len(messages):
+            if message_num != 0 and messages[message_num]["role"] != "user":
+                chunk_list = []
+                if "chunks" in conv["reference"][chunk_num]:
+                    chunks = conv["reference"][chunk_num]["chunks"]
+                    for chunk in chunks:
+                        new_chunk = {
+                            "id": chunk["chunk_id"],
+                            "content": chunk["content_with_weight"],
+                            "document_id": chunk["doc_id"],
+                            "document_name": chunk["docnm_kwd"],
+                            "knowledgebase_id": chunk["kb_id"],
+                            "image_id": chunk["img_id"],
+                            "similarity": chunk["similarity"],
+                            "vector_similarity": chunk["vector_similarity"],
+                            "term_similarity": chunk["term_similarity"],
+                            "positions": chunk["positions"],
+                        }
+                        chunk_list.append(new_chunk)
+                chunk_num += 1
+                messages[message_num]["reference"] = chunk_list
+            message_num += 1
+    del conv["reference"]
+    return get_json_result(data=conv)
+
+
+@manager.route('/list', methods=["GET"])
+@token_required
+def list(tenant_id):
+    assistant_id = request.args["assistant_id"]
+    if not DialogService.query(tenant_id=tenant_id, id=assistant_id, status=StatusEnum.VALID.value):
+        return get_json_result(
+            data=False, retmsg=f"You don't own the assistant.",
+            retcode=RetCode.OPERATING_ERROR)
+    convs = ConversationService.query(
+        dialog_id=assistant_id,
+        order_by=ConversationService.model.create_time,
+        reverse=True)
+    convs = [d.to_dict() for d in convs]
+    for conv in convs:
+        conv['messages'] = conv.pop("message")
+        conv["assistant_id"] = conv.pop("dialog_id")
+        if conv["reference"]:
+            messages = conv["messages"]
+            message_num = 0
+            chunk_num = 0
+            while message_num < len(messages):
+                if message_num != 0 and messages[message_num]["role"] != "user":
+                    chunk_list = []
+                    if "chunks" in conv["reference"][chunk_num]:
+                        chunks = conv["reference"][chunk_num]["chunks"]
+                        for chunk in chunks:
+                            new_chunk = {
+                                "id": chunk["chunk_id"],
+                                "content": chunk["content_with_weight"],
+                                "document_id": chunk["doc_id"],
+                                "document_name": chunk["docnm_kwd"],
+                                "knowledgebase_id": chunk["kb_id"],
+                                "image_id": chunk["img_id"],
+                                "similarity": chunk["similarity"],
+                                "vector_similarity": chunk["vector_similarity"],
+                                "term_similarity": chunk["term_similarity"],
+                                "positions": chunk["positions"],
+                            }
+                            chunk_list.append(new_chunk)
+                    chunk_num += 1
+                    messages[message_num]["reference"] = chunk_list
+                message_num += 1
+        del conv["reference"]
+    return get_json_result(data=convs)
+
+
+@manager.route('/delete', methods=["DELETE"])
+@token_required
+def delete(tenant_id):
+    id = request.args.get("id")
+    if not id:
+        return get_data_error_result(retmsg="`id` is required in deleting operation")
+    conv = ConversationService.query(id=id)
+    if not conv:
+        return get_data_error_result(retmsg="Session doesn't exist")
+    conv = conv[0]
+    if not DialogService.query(id=conv.dialog_id, tenant_id=tenant_id, status=StatusEnum.VALID.value):
+        return get_data_error_result(retmsg="You don't own the session")
+    ConversationService.delete_by_id(id)
+    return get_json_result(data=True)

api/apps/system_app.py

@@ -13,14 +13,17 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License
 #
+import json
+
 from flask_login import login_required
 
 from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.settings import DATABASE_TYPE
 from api.utils.api_utils import get_json_result
 from api.versions import get_rag_version
 from rag.settings import SVR_QUEUE_NAME
 from rag.utils.es_conn import ELASTICSEARCH
-from rag.utils.minio_conn import MINIO
+from rag.utils.storage_factory import STORAGE_IMPL, STORAGE_IMPL_TYPE
 from timeit import default_timer as timer
 
 from rag.utils.redis_conn import REDIS_CONN
@@ -45,17 +48,17 @@ def status():
 
     st = timer()
     try:
-        MINIO.health()
-        res["minio"] = {"status": "green", "elapsed": "{:.1f}".format((timer() - st)*1000.)}
+        STORAGE_IMPL.health()
+        res["storage"] = {"storage": STORAGE_IMPL_TYPE.lower(), "status": "green", "elapsed": "{:.1f}".format((timer() - st)*1000.)}
     except Exception as e:
-        res["minio"] = {"status": "red", "elapsed": "{:.1f}".format((timer() - st)*1000.), "error": str(e)}
+        res["storage"] = {"storage": STORAGE_IMPL_TYPE.lower(), "status": "red", "elapsed": "{:.1f}".format((timer() - st)*1000.), "error": str(e)}
 
     st = timer()
     try:
         KnowledgebaseService.get_by_id("x")
-        res["mysql"] = {"status": "green", "elapsed": "{:.1f}".format((timer() - st)*1000.)}
+        res["database"] = {"database": DATABASE_TYPE.lower(), "status": "green", "elapsed": "{:.1f}".format((timer() - st)*1000.)}
     except Exception as e:
-        res["mysql"] = {"status": "red", "elapsed": "{:.1f}".format((timer() - st)*1000.), "error": str(e)}
+        res["database"] = {"database": DATABASE_TYPE.lower(), "status": "red", "elapsed": "{:.1f}".format((timer() - st)*1000.), "error": str(e)}
 
     st = timer()
     try:
@@ -65,4 +68,23 @@ def status():
     except Exception as e:
         res["redis"] = {"status": "red", "elapsed": "{:.1f}".format((timer() - st)*1000.), "error": str(e)}
 
+    try:
+        v = REDIS_CONN.get("TASKEXE")
+        if not v:
+            raise Exception("No task executor running!")
+        obj = json.loads(v)
+        color = "green"
+        for id in obj.keys():
+            arr = obj[id]
+            if len(arr) == 1:
+                obj[id] = [0]
+            else:
+                obj[id] = [arr[i+1]-arr[i] for i in range(len(arr)-1)]
+            elapsed = max(obj[id])
+            if elapsed > 50: color = "yellow"
+            if elapsed > 120: color = "red"
+        res["task_executor"] = {"status": color, "elapsed": obj}
+    except Exception as e:
+        res["task_executor"] = {"status": "red", "error": str(e)}
+
     return get_json_result(data=res)

api/apps/tenant_app.py

@@ -0,0 +1,85 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+from flask import request
+from flask_login import current_user, login_required
+
+from api.db import UserTenantRole, StatusEnum
+from api.db.db_models import UserTenant
+from api.db.services.user_service import TenantService, UserTenantService
+from api.settings import RetCode
+
+from api.utils import get_uuid
+from api.utils.api_utils import get_json_result, validate_request, server_error_response
+
+
+@manager.route("/list", methods=["GET"])
+@login_required
+def tenant_list():
+    try:
+        tenants = TenantService.get_by_user_id(current_user.id)
+        return get_json_result(data=tenants)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route("/<tenant_id>/user/list", methods=["GET"])
+@login_required
+def user_list(tenant_id):
+    try:
+        users = UserTenantService.get_by_tenant_id(tenant_id)
+        return get_json_result(data=users)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/<tenant_id>/user', methods=['POST'])
+@login_required
+@validate_request("user_id")
+def create(tenant_id):
+    user_id = request.json.get("user_id")
+    if not user_id:
+        return get_json_result(
+            data=False, retmsg='Lack of "USER ID"', retcode=RetCode.ARGUMENT_ERROR)
+
+    try:
+        user_tenants = UserTenantService.query(user_id=user_id, tenant_id=tenant_id)
+        if user_tenants:
+            uuid = user_tenants[0].id
+            return get_json_result(data={"id": uuid})
+
+        uuid = get_uuid()
+        UserTenantService.save(
+            id = uuid,
+            user_id = user_id,
+            tenant_id = tenant_id,
+            role = UserTenantRole.NORMAL.value,
+            status = StatusEnum.VALID.value)
+
+        return get_json_result(data={"id": uuid})
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/<tenant_id>/user/<user_id>', methods=['DELETE'])
+@login_required
+def rm(tenant_id, user_id):
+    try:
+        UserTenantService.filter_delete([UserTenant.tenant_id == tenant_id, UserTenant.user_id == user_id])
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+    

api/apps/user_app.py

@@ -1,391 +1,422 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import json
-import re
-from datetime import datetime
-
-from flask import request, session, redirect
-from werkzeug.security import generate_password_hash, check_password_hash
-from flask_login import login_required, current_user, login_user, logout_user
-
-from api.db.db_models import TenantLLM
-from api.db.services.llm_service import TenantLLMService, LLMService
-from api.utils.api_utils import server_error_response, validate_request
-from api.utils import get_uuid, get_format_time, decrypt, download_img, current_timestamp, datetime_format
-from api.db import UserTenantRole, LLMType, FileType
-from api.settings import RetCode, GITHUB_OAUTH, FEISHU_OAUTH, CHAT_MDL, EMBEDDING_MDL, ASR_MDL, IMAGE2TEXT_MDL, PARSERS, \
-    API_KEY, \
-    LLM_FACTORY, LLM_BASE_URL, RERANK_MDL
-from api.db.services.user_service import UserService, TenantService, UserTenantService
-from api.db.services.file_service import FileService
-from api.settings import stat_logger
-from api.utils.api_utils import get_json_result, cors_reponse
-
-
-@manager.route('/login', methods=['POST', 'GET'])
-def login():
-    login_channel = "password"
-    if not request.json:
-        return get_json_result(data=False, retcode=RetCode.AUTHENTICATION_ERROR,
-                               retmsg='Unautherized!')
-
-    email = request.json.get('email', "")
-    users = UserService.query(email=email)
-    if not users:
-        return get_json_result(
-            data=False, retcode=RetCode.AUTHENTICATION_ERROR, retmsg=f'This Email is not registered!')
-
-    password = request.json.get('password')
-    try:
-        password = decrypt(password)
-    except BaseException:
-        return get_json_result(
-            data=False, retcode=RetCode.SERVER_ERROR, retmsg='Fail to crypt password')
-
-    user = UserService.query_user(email, password)
-    if user:
-        response_data = user.to_json()
-        user.access_token = get_uuid()
-        login_user(user)
-        user.update_time = current_timestamp(),
-        user.update_date = datetime_format(datetime.now()),
-        user.save()
-        msg = "Welcome back!"
-        return cors_reponse(data=response_data, auth=user.get_id(), retmsg=msg)
-    else:
-        return get_json_result(data=False, retcode=RetCode.AUTHENTICATION_ERROR,
-                               retmsg='Email and Password do not match!')
-
-
-@manager.route('/github_callback', methods=['GET'])
-def github_callback():
-    import requests
-    res = requests.post(GITHUB_OAUTH.get("url"), data={
-        "client_id": GITHUB_OAUTH.get("client_id"),
-        "client_secret": GITHUB_OAUTH.get("secret_key"),
-        "code": request.args.get('code')
-    }, headers={"Accept": "application/json"})
-    res = res.json()
-    if "error" in res:
-        return redirect("/?error=%s" % res["error_description"])
-
-    if "user:email" not in res["scope"].split(","):
-        return redirect("/?error=user:email not in scope")
-
-    session["access_token"] = res["access_token"]
-    session["access_token_from"] = "github"
-    userinfo = user_info_from_github(session["access_token"])
-    users = UserService.query(email=userinfo["email"])
-    user_id = get_uuid()
-    if not users:
-        try:
-            try:
-                avatar = download_img(userinfo["avatar_url"])
-            except Exception as e:
-                stat_logger.exception(e)
-                avatar = ""
-            users = user_register(user_id, {
-                "access_token": session["access_token"],
-                "email": userinfo["email"],
-                "avatar": avatar,
-                "nickname": userinfo["login"],
-                "login_channel": "github",
-                "last_login_time": get_format_time(),
-                "is_superuser": False,
-            })
-            if not users:
-                raise Exception('Register user failure.')
-            if len(users) > 1:
-                raise Exception('Same E-mail exist!')
-            user = users[0]
-            login_user(user)
-            return redirect("/?auth=%s" % user.get_id())
-        except Exception as e:
-            rollback_user_registration(user_id)
-            stat_logger.exception(e)
-            return redirect("/?error=%s" % str(e))
-    user = users[0]
-    user.access_token = get_uuid()
-    login_user(user)
-    user.save()
-    return redirect("/?auth=%s" % user.get_id())
-
-
-@manager.route('/feishu_callback', methods=['GET'])
-def feishu_callback():
-    import requests
-    app_access_token_res = requests.post(FEISHU_OAUTH.get("app_access_token_url"), data=json.dumps({
-        "app_id": FEISHU_OAUTH.get("app_id"),
-        "app_secret": FEISHU_OAUTH.get("app_secret")
-    }), headers={"Content-Type": "application/json; charset=utf-8"})
-    app_access_token_res = app_access_token_res.json()
-    if app_access_token_res['code'] != 0:
-        return redirect("/?error=%s" % app_access_token_res)
-
-    res = requests.post(FEISHU_OAUTH.get("user_access_token_url"), data=json.dumps({
-        "grant_type": FEISHU_OAUTH.get("grant_type"),
-        "code": request.args.get('code')
-    }), headers={"Content-Type": "application/json; charset=utf-8",
-                 'Authorization': f"Bearer {app_access_token_res['app_access_token']}"})
-    res = res.json()
-    if res['code'] != 0:
-        return redirect("/?error=%s" % res["message"])
-
-    if "contact:user.email:readonly" not in res["data"]["scope"].split(" "):
-        return redirect("/?error=contact:user.email:readonly not in scope")
-    session["access_token"] = res["data"]["access_token"]
-    session["access_token_from"] = "feishu"
-    userinfo = user_info_from_feishu(session["access_token"])
-    users = UserService.query(email=userinfo["email"])
-    user_id = get_uuid()
-    if not users:
-        try:
-            try:
-                avatar = download_img(userinfo["avatar_url"])
-            except Exception as e:
-                stat_logger.exception(e)
-                avatar = ""
-            users = user_register(user_id, {
-                "access_token": session["access_token"],
-                "email": userinfo["email"],
-                "avatar": avatar,
-                "nickname": userinfo["en_name"],
-                "login_channel": "feishu",
-                "last_login_time": get_format_time(),
-                "is_superuser": False,
-            })
-            if not users:
-                raise Exception('Register user failure.')
-            if len(users) > 1:
-                raise Exception('Same E-mail exist!')
-            user = users[0]
-            login_user(user)
-            return redirect("/?auth=%s" % user.get_id())
-        except Exception as e:
-            rollback_user_registration(user_id)
-            stat_logger.exception(e)
-            return redirect("/?error=%s" % str(e))
-    user = users[0]
-    user.access_token = get_uuid()
-    login_user(user)
-    user.save()
-    return redirect("/?auth=%s" % user.get_id())
-
-
-def user_info_from_feishu(access_token):
-    import requests
-    headers = {"Content-Type": "application/json; charset=utf-8",
-               'Authorization': f"Bearer {access_token}"}
-    res = requests.get(
-        f"https://open.feishu.cn/open-apis/authen/v1/user_info",
-        headers=headers)
-    user_info = res.json()["data"]
-    user_info["email"] = None if user_info.get("email") == "" else user_info["email"]
-    return user_info
-
-
-def user_info_from_github(access_token):
-    import requests
-    headers = {"Accept": "application/json",
-               'Authorization': f"token {access_token}"}
-    res = requests.get(
-        f"https://api.github.com/user?access_token={access_token}",
-        headers=headers)
-    user_info = res.json()
-    email_info = requests.get(
-        f"https://api.github.com/user/emails?access_token={access_token}",
-        headers=headers).json()
-    user_info["email"] = next(
-        (email for email in email_info if email['primary'] == True),
-        None)["email"]
-    return user_info
-
-
-@manager.route("/logout", methods=['GET'])
-@login_required
-def log_out():
-    current_user.access_token = ""
-    current_user.save()
-    logout_user()
-    return get_json_result(data=True)
-
-
-@manager.route("/setting", methods=["POST"])
-@login_required
-def setting_user():
-    update_dict = {}
-    request_data = request.json
-    if request_data.get("password"):
-        new_password = request_data.get("new_password")
-        if not check_password_hash(
-                current_user.password, decrypt(request_data["password"])):
-            return get_json_result(
-                data=False, retcode=RetCode.AUTHENTICATION_ERROR, retmsg='Password error!')
-
-        if new_password:
-            update_dict["password"] = generate_password_hash(
-                decrypt(new_password))
-
-    for k in request_data.keys():
-        if k in ["password", "new_password"]:
-            continue
-        update_dict[k] = request_data[k]
-
-    try:
-        UserService.update_by_id(current_user.id, update_dict)
-        return get_json_result(data=True)
-    except Exception as e:
-        stat_logger.exception(e)
-        return get_json_result(
-            data=False, retmsg='Update failure!', retcode=RetCode.EXCEPTION_ERROR)
-
-
-@manager.route("/info", methods=["GET"])
-@login_required
-def user_info():
-    return get_json_result(data=current_user.to_dict())
-
-
-def rollback_user_registration(user_id):
-    try:
-        UserService.delete_by_id(user_id)
-    except Exception as e:
-        pass
-    try:
-        TenantService.delete_by_id(user_id)
-    except Exception as e:
-        pass
-    try:
-        u = UserTenantService.query(tenant_id=user_id)
-        if u:
-            UserTenantService.delete_by_id(u[0].id)
-    except Exception as e:
-        pass
-    try:
-        TenantLLM.delete().where(TenantLLM.tenant_id == user_id).execute()
-    except Exception as e:
-        pass
-
-
-def user_register(user_id, user):
-    user["id"] = user_id
-    tenant = {
-        "id": user_id,
-        "name": user["nickname"] + "‘s Kingdom",
-        "llm_id": CHAT_MDL,
-        "embd_id": EMBEDDING_MDL,
-        "asr_id": ASR_MDL,
-        "parser_ids": PARSERS,
-        "img2txt_id": IMAGE2TEXT_MDL,
-        "rerank_id": RERANK_MDL
-    }
-    usr_tenant = {
-        "tenant_id": user_id,
-        "user_id": user_id,
-        "invited_by": user_id,
-        "role": UserTenantRole.OWNER
-    }
-    file_id = get_uuid()
-    file = {
-        "id": file_id,
-        "parent_id": file_id,
-        "tenant_id": user_id,
-        "created_by": user_id,
-        "name": "/",
-        "type": FileType.FOLDER.value,
-        "size": 0,
-        "location": "",
-    }
-    tenant_llm = []
-    for llm in LLMService.query(fid=LLM_FACTORY):
-        tenant_llm.append({"tenant_id": user_id,
-                           "llm_factory": LLM_FACTORY,
-                           "llm_name": llm.llm_name,
-                           "model_type": llm.model_type,
-                           "api_key": API_KEY,
-                           "api_base": LLM_BASE_URL
-                           })
-
-    if not UserService.save(**user):
-        return
-    TenantService.insert(**tenant)
-    UserTenantService.insert(**usr_tenant)
-    TenantLLMService.insert_many(tenant_llm)
-    FileService.insert(file)
-    return UserService.query(email=user["email"])
-
-
-@manager.route("/register", methods=["POST"])
-@validate_request("nickname", "email", "password")
-def user_add():
-    req = request.json
-    if UserService.query(email=req["email"]):
-        return get_json_result(
-            data=False, retmsg=f'Email: {req["email"]} has already registered!', retcode=RetCode.OPERATING_ERROR)
-    if not re.match(r"^[\w\._-]+@([\w_-]+\.)+[\w-]{2,4}$", req["email"]):
-        return get_json_result(data=False, retmsg=f'Invaliad e-mail: {req["email"]}!',
-                               retcode=RetCode.OPERATING_ERROR)
-
-    user_dict = {
-        "access_token": get_uuid(),
-        "email": req["email"],
-        "nickname": req["nickname"],
-        "password": decrypt(req["password"]),
-        "login_channel": "password",
-        "last_login_time": get_format_time(),
-        "is_superuser": False,
-    }
-
-    user_id = get_uuid()
-    try:
-        users = user_register(user_id, user_dict)
-        if not users:
-            raise Exception('Register user failure.')
-        if len(users) > 1:
-            raise Exception('Same E-mail exist!')
-        user = users[0]
-        login_user(user)
-        return cors_reponse(data=user.to_json(),
-                            auth=user.get_id(), retmsg="Welcome aboard!")
-    except Exception as e:
-        rollback_user_registration(user_id)
-        stat_logger.exception(e)
-        return get_json_result(
-            data=False, retmsg='User registration failure!', retcode=RetCode.EXCEPTION_ERROR)
-
-
-@manager.route("/tenant_info", methods=["GET"])
-@login_required
-def tenant_info():
-    try:
-        tenants = TenantService.get_by_user_id(current_user.id)[0]
-        return get_json_result(data=tenants)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route("/set_tenant_info", methods=["POST"])
-@login_required
-@validate_request("tenant_id", "asr_id", "embd_id", "img2txt_id", "llm_id")
-def set_tenant_info():
-    req = request.json
-    try:
-        tid = req["tenant_id"]
-        del req["tenant_id"]
-        TenantService.update_by_id(tid, req)
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import json
+import re
+from datetime import datetime
+
+from flask import request, session, redirect
+from werkzeug.security import generate_password_hash, check_password_hash
+from flask_login import login_required, current_user, login_user, logout_user
+
+from api.db.db_models import TenantLLM
+from api.db.services.llm_service import TenantLLMService, LLMService
+from api.utils.api_utils import server_error_response, validate_request
+from api.utils import get_uuid, get_format_time, decrypt, download_img, current_timestamp, datetime_format
+from api.db import UserTenantRole, LLMType, FileType
+from api.settings import RetCode, GITHUB_OAUTH, FEISHU_OAUTH, CHAT_MDL, EMBEDDING_MDL, ASR_MDL, IMAGE2TEXT_MDL, PARSERS, \
+    API_KEY, \
+    LLM_FACTORY, LLM_BASE_URL, RERANK_MDL
+from api.db.services.user_service import UserService, TenantService, UserTenantService
+from api.db.services.file_service import FileService
+from api.settings import stat_logger
+from api.utils.api_utils import get_json_result, construct_response
+
+
+@manager.route('/login', methods=['POST', 'GET'])
+def login():
+    if not request.json:
+        return get_json_result(data=False,
+                               retcode=RetCode.AUTHENTICATION_ERROR,
+                               retmsg='Unauthorized!')
+
+    email = request.json.get('email', "")
+    users = UserService.query(email=email)
+    if not users:
+        return get_json_result(data=False,
+                               retcode=RetCode.AUTHENTICATION_ERROR,
+                               retmsg=f'Email: {email} is not registered!')
+
+    password = request.json.get('password')
+    try:
+        password = decrypt(password)
+    except BaseException:
+        return get_json_result(data=False,
+                               retcode=RetCode.SERVER_ERROR,
+                               retmsg='Fail to crypt password')
+
+    user = UserService.query_user(email, password)
+    if user:
+        response_data = user.to_json()
+        user.access_token = get_uuid()
+        login_user(user)
+        user.update_time = current_timestamp(),
+        user.update_date = datetime_format(datetime.now()),
+        user.save()
+        msg = "Welcome back!"
+        return construct_response(data=response_data, auth=user.get_id(), retmsg=msg)
+    else:
+        return get_json_result(data=False,
+                               retcode=RetCode.AUTHENTICATION_ERROR,
+                               retmsg='Email and password do not match!')
+
+
+@manager.route('/github_callback', methods=['GET'])
+def github_callback():
+    import requests
+    res = requests.post(GITHUB_OAUTH.get("url"),
+                        data={
+                            "client_id": GITHUB_OAUTH.get("client_id"),
+                            "client_secret": GITHUB_OAUTH.get("secret_key"),
+                            "code": request.args.get('code')},
+                        headers={"Accept": "application/json"})
+    res = res.json()
+    if "error" in res:
+        return redirect("/?error=%s" % res["error_description"])
+
+    if "user:email" not in res["scope"].split(","):
+        return redirect("/?error=user:email not in scope")
+
+    session["access_token"] = res["access_token"]
+    session["access_token_from"] = "github"
+    user_info = user_info_from_github(session["access_token"])
+    email_address = user_info["email"]
+    users = UserService.query(email=email_address)
+    user_id = get_uuid()
+    if not users:
+        # User isn't try to register
+        try:
+            try:
+                avatar = download_img(user_info["avatar_url"])
+            except Exception as e:
+                stat_logger.exception(e)
+                avatar = ""
+            users = user_register(user_id, {
+                "access_token": session["access_token"],
+                "email": email_address,
+                "avatar": avatar,
+                "nickname": user_info["login"],
+                "login_channel": "github",
+                "last_login_time": get_format_time(),
+                "is_superuser": False,
+            })
+            if not users:
+                raise Exception(f'Fail to register {email_address}.')
+            if len(users) > 1:
+                raise Exception(f'Same email: {email_address} exists!')
+
+            # Try to log in
+            user = users[0]
+            login_user(user)
+            return redirect("/?auth=%s" % user.get_id())
+        except Exception as e:
+            rollback_user_registration(user_id)
+            stat_logger.exception(e)
+            return redirect("/?error=%s" % str(e))
+
+    # User has already registered, try to log in
+    user = users[0]
+    user.access_token = get_uuid()
+    login_user(user)
+    user.save()
+    return redirect("/?auth=%s" % user.get_id())
+
+
+@manager.route('/feishu_callback', methods=['GET'])
+def feishu_callback():
+    import requests
+    app_access_token_res = requests.post(FEISHU_OAUTH.get("app_access_token_url"),
+                                         data=json.dumps({
+                                             "app_id": FEISHU_OAUTH.get("app_id"),
+                                             "app_secret": FEISHU_OAUTH.get("app_secret")
+                                         }),
+                                         headers={"Content-Type": "application/json; charset=utf-8"})
+    app_access_token_res = app_access_token_res.json()
+    if app_access_token_res['code'] != 0:
+        return redirect("/?error=%s" % app_access_token_res)
+
+    res = requests.post(FEISHU_OAUTH.get("user_access_token_url"),
+                        data=json.dumps({
+                            "grant_type": FEISHU_OAUTH.get("grant_type"),
+                            "code": request.args.get('code')
+                        }),
+                        headers={
+                            "Content-Type": "application/json; charset=utf-8",
+                            'Authorization': f"Bearer {app_access_token_res['app_access_token']}"
+                        })
+    res = res.json()
+    if res['code'] != 0:
+        return redirect("/?error=%s" % res["message"])
+
+    if "contact:user.email:readonly" not in res["data"]["scope"].split(" "):
+        return redirect("/?error=contact:user.email:readonly not in scope")
+    session["access_token"] = res["data"]["access_token"]
+    session["access_token_from"] = "feishu"
+    user_info = user_info_from_feishu(session["access_token"])
+    email_address = user_info["email"]
+    users = UserService.query(email=email_address)
+    user_id = get_uuid()
+    if not users:
+        # User isn't try to register
+        try:
+            try:
+                avatar = download_img(user_info["avatar_url"])
+            except Exception as e:
+                stat_logger.exception(e)
+                avatar = ""
+            users = user_register(user_id, {
+                "access_token": session["access_token"],
+                "email": email_address,
+                "avatar": avatar,
+                "nickname": user_info["en_name"],
+                "login_channel": "feishu",
+                "last_login_time": get_format_time(),
+                "is_superuser": False,
+            })
+            if not users:
+                raise Exception(f'Fail to register {email_address}.')
+            if len(users) > 1:
+                raise Exception(f'Same email: {email_address} exists!')
+
+            # Try to log in
+            user = users[0]
+            login_user(user)
+            return redirect("/?auth=%s" % user.get_id())
+        except Exception as e:
+            rollback_user_registration(user_id)
+            stat_logger.exception(e)
+            return redirect("/?error=%s" % str(e))
+
+    # User has already registered, try to log in
+    user = users[0]
+    user.access_token = get_uuid()
+    login_user(user)
+    user.save()
+    return redirect("/?auth=%s" % user.get_id())
+
+
+def user_info_from_feishu(access_token):
+    import requests
+    headers = {"Content-Type": "application/json; charset=utf-8",
+               'Authorization': f"Bearer {access_token}"}
+    res = requests.get(
+        f"https://open.feishu.cn/open-apis/authen/v1/user_info",
+        headers=headers)
+    user_info = res.json()["data"]
+    user_info["email"] = None if user_info.get("email") == "" else user_info["email"]
+    return user_info
+
+
+def user_info_from_github(access_token):
+    import requests
+    headers = {"Accept": "application/json",
+               'Authorization': f"token {access_token}"}
+    res = requests.get(
+        f"https://api.github.com/user?access_token={access_token}",
+        headers=headers)
+    user_info = res.json()
+    email_info = requests.get(
+        f"https://api.github.com/user/emails?access_token={access_token}",
+        headers=headers).json()
+    user_info["email"] = next(
+        (email for email in email_info if email['primary'] == True),
+        None)["email"]
+    return user_info
+
+
+@manager.route("/logout", methods=['GET'])
+@login_required
+def log_out():
+    current_user.access_token = ""
+    current_user.save()
+    logout_user()
+    return get_json_result(data=True)
+
+
+@manager.route("/setting", methods=["POST"])
+@login_required
+def setting_user():
+    update_dict = {}
+    request_data = request.json
+    if request_data.get("password"):
+        new_password = request_data.get("new_password")
+        if not check_password_hash(
+                current_user.password, decrypt(request_data["password"])):
+            return get_json_result(data=False, retcode=RetCode.AUTHENTICATION_ERROR, retmsg='Password error!')
+
+        if new_password:
+            update_dict["password"] = generate_password_hash(decrypt(new_password))
+
+    for k in request_data.keys():
+        if k in ["password", "new_password"]:
+            continue
+        update_dict[k] = request_data[k]
+
+    try:
+        UserService.update_by_id(current_user.id, update_dict)
+        return get_json_result(data=True)
+    except Exception as e:
+        stat_logger.exception(e)
+        return get_json_result(data=False, retmsg='Update failure!', retcode=RetCode.EXCEPTION_ERROR)
+
+
+@manager.route("/info", methods=["GET"])
+@login_required
+def user_profile():
+    return get_json_result(data=current_user.to_dict())
+
+
+def rollback_user_registration(user_id):
+    try:
+        UserService.delete_by_id(user_id)
+    except Exception as e:
+        pass
+    try:
+        TenantService.delete_by_id(user_id)
+    except Exception as e:
+        pass
+    try:
+        u = UserTenantService.query(tenant_id=user_id)
+        if u:
+            UserTenantService.delete_by_id(u[0].id)
+    except Exception as e:
+        pass
+    try:
+        TenantLLM.delete().where(TenantLLM.tenant_id == user_id).execute()
+    except Exception as e:
+        pass
+
+
+def user_register(user_id, user):
+    user["id"] = user_id
+    tenant = {
+        "id": user_id,
+        "name": user["nickname"] + "‘s Kingdom",
+        "llm_id": CHAT_MDL,
+        "embd_id": EMBEDDING_MDL,
+        "asr_id": ASR_MDL,
+        "parser_ids": PARSERS,
+        "img2txt_id": IMAGE2TEXT_MDL,
+        "rerank_id": RERANK_MDL
+    }
+    usr_tenant = {
+        "tenant_id": user_id,
+        "user_id": user_id,
+        "invited_by": user_id,
+        "role": UserTenantRole.OWNER
+    }
+    file_id = get_uuid()
+    file = {
+        "id": file_id,
+        "parent_id": file_id,
+        "tenant_id": user_id,
+        "created_by": user_id,
+        "name": "/",
+        "type": FileType.FOLDER.value,
+        "size": 0,
+        "location": "",
+    }
+    tenant_llm = []
+    for llm in LLMService.query(fid=LLM_FACTORY):
+        tenant_llm.append({"tenant_id": user_id,
+                           "llm_factory": LLM_FACTORY,
+                           "llm_name": llm.llm_name,
+                           "model_type": llm.model_type,
+                           "api_key": API_KEY,
+                           "api_base": LLM_BASE_URL
+                           })
+
+    if not UserService.save(**user):
+        return
+    TenantService.insert(**tenant)
+    UserTenantService.insert(**usr_tenant)
+    TenantLLMService.insert_many(tenant_llm)
+    FileService.insert(file)
+    return UserService.query(email=user["email"])
+
+
+@manager.route("/register", methods=["POST"])
+@validate_request("nickname", "email", "password")
+def user_add():
+    req = request.json
+    email_address = req["email"]
+
+    # Validate the email address
+    if not re.match(r"^[\w\._-]+@([\w_-]+\.)+[\w-]{2,4}$", email_address):
+        return get_json_result(data=False,
+                               retmsg=f'Invalid email address: {email_address}!',
+                               retcode=RetCode.OPERATING_ERROR)
+
+    # Check if the email address is already used
+    if UserService.query(email=email_address):
+        return get_json_result(
+            data=False,
+            retmsg=f'Email: {email_address} has already registered!',
+            retcode=RetCode.OPERATING_ERROR)
+
+    # Construct user info data
+    nickname = req["nickname"]
+    user_dict = {
+        "access_token": get_uuid(),
+        "email": email_address,
+        "nickname": nickname,
+        "password": decrypt(req["password"]),
+        "login_channel": "password",
+        "last_login_time": get_format_time(),
+        "is_superuser": False,
+    }
+
+    user_id = get_uuid()
+    try:
+        users = user_register(user_id, user_dict)
+        if not users:
+            raise Exception(f'Fail to register {email_address}.')
+        if len(users) > 1:
+            raise Exception(f'Same email: {email_address} exists!')
+        user = users[0]
+        login_user(user)
+        return construct_response(data=user.to_json(),
+                                  auth=user.get_id(),
+                                  retmsg=f"{nickname}, welcome aboard!")
+    except Exception as e:
+        rollback_user_registration(user_id)
+        stat_logger.exception(e)
+        return get_json_result(data=False,
+                               retmsg=f'User registration failure, error: {str(e)}',
+                               retcode=RetCode.EXCEPTION_ERROR)
+
+
+@manager.route("/tenant_info", methods=["GET"])
+@login_required
+def tenant_info():
+    try:
+        tenants = TenantService.get_by_user_id(current_user.id)[0]
+        return get_json_result(data=tenants)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route("/set_tenant_info", methods=["POST"])
+@login_required
+@validate_request("tenant_id", "asr_id", "embd_id", "img2txt_id", "llm_id")
+def set_tenant_info():
+    req = request.json
+    try:
+        tid = req["tenant_id"]
+        del req["tenant_id"]
+        TenantService.update_by_id(tid, req)
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)

api/db/__init__.py

@@ -1,101 +1,103 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from enum import Enum
-from enum import IntEnum
-from strenum import StrEnum
-
-
-class StatusEnum(Enum):
-    VALID = "1"
-    INVALID = "0"
-
-
-class UserTenantRole(StrEnum):
-    OWNER = 'owner'
-    ADMIN = 'admin'
-    NORMAL = 'normal'
-
-
-class TenantPermission(StrEnum):
-    ME = 'me'
-    TEAM = 'team'
-
-
-class SerializedType(IntEnum):
-    PICKLE = 1
-    JSON = 2
-
-
-class FileType(StrEnum):
-    PDF = 'pdf'
-    DOC = 'doc'
-    VISUAL = 'visual'
-    AURAL = 'aural'
-    VIRTUAL = 'virtual'
-    FOLDER = 'folder'
-    OTHER = "other"
-
-
-class LLMType(StrEnum):
-    CHAT = 'chat'
-    EMBEDDING = 'embedding'
-    SPEECH2TEXT = 'speech2text'
-    IMAGE2TEXT = 'image2text'
-    RERANK = 'rerank'
-
-
-class ChatStyle(StrEnum):
-    CREATIVE = 'Creative'
-    PRECISE = 'Precise'
-    EVENLY = 'Evenly'
-    CUSTOM = 'Custom'
-
-
-class TaskStatus(StrEnum):
-    UNSTART = "0"
-    RUNNING = "1"
-    CANCEL = "2"
-    DONE = "3"
-    FAIL = "4"
-
-
-class ParserType(StrEnum):
-    PRESENTATION = "presentation"
-    LAWS = "laws"
-    MANUAL = "manual"
-    PAPER = "paper"
-    RESUME = "resume"
-    BOOK = "book"
-    QA = "qa"
-    TABLE = "table"
-    NAIVE = "naive"
-    PICTURE = "picture"
-    ONE = "one"
-    AUDIO = "audio"
-    KG = "knowledge_graph"
-
-
-class FileSource(StrEnum):
-    LOCAL = ""
-    KNOWLEDGEBASE = "knowledgebase"
-    S3 = "s3"
-
-
-class CanvasType(StrEnum):
-    ChatBot = "chatbot"
-    DocBot = "docbot"
-
-KNOWLEDGEBASE_FOLDER_NAME=".knowledgebase"
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from enum import Enum
+from enum import IntEnum
+from strenum import StrEnum
+
+
+class StatusEnum(Enum):
+    VALID = "1"
+    INVALID = "0"
+
+
+class UserTenantRole(StrEnum):
+    OWNER = 'owner'
+    ADMIN = 'admin'
+    NORMAL = 'normal'
+
+
+class TenantPermission(StrEnum):
+    ME = 'me'
+    TEAM = 'team'
+
+
+class SerializedType(IntEnum):
+    PICKLE = 1
+    JSON = 2
+
+
+class FileType(StrEnum):
+    PDF = 'pdf'
+    DOC = 'doc'
+    VISUAL = 'visual'
+    AURAL = 'aural'
+    VIRTUAL = 'virtual'
+    FOLDER = 'folder'
+    OTHER = "other"
+
+
+class LLMType(StrEnum):
+    CHAT = 'chat'
+    EMBEDDING = 'embedding'
+    SPEECH2TEXT = 'speech2text'
+    IMAGE2TEXT = 'image2text'
+    RERANK = 'rerank'
+    TTS    = 'tts'
+
+
+class ChatStyle(StrEnum):
+    CREATIVE = 'Creative'
+    PRECISE = 'Precise'
+    EVENLY = 'Evenly'
+    CUSTOM = 'Custom'
+
+
+class TaskStatus(StrEnum):
+    UNSTART = "0"
+    RUNNING = "1"
+    CANCEL = "2"
+    DONE = "3"
+    FAIL = "4"
+
+
+class ParserType(StrEnum):
+    PRESENTATION = "presentation"
+    LAWS = "laws"
+    MANUAL = "manual"
+    PAPER = "paper"
+    RESUME = "resume"
+    BOOK = "book"
+    QA = "qa"
+    TABLE = "table"
+    NAIVE = "naive"
+    PICTURE = "picture"
+    ONE = "one"
+    AUDIO = "audio"
+    EMAIL = "email"
+    KG = "knowledge_graph"
+
+
+class FileSource(StrEnum):
+    LOCAL = ""
+    KNOWLEDGEBASE = "knowledgebase"
+    S3 = "s3"
+
+
+class CanvasType(StrEnum):
+    ChatBot = "chatbot"
+    DocBot = "docbot"
+
+KNOWLEDGEBASE_FOLDER_NAME=".knowledgebase"

api/db/db_utils.py

@@ -1,130 +1,135 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import operator
-from functools import reduce
-from typing import Dict, Type, Union
-
-from api.utils import current_timestamp, timestamp_to_date
-
-from api.db.db_models import DB, DataBaseModel
-from api.db.runtime_config import RuntimeConfig
-from api.utils.log_utils import getLogger
-from enum import Enum
-
-
-LOGGER = getLogger()
-
-
-@DB.connection_context()
-def bulk_insert_into_db(model, data_source, replace_on_conflict=False):
-    DB.create_tables([model])
-
-    for i, data in enumerate(data_source):
-        current_time = current_timestamp() + i
-        current_date = timestamp_to_date(current_time)
-        if 'create_time' not in data:
-            data['create_time'] = current_time
-        data['create_date'] = timestamp_to_date(data['create_time'])
-        data['update_time'] = current_time
-        data['update_date'] = current_date
-
-    preserve = tuple(data_source[0].keys() - {'create_time', 'create_date'})
-
-    batch_size = 1000
-
-    for i in range(0, len(data_source), batch_size):
-        with DB.atomic():
-            query = model.insert_many(data_source[i:i + batch_size])
-            if replace_on_conflict:
-                query = query.on_conflict(preserve=preserve)
-            query.execute()
-
-
-def get_dynamic_db_model(base, job_id):
-    return type(base.model(
-        table_index=get_dynamic_tracking_table_index(job_id=job_id)))
-
-
-def get_dynamic_tracking_table_index(job_id):
-    return job_id[:8]
-
-
-def fill_db_model_object(model_object, human_model_dict):
-    for k, v in human_model_dict.items():
-        attr_name = 'f_%s' % k
-        if hasattr(model_object.__class__, attr_name):
-            setattr(model_object, attr_name, v)
-    return model_object
-
-
-# https://docs.peewee-orm.com/en/latest/peewee/query_operators.html
-supported_operators = {
-    '==': operator.eq,
-    '<': operator.lt,
-    '<=': operator.le,
-    '>': operator.gt,
-    '>=': operator.ge,
-    '!=': operator.ne,
-    '<<': operator.lshift,
-    '>>': operator.rshift,
-    '%': operator.mod,
-    '**': operator.pow,
-    '^': operator.xor,
-    '~': operator.inv,
-}
-
-
-def query_dict2expression(
-        model: Type[DataBaseModel], query: Dict[str, Union[bool, int, str, list, tuple]]):
-    expression = []
-
-    for field, value in query.items():
-        if not isinstance(value, (list, tuple)):
-            value = ('==', value)
-        op, *val = value
-
-        field = getattr(model, f'f_{field}')
-        value = supported_operators[op](
-            field, val[0]) if op in supported_operators else getattr(
-            field, op)(
-            *val)
-        expression.append(value)
-
-    return reduce(operator.iand, expression)
-
-
-def query_db(model: Type[DataBaseModel], limit: int = 0, offset: int = 0,
-             query: dict = None, order_by: Union[str, list, tuple] = None):
-    data = model.select()
-    if query:
-        data = data.where(query_dict2expression(model, query))
-    count = data.count()
-
-    if not order_by:
-        order_by = 'create_time'
-    if not isinstance(order_by, (list, tuple)):
-        order_by = (order_by, 'asc')
-    order_by, order = order_by
-    order_by = getattr(model, f'f_{order_by}')
-    order_by = getattr(order_by, order)()
-    data = data.order_by(order_by)
-
-    if limit > 0:
-        data = data.limit(limit)
-    if offset > 0:
-        data = data.offset(offset)
-
-    return list(data), count
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import operator
+from functools import reduce
+from typing import Dict, Type, Union
+
+from playhouse.pool import PooledMySQLDatabase
+
+from api.utils import current_timestamp, timestamp_to_date
+
+from api.db.db_models import DB, DataBaseModel
+from api.db.runtime_config import RuntimeConfig
+from api.utils.log_utils import getLogger
+from enum import Enum
+
+
+LOGGER = getLogger()
+
+
+@DB.connection_context()
+def bulk_insert_into_db(model, data_source, replace_on_conflict=False):
+    DB.create_tables([model])
+
+    for i, data in enumerate(data_source):
+        current_time = current_timestamp() + i
+        current_date = timestamp_to_date(current_time)
+        if 'create_time' not in data:
+            data['create_time'] = current_time
+        data['create_date'] = timestamp_to_date(data['create_time'])
+        data['update_time'] = current_time
+        data['update_date'] = current_date
+
+    preserve = tuple(data_source[0].keys() - {'create_time', 'create_date'})
+
+    batch_size = 1000
+
+    for i in range(0, len(data_source), batch_size):
+        with DB.atomic():
+            query = model.insert_many(data_source[i:i + batch_size])
+            if replace_on_conflict:
+                if isinstance(DB, PooledMySQLDatabase):
+                    query = query.on_conflict(preserve=preserve)
+                else:
+                    query = query.on_conflict(conflict_target="id", preserve=preserve)
+            query.execute()
+
+
+def get_dynamic_db_model(base, job_id):
+    return type(base.model(
+        table_index=get_dynamic_tracking_table_index(job_id=job_id)))
+
+
+def get_dynamic_tracking_table_index(job_id):
+    return job_id[:8]
+
+
+def fill_db_model_object(model_object, human_model_dict):
+    for k, v in human_model_dict.items():
+        attr_name = 'f_%s' % k
+        if hasattr(model_object.__class__, attr_name):
+            setattr(model_object, attr_name, v)
+    return model_object
+
+
+# https://docs.peewee-orm.com/en/latest/peewee/query_operators.html
+supported_operators = {
+    '==': operator.eq,
+    '<': operator.lt,
+    '<=': operator.le,
+    '>': operator.gt,
+    '>=': operator.ge,
+    '!=': operator.ne,
+    '<<': operator.lshift,
+    '>>': operator.rshift,
+    '%': operator.mod,
+    '**': operator.pow,
+    '^': operator.xor,
+    '~': operator.inv,
+}
+
+
+def query_dict2expression(
+        model: Type[DataBaseModel], query: Dict[str, Union[bool, int, str, list, tuple]]):
+    expression = []
+
+    for field, value in query.items():
+        if not isinstance(value, (list, tuple)):
+            value = ('==', value)
+        op, *val = value
+
+        field = getattr(model, f'f_{field}')
+        value = supported_operators[op](
+            field, val[0]) if op in supported_operators else getattr(
+            field, op)(
+            *val)
+        expression.append(value)
+
+    return reduce(operator.iand, expression)
+
+
+def query_db(model: Type[DataBaseModel], limit: int = 0, offset: int = 0,
+             query: dict = None, order_by: Union[str, list, tuple] = None):
+    data = model.select()
+    if query:
+        data = data.where(query_dict2expression(model, query))
+    count = data.count()
+
+    if not order_by:
+        order_by = 'create_time'
+    if not isinstance(order_by, (list, tuple)):
+        order_by = (order_by, 'asc')
+    order_by, order = order_by
+    order_by = getattr(model, f'f_{order_by}')
+    order_by = getattr(order_by, order)()
+    data = data.order_by(order_by)
+
+    if limit > 0:
+        data = data.limit(limit)
+    if offset > 0:
+        data = data.offset(offset)
+
+    return list(data), count

api/db/init_data.py

@@ -1,182 +1,190 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import json
-import os
-import time
-import uuid
-from copy import deepcopy
-
-from api.db import LLMType, UserTenantRole
-from api.db.db_models import init_database_tables as init_web_db, LLMFactories, LLM, TenantLLM
-from api.db.services import UserService
-from api.db.services.canvas_service import CanvasTemplateService
-from api.db.services.document_service import DocumentService
-from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.services.llm_service import LLMFactoriesService, LLMService, TenantLLMService, LLMBundle
-from api.db.services.user_service import TenantService, UserTenantService
-from api.settings import CHAT_MDL, EMBEDDING_MDL, ASR_MDL, IMAGE2TEXT_MDL, PARSERS, LLM_FACTORY, API_KEY, LLM_BASE_URL
-from api.utils.file_utils import get_project_base_directory
-
-
-def init_superuser():
-    user_info = {
-        "id": uuid.uuid1().hex,
-        "password": "admin",
-        "nickname": "admin",
-        "is_superuser": True,
-        "email": "admin@ragflow.io",
-        "creator": "system",
-        "status": "1",
-    }
-    tenant = {
-        "id": user_info["id"],
-        "name": user_info["nickname"] + "‘s Kingdom",
-        "llm_id": CHAT_MDL,
-        "embd_id": EMBEDDING_MDL,
-        "asr_id": ASR_MDL,
-        "parser_ids": PARSERS,
-        "img2txt_id": IMAGE2TEXT_MDL
-    }
-    usr_tenant = {
-        "tenant_id": user_info["id"],
-        "user_id": user_info["id"],
-        "invited_by": user_info["id"],
-        "role": UserTenantRole.OWNER
-    }
-    tenant_llm = []
-    for llm in LLMService.query(fid=LLM_FACTORY):
-        tenant_llm.append(
-            {"tenant_id": user_info["id"], "llm_factory": LLM_FACTORY, "llm_name": llm.llm_name, "model_type": llm.model_type,
-             "api_key": API_KEY, "api_base": LLM_BASE_URL})
-
-    if not UserService.save(**user_info):
-        print("\033[93m【ERROR】\033[0mcan't init admin.")
-        return
-    TenantService.insert(**tenant)
-    UserTenantService.insert(**usr_tenant)
-    TenantLLMService.insert_many(tenant_llm)
-    print(
-        "【INFO】Super user initialized. \033[93memail: admin@ragflow.io, password: admin\033[0m. Changing the password after logining is strongly recomanded.")
-
-    chat_mdl = LLMBundle(tenant["id"], LLMType.CHAT, tenant["llm_id"])
-    msg = chat_mdl.chat(system="", history=[
-                        {"role": "user", "content": "Hello!"}], gen_conf={})
-    if msg.find("ERROR: ") == 0:
-        print(
-            "\33[91m【ERROR】\33[0m: ",
-            "'{}' dosen't work. {}".format(
-                tenant["llm_id"],
-                msg))
-    embd_mdl = LLMBundle(tenant["id"], LLMType.EMBEDDING, tenant["embd_id"])
-    v, c = embd_mdl.encode(["Hello!"])
-    if c == 0:
-        print(
-            "\33[91m【ERROR】\33[0m:",
-            " '{}' dosen't work!".format(
-                tenant["embd_id"]))
-
-
-def init_llm_factory():
-    try:
-        LLMService.filter_delete([(LLM.fid == "MiniMax" or LLM.fid == "Minimax")])
-    except Exception as e:
-        pass
-
-    factory_llm_infos = json.load(
-        open(
-            os.path.join(get_project_base_directory(), "conf", "llm_factories.json"),
-            "r",
-        )
-    )
-    for factory_llm_info in factory_llm_infos["factory_llm_infos"]:
-        llm_infos = factory_llm_info.pop("llm")
-        try:
-            LLMFactoriesService.save(**factory_llm_info)
-        except Exception as e:
-            pass
-        for llm_info in llm_infos:
-            llm_info["fid"] = factory_llm_info["name"]
-            try:
-                LLMService.save(**llm_info)
-            except Exception as e:
-                pass
-
-    LLMFactoriesService.filter_delete([LLMFactories.name == "Local"])
-    LLMService.filter_delete([LLM.fid == "Local"])
-    LLMService.filter_delete([LLM.fid == "Moonshot", LLM.llm_name == "flag-embedding"])
-    TenantLLMService.filter_delete([TenantLLM.llm_factory == "Moonshot", TenantLLM.llm_name == "flag-embedding"])
-    LLMFactoriesService.filter_delete([LLMFactoriesService.model.name == "QAnything"])
-    LLMService.filter_delete([LLMService.model.fid == "QAnything"])
-    TenantLLMService.filter_update([TenantLLMService.model.llm_factory == "QAnything"], {"llm_factory": "Youdao"})
-    TenantService.filter_update([1 == 1], {
-        "parser_ids": "naive:General,qa:Q&A,resume:Resume,manual:Manual,table:Table,paper:Paper,book:Book,laws:Laws,presentation:Presentation,picture:Picture,one:One,audio:Audio,knowledge_graph:Knowledge Graph"})
-    ## insert openai two embedding models to the current openai user.
-    print("Start to insert 2 OpenAI embedding models...")
-    tenant_ids = set([row["tenant_id"] for row in TenantLLMService.get_openai_models()])
-    for tid in tenant_ids:
-        for row in TenantLLMService.query(llm_factory="OpenAI", tenant_id=tid):
-            row = row.to_dict()
-            row["model_type"] = LLMType.EMBEDDING.value
-            row["llm_name"] = "text-embedding-3-small"
-            row["used_tokens"] = 0
-            try:
-                TenantLLMService.save(**row)
-                row = deepcopy(row)
-                row["llm_name"] = "text-embedding-3-large"
-                TenantLLMService.save(**row)
-            except Exception as e:
-                pass
-            break
-    for kb_id in KnowledgebaseService.get_all_ids():
-        KnowledgebaseService.update_by_id(kb_id, {"doc_num": DocumentService.get_kb_doc_count(kb_id)})
-    """
-    drop table llm;
-    drop table llm_factories;
-    update tenant set parser_ids='naive:General,qa:Q&A,resume:Resume,manual:Manual,table:Table,paper:Paper,book:Book,laws:Laws,presentation:Presentation,picture:Picture,one:One,audio:Audio,knowledge_graph:Knowledge Graph';
-    alter table knowledgebase modify avatar longtext;
-    alter table user modify avatar longtext;
-    alter table dialog modify icon longtext;
-    """
-
-
-def add_graph_templates():
-    dir = os.path.join(get_project_base_directory(), "agent", "templates")
-    for fnm in os.listdir(dir):
-        try:
-            cnvs = json.load(open(os.path.join(dir, fnm), "r"))
-            try:
-                CanvasTemplateService.save(**cnvs)
-            except:
-                CanvasTemplateService.update_by_id(cnvs["id"], cnvs)
-        except Exception as e:
-            print("Add graph templates error: ", e)
-            print("------------", flush=True)
-
-
-def init_web_data():
-    start_time = time.time()
-
-    init_llm_factory()
-    if not UserService.get_all().count():
-        init_superuser()
-
-    add_graph_templates()
-    print("init web data success:{}".format(time.time() - start_time))
-
-
-if __name__ == '__main__':
-    init_web_db()
-    init_web_data()
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import base64
+import json
+import os
+import time
+import uuid
+from copy import deepcopy
+
+from api.db import LLMType, UserTenantRole
+from api.db.db_models import init_database_tables as init_web_db, LLMFactories, LLM, TenantLLM
+from api.db.services import UserService
+from api.db.services.canvas_service import CanvasTemplateService
+from api.db.services.document_service import DocumentService
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.services.llm_service import LLMFactoriesService, LLMService, TenantLLMService, LLMBundle
+from api.db.services.user_service import TenantService, UserTenantService
+from api.settings import CHAT_MDL, EMBEDDING_MDL, ASR_MDL, IMAGE2TEXT_MDL, PARSERS, LLM_FACTORY, API_KEY, LLM_BASE_URL
+from api.utils.file_utils import get_project_base_directory
+
+
+def encode_to_base64(input_string):
+    base64_encoded = base64.b64encode(input_string.encode('utf-8'))
+    return base64_encoded.decode('utf-8')
+
+
+def init_superuser():
+    user_info = {
+        "id": uuid.uuid1().hex,
+        "password": encode_to_base64("admin"),
+        "nickname": "admin",
+        "is_superuser": True,
+        "email": "admin@ragflow.io",
+        "creator": "system",
+        "status": "1",
+    }
+    tenant = {
+        "id": user_info["id"],
+        "name": user_info["nickname"] + "‘s Kingdom",
+        "llm_id": CHAT_MDL,
+        "embd_id": EMBEDDING_MDL,
+        "asr_id": ASR_MDL,
+        "parser_ids": PARSERS,
+        "img2txt_id": IMAGE2TEXT_MDL
+    }
+    usr_tenant = {
+        "tenant_id": user_info["id"],
+        "user_id": user_info["id"],
+        "invited_by": user_info["id"],
+        "role": UserTenantRole.OWNER
+    }
+    tenant_llm = []
+    for llm in LLMService.query(fid=LLM_FACTORY):
+        tenant_llm.append(
+            {"tenant_id": user_info["id"], "llm_factory": LLM_FACTORY, "llm_name": llm.llm_name, "model_type": llm.model_type,
+             "api_key": API_KEY, "api_base": LLM_BASE_URL})
+
+    if not UserService.save(**user_info):
+        print("\033[93m【ERROR】\033[0mcan't init admin.")
+        return
+    TenantService.insert(**tenant)
+    UserTenantService.insert(**usr_tenant)
+    TenantLLMService.insert_many(tenant_llm)
+    print(
+        "【INFO】Super user initialized. \033[93memail: admin@ragflow.io, password: admin\033[0m. Changing the password after logining is strongly recomanded.")
+
+    chat_mdl = LLMBundle(tenant["id"], LLMType.CHAT, tenant["llm_id"])
+    msg = chat_mdl.chat(system="", history=[
+                        {"role": "user", "content": "Hello!"}], gen_conf={})
+    if msg.find("ERROR: ") == 0:
+        print(
+            "\33[91m【ERROR】\33[0m: ",
+            "'{}' dosen't work. {}".format(
+                tenant["llm_id"],
+                msg))
+    embd_mdl = LLMBundle(tenant["id"], LLMType.EMBEDDING, tenant["embd_id"])
+    v, c = embd_mdl.encode(["Hello!"])
+    if c == 0:
+        print(
+            "\33[91m【ERROR】\33[0m:",
+            " '{}' dosen't work!".format(
+                tenant["embd_id"]))
+
+
+def init_llm_factory():
+    try:
+        LLMService.filter_delete([(LLM.fid == "MiniMax" or LLM.fid == "Minimax")])
+    except Exception as e:
+        pass
+
+    factory_llm_infos = json.load(
+        open(
+            os.path.join(get_project_base_directory(), "conf", "llm_factories.json"),
+            "r",
+        )
+    )
+    for factory_llm_info in factory_llm_infos["factory_llm_infos"]:
+        llm_infos = factory_llm_info.pop("llm")
+        try:
+            LLMFactoriesService.save(**factory_llm_info)
+        except Exception as e:
+            pass
+        LLMService.filter_delete([LLM.fid == factory_llm_info["name"]])
+        for llm_info in llm_infos:
+            llm_info["fid"] = factory_llm_info["name"]
+            try:
+                LLMService.save(**llm_info)
+            except Exception as e:
+                pass
+
+    LLMFactoriesService.filter_delete([LLMFactories.name == "Local"])
+    LLMService.filter_delete([LLM.fid == "Local"])
+    LLMService.filter_delete([LLM.llm_name == "qwen-vl-max"])
+    LLMService.filter_delete([LLM.fid == "Moonshot", LLM.llm_name == "flag-embedding"])
+    TenantLLMService.filter_delete([TenantLLM.llm_factory == "Moonshot", TenantLLM.llm_name == "flag-embedding"])
+    LLMFactoriesService.filter_delete([LLMFactoriesService.model.name == "QAnything"])
+    LLMService.filter_delete([LLMService.model.fid == "QAnything"])
+    TenantLLMService.filter_update([TenantLLMService.model.llm_factory == "QAnything"], {"llm_factory": "Youdao"})
+    TenantService.filter_update([1 == 1], {
+        "parser_ids": "naive:General,qa:Q&A,resume:Resume,manual:Manual,table:Table,paper:Paper,book:Book,laws:Laws,presentation:Presentation,picture:Picture,one:One,audio:Audio,knowledge_graph:Knowledge Graph,email:Email"})
+    ## insert openai two embedding models to the current openai user.
+    print("Start to insert 2 OpenAI embedding models...")
+    tenant_ids = set([row["tenant_id"] for row in TenantLLMService.get_openai_models()])
+    for tid in tenant_ids:
+        for row in TenantLLMService.query(llm_factory="OpenAI", tenant_id=tid):
+            row = row.to_dict()
+            row["model_type"] = LLMType.EMBEDDING.value
+            row["llm_name"] = "text-embedding-3-small"
+            row["used_tokens"] = 0
+            try:
+                TenantLLMService.save(**row)
+                row = deepcopy(row)
+                row["llm_name"] = "text-embedding-3-large"
+                TenantLLMService.save(**row)
+            except Exception as e:
+                pass
+            break
+    for kb_id in KnowledgebaseService.get_all_ids():
+        KnowledgebaseService.update_by_id(kb_id, {"doc_num": DocumentService.get_kb_doc_count(kb_id)})
+    """
+    drop table llm;
+    drop table llm_factories;
+    update tenant set parser_ids='naive:General,qa:Q&A,resume:Resume,manual:Manual,table:Table,paper:Paper,book:Book,laws:Laws,presentation:Presentation,picture:Picture,one:One,audio:Audio,knowledge_graph:Knowledge Graph';
+    alter table knowledgebase modify avatar longtext;
+    alter table user modify avatar longtext;
+    alter table dialog modify icon longtext;
+    """
+
+
+def add_graph_templates():
+    dir = os.path.join(get_project_base_directory(), "agent", "templates")
+    for fnm in os.listdir(dir):
+        try:
+            cnvs = json.load(open(os.path.join(dir, fnm), "r"))
+            try:
+                CanvasTemplateService.save(**cnvs)
+            except:
+                CanvasTemplateService.update_by_id(cnvs["id"], cnvs)
+        except Exception as e:
+            print("Add graph templates error: ", e)
+            print("------------", flush=True)
+
+
+def init_web_data():
+    start_time = time.time()
+
+    init_llm_factory()
+    #if not UserService.get_all().count():
+    #    init_superuser()
+
+    add_graph_templates()
+    print("init web data success:{}".format(time.time() - start_time))
+
+
+if __name__ == '__main__':
+    init_web_db()
+    init_web_data()

api/db/operatioins.py

@@ -1,21 +1,21 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-
-import operator
-import time
-import typing
-from api.utils.log_utils import sql_logger
-import peewee
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+import operator
+import time
+import typing
+from api.utils.log_utils import sql_logger
+import peewee

api/db/reload_config_base.py

@@ -1,28 +1,28 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-class ReloadConfigBase:
-    @classmethod
-    def get_all(cls):
-        configs = {}
-        for k, v in cls.__dict__.items():
-            if not callable(getattr(cls, k)) and not k.startswith(
-                    "__") and not k.startswith("_"):
-                configs[k] = v
-        return configs
-
-    @classmethod
-    def get(cls, config_name):
-        return getattr(cls, config_name) if hasattr(cls, config_name) else None
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+class ReloadConfigBase:
+    @classmethod
+    def get_all(cls):
+        configs = {}
+        for k, v in cls.__dict__.items():
+            if not callable(getattr(cls, k)) and not k.startswith(
+                    "__") and not k.startswith("_"):
+                configs[k] = v
+        return configs
+
+    @classmethod
+    def get(cls, config_name):
+        return getattr(cls, config_name) if hasattr(cls, config_name) else None

api/db/runtime_config.py

@@ -1,54 +1,54 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from api.versions import get_versions
-from .reload_config_base import ReloadConfigBase
-
-
-class RuntimeConfig(ReloadConfigBase):
-    DEBUG = None
-    WORK_MODE = None
-    HTTP_PORT = None
-    JOB_SERVER_HOST = None
-    JOB_SERVER_VIP = None
-    ENV = dict()
-    SERVICE_DB = None
-    LOAD_CONFIG_MANAGER = False
-
-    @classmethod
-    def init_config(cls, **kwargs):
-        for k, v in kwargs.items():
-            if hasattr(cls, k):
-                setattr(cls, k, v)
-
-    @classmethod
-    def init_env(cls):
-        cls.ENV.update(get_versions())
-
-    @classmethod
-    def load_config_manager(cls):
-        cls.LOAD_CONFIG_MANAGER = True
-
-    @classmethod
-    def get_env(cls, key):
-        return cls.ENV.get(key, None)
-
-    @classmethod
-    def get_all_env(cls):
-        return cls.ENV
-
-    @classmethod
-    def set_service_db(cls, service_db):
-        cls.SERVICE_DB = service_db
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from api.versions import get_versions
+from .reload_config_base import ReloadConfigBase
+
+
+class RuntimeConfig(ReloadConfigBase):
+    DEBUG = None
+    WORK_MODE = None
+    HTTP_PORT = None
+    JOB_SERVER_HOST = None
+    JOB_SERVER_VIP = None
+    ENV = dict()
+    SERVICE_DB = None
+    LOAD_CONFIG_MANAGER = False
+
+    @classmethod
+    def init_config(cls, **kwargs):
+        for k, v in kwargs.items():
+            if hasattr(cls, k):
+                setattr(cls, k, v)
+
+    @classmethod
+    def init_env(cls):
+        cls.ENV.update(get_versions())
+
+    @classmethod
+    def load_config_manager(cls):
+        cls.LOAD_CONFIG_MANAGER = True
+
+    @classmethod
+    def get_env(cls, key):
+        return cls.ENV.get(key, None)
+
+    @classmethod
+    def get_all_env(cls):
+        return cls.ENV
+
+    @classmethod
+    def set_service_db(cls, service_db):
+        cls.SERVICE_DB = service_db

api/db/services/__init__.py

@@ -1,38 +1,38 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import pathlib
-import re
-from .user_service import UserService
-
-
-def duplicate_name(query_func, **kwargs):
-    fnm = kwargs["name"]
-    objs = query_func(**kwargs)
-    if not objs: return fnm
-    ext = pathlib.Path(fnm).suffix #.jpg
-    nm = re.sub(r"%s$"%ext, "", fnm)
-    r = re.search(r"\(([0-9]+)\)$", nm)
-    c = 0
-    if r:
-        c = int(r.group(1))
-        nm = re.sub(r"\([0-9]+\)$", "", nm)
-    c += 1
-    nm = f"{nm}({c})"
-    if ext: nm += f"{ext}"
-
-    kwargs["name"] = nm
-    return duplicate_name(query_func, **kwargs)
-
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import pathlib
+import re
+from .user_service import UserService
+
+
+def duplicate_name(query_func, **kwargs):
+    fnm = kwargs["name"]
+    objs = query_func(**kwargs)
+    if not objs: return fnm
+    ext = pathlib.Path(fnm).suffix #.jpg
+    nm = re.sub(r"%s$"%ext, "", fnm)
+    r = re.search(r"\(([0-9]+)\)$", nm)
+    c = 0
+    if r:
+        c = int(r.group(1))
+        nm = re.sub(r"\([0-9]+\)$", "", nm)
+    c += 1
+    nm = f"{nm}({c})"
+    if ext: nm += f"{ext}"
+
+    kwargs["name"] = nm
+    return duplicate_name(query_func, **kwargs)
+

api/db/services/api_service.py

@@ -1,66 +1,70 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from datetime import datetime
-import peewee
-from api.db.db_models import DB, API4Conversation, APIToken, Dialog
-from api.db.services.common_service import CommonService
-from api.utils import current_timestamp, datetime_format
-
-
-class APITokenService(CommonService):
-    model = APIToken
-
-    @classmethod
-    @DB.connection_context()
-    def used(cls, token):
-        return cls.model.update({
-            "update_time": current_timestamp(),
-            "update_date": datetime_format(datetime.now()),
-        }).where(
-            cls.model.token == token
-        )
-
-
-class API4ConversationService(CommonService):
-    model = API4Conversation
-
-    @classmethod
-    @DB.connection_context()
-    def append_message(cls, id, conversation):
-        cls.update_by_id(id, conversation)
-        return cls.model.update(round=cls.model.round + 1).where(cls.model.id==id).execute()
-
-    @classmethod
-    @DB.connection_context()
-    def stats(cls, tenant_id, from_date, to_date):
-        return cls.model.select(
-            cls.model.create_date.truncate("day").alias("dt"),
-            peewee.fn.COUNT(
-                cls.model.id).alias("pv"),
-            peewee.fn.COUNT(
-                cls.model.user_id.distinct()).alias("uv"),
-            peewee.fn.SUM(
-                cls.model.tokens).alias("tokens"),
-            peewee.fn.SUM(
-                cls.model.duration).alias("duration"),
-            peewee.fn.AVG(
-                cls.model.round).alias("round"),
-            peewee.fn.SUM(
-                cls.model.thumb_up).alias("thumb_up")
-        ).join(Dialog, on=(cls.model.dialog_id == Dialog.id & Dialog.tenant_id == tenant_id)).where(
-            cls.model.create_date >= from_date,
-            cls.model.create_date <= to_date
-        ).group_by(cls.model.create_date.truncate("day")).dicts()
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from datetime import datetime
+
+import peewee
+
+from api.db.db_models import DB, API4Conversation, APIToken, Dialog
+from api.db.services.common_service import CommonService
+from api.utils import current_timestamp, datetime_format
+
+
+class APITokenService(CommonService):
+    model = APIToken
+
+    @classmethod
+    @DB.connection_context()
+    def used(cls, token):
+        return cls.model.update({
+            "update_time": current_timestamp(),
+            "update_date": datetime_format(datetime.now()),
+        }).where(
+            cls.model.token == token
+        )
+
+
+class API4ConversationService(CommonService):
+    model = API4Conversation
+
+    @classmethod
+    @DB.connection_context()
+    def append_message(cls, id, conversation):
+        cls.update_by_id(id, conversation)
+        return cls.model.update(round=cls.model.round + 1).where(cls.model.id == id).execute()
+
+    @classmethod
+    @DB.connection_context()
+    def stats(cls, tenant_id, from_date, to_date, source=None):
+        if len(to_date) == 10: to_date += " 23:59:59"
+        return cls.model.select(
+            cls.model.create_date.truncate("day").alias("dt"),
+            peewee.fn.COUNT(
+                cls.model.id).alias("pv"),
+            peewee.fn.COUNT(
+                cls.model.user_id.distinct()).alias("uv"),
+            peewee.fn.SUM(
+                cls.model.tokens).alias("tokens"),
+            peewee.fn.SUM(
+                cls.model.duration).alias("duration"),
+            peewee.fn.AVG(
+                cls.model.round).alias("round"),
+            peewee.fn.SUM(
+                cls.model.thumb_up).alias("thumb_up")
+        ).join(Dialog, on=((cls.model.dialog_id == Dialog.id) & (Dialog.tenant_id == tenant_id))).where(
+            cls.model.create_date >= from_date,
+            cls.model.create_date <= to_date,
+            cls.model.source == source
+        ).group_by(cls.model.create_date.truncate("day")).dicts()

api/db/services/common_service.py

@@ -1,183 +1,183 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from datetime import datetime
-
-import peewee
-
-from api.db.db_models import DB
-from api.utils import datetime_format, current_timestamp, get_uuid
-
-
-class CommonService:
-    model = None
-
-    @classmethod
-    @DB.connection_context()
-    def query(cls, cols=None, reverse=None, order_by=None, **kwargs):
-        return cls.model.query(cols=cols, reverse=reverse,
-                               order_by=order_by, **kwargs)
-
-    @classmethod
-    @DB.connection_context()
-    def get_all(cls, cols=None, reverse=None, order_by=None):
-        if cols:
-            query_records = cls.model.select(*cols)
-        else:
-            query_records = cls.model.select()
-        if reverse is not None:
-            if not order_by or not hasattr(cls, order_by):
-                order_by = "create_time"
-            if reverse is True:
-                query_records = query_records.order_by(
-                    cls.model.getter_by(order_by).desc())
-            elif reverse is False:
-                query_records = query_records.order_by(
-                    cls.model.getter_by(order_by).asc())
-        return query_records
-
-    @classmethod
-    @DB.connection_context()
-    def get(cls, **kwargs):
-        return cls.model.get(**kwargs)
-
-    @classmethod
-    @DB.connection_context()
-    def get_or_none(cls, **kwargs):
-        try:
-            return cls.model.get(**kwargs)
-        except peewee.DoesNotExist:
-            return None
-
-    @classmethod
-    @DB.connection_context()
-    def save(cls, **kwargs):
-        # if "id" not in kwargs:
-        #    kwargs["id"] = get_uuid()
-        sample_obj = cls.model(**kwargs).save(force_insert=True)
-        return sample_obj
-
-    @classmethod
-    @DB.connection_context()
-    def insert(cls, **kwargs):
-        if "id" not in kwargs:
-            kwargs["id"] = get_uuid()
-        kwargs["create_time"] = current_timestamp()
-        kwargs["create_date"] = datetime_format(datetime.now())
-        kwargs["update_time"] = current_timestamp()
-        kwargs["update_date"] = datetime_format(datetime.now())
-        sample_obj = cls.model(**kwargs).save(force_insert=True)
-        return sample_obj
-
-    @classmethod
-    @DB.connection_context()
-    def insert_many(cls, data_list, batch_size=100):
-        with DB.atomic():
-            for d in data_list:
-                d["create_time"] = current_timestamp()
-                d["create_date"] = datetime_format(datetime.now())
-            for i in range(0, len(data_list), batch_size):
-                cls.model.insert_many(data_list[i:i + batch_size]).execute()
-
-    @classmethod
-    @DB.connection_context()
-    def update_many_by_id(cls, data_list):
-        with DB.atomic():
-            for data in data_list:
-                data["update_time"] = current_timestamp()
-                data["update_date"] = datetime_format(datetime.now())
-                cls.model.update(data).where(
-                    cls.model.id == data["id"]).execute()
-
-    @classmethod
-    @DB.connection_context()
-    def update_by_id(cls, pid, data):
-        data["update_time"] = current_timestamp()
-        data["update_date"] = datetime_format(datetime.now())
-        num = cls.model.update(data).where(cls.model.id == pid).execute()
-        return num
-
-    @classmethod
-    @DB.connection_context()
-    def get_by_id(cls, pid):
-        try:
-            obj = cls.model.query(id=pid)[0]
-            return True, obj
-        except Exception as e:
-            return False, None
-
-    @classmethod
-    @DB.connection_context()
-    def get_by_ids(cls, pids, cols=None):
-        if cols:
-            objs = cls.model.select(*cols)
-        else:
-            objs = cls.model.select()
-        return objs.where(cls.model.id.in_(pids))
-
-    @classmethod
-    @DB.connection_context()
-    def delete_by_id(cls, pid):
-        return cls.model.delete().where(cls.model.id == pid).execute()
-
-    @classmethod
-    @DB.connection_context()
-    def filter_delete(cls, filters):
-        with DB.atomic():
-            num = cls.model.delete().where(*filters).execute()
-            return num
-
-    @classmethod
-    @DB.connection_context()
-    def filter_update(cls, filters, update_data):
-        with DB.atomic():
-            return cls.model.update(update_data).where(*filters).execute()
-
-    @staticmethod
-    def cut_list(tar_list, n):
-        length = len(tar_list)
-        arr = range(length)
-        result = [tuple(tar_list[x:(x + n)]) for x in arr[::n]]
-        return result
-
-    @classmethod
-    @DB.connection_context()
-    def filter_scope_list(cls, in_key, in_filters_list,
-                          filters=None, cols=None):
-        in_filters_tuple_list = cls.cut_list(in_filters_list, 20)
-        if not filters:
-            filters = []
-        res_list = []
-        if cols:
-            for i in in_filters_tuple_list:
-                query_records = cls.model.select(
-                    *
-                    cols).where(
-                    getattr(
-                        cls.model,
-                        in_key).in_(i),
-                    *
-                    filters)
-                if query_records:
-                    res_list.extend(
-                        [query_record for query_record in query_records])
-        else:
-            for i in in_filters_tuple_list:
-                query_records = cls.model.select().where(
-                    getattr(cls.model, in_key).in_(i), *filters)
-                if query_records:
-                    res_list.extend(
-                        [query_record for query_record in query_records])
-        return res_list
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from datetime import datetime
+
+import peewee
+
+from api.db.db_models import DB
+from api.utils import datetime_format, current_timestamp, get_uuid
+
+
+class CommonService:
+    model = None
+
+    @classmethod
+    @DB.connection_context()
+    def query(cls, cols=None, reverse=None, order_by=None, **kwargs):
+        return cls.model.query(cols=cols, reverse=reverse,
+                               order_by=order_by, **kwargs)
+
+    @classmethod
+    @DB.connection_context()
+    def get_all(cls, cols=None, reverse=None, order_by=None):
+        if cols:
+            query_records = cls.model.select(*cols)
+        else:
+            query_records = cls.model.select()
+        if reverse is not None:
+            if not order_by or not hasattr(cls, order_by):
+                order_by = "create_time"
+            if reverse is True:
+                query_records = query_records.order_by(
+                    cls.model.getter_by(order_by).desc())
+            elif reverse is False:
+                query_records = query_records.order_by(
+                    cls.model.getter_by(order_by).asc())
+        return query_records
+
+    @classmethod
+    @DB.connection_context()
+    def get(cls, **kwargs):
+        return cls.model.get(**kwargs)
+
+    @classmethod
+    @DB.connection_context()
+    def get_or_none(cls, **kwargs):
+        try:
+            return cls.model.get(**kwargs)
+        except peewee.DoesNotExist:
+            return None
+
+    @classmethod
+    @DB.connection_context()
+    def save(cls, **kwargs):
+        # if "id" not in kwargs:
+        #    kwargs["id"] = get_uuid()
+        sample_obj = cls.model(**kwargs).save(force_insert=True)
+        return sample_obj
+
+    @classmethod
+    @DB.connection_context()
+    def insert(cls, **kwargs):
+        if "id" not in kwargs:
+            kwargs["id"] = get_uuid()
+        kwargs["create_time"] = current_timestamp()
+        kwargs["create_date"] = datetime_format(datetime.now())
+        kwargs["update_time"] = current_timestamp()
+        kwargs["update_date"] = datetime_format(datetime.now())
+        sample_obj = cls.model(**kwargs).save(force_insert=True)
+        return sample_obj
+
+    @classmethod
+    @DB.connection_context()
+    def insert_many(cls, data_list, batch_size=100):
+        with DB.atomic():
+            for d in data_list:
+                d["create_time"] = current_timestamp()
+                d["create_date"] = datetime_format(datetime.now())
+            for i in range(0, len(data_list), batch_size):
+                cls.model.insert_many(data_list[i:i + batch_size]).execute()
+
+    @classmethod
+    @DB.connection_context()
+    def update_many_by_id(cls, data_list):
+        with DB.atomic():
+            for data in data_list:
+                data["update_time"] = current_timestamp()
+                data["update_date"] = datetime_format(datetime.now())
+                cls.model.update(data).where(
+                    cls.model.id == data["id"]).execute()
+
+    @classmethod
+    @DB.connection_context()
+    def update_by_id(cls, pid, data):
+        data["update_time"] = current_timestamp()
+        data["update_date"] = datetime_format(datetime.now())
+        num = cls.model.update(data).where(cls.model.id == pid).execute()
+        return num
+
+    @classmethod
+    @DB.connection_context()
+    def get_by_id(cls, pid):
+        try:
+            obj = cls.model.query(id=pid)[0]
+            return True, obj
+        except Exception as e:
+            return False, None
+
+    @classmethod
+    @DB.connection_context()
+    def get_by_ids(cls, pids, cols=None):
+        if cols:
+            objs = cls.model.select(*cols)
+        else:
+            objs = cls.model.select()
+        return objs.where(cls.model.id.in_(pids))
+
+    @classmethod
+    @DB.connection_context()
+    def delete_by_id(cls, pid):
+        return cls.model.delete().where(cls.model.id == pid).execute()
+
+    @classmethod
+    @DB.connection_context()
+    def filter_delete(cls, filters):
+        with DB.atomic():
+            num = cls.model.delete().where(*filters).execute()
+            return num
+
+    @classmethod
+    @DB.connection_context()
+    def filter_update(cls, filters, update_data):
+        with DB.atomic():
+            return cls.model.update(update_data).where(*filters).execute()
+
+    @staticmethod
+    def cut_list(tar_list, n):
+        length = len(tar_list)
+        arr = range(length)
+        result = [tuple(tar_list[x:(x + n)]) for x in arr[::n]]
+        return result
+
+    @classmethod
+    @DB.connection_context()
+    def filter_scope_list(cls, in_key, in_filters_list,
+                          filters=None, cols=None):
+        in_filters_tuple_list = cls.cut_list(in_filters_list, 20)
+        if not filters:
+            filters = []
+        res_list = []
+        if cols:
+            for i in in_filters_tuple_list:
+                query_records = cls.model.select(
+                    *
+                    cols).where(
+                    getattr(
+                        cls.model,
+                        in_key).in_(i),
+                    *
+                    filters)
+                if query_records:
+                    res_list.extend(
+                        [query_record for query_record in query_records])
+        else:
+            for i in in_filters_tuple_list:
+                query_records = cls.model.select().where(
+                    getattr(cls.model, in_key).in_(i), *filters)
+                if query_records:
+                    res_list.extend(
+                        [query_record for query_record in query_records])
+        return res_list

api/db/services/dialog_service.py

@@ -1,385 +1,546 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import os
-import json
-import re
-from copy import deepcopy
-
-from api.db import LLMType, ParserType
-from api.db.db_models import Dialog, Conversation
-from api.db.services.common_service import CommonService
-from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.services.llm_service import LLMService, TenantLLMService, LLMBundle
-from api.settings import chat_logger, retrievaler, kg_retrievaler
-from rag.app.resume import forbidden_select_fields4resume
-from rag.nlp import keyword_extraction
-from rag.nlp.search import index_name
-from rag.utils import rmSpace, num_tokens_from_string, encoder
-from api.utils.file_utils import get_project_base_directory
-
-
-class DialogService(CommonService):
-    model = Dialog
-
-
-class ConversationService(CommonService):
-    model = Conversation
-
-
-def message_fit_in(msg, max_length=4000):
-    def count():
-        nonlocal msg
-        tks_cnts = []
-        for m in msg:
-            tks_cnts.append(
-                {"role": m["role"], "count": num_tokens_from_string(m["content"])})
-        total = 0
-        for m in tks_cnts:
-            total += m["count"]
-        return total
-
-    c = count()
-    if c < max_length:
-        return c, msg
-
-    msg_ = [m for m in msg[:-1] if m["role"] == "system"]
-    msg_.append(msg[-1])
-    msg = msg_
-    c = count()
-    if c < max_length:
-        return c, msg
-
-    ll = num_tokens_from_string(msg_[0]["content"])
-    l = num_tokens_from_string(msg_[-1]["content"])
-    if ll / (ll + l) > 0.8:
-        m = msg_[0]["content"]
-        m = encoder.decode(encoder.encode(m)[:max_length - l])
-        msg[0]["content"] = m
-        return max_length, msg
-
-    m = msg_[1]["content"]
-    m = encoder.decode(encoder.encode(m)[:max_length - l])
-    msg[1]["content"] = m
-    return max_length, msg
-
-
-def llm_id2llm_type(llm_id):
-    fnm = os.path.join(get_project_base_directory(), "conf")
-    llm_factories = json.load(open(os.path.join(fnm, "llm_factories.json"), "r"))
-    for llm_factory in llm_factories["factory_llm_infos"]:
-        for llm in llm_factory["llm"]:
-            if llm_id == llm["llm_name"]:
-                return llm["model_type"].strip(",")[-1]
-                
-
-def chat(dialog, messages, stream=True, **kwargs):
-    assert messages[-1]["role"] == "user", "The last content of this conversation is not from user."
-    llm = LLMService.query(llm_name=dialog.llm_id)
-    if not llm:
-        llm = TenantLLMService.query(tenant_id=dialog.tenant_id, llm_name=dialog.llm_id)
-        if not llm:
-            raise LookupError("LLM(%s) not found" % dialog.llm_id)
-        max_tokens = 8192
-    else:
-        max_tokens = llm[0].max_tokens
-    kbs = KnowledgebaseService.get_by_ids(dialog.kb_ids)
-    embd_nms = list(set([kb.embd_id for kb in kbs]))
-    if len(embd_nms) != 1:
-        yield {"answer": "**ERROR**: Knowledge bases use different embedding models.", "reference": []}
-        return {"answer": "**ERROR**: Knowledge bases use different embedding models.", "reference": []}
-
-    is_kg = all([kb.parser_id == ParserType.KG for kb in kbs])
-    retr = retrievaler if not is_kg else kg_retrievaler
-
-    questions = [m["content"] for m in messages if m["role"] == "user"]
-    embd_mdl = LLMBundle(dialog.tenant_id, LLMType.EMBEDDING, embd_nms[0])
-    if llm_id2llm_type(dialog.llm_id) == "image2text":
-        chat_mdl = LLMBundle(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id)
-    else:
-        chat_mdl = LLMBundle(dialog.tenant_id, LLMType.CHAT, dialog.llm_id)
-
-    prompt_config = dialog.prompt_config
-    field_map = KnowledgebaseService.get_field_map(dialog.kb_ids)
-    # try to use sql if field mapping is good to go
-    if field_map:
-        chat_logger.info("Use SQL to retrieval:{}".format(questions[-1]))
-        ans = use_sql(questions[-1], field_map, dialog.tenant_id, chat_mdl, prompt_config.get("quote", True))
-        if ans:
-            yield ans
-            return
-
-    for p in prompt_config["parameters"]:
-        if p["key"] == "knowledge":
-            continue
-        if p["key"] not in kwargs and not p["optional"]:
-            raise KeyError("Miss parameter: " + p["key"])
-        if p["key"] not in kwargs:
-            prompt_config["system"] = prompt_config["system"].replace(
-                "{%s}" % p["key"], " ")
-
-    rerank_mdl = None
-    if dialog.rerank_id:
-        rerank_mdl = LLMBundle(dialog.tenant_id, LLMType.RERANK, dialog.rerank_id)
-
-    for _ in range(len(questions) // 2):
-        questions.append(questions[-1])
-    if "knowledge" not in [p["key"] for p in prompt_config["parameters"]]:
-        kbinfos = {"total": 0, "chunks": [], "doc_aggs": []}
-    else:
-        if prompt_config.get("keyword", False):
-            questions[-1] += keyword_extraction(chat_mdl, questions[-1])
-        kbinfos = retr.retrieval(" ".join(questions), embd_mdl, dialog.tenant_id, dialog.kb_ids, 1, dialog.top_n,
-                                        dialog.similarity_threshold,
-                                        dialog.vector_similarity_weight,
-                                        doc_ids=kwargs["doc_ids"].split(",") if "doc_ids" in kwargs else None,
-                                        top=dialog.top_k, aggs=False, rerank_mdl=rerank_mdl)
-    knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
-    #self-rag
-    if dialog.prompt_config.get("self_rag") and not relevant(dialog.tenant_id, dialog.llm_id, questions[-1], knowledges):
-        questions[-1] = rewrite(dialog.tenant_id, dialog.llm_id, questions[-1])
-        kbinfos = retr.retrieval(" ".join(questions), embd_mdl, dialog.tenant_id, dialog.kb_ids, 1, dialog.top_n,
-                                        dialog.similarity_threshold,
-                                        dialog.vector_similarity_weight,
-                                        doc_ids=kwargs["doc_ids"].split(",") if "doc_ids" in kwargs else None,
-                                        top=dialog.top_k, aggs=False, rerank_mdl=rerank_mdl)
-        knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
-
-    chat_logger.info(
-        "{}->{}".format(" ".join(questions), "\n->".join(knowledges)))
-
-    if not knowledges and prompt_config.get("empty_response"):
-        yield {"answer": prompt_config["empty_response"], "reference": kbinfos}
-        return {"answer": prompt_config["empty_response"], "reference": kbinfos}
-
-    kwargs["knowledge"] = "\n".join(knowledges)
-    gen_conf = dialog.llm_setting
-
-    msg = [{"role": "system", "content": prompt_config["system"].format(**kwargs)}]
-    msg.extend([{"role": m["role"], "content": m["content"]}
-                for m in messages if m["role"] != "system"])
-    used_token_count, msg = message_fit_in(msg, int(max_tokens * 0.97))
-    assert len(msg) >= 2, f"message_fit_in has bug: {msg}"
-
-    if "max_tokens" in gen_conf:
-        gen_conf["max_tokens"] = min(
-            gen_conf["max_tokens"],
-            max_tokens - used_token_count)
-
-    def decorate_answer(answer):
-        nonlocal prompt_config, knowledges, kwargs, kbinfos
-        refs = []
-        if knowledges and (prompt_config.get("quote", True) and kwargs.get("quote", True)):
-            answer, idx = retr.insert_citations(answer,
-                                                       [ck["content_ltks"]
-                                                        for ck in kbinfos["chunks"]],
-                                                       [ck["vector"]
-                                                        for ck in kbinfos["chunks"]],
-                                                       embd_mdl,
-                                                       tkweight=1 - dialog.vector_similarity_weight,
-                                                       vtweight=dialog.vector_similarity_weight)
-            idx = set([kbinfos["chunks"][int(i)]["doc_id"] for i in idx])
-            recall_docs = [
-                d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx]
-            if not recall_docs: recall_docs = kbinfos["doc_aggs"]
-            kbinfos["doc_aggs"] = recall_docs
-
-            refs = deepcopy(kbinfos)
-            for c in refs["chunks"]:
-                if c.get("vector"):
-                    del c["vector"]
-
-        if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0:
-            answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'"
-        return {"answer": answer, "reference": refs}
-
-    if stream:
-        answer = ""
-        for ans in chat_mdl.chat_streamly(msg[0]["content"], msg[1:], gen_conf):
-            answer = ans
-            yield {"answer": answer, "reference": {}}
-        yield decorate_answer(answer)
-    else:
-        answer = chat_mdl.chat(
-            msg[0]["content"], msg[1:], gen_conf)
-        chat_logger.info("User: {}|Assistant: {}".format(
-            msg[-1]["content"], answer))
-        yield decorate_answer(answer)
-
-
-def use_sql(question, field_map, tenant_id, chat_mdl, quota=True):
-    sys_prompt = "你是一个DBA。你需要这对以下表的字段结构，根据用户的问题列表，写出最后一个问题对应的SQL。"
-    user_promt = """
-表名：{}；
-数据库表字段说明如下：
-{}
-
-问题如下：
-{}
-请写出SQL, 且只要SQL，不要有其他说明及文字。
-""".format(
-        index_name(tenant_id),
-        "\n".join([f"{k}: {v}" for k, v in field_map.items()]),
-        question
-    )
-    tried_times = 0
-
-    def get_table():
-        nonlocal sys_prompt, user_promt, question, tried_times
-        sql = chat_mdl.chat(sys_prompt, [{"role": "user", "content": user_promt}], {
-            "temperature": 0.06})
-        print(user_promt, sql)
-        chat_logger.info(f"“{question}”==>{user_promt} get SQL: {sql}")
-        sql = re.sub(r"[\r\n]+", " ", sql.lower())
-        sql = re.sub(r".*select ", "select ", sql.lower())
-        sql = re.sub(r" +", " ", sql)
-        sql = re.sub(r"([;；]|```).*", "", sql)
-        if sql[:len("select ")] != "select ":
-            return None, None
-        if not re.search(r"((sum|avg|max|min)\(|group by )", sql.lower()):
-            if sql[:len("select *")] != "select *":
-                sql = "select doc_id,docnm_kwd," + sql[6:]
-            else:
-                flds = []
-                for k in field_map.keys():
-                    if k in forbidden_select_fields4resume:
-                        continue
-                    if len(flds) > 11:
-                        break
-                    flds.append(k)
-                sql = "select doc_id,docnm_kwd," + ",".join(flds) + sql[8:]
-
-        print(f"“{question}” get SQL(refined): {sql}")
-
-        chat_logger.info(f"“{question}” get SQL(refined): {sql}")
-        tried_times += 1
-        return retrievaler.sql_retrieval(sql, format="json"), sql
-
-    tbl, sql = get_table()
-    if tbl is None:
-        return None
-    if tbl.get("error") and tried_times <= 2:
-        user_promt = """
-        表名：{}；
-        数据库表字段说明如下：
-        {}
-
-        问题如下：
-        {}
-
-        你上一次给出的错误SQL如下：
-        {}
-
-        后台报错如下：
-        {}
-
-        请纠正SQL中的错误再写一遍，且只要SQL，不要有其他说明及文字。
-        """.format(
-            index_name(tenant_id),
-            "\n".join([f"{k}: {v}" for k, v in field_map.items()]),
-            question, sql, tbl["error"]
-        )
-        tbl, sql = get_table()
-        chat_logger.info("TRY it again: {}".format(sql))
-
-    chat_logger.info("GET table: {}".format(tbl))
-    print(tbl)
-    if tbl.get("error") or len(tbl["rows"]) == 0:
-        return None
-
-    docid_idx = set([ii for ii, c in enumerate(
-        tbl["columns"]) if c["name"] == "doc_id"])
-    docnm_idx = set([ii for ii, c in enumerate(
-        tbl["columns"]) if c["name"] == "docnm_kwd"])
-    clmn_idx = [ii for ii in range(
-        len(tbl["columns"])) if ii not in (docid_idx | docnm_idx)]
-
-    # compose markdown table
-    clmns = "|" + "|".join([re.sub(r"(/.*|（[^（）]+）)", "", field_map.get(tbl["columns"][i]["name"],
-                                                                        tbl["columns"][i]["name"])) for i in
-                            clmn_idx]) + ("|Source|" if docid_idx and docid_idx else "|")
-
-    line = "|" + "|".join(["------" for _ in range(len(clmn_idx))]) + \
-           ("|------|" if docid_idx and docid_idx else "")
-
-    rows = ["|" +
-            "|".join([rmSpace(str(r[i])) for i in clmn_idx]).replace("None", " ") +
-            "|" for r in tbl["rows"]]
-    if quota:
-        rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)])
-    else:
-        rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)])
-    rows = re.sub(r"T[0-9]{2}:[0-9]{2}:[0-9]{2}(\.[0-9]+Z)?\|", "|", rows)
-
-    if not docid_idx or not docnm_idx:
-        chat_logger.warning("SQL missing field: " + sql)
-        return {
-            "answer": "\n".join([clmns, line, rows]),
-            "reference": {"chunks": [], "doc_aggs": []}
-        }
-
-    docid_idx = list(docid_idx)[0]
-    docnm_idx = list(docnm_idx)[0]
-    doc_aggs = {}
-    for r in tbl["rows"]:
-        if r[docid_idx] not in doc_aggs:
-            doc_aggs[r[docid_idx]] = {"doc_name": r[docnm_idx], "count": 0}
-        doc_aggs[r[docid_idx]]["count"] += 1
-    return {
-        "answer": "\n".join([clmns, line, rows]),
-        "reference": {"chunks": [{"doc_id": r[docid_idx], "docnm_kwd": r[docnm_idx]} for r in tbl["rows"]],
-                      "doc_aggs": [{"doc_id": did, "doc_name": d["doc_name"], "count": d["count"]} for did, d in
-                                   doc_aggs.items()]}
-    }
-
-
-def relevant(tenant_id, llm_id, question, contents: list):
-    if llm_id2llm_type(llm_id) == "image2text":
-        chat_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, llm_id)
-    else:
-        chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id)
-    prompt = """
-        You are a grader assessing relevance of a retrieved document to a user question. 
-        It does not need to be a stringent test. The goal is to filter out erroneous retrievals.
-        If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. 
-        Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.
-        No other words needed except 'yes' or 'no'.
-    """
-    if not contents:return False
-    contents = "Documents: \n" + "   - ".join(contents)
-    contents = f"Question: {question}\n" + contents
-    if num_tokens_from_string(contents) >= chat_mdl.max_length - 4:
-        contents = encoder.decode(encoder.encode(contents)[:chat_mdl.max_length - 4])
-    ans = chat_mdl.chat(prompt, [{"role": "user", "content": contents}], {"temperature": 0.01})
-    if ans.lower().find("yes") >= 0: return True
-    return False
-
-
-def rewrite(tenant_id, llm_id, question):
-    if llm_id2llm_type(llm_id) == "image2text":
-        chat_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, llm_id)
-    else:
-        chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id)
-    prompt = """
-        You are an expert at query expansion to generate a paraphrasing of a question.
-        I can't retrieval relevant information from the knowledge base by using user's question directly.     
-        You need to expand or paraphrase user's question by multiple ways such as using synonyms words/phrase, 
-        writing the abbreviation in its entirety, adding some extra descriptions or explanations, 
-        changing the way of expression, translating the original question into another language (English/Chinese), etc. 
-        And return 5 versions of question and one is from translation.
-        Just list the question. No other words are needed.
-    """
-    ans = chat_mdl.chat(prompt, [{"role": "user", "content": question}], {"temperature": 0.8})
-    return ans
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import binascii
+import os
+import json
+import re
+from copy import deepcopy
+from timeit import default_timer as timer
+from api.db import LLMType, ParserType
+from api.db.db_models import Dialog, Conversation
+from api.db.services.common_service import CommonService
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.services.llm_service import LLMService, TenantLLMService, LLMBundle
+from api.settings import chat_logger, retrievaler, kg_retrievaler
+from rag.app.resume import forbidden_select_fields4resume
+from rag.nlp import keyword_extraction
+from rag.nlp.search import index_name
+from rag.utils import rmSpace, num_tokens_from_string, encoder
+from api.utils.file_utils import get_project_base_directory
+
+
+class DialogService(CommonService):
+    model = Dialog
+
+
+class ConversationService(CommonService):
+    model = Conversation
+
+
+def message_fit_in(msg, max_length=4000):
+    def count():
+        nonlocal msg
+        tks_cnts = []
+        for m in msg:
+            tks_cnts.append(
+                {"role": m["role"], "count": num_tokens_from_string(m["content"])})
+        total = 0
+        for m in tks_cnts:
+            total += m["count"]
+        return total
+
+    c = count()
+    if c < max_length:
+        return c, msg
+
+    msg_ = [m for m in msg[:-1] if m["role"] == "system"]
+    msg_.append(msg[-1])
+    msg = msg_
+    c = count()
+    if c < max_length:
+        return c, msg
+
+    ll = num_tokens_from_string(msg_[0]["content"])
+    l = num_tokens_from_string(msg_[-1]["content"])
+    if ll / (ll + l) > 0.8:
+        m = msg_[0]["content"]
+        m = encoder.decode(encoder.encode(m)[:max_length - l])
+        msg[0]["content"] = m
+        return max_length, msg
+
+    m = msg_[1]["content"]
+    m = encoder.decode(encoder.encode(m)[:max_length - l])
+    msg[1]["content"] = m
+    return max_length, msg
+
+
+def llm_id2llm_type(llm_id):
+    llm_id = llm_id.split("@")[0]
+    fnm = os.path.join(get_project_base_directory(), "conf")
+    llm_factories = json.load(open(os.path.join(fnm, "llm_factories.json"), "r"))
+    for llm_factory in llm_factories["factory_llm_infos"]:
+        for llm in llm_factory["llm"]:
+            if llm_id == llm["llm_name"]:
+                return llm["model_type"].strip(",")[-1]
+                
+
+def chat(dialog, messages, stream=True, **kwargs):
+    assert messages[-1]["role"] == "user", "The last content of this conversation is not from user."
+    st = timer()
+    tmp = dialog.llm_id.split("@")
+    fid = None
+    llm_id = tmp[0]
+    if len(tmp)>1: fid = tmp[1]
+
+    llm = LLMService.query(llm_name=llm_id) if not fid else LLMService.query(llm_name=llm_id, fid=fid)
+    if not llm:
+        llm = TenantLLMService.query(tenant_id=dialog.tenant_id, llm_name=llm_id) if not fid else \
+            TenantLLMService.query(tenant_id=dialog.tenant_id, llm_name=llm_id, llm_factory=fid)
+        if not llm:
+            raise LookupError("LLM(%s) not found" % dialog.llm_id)
+        max_tokens = 8192
+    else:
+        max_tokens = llm[0].max_tokens
+    kbs = KnowledgebaseService.get_by_ids(dialog.kb_ids)
+    embd_nms = list(set([kb.embd_id for kb in kbs]))
+    if len(embd_nms) != 1:
+        yield {"answer": "**ERROR**: Knowledge bases use different embedding models.", "reference": []}
+        return {"answer": "**ERROR**: Knowledge bases use different embedding models.", "reference": []}
+
+    is_kg = all([kb.parser_id == ParserType.KG for kb in kbs])
+    retr = retrievaler if not is_kg else kg_retrievaler
+
+    questions = [m["content"] for m in messages if m["role"] == "user"][-3:]
+    attachments = kwargs["doc_ids"].split(",") if "doc_ids" in kwargs else None
+    if "doc_ids" in messages[-1]:
+        attachments = messages[-1]["doc_ids"]
+        for m in messages[:-1]:
+            if "doc_ids" in m:
+                attachments.extend(m["doc_ids"])
+
+    embd_mdl = LLMBundle(dialog.tenant_id, LLMType.EMBEDDING, embd_nms[0])
+    if llm_id2llm_type(dialog.llm_id) == "image2text":
+        chat_mdl = LLMBundle(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id)
+    else:
+        chat_mdl = LLMBundle(dialog.tenant_id, LLMType.CHAT, dialog.llm_id)
+
+    prompt_config = dialog.prompt_config
+    field_map = KnowledgebaseService.get_field_map(dialog.kb_ids)
+    tts_mdl = None
+    if prompt_config.get("tts"):
+        tts_mdl = LLMBundle(dialog.tenant_id, LLMType.TTS)
+    # try to use sql if field mapping is good to go
+    if field_map:
+        chat_logger.info("Use SQL to retrieval:{}".format(questions[-1]))
+        ans = use_sql(questions[-1], field_map, dialog.tenant_id, chat_mdl, prompt_config.get("quote", True))
+        if ans:
+            yield ans
+            return
+
+    for p in prompt_config["parameters"]:
+        if p["key"] == "knowledge":
+            continue
+        if p["key"] not in kwargs and not p["optional"]:
+            raise KeyError("Miss parameter: " + p["key"])
+        if p["key"] not in kwargs:
+            prompt_config["system"] = prompt_config["system"].replace(
+                "{%s}" % p["key"], " ")
+
+    if len(questions) > 1 and prompt_config.get("refine_multiturn"):
+        questions = [full_question(dialog.tenant_id, dialog.llm_id, messages)]
+    else:
+        questions = questions[-1:]
+
+    rerank_mdl = None
+    if dialog.rerank_id:
+        rerank_mdl = LLMBundle(dialog.tenant_id, LLMType.RERANK, dialog.rerank_id)
+
+    for _ in range(len(questions) // 2):
+        questions.append(questions[-1])
+    if "knowledge" not in [p["key"] for p in prompt_config["parameters"]]:
+        kbinfos = {"total": 0, "chunks": [], "doc_aggs": []}
+    else:
+        if prompt_config.get("keyword", False):
+            questions[-1] += keyword_extraction(chat_mdl, questions[-1])
+        kbinfos = retr.retrieval(" ".join(questions), embd_mdl, dialog.tenant_id, dialog.kb_ids, 1, dialog.top_n,
+                                        dialog.similarity_threshold,
+                                        dialog.vector_similarity_weight,
+                                        doc_ids=attachments,
+                                        top=dialog.top_k, aggs=False, rerank_mdl=rerank_mdl)
+    knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
+    chat_logger.info(
+        "{}->{}".format(" ".join(questions), "\n->".join(knowledges)))
+    retrieval_tm = timer()
+
+    if not knowledges and prompt_config.get("empty_response"):
+        empty_res = prompt_config["empty_response"]
+        yield {"answer": empty_res, "reference": kbinfos, "audio_binary": tts(tts_mdl, empty_res)}
+        return {"answer": prompt_config["empty_response"], "reference": kbinfos}
+
+    kwargs["knowledge"] = "\n\n------\n\n".join(knowledges)
+    gen_conf = dialog.llm_setting
+
+    msg = [{"role": "system", "content": prompt_config["system"].format(**kwargs)}]
+    msg.extend([{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])}
+                for m in messages if m["role"] != "system"])
+    used_token_count, msg = message_fit_in(msg, int(max_tokens * 0.97))
+    assert len(msg) >= 2, f"message_fit_in has bug: {msg}"
+    prompt = msg[0]["content"]
+
+    if "max_tokens" in gen_conf:
+        gen_conf["max_tokens"] = min(
+            gen_conf["max_tokens"],
+            max_tokens - used_token_count)
+
+    def decorate_answer(answer):
+        nonlocal prompt_config, knowledges, kwargs, kbinfos, prompt, retrieval_tm
+        refs = []
+        if knowledges and (prompt_config.get("quote", True) and kwargs.get("quote", True)):
+            answer, idx = retr.insert_citations(answer,
+                                                       [ck["content_ltks"]
+                                                        for ck in kbinfos["chunks"]],
+                                                       [ck["vector"]
+                                                        for ck in kbinfos["chunks"]],
+                                                       embd_mdl,
+                                                       tkweight=1 - dialog.vector_similarity_weight,
+                                                       vtweight=dialog.vector_similarity_weight)
+            idx = set([kbinfos["chunks"][int(i)]["doc_id"] for i in idx])
+            recall_docs = [
+                d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx]
+            if not recall_docs: recall_docs = kbinfos["doc_aggs"]
+            kbinfos["doc_aggs"] = recall_docs
+
+            refs = deepcopy(kbinfos)
+            for c in refs["chunks"]:
+                if c.get("vector"):
+                    del c["vector"]
+
+        if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0:
+            answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'"
+        done_tm = timer()
+        prompt += "\n\n### Elapsed\n  - Retrieval: %.1f ms\n  - LLM: %.1f ms"%((retrieval_tm-st)*1000, (done_tm-st)*1000)
+        return {"answer": answer, "reference": refs, "prompt": prompt}
+
+    if stream:
+        last_ans = ""
+        answer = ""
+        for ans in chat_mdl.chat_streamly(prompt, msg[1:], gen_conf):
+            answer = ans
+            delta_ans = ans[len(last_ans):]
+            if num_tokens_from_string(delta_ans) < 16:
+                continue
+            last_ans = answer
+            yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
+        delta_ans = answer[len(last_ans):]
+        if delta_ans:
+            yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
+        yield decorate_answer(answer)
+    else:
+        answer = chat_mdl.chat(prompt, msg[1:], gen_conf)
+        chat_logger.info("User: {}|Assistant: {}".format(
+            msg[-1]["content"], answer))
+        res = decorate_answer(answer)
+        res["audio_binary"] = tts(tts_mdl, answer)
+        yield res
+
+
+def use_sql(question, field_map, tenant_id, chat_mdl, quota=True):
+    sys_prompt = "你是一个DBA。你需要这对以下表的字段结构，根据用户的问题列表，写出最后一个问题对应的SQL。"
+    user_promt = """
+表名：{}；
+数据库表字段说明如下：
+{}
+
+问题如下：
+{}
+请写出SQL, 且只要SQL，不要有其他说明及文字。
+""".format(
+        index_name(tenant_id),
+        "\n".join([f"{k}: {v}" for k, v in field_map.items()]),
+        question
+    )
+    tried_times = 0
+
+    def get_table():
+        nonlocal sys_prompt, user_promt, question, tried_times
+        sql = chat_mdl.chat(sys_prompt, [{"role": "user", "content": user_promt}], {
+            "temperature": 0.06})
+        print(user_promt, sql)
+        chat_logger.info(f"“{question}”==>{user_promt} get SQL: {sql}")
+        sql = re.sub(r"[\r\n]+", " ", sql.lower())
+        sql = re.sub(r".*select ", "select ", sql.lower())
+        sql = re.sub(r" +", " ", sql)
+        sql = re.sub(r"([;；]|```).*", "", sql)
+        if sql[:len("select ")] != "select ":
+            return None, None
+        if not re.search(r"((sum|avg|max|min)\(|group by )", sql.lower()):
+            if sql[:len("select *")] != "select *":
+                sql = "select doc_id,docnm_kwd," + sql[6:]
+            else:
+                flds = []
+                for k in field_map.keys():
+                    if k in forbidden_select_fields4resume:
+                        continue
+                    if len(flds) > 11:
+                        break
+                    flds.append(k)
+                sql = "select doc_id,docnm_kwd," + ",".join(flds) + sql[8:]
+
+        print(f"“{question}” get SQL(refined): {sql}")
+
+        chat_logger.info(f"“{question}” get SQL(refined): {sql}")
+        tried_times += 1
+        return retrievaler.sql_retrieval(sql, format="json"), sql
+
+    tbl, sql = get_table()
+    if tbl is None:
+        return None
+    if tbl.get("error") and tried_times <= 2:
+        user_promt = """
+        表名：{}；
+        数据库表字段说明如下：
+        {}
+
+        问题如下：
+        {}
+
+        你上一次给出的错误SQL如下：
+        {}
+
+        后台报错如下：
+        {}
+
+        请纠正SQL中的错误再写一遍，且只要SQL，不要有其他说明及文字。
+        """.format(
+            index_name(tenant_id),
+            "\n".join([f"{k}: {v}" for k, v in field_map.items()]),
+            question, sql, tbl["error"]
+        )
+        tbl, sql = get_table()
+        chat_logger.info("TRY it again: {}".format(sql))
+
+    chat_logger.info("GET table: {}".format(tbl))
+    print(tbl)
+    if tbl.get("error") or len(tbl["rows"]) == 0:
+        return None
+
+    docid_idx = set([ii for ii, c in enumerate(
+        tbl["columns"]) if c["name"] == "doc_id"])
+    docnm_idx = set([ii for ii, c in enumerate(
+        tbl["columns"]) if c["name"] == "docnm_kwd"])
+    clmn_idx = [ii for ii in range(
+        len(tbl["columns"])) if ii not in (docid_idx | docnm_idx)]
+
+    # compose markdown table
+    clmns = "|" + "|".join([re.sub(r"(/.*|（[^（）]+）)", "", field_map.get(tbl["columns"][i]["name"],
+                                                                        tbl["columns"][i]["name"])) for i in
+                            clmn_idx]) + ("|Source|" if docid_idx and docid_idx else "|")
+
+    line = "|" + "|".join(["------" for _ in range(len(clmn_idx))]) + \
+           ("|------|" if docid_idx and docid_idx else "")
+
+    rows = ["|" +
+            "|".join([rmSpace(str(r[i])) for i in clmn_idx]).replace("None", " ") +
+            "|" for r in tbl["rows"]]
+    if quota:
+        rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)])
+    else:
+        rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)])
+    rows = re.sub(r"T[0-9]{2}:[0-9]{2}:[0-9]{2}(\.[0-9]+Z)?\|", "|", rows)
+
+    if not docid_idx or not docnm_idx:
+        chat_logger.warning("SQL missing field: " + sql)
+        return {
+            "answer": "\n".join([clmns, line, rows]),
+            "reference": {"chunks": [], "doc_aggs": []},
+            "prompt": sys_prompt
+        }
+
+    docid_idx = list(docid_idx)[0]
+    docnm_idx = list(docnm_idx)[0]
+    doc_aggs = {}
+    for r in tbl["rows"]:
+        if r[docid_idx] not in doc_aggs:
+            doc_aggs[r[docid_idx]] = {"doc_name": r[docnm_idx], "count": 0}
+        doc_aggs[r[docid_idx]]["count"] += 1
+    return {
+        "answer": "\n".join([clmns, line, rows]),
+        "reference": {"chunks": [{"doc_id": r[docid_idx], "docnm_kwd": r[docnm_idx]} for r in tbl["rows"]],
+                      "doc_aggs": [{"doc_id": did, "doc_name": d["doc_name"], "count": d["count"]} for did, d in
+                                   doc_aggs.items()]},
+        "prompt": sys_prompt
+    }
+
+
+def relevant(tenant_id, llm_id, question, contents: list):
+    if llm_id2llm_type(llm_id) == "image2text":
+        chat_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, llm_id)
+    else:
+        chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id)
+    prompt = """
+        You are a grader assessing relevance of a retrieved document to a user question. 
+        It does not need to be a stringent test. The goal is to filter out erroneous retrievals.
+        If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. 
+        Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.
+        No other words needed except 'yes' or 'no'.
+    """
+    if not contents:return False
+    contents = "Documents: \n" + "   - ".join(contents)
+    contents = f"Question: {question}\n" + contents
+    if num_tokens_from_string(contents) >= chat_mdl.max_length - 4:
+        contents = encoder.decode(encoder.encode(contents)[:chat_mdl.max_length - 4])
+    ans = chat_mdl.chat(prompt, [{"role": "user", "content": contents}], {"temperature": 0.01})
+    if ans.lower().find("yes") >= 0: return True
+    return False
+
+
+def rewrite(tenant_id, llm_id, question):
+    if llm_id2llm_type(llm_id) == "image2text":
+        chat_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, llm_id)
+    else:
+        chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id)
+    prompt = """
+        You are an expert at query expansion to generate a paraphrasing of a question.
+        I can't retrieval relevant information from the knowledge base by using user's question directly.     
+        You need to expand or paraphrase user's question by multiple ways such as using synonyms words/phrase, 
+        writing the abbreviation in its entirety, adding some extra descriptions or explanations, 
+        changing the way of expression, translating the original question into another language (English/Chinese), etc. 
+        And return 5 versions of question and one is from translation.
+        Just list the question. No other words are needed.
+    """
+    ans = chat_mdl.chat(prompt, [{"role": "user", "content": question}], {"temperature": 0.8})
+    return ans
+
+
+def full_question(tenant_id, llm_id, messages):
+    if llm_id2llm_type(llm_id) == "image2text":
+        chat_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, llm_id)
+    else:
+        chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id)
+    conv = []
+    for m in messages:
+        if m["role"] not in ["user", "assistant"]: continue
+        conv.append("{}: {}".format(m["role"].upper(), m["content"]))
+    conv = "\n".join(conv)
+    prompt = f"""
+Role: A helpful assistant
+Task: Generate a full user question that would follow the conversation.
+Requirements & Restrictions:
+  - Text generated MUST be in the same language of the original user's question.
+  - If the user's latest question is completely, don't do anything, just return the original question.
+  - DON'T generate anything except a refined question.
+
+######################
+-Examples-
+######################
+
+# Example 1
+## Conversation
+USER: What is the name of Donald Trump's father?
+ASSISTANT:  Fred Trump.
+USER: And his mother?
+###############
+Output: What's the name of Donald Trump's mother?
+
+------------
+# Example 2
+## Conversation
+USER: What is the name of Donald Trump's father?
+ASSISTANT:  Fred Trump.
+USER: And his mother?
+ASSISTANT:  Mary Trump.
+User: What's her full name?
+###############
+Output: What's the full name of Donald Trump's mother Mary Trump?
+
+######################
+
+# Real Data
+## Conversation
+{conv}
+###############
+    """
+    ans = chat_mdl.chat(prompt, [{"role": "user", "content": "Output: "}], {"temperature": 0.2})
+    return ans if ans.find("**ERROR**") < 0 else messages[-1]["content"]
+
+
+def tts(tts_mdl, text):
+    if not tts_mdl or not text: return
+    bin = b""
+    for chunk in tts_mdl.tts(text):
+        bin += chunk
+    return binascii.hexlify(bin).decode("utf-8")
+
+
+def ask(question, kb_ids, tenant_id):
+    kbs = KnowledgebaseService.get_by_ids(kb_ids)
+    embd_nms = list(set([kb.embd_id for kb in kbs]))
+
+    is_kg = all([kb.parser_id == ParserType.KG for kb in kbs])
+    retr = retrievaler if not is_kg else kg_retrievaler
+
+    embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embd_nms[0])
+    chat_mdl = LLMBundle(tenant_id, LLMType.CHAT)
+    max_tokens = chat_mdl.max_length
+
+    kbinfos = retr.retrieval(question, embd_mdl, tenant_id, kb_ids, 1, 12, 0.1, 0.3, aggs=False)
+    knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
+
+    used_token_count = 0
+    for i, c in enumerate(knowledges):
+        used_token_count += num_tokens_from_string(c)
+        if max_tokens * 0.97 < used_token_count:
+            knowledges = knowledges[:i]
+            break
+
+    prompt = """
+    Role: You're a smart assistant. Your name is Miss R.
+    Task: Summarize the information from knowledge bases and answer user's question.
+    Requirements and restriction:
+      - DO NOT make things up, especially for numbers.
+      - If the information from knowledge is irrelevant with user's question, JUST SAY: Sorry, no relevant information provided.
+      - Answer with markdown format text.
+      - Answer in language of user's question.
+      - DO NOT make things up, especially for numbers.
+      
+    ### Information from knowledge bases
+    %s
+    
+    The above is information from knowledge bases.
+     
+    """%"\n".join(knowledges)
+    msg = [{"role": "user", "content": question}]
+
+    def decorate_answer(answer):
+        nonlocal knowledges, kbinfos, prompt
+        answer, idx = retr.insert_citations(answer,
+                                           [ck["content_ltks"]
+                                            for ck in kbinfos["chunks"]],
+                                           [ck["vector"]
+                                            for ck in kbinfos["chunks"]],
+                                           embd_mdl,
+                                           tkweight=0.7,
+                                           vtweight=0.3)
+        idx = set([kbinfos["chunks"][int(i)]["doc_id"] for i in idx])
+        recall_docs = [
+            d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx]
+        if not recall_docs: recall_docs = kbinfos["doc_aggs"]
+        kbinfos["doc_aggs"] = recall_docs
+        refs = deepcopy(kbinfos)
+        for c in refs["chunks"]:
+            if c.get("vector"):
+                del c["vector"]
+
+        if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0:
+            answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'"
+        return {"answer": answer, "reference": refs}
+
+    answer = ""
+    for ans in chat_mdl.chat_streamly(prompt, msg, {"temperature": 0.1}):
+        answer = ans
+        yield {"answer": answer, "reference": {}}
+    yield decorate_answer(answer)
+

api/db/services/document_service.py

@@ -1,381 +1,532 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import random
-from datetime import datetime
-from elasticsearch_dsl import Q
-from peewee import fn
-
-from api.db.db_utils import bulk_insert_into_db
-from api.settings import stat_logger
-from api.utils import current_timestamp, get_format_time, get_uuid
-from rag.settings import SVR_QUEUE_NAME
-from rag.utils.es_conn import ELASTICSEARCH
-from rag.utils.minio_conn import MINIO
-from rag.nlp import search
-
-from api.db import FileType, TaskStatus
-from api.db.db_models import DB, Knowledgebase, Tenant, Task
-from api.db.db_models import Document
-from api.db.services.common_service import CommonService
-from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db import StatusEnum
-from rag.utils.redis_conn import REDIS_CONN
-
-
-class DocumentService(CommonService):
-    model = Document
-
-    @classmethod
-    @DB.connection_context()
-    def get_by_kb_id(cls, kb_id, page_number, items_per_page,
-                     orderby, desc, keywords):
-        if keywords:
-            docs = cls.model.select().where(
-                (cls.model.kb_id == kb_id),
-                (fn.LOWER(cls.model.name).contains(keywords.lower()))
-            )
-        else:
-            docs = cls.model.select().where(cls.model.kb_id == kb_id)
-        count = docs.count()
-        if desc:
-            docs = docs.order_by(cls.model.getter_by(orderby).desc())
-        else:
-            docs = docs.order_by(cls.model.getter_by(orderby).asc())
-
-        docs = docs.paginate(page_number, items_per_page)
-
-        return list(docs.dicts()), count
-
-    @classmethod
-    @DB.connection_context()
-    def list_documents_in_dataset(cls, dataset_id, offset, count, order_by, descend, keywords):
-        if keywords:
-            docs = cls.model.select().where(
-                (cls.model.kb_id == dataset_id),
-                (fn.LOWER(cls.model.name).contains(keywords.lower()))
-            )
-        else:
-            docs = cls.model.select().where(cls.model.kb_id == dataset_id)
-
-        total = docs.count()
-
-        if descend == 'True':
-            docs = docs.order_by(cls.model.getter_by(order_by).desc())
-        if descend == 'False':
-            docs = docs.order_by(cls.model.getter_by(order_by).asc())
-
-        docs = list(docs.dicts())
-        docs_length = len(docs)
-
-        if offset < 0 or offset > docs_length:
-            raise IndexError("Offset is out of the valid range.")
-
-        if count == -1:
-            return docs[offset:], total
-
-        return docs[offset:offset + count], total
-
-    @classmethod
-    @DB.connection_context()
-    def insert(cls, doc):
-        if not cls.save(**doc):
-            raise RuntimeError("Database error (Document)!")
-        e, doc = cls.get_by_id(doc["id"])
-        if not e:
-            raise RuntimeError("Database error (Document retrieval)!")
-        e, kb = KnowledgebaseService.get_by_id(doc.kb_id)
-        if not KnowledgebaseService.update_by_id(
-                kb.id, {"doc_num": kb.doc_num + 1}):
-            raise RuntimeError("Database error (Knowledgebase)!")
-        return doc
-
-    @classmethod
-    @DB.connection_context()
-    def remove_document(cls, doc, tenant_id):
-        ELASTICSEARCH.deleteByQuery(
-                Q("match", doc_id=doc.id), idxnm=search.index_name(tenant_id))
-        cls.clear_chunk_num(doc.id)
-        return cls.delete_by_id(doc.id)
-
-    @classmethod
-    @DB.connection_context()
-    def get_newly_uploaded(cls):
-        fields = [
-            cls.model.id,
-            cls.model.kb_id,
-            cls.model.parser_id,
-            cls.model.parser_config,
-            cls.model.name,
-            cls.model.type,
-            cls.model.location,
-            cls.model.size,
-            Knowledgebase.tenant_id,
-            Tenant.embd_id,
-            Tenant.img2txt_id,
-            Tenant.asr_id,
-            cls.model.update_time]
-        docs = cls.model.select(*fields) \
-            .join(Knowledgebase, on=(cls.model.kb_id == Knowledgebase.id)) \
-            .join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id))\
-            .where(
-                cls.model.status == StatusEnum.VALID.value,
-                ~(cls.model.type == FileType.VIRTUAL.value),
-                cls.model.progress == 0,
-                cls.model.update_time >= current_timestamp() - 1000 * 600,
-                cls.model.run == TaskStatus.RUNNING.value)\
-            .order_by(cls.model.update_time.asc())
-        return list(docs.dicts())
-
-    @classmethod
-    @DB.connection_context()
-    def get_unfinished_docs(cls):
-        fields = [cls.model.id, cls.model.process_begin_at, cls.model.parser_config, cls.model.progress_msg, cls.model.run]
-        docs = cls.model.select(*fields) \
-            .where(
-                cls.model.status == StatusEnum.VALID.value,
-                ~(cls.model.type == FileType.VIRTUAL.value),
-                cls.model.progress < 1,
-                cls.model.progress > 0)
-        return list(docs.dicts())
-
-    @classmethod
-    @DB.connection_context()
-    def increment_chunk_num(cls, doc_id, kb_id, token_num, chunk_num, duation):
-        num = cls.model.update(token_num=cls.model.token_num + token_num,
-                               chunk_num=cls.model.chunk_num + chunk_num,
-                               process_duation=cls.model.process_duation + duation).where(
-            cls.model.id == doc_id).execute()
-        if num == 0:
-            raise LookupError(
-                "Document not found which is supposed to be there")
-        num = Knowledgebase.update(
-            token_num=Knowledgebase.token_num +
-            token_num,
-            chunk_num=Knowledgebase.chunk_num +
-            chunk_num).where(
-            Knowledgebase.id == kb_id).execute()
-        return num
-    
-    @classmethod
-    @DB.connection_context()
-    def decrement_chunk_num(cls, doc_id, kb_id, token_num, chunk_num, duation):
-        num = cls.model.update(token_num=cls.model.token_num - token_num,
-                               chunk_num=cls.model.chunk_num - chunk_num,
-                               process_duation=cls.model.process_duation + duation).where(
-            cls.model.id == doc_id).execute()
-        if num == 0:
-            raise LookupError(
-                "Document not found which is supposed to be there")
-        num = Knowledgebase.update(
-            token_num=Knowledgebase.token_num -
-            token_num,
-            chunk_num=Knowledgebase.chunk_num -
-            chunk_num
-        ).where(
-            Knowledgebase.id == kb_id).execute()
-        return num
-    
-    @classmethod
-    @DB.connection_context()
-    def clear_chunk_num(cls, doc_id):
-        doc = cls.model.get_by_id(doc_id)
-        assert doc, "Can't fine document in database."
-
-        num = Knowledgebase.update(
-            token_num=Knowledgebase.token_num -
-            doc.token_num,
-            chunk_num=Knowledgebase.chunk_num -
-            doc.chunk_num,
-            doc_num=Knowledgebase.doc_num-1
-        ).where(
-            Knowledgebase.id == doc.kb_id).execute()
-        return num
-
-    @classmethod
-    @DB.connection_context()
-    def get_tenant_id(cls, doc_id):
-        docs = cls.model.select(
-            Knowledgebase.tenant_id).join(
-            Knowledgebase, on=(
-                Knowledgebase.id == cls.model.kb_id)).where(
-                cls.model.id == doc_id, Knowledgebase.status == StatusEnum.VALID.value)
-        docs = docs.dicts()
-        if not docs:
-            return
-        return docs[0]["tenant_id"]
-
-    @classmethod
-    @DB.connection_context()
-    def get_tenant_id_by_name(cls, name):
-        docs = cls.model.select(
-            Knowledgebase.tenant_id).join(
-            Knowledgebase, on=(
-                    Knowledgebase.id == cls.model.kb_id)).where(
-            cls.model.name == name, Knowledgebase.status == StatusEnum.VALID.value)
-        docs = docs.dicts()
-        if not docs:
-            return
-        return docs[0]["tenant_id"]
-
-    @classmethod
-    @DB.connection_context()
-    def get_embd_id(cls, doc_id):
-        docs = cls.model.select(
-            Knowledgebase.embd_id).join(
-            Knowledgebase, on=(
-                Knowledgebase.id == cls.model.kb_id)).where(
-                cls.model.id == doc_id, Knowledgebase.status == StatusEnum.VALID.value)
-        docs = docs.dicts()
-        if not docs:
-            return
-        return docs[0]["embd_id"]
-    
-    @classmethod
-    @DB.connection_context()
-    def get_doc_id_by_doc_name(cls, doc_name):
-        fields = [cls.model.id]
-        doc_id = cls.model.select(*fields) \
-            .where(cls.model.name == doc_name)
-        doc_id = doc_id.dicts()
-        if not doc_id:
-            return
-        return doc_id[0]["id"]
-
-    @classmethod
-    @DB.connection_context()
-    def get_thumbnails(cls, docids):
-        fields = [cls.model.id, cls.model.thumbnail]
-        return list(cls.model.select(
-            *fields).where(cls.model.id.in_(docids)).dicts())
-
-    @classmethod
-    @DB.connection_context()
-    def update_parser_config(cls, id, config):
-        e, d = cls.get_by_id(id)
-        if not e:
-            raise LookupError(f"Document({id}) not found.")
-
-        def dfs_update(old, new):
-            for k, v in new.items():
-                if k not in old:
-                    old[k] = v
-                    continue
-                if isinstance(v, dict):
-                    assert isinstance(old[k], dict)
-                    dfs_update(old[k], v)
-                else:
-                    old[k] = v
-        dfs_update(d.parser_config, config)
-        cls.update_by_id(id, {"parser_config": d.parser_config})
-
-    @classmethod
-    @DB.connection_context()
-    def get_doc_count(cls, tenant_id):
-        docs = cls.model.select(cls.model.id).join(Knowledgebase,
-                                                   on=(Knowledgebase.id == cls.model.kb_id)).where(
-            Knowledgebase.tenant_id == tenant_id)
-        return len(docs)
-
-    @classmethod
-    @DB.connection_context()
-    def begin2parse(cls, docid):
-        cls.update_by_id(
-            docid, {"progress": random.random() * 1 / 100.,
-                    "progress_msg": "Task dispatched...",
-                    "process_begin_at": get_format_time()
-                    })
-
-    @classmethod
-    @DB.connection_context()
-    def update_progress(cls):
-        docs = cls.get_unfinished_docs()
-        for d in docs:
-            try:
-                tsks = Task.query(doc_id=d["id"], order_by=Task.create_time)
-                if not tsks:
-                    continue
-                msg = []
-                prg = 0
-                finished = True
-                bad = 0
-                e, doc = DocumentService.get_by_id(d["id"])
-                status = doc.run#TaskStatus.RUNNING.value
-                for t in tsks:
-                    if 0 <= t.progress < 1:
-                        finished = False
-                    prg += t.progress if t.progress >= 0 else 0
-                    msg.append(t.progress_msg)
-                    if t.progress == -1:
-                        bad += 1
-                prg /= len(tsks)
-                if finished and bad:
-                    prg = -1
-                    status = TaskStatus.FAIL.value
-                elif finished:
-                    if d["parser_config"].get("raptor", {}).get("use_raptor") and d["progress_msg"].lower().find(" raptor")<0:
-                        queue_raptor_tasks(d)
-                        prg *= 0.98
-                        msg.append("------ RAPTOR -------")
-                    else:
-                        status = TaskStatus.DONE.value
-
-                msg = "\n".join(msg)
-                info = {
-                    "process_duation": datetime.timestamp(
-                        datetime.now()) -
-                                       d["process_begin_at"].timestamp(),
-                    "run": status}
-                if prg != 0:
-                    info["progress"] = prg
-                if msg:
-                    info["progress_msg"] = msg
-                cls.update_by_id(d["id"], info)
-            except Exception as e:
-                stat_logger.error("fetch task exception:" + str(e))
-
-    @classmethod
-    @DB.connection_context()
-    def get_kb_doc_count(cls, kb_id):
-        return len(cls.model.select(cls.model.id).where(
-            cls.model.kb_id == kb_id).dicts())
-
-
-    @classmethod
-    @DB.connection_context()
-    def do_cancel(cls, doc_id):
-        try:
-            _, doc = DocumentService.get_by_id(doc_id)
-            return doc.run == TaskStatus.CANCEL.value or doc.progress < 0
-        except Exception as e:
-            pass
-        return False
-
-
-def queue_raptor_tasks(doc):
-    def new_task():
-        nonlocal doc
-        return {
-            "id": get_uuid(),
-            "doc_id": doc["id"],
-            "from_page": 0,
-            "to_page": -1,
-            "progress_msg": "Start to do RAPTOR (Recursive Abstractive Processing For Tree-Organized Retrieval)."
-        }
-
-    task = new_task()
-    bulk_insert_into_db(Task, [task], True)
-    task["type"] = "raptor"
-    assert REDIS_CONN.queue_product(SVR_QUEUE_NAME, message=task), "Can't access Redis. Please check the Redis' status."
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import hashlib
+import json
+import os
+import random
+import re
+import traceback
+from concurrent.futures import ThreadPoolExecutor
+from copy import deepcopy
+from datetime import datetime
+from io import BytesIO
+
+from elasticsearch_dsl import Q
+from peewee import fn
+
+from api.db.db_utils import bulk_insert_into_db
+from api.settings import stat_logger
+from api.utils import current_timestamp, get_format_time, get_uuid
+from api.utils.file_utils import get_project_base_directory
+from graphrag.mind_map_extractor import MindMapExtractor
+from rag.settings import SVR_QUEUE_NAME
+from rag.utils.es_conn import ELASTICSEARCH
+from rag.utils.storage_factory import STORAGE_IMPL
+from rag.nlp import search, rag_tokenizer
+
+from api.db import FileType, TaskStatus, ParserType, LLMType
+from api.db.db_models import DB, Knowledgebase, Tenant, Task
+from api.db.db_models import Document
+from api.db.services.common_service import CommonService
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db import StatusEnum
+from rag.utils.redis_conn import REDIS_CONN
+
+
+class DocumentService(CommonService):
+    model = Document
+
+    @classmethod
+    @DB.connection_context()
+    def get_by_kb_id(cls, kb_id, page_number, items_per_page,
+                     orderby, desc, keywords):
+        if keywords:
+            docs = cls.model.select().where(
+                (cls.model.kb_id == kb_id),
+                (fn.LOWER(cls.model.name).contains(keywords.lower()))
+            )
+        else:
+            docs = cls.model.select().where(cls.model.kb_id == kb_id)
+        count = docs.count()
+        if desc:
+            docs = docs.order_by(cls.model.getter_by(orderby).desc())
+        else:
+            docs = docs.order_by(cls.model.getter_by(orderby).asc())
+
+        docs = docs.paginate(page_number, items_per_page)
+
+        return list(docs.dicts()), count
+
+    @classmethod
+    @DB.connection_context()
+    def list_documents_in_dataset(cls, dataset_id, offset, count, order_by, descend, keywords):
+        if keywords:
+            docs = cls.model.select().where(
+                (cls.model.kb_id == dataset_id),
+                (fn.LOWER(cls.model.name).contains(keywords.lower()))
+            )
+        else:
+            docs = cls.model.select().where(cls.model.kb_id == dataset_id)
+
+        total = docs.count()
+
+        if descend == 'True':
+            docs = docs.order_by(cls.model.getter_by(order_by).desc())
+        if descend == 'False':
+            docs = docs.order_by(cls.model.getter_by(order_by).asc())
+
+        docs = list(docs.dicts())
+        docs_length = len(docs)
+
+        if offset < 0 or offset > docs_length:
+            raise IndexError("Offset is out of the valid range.")
+
+        if count == -1:
+            return docs[offset:], total
+
+        return docs[offset:offset + count], total
+
+    @classmethod
+    @DB.connection_context()
+    def insert(cls, doc):
+        if not cls.save(**doc):
+            raise RuntimeError("Database error (Document)!")
+        e, doc = cls.get_by_id(doc["id"])
+        if not e:
+            raise RuntimeError("Database error (Document retrieval)!")
+        e, kb = KnowledgebaseService.get_by_id(doc.kb_id)
+        if not KnowledgebaseService.update_by_id(
+                kb.id, {"doc_num": kb.doc_num + 1}):
+            raise RuntimeError("Database error (Knowledgebase)!")
+        return doc
+
+    @classmethod
+    @DB.connection_context()
+    def remove_document(cls, doc, tenant_id):
+        ELASTICSEARCH.deleteByQuery(
+                Q("match", doc_id=doc.id), idxnm=search.index_name(tenant_id))
+        cls.clear_chunk_num(doc.id)
+        return cls.delete_by_id(doc.id)
+
+    @classmethod
+    @DB.connection_context()
+    def get_newly_uploaded(cls):
+        fields = [
+            cls.model.id,
+            cls.model.kb_id,
+            cls.model.parser_id,
+            cls.model.parser_config,
+            cls.model.name,
+            cls.model.type,
+            cls.model.location,
+            cls.model.size,
+            Knowledgebase.tenant_id,
+            Tenant.embd_id,
+            Tenant.img2txt_id,
+            Tenant.asr_id,
+            cls.model.update_time]
+        docs = cls.model.select(*fields) \
+            .join(Knowledgebase, on=(cls.model.kb_id == Knowledgebase.id)) \
+            .join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id))\
+            .where(
+                cls.model.status == StatusEnum.VALID.value,
+                ~(cls.model.type == FileType.VIRTUAL.value),
+                cls.model.progress == 0,
+                cls.model.update_time >= current_timestamp() - 1000 * 600,
+                cls.model.run == TaskStatus.RUNNING.value)\
+            .order_by(cls.model.update_time.asc())
+        return list(docs.dicts())
+
+    @classmethod
+    @DB.connection_context()
+    def get_unfinished_docs(cls):
+        fields = [cls.model.id, cls.model.process_begin_at, cls.model.parser_config, cls.model.progress_msg, cls.model.run]
+        docs = cls.model.select(*fields) \
+            .where(
+                cls.model.status == StatusEnum.VALID.value,
+                ~(cls.model.type == FileType.VIRTUAL.value),
+                cls.model.progress < 1,
+                cls.model.progress > 0)
+        return list(docs.dicts())
+
+    @classmethod
+    @DB.connection_context()
+    def increment_chunk_num(cls, doc_id, kb_id, token_num, chunk_num, duation):
+        num = cls.model.update(token_num=cls.model.token_num + token_num,
+                               chunk_num=cls.model.chunk_num + chunk_num,
+                               process_duation=cls.model.process_duation + duation).where(
+            cls.model.id == doc_id).execute()
+        if num == 0:
+            raise LookupError(
+                "Document not found which is supposed to be there")
+        num = Knowledgebase.update(
+            token_num=Knowledgebase.token_num +
+            token_num,
+            chunk_num=Knowledgebase.chunk_num +
+            chunk_num).where(
+            Knowledgebase.id == kb_id).execute()
+        return num
+    
+    @classmethod
+    @DB.connection_context()
+    def decrement_chunk_num(cls, doc_id, kb_id, token_num, chunk_num, duation):
+        num = cls.model.update(token_num=cls.model.token_num - token_num,
+                               chunk_num=cls.model.chunk_num - chunk_num,
+                               process_duation=cls.model.process_duation + duation).where(
+            cls.model.id == doc_id).execute()
+        if num == 0:
+            raise LookupError(
+                "Document not found which is supposed to be there")
+        num = Knowledgebase.update(
+            token_num=Knowledgebase.token_num -
+            token_num,
+            chunk_num=Knowledgebase.chunk_num -
+            chunk_num
+        ).where(
+            Knowledgebase.id == kb_id).execute()
+        return num
+    
+    @classmethod
+    @DB.connection_context()
+    def clear_chunk_num(cls, doc_id):
+        doc = cls.model.get_by_id(doc_id)
+        assert doc, "Can't fine document in database."
+
+        num = Knowledgebase.update(
+            token_num=Knowledgebase.token_num -
+            doc.token_num,
+            chunk_num=Knowledgebase.chunk_num -
+            doc.chunk_num,
+            doc_num=Knowledgebase.doc_num-1
+        ).where(
+            Knowledgebase.id == doc.kb_id).execute()
+        return num
+
+    @classmethod
+    @DB.connection_context()
+    def get_tenant_id(cls, doc_id):
+        docs = cls.model.select(
+            Knowledgebase.tenant_id).join(
+            Knowledgebase, on=(
+                Knowledgebase.id == cls.model.kb_id)).where(
+                cls.model.id == doc_id, Knowledgebase.status == StatusEnum.VALID.value)
+        docs = docs.dicts()
+        if not docs:
+            return
+        return docs[0]["tenant_id"]
+
+    @classmethod
+    @DB.connection_context()
+    def get_tenant_id_by_name(cls, name):
+        docs = cls.model.select(
+            Knowledgebase.tenant_id).join(
+            Knowledgebase, on=(
+                    Knowledgebase.id == cls.model.kb_id)).where(
+            cls.model.name == name, Knowledgebase.status == StatusEnum.VALID.value)
+        docs = docs.dicts()
+        if not docs:
+            return
+        return docs[0]["tenant_id"]
+
+    @classmethod
+    @DB.connection_context()
+    def get_embd_id(cls, doc_id):
+        docs = cls.model.select(
+            Knowledgebase.embd_id).join(
+            Knowledgebase, on=(
+                Knowledgebase.id == cls.model.kb_id)).where(
+                cls.model.id == doc_id, Knowledgebase.status == StatusEnum.VALID.value)
+        docs = docs.dicts()
+        if not docs:
+            return
+        return docs[0]["embd_id"]
+    
+    @classmethod
+    @DB.connection_context()
+    def get_doc_id_by_doc_name(cls, doc_name):
+        fields = [cls.model.id]
+        doc_id = cls.model.select(*fields) \
+            .where(cls.model.name == doc_name)
+        doc_id = doc_id.dicts()
+        if not doc_id:
+            return
+        return doc_id[0]["id"]
+
+    @classmethod
+    @DB.connection_context()
+    def get_thumbnails(cls, docids):
+        fields = [cls.model.id, cls.model.thumbnail]
+        return list(cls.model.select(
+            *fields).where(cls.model.id.in_(docids)).dicts())
+
+    @classmethod
+    @DB.connection_context()
+    def update_parser_config(cls, id, config):
+        e, d = cls.get_by_id(id)
+        if not e:
+            raise LookupError(f"Document({id}) not found.")
+
+        def dfs_update(old, new):
+            for k, v in new.items():
+                if k not in old:
+                    old[k] = v
+                    continue
+                if isinstance(v, dict):
+                    assert isinstance(old[k], dict)
+                    dfs_update(old[k], v)
+                else:
+                    old[k] = v
+        dfs_update(d.parser_config, config)
+        cls.update_by_id(id, {"parser_config": d.parser_config})
+
+    @classmethod
+    @DB.connection_context()
+    def get_doc_count(cls, tenant_id):
+        docs = cls.model.select(cls.model.id).join(Knowledgebase,
+                                                   on=(Knowledgebase.id == cls.model.kb_id)).where(
+            Knowledgebase.tenant_id == tenant_id)
+        return len(docs)
+
+    @classmethod
+    @DB.connection_context()
+    def begin2parse(cls, docid):
+        cls.update_by_id(
+            docid, {"progress": random.random() * 1 / 100.,
+                    "progress_msg": "Task dispatched...",
+                    "process_begin_at": get_format_time()
+                    })
+
+    @classmethod
+    @DB.connection_context()
+    def update_progress(cls):
+        docs = cls.get_unfinished_docs()
+        for d in docs:
+            try:
+                tsks = Task.query(doc_id=d["id"], order_by=Task.create_time)
+                if not tsks:
+                    continue
+                msg = []
+                prg = 0
+                finished = True
+                bad = 0
+                e, doc = DocumentService.get_by_id(d["id"])
+                status = doc.run#TaskStatus.RUNNING.value
+                for t in tsks:
+                    if 0 <= t.progress < 1:
+                        finished = False
+                    prg += t.progress if t.progress >= 0 else 0
+                    if t.progress_msg not in msg:
+                        msg.append(t.progress_msg)
+                    if t.progress == -1:
+                        bad += 1
+                prg /= len(tsks)
+                if finished and bad:
+                    prg = -1
+                    status = TaskStatus.FAIL.value
+                elif finished:
+                    if d["parser_config"].get("raptor", {}).get("use_raptor") and d["progress_msg"].lower().find(" raptor")<0:
+                        queue_raptor_tasks(d)
+                        prg *= 0.98
+                        msg.append("------ RAPTOR -------")
+                    else:
+                        status = TaskStatus.DONE.value
+
+                msg = "\n".join(msg)
+                info = {
+                    "process_duation": datetime.timestamp(
+                        datetime.now()) -
+                                       d["process_begin_at"].timestamp(),
+                    "run": status}
+                if prg != 0:
+                    info["progress"] = prg
+                if msg:
+                    info["progress_msg"] = msg
+                cls.update_by_id(d["id"], info)
+            except Exception as e:
+                stat_logger.error("fetch task exception:" + str(e))
+
+    @classmethod
+    @DB.connection_context()
+    def get_kb_doc_count(cls, kb_id):
+        return len(cls.model.select(cls.model.id).where(
+            cls.model.kb_id == kb_id).dicts())
+
+
+    @classmethod
+    @DB.connection_context()
+    def do_cancel(cls, doc_id):
+        try:
+            _, doc = DocumentService.get_by_id(doc_id)
+            return doc.run == TaskStatus.CANCEL.value or doc.progress < 0
+        except Exception as e:
+            pass
+        return False
+
+
+def queue_raptor_tasks(doc):
+    def new_task():
+        nonlocal doc
+        return {
+            "id": get_uuid(),
+            "doc_id": doc["id"],
+            "from_page": 0,
+            "to_page": -1,
+            "progress_msg": "Start to do RAPTOR (Recursive Abstractive Processing For Tree-Organized Retrieval)."
+        }
+
+    task = new_task()
+    bulk_insert_into_db(Task, [task], True)
+    task["type"] = "raptor"
+    assert REDIS_CONN.queue_product(SVR_QUEUE_NAME, message=task), "Can't access Redis. Please check the Redis' status."
+
+
+def doc_upload_and_parse(conversation_id, file_objs, user_id):
+    from rag.app import presentation, picture, naive, audio, email
+    from api.db.services.dialog_service import ConversationService, DialogService
+    from api.db.services.file_service import FileService
+    from api.db.services.llm_service import LLMBundle
+    from api.db.services.user_service import TenantService
+    from api.db.services.api_service import API4ConversationService
+
+    e, conv = ConversationService.get_by_id(conversation_id)
+    if not e:
+        e, conv = API4ConversationService.get_by_id(conversation_id)
+    assert e, "Conversation not found!"
+
+    e, dia = DialogService.get_by_id(conv.dialog_id)
+    kb_id = dia.kb_ids[0]
+    e, kb = KnowledgebaseService.get_by_id(kb_id)
+    if not e:
+        raise LookupError("Can't find this knowledgebase!")
+
+    idxnm = search.index_name(kb.tenant_id)
+    if not ELASTICSEARCH.indexExist(idxnm):
+        ELASTICSEARCH.createIdx(idxnm, json.load(
+            open(os.path.join(get_project_base_directory(), "conf", "mapping.json"), "r")))
+
+    embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id, lang=kb.language)
+
+    err, files = FileService.upload_document(kb, file_objs, user_id)
+    assert not err, "\n".join(err)
+
+    def dummy(prog=None, msg=""):
+        pass
+
+    FACTORY = {
+        ParserType.PRESENTATION.value: presentation,
+        ParserType.PICTURE.value: picture,
+        ParserType.AUDIO.value: audio,
+        ParserType.EMAIL.value: email
+    }
+    parser_config = {"chunk_token_num": 4096, "delimiter": "\n!?;。；！？", "layout_recognize": False}
+    exe = ThreadPoolExecutor(max_workers=12)
+    threads = []
+    doc_nm = {}
+    for d, blob in files:
+        doc_nm[d["id"]] = d["name"]
+    for d, blob in files:
+        kwargs = {
+            "callback": dummy,
+            "parser_config": parser_config,
+            "from_page": 0,
+            "to_page": 100000,
+            "tenant_id": kb.tenant_id,
+            "lang": kb.language
+        }
+        threads.append(exe.submit(FACTORY.get(d["parser_id"], naive).chunk, d["name"], blob, **kwargs))
+
+    for (docinfo, _), th in zip(files, threads):
+        docs = []
+        doc = {
+            "doc_id": docinfo["id"],
+            "kb_id": [kb.id]
+        }
+        for ck in th.result():
+            d = deepcopy(doc)
+            d.update(ck)
+            md5 = hashlib.md5()
+            md5.update((ck["content_with_weight"] +
+                        str(d["doc_id"])).encode("utf-8"))
+            d["_id"] = md5.hexdigest()
+            d["create_time"] = str(datetime.now()).replace("T", " ")[:19]
+            d["create_timestamp_flt"] = datetime.now().timestamp()
+            if not d.get("image"):
+                docs.append(d)
+                continue
+
+            output_buffer = BytesIO()
+            if isinstance(d["image"], bytes):
+                output_buffer = BytesIO(d["image"])
+            else:
+                d["image"].save(output_buffer, format='JPEG')
+
+            STORAGE_IMPL.put(kb.id, d["_id"], output_buffer.getvalue())
+            d["img_id"] = "{}-{}".format(kb.id, d["_id"])
+            del d["image"]
+            docs.append(d)
+
+    parser_ids = {d["id"]: d["parser_id"] for d, _ in files}
+    docids = [d["id"] for d, _ in files]
+    chunk_counts = {id: 0 for id in docids}
+    token_counts = {id: 0 for id in docids}
+    es_bulk_size = 64
+
+    def embedding(doc_id, cnts, batch_size=16):
+        nonlocal embd_mdl, chunk_counts, token_counts
+        vects = []
+        for i in range(0, len(cnts), batch_size):
+            vts, c = embd_mdl.encode(cnts[i: i + batch_size])
+            vects.extend(vts.tolist())
+            chunk_counts[doc_id] += len(cnts[i:i + batch_size])
+            token_counts[doc_id] += c
+        return vects
+
+    _, tenant = TenantService.get_by_id(kb.tenant_id)
+    llm_bdl = LLMBundle(kb.tenant_id, LLMType.CHAT, tenant.llm_id)
+    for doc_id in docids:
+        cks = [c for c in docs if c["doc_id"] == doc_id]
+
+        if parser_ids[doc_id] != ParserType.PICTURE.value:
+            mindmap = MindMapExtractor(llm_bdl)
+            try:
+                mind_map = json.dumps(mindmap([c["content_with_weight"] for c in docs if c["doc_id"] == doc_id]).output,
+                                      ensure_ascii=False, indent=2)
+                if len(mind_map) < 32: raise Exception("Few content: " + mind_map)
+                cks.append({
+                    "id": get_uuid(),
+                    "doc_id": doc_id,
+                    "kb_id": [kb.id],
+                    "docnm_kwd": doc_nm[doc_id],
+                    "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", doc_nm[doc_id])),
+                    "content_ltks": "",
+                    "content_with_weight": mind_map,
+                    "knowledge_graph_kwd": "mind_map"
+                })
+            except Exception as e:
+                stat_logger.error("Mind map generation error:", traceback.format_exc())
+
+        vects = embedding(doc_id, [c["content_with_weight"] for c in cks])
+        assert len(cks) == len(vects)
+        for i, d in enumerate(cks):
+            v = vects[i]
+            d["q_%d_vec" % len(v)] = v
+        for b in range(0, len(cks), es_bulk_size):
+            ELASTICSEARCH.bulk(cks[b:b + es_bulk_size], idxnm)
+
+        DocumentService.increment_chunk_num(
+            doc_id, kb.id, token_counts[doc_id], chunk_counts[doc_id], 0)
+
+    return [d["id"] for d,_ in files]

api/db/services/file2document_service.py

@@ -69,14 +69,14 @@ class File2DocumentService(CommonService):
 
     @classmethod
     @DB.connection_context()
-    def get_minio_address(cls, doc_id=None, file_id=None):
+    def get_storage_address(cls, doc_id=None, file_id=None):
         if doc_id:
             f2d = cls.get_by_document_id(doc_id)
         else:
             f2d = cls.get_by_file_id(file_id)
         if f2d:
             file = File.get_by_id(f2d[0].file_id)
-            if file.source_type == FileSource.LOCAL:
+            if not file.source_type or file.source_type == FileSource.LOCAL:
                 return file.parent_id, file.location
             doc_id = f2d[0].document_id
 

api/db/services/file_service.py

@@ -13,16 +13,21 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import re
+import os
 from flask_login import current_user
 from peewee import fn
 
-from api.db import FileType, KNOWLEDGEBASE_FOLDER_NAME, FileSource
+from api.db import FileType, KNOWLEDGEBASE_FOLDER_NAME, FileSource, ParserType
 from api.db.db_models import DB, File2Document, Knowledgebase
 from api.db.db_models import File, Document
+from api.db.services import duplicate_name
 from api.db.services.common_service import CommonService
 from api.db.services.document_service import DocumentService
 from api.db.services.file2document_service import File2DocumentService
 from api.utils import get_uuid
+from api.utils.file_utils import filename_type, thumbnail
+from rag.utils.storage_factory import STORAGE_IMPL
 
 
 class FileService(CommonService):
@@ -57,6 +62,12 @@ class FileService(CommonService):
             if file["type"] == FileType.FOLDER.value:
                 file["size"] = cls.get_folder_size(file["id"])
                 file['kbs_info'] = []
+                children = list(cls.model.select().where(
+                    (cls.model.tenant_id == tenant_id),
+                    (cls.model.parent_id == file["id"]),
+                    ~(cls.model.id == file["id"]),
+                ).dicts())
+                file["has_child_folder"] = any(value["type"] == FileType.FOLDER.value for value in children)                       
                 continue
             kbs_info = cls.get_kb_id_by_file_id(file['id'])
             file['kbs_info'] = kbs_info
@@ -312,4 +323,66 @@ class FileService(CommonService):
             cls.filter_update((cls.model.id << file_ids, ), { 'parent_id': folder_id })
         except Exception as e:
             print(e)
-            raise RuntimeError("Database error (File move)!")
+            raise RuntimeError("Database error (File move)!")
+
+    @classmethod
+    @DB.connection_context()
+    def upload_document(self, kb, file_objs, user_id):
+        root_folder = self.get_root_folder(user_id)
+        pf_id = root_folder["id"]
+        self.init_knowledgebase_docs(pf_id, user_id)
+        kb_root_folder = self.get_kb_folder(user_id)
+        kb_folder = self.new_a_file_from_kb(kb.tenant_id, kb.name, kb_root_folder["id"])
+
+        err, files = [], []
+        for file in file_objs:
+            try:
+                MAX_FILE_NUM_PER_USER = int(os.environ.get('MAX_FILE_NUM_PER_USER', 0))
+                if MAX_FILE_NUM_PER_USER > 0 and DocumentService.get_doc_count(kb.tenant_id) >= MAX_FILE_NUM_PER_USER:
+                    raise RuntimeError("Exceed the maximum file number of a free user!")
+
+                filename = duplicate_name(
+                    DocumentService.query,
+                    name=file.filename,
+                    kb_id=kb.id)
+                filetype = filename_type(filename)
+                if filetype == FileType.OTHER.value:
+                    raise RuntimeError("This type of file has not been supported yet!")
+
+                location = filename
+                while STORAGE_IMPL.obj_exist(kb.id, location):
+                    location += "_"
+                blob = file.read()
+                STORAGE_IMPL.put(kb.id, location, blob)
+                doc = {
+                    "id": get_uuid(),
+                    "kb_id": kb.id,
+                    "parser_id": self.get_parser(filetype, filename, kb.parser_id),
+                    "parser_config": kb.parser_config,
+                    "created_by": user_id,
+                    "type": filetype,
+                    "name": filename,
+                    "location": location,
+                    "size": len(blob),
+                    "thumbnail": thumbnail(filename, blob)
+                }
+                DocumentService.insert(doc)
+
+                FileService.add_file_from_kb(doc, kb_folder["id"], kb.tenant_id)
+                files.append((doc, blob))
+            except Exception as e:
+                err.append(file.filename + ": " + str(e))
+
+        return err, files
+
+    @staticmethod
+    def get_parser(doc_type, filename, default):
+        if doc_type == FileType.VISUAL:
+            return ParserType.PICTURE.value
+        if doc_type == FileType.AURAL:
+            return ParserType.AUDIO.value
+        if re.search(r"\.(ppt|pptx|pages)$", filename):
+            return ParserType.PRESENTATION.value
+        if re.search(r"\.(eml)$", filename):
+            return ParserType.EMAIL.value
+        return default

api/db/services/knowledgebase_service.py

@@ -1,144 +1,144 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from api.db import StatusEnum, TenantPermission
-from api.db.db_models import Knowledgebase, DB, Tenant
-from api.db.services.common_service import CommonService
-
-
-class KnowledgebaseService(CommonService):
-    model = Knowledgebase
-
-    @classmethod
-    @DB.connection_context()
-    def get_by_tenant_ids(cls, joined_tenant_ids, user_id,
-                          page_number, items_per_page, orderby, desc):
-        kbs = cls.model.select().where(
-            ((cls.model.tenant_id.in_(joined_tenant_ids) & (cls.model.permission ==
-                                                            TenantPermission.TEAM.value)) | (
-                         cls.model.tenant_id == user_id))
-            & (cls.model.status == StatusEnum.VALID.value)
-        )
-        if desc:
-            kbs = kbs.order_by(cls.model.getter_by(orderby).desc())
-        else:
-            kbs = kbs.order_by(cls.model.getter_by(orderby).asc())
-
-        kbs = kbs.paginate(page_number, items_per_page)
-
-        return list(kbs.dicts())
-
-    @classmethod
-    @DB.connection_context()
-    def get_by_tenant_ids_by_offset(cls, joined_tenant_ids, user_id, offset, count, orderby, desc):
-        kbs = cls.model.select().where(
-            ((cls.model.tenant_id.in_(joined_tenant_ids) & (cls.model.permission ==
-                                                            TenantPermission.TEAM.value)) | (
-                     cls.model.tenant_id == user_id))
-            & (cls.model.status == StatusEnum.VALID.value)
-        )
-        if desc:
-            kbs = kbs.order_by(cls.model.getter_by(orderby).desc())
-        else:
-            kbs = kbs.order_by(cls.model.getter_by(orderby).asc())
-
-        kbs = list(kbs.dicts())
-
-        kbs_length = len(kbs)
-        if offset < 0 or offset > kbs_length:
-            raise IndexError("Offset is out of the valid range.")
-
-        if count == -1:
-            return kbs[offset:]
-
-        return kbs[offset:offset+count]
-
-    @classmethod
-    @DB.connection_context()
-    def get_detail(cls, kb_id):
-        fields = [
-            cls.model.id,
-            #Tenant.embd_id,
-            cls.model.embd_id,
-            cls.model.avatar,
-            cls.model.name,
-            cls.model.language,
-            cls.model.description,
-            cls.model.permission,
-            cls.model.doc_num,
-            cls.model.token_num,
-            cls.model.chunk_num,
-            cls.model.parser_id,
-            cls.model.parser_config]
-        kbs = cls.model.select(*fields).join(Tenant, on=(
-                    (Tenant.id == cls.model.tenant_id) & (Tenant.status == StatusEnum.VALID.value))).where(
-            (cls.model.id == kb_id),
-            (cls.model.status == StatusEnum.VALID.value)
-        )
-        if not kbs:
-            return
-        d = kbs[0].to_dict()
-        #d["embd_id"] = kbs[0].tenant.embd_id
-        return d
-
-    @classmethod
-    @DB.connection_context()
-    def update_parser_config(cls, id, config):
-        e, m = cls.get_by_id(id)
-        if not e:
-            raise LookupError(f"knowledgebase({id}) not found.")
-
-        def dfs_update(old, new):
-            for k, v in new.items():
-                if k not in old:
-                    old[k] = v
-                    continue
-                if isinstance(v, dict):
-                    assert isinstance(old[k], dict)
-                    dfs_update(old[k], v)
-                elif isinstance(v, list):
-                    assert isinstance(old[k], list)
-                    old[k] = list(set(old[k] + v))
-                else:
-                    old[k] = v
-
-        dfs_update(m.parser_config, config)
-        cls.update_by_id(id, {"parser_config": m.parser_config})
-
-    @classmethod
-    @DB.connection_context()
-    def get_field_map(cls, ids):
-        conf = {}
-        for k in cls.get_by_ids(ids):
-            if k.parser_config and "field_map" in k.parser_config:
-                conf.update(k.parser_config["field_map"])
-        return conf
-
-    @classmethod
-    @DB.connection_context()
-    def get_by_name(cls, kb_name, tenant_id):
-        kb = cls.model.select().where(
-            (cls.model.name == kb_name)
-            & (cls.model.tenant_id == tenant_id)
-            & (cls.model.status == StatusEnum.VALID.value)
-        )
-        if kb:
-            return True, kb[0]
-        return False, None
-
-    @classmethod
-    @DB.connection_context()
-    def get_all_ids(cls):
-        return [m["id"] for m in cls.model.select(cls.model.id).dicts()]
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from api.db import StatusEnum, TenantPermission
+from api.db.db_models import Knowledgebase, DB, Tenant
+from api.db.services.common_service import CommonService
+
+
+class KnowledgebaseService(CommonService):
+    model = Knowledgebase
+
+    @classmethod
+    @DB.connection_context()
+    def get_by_tenant_ids(cls, joined_tenant_ids, user_id,
+                          page_number, items_per_page, orderby, desc):
+        kbs = cls.model.select().where(
+            ((cls.model.tenant_id.in_(joined_tenant_ids) & (cls.model.permission ==
+                                                            TenantPermission.TEAM.value)) | (
+                         cls.model.tenant_id == user_id))
+            & (cls.model.status == StatusEnum.VALID.value)
+        )
+        if desc:
+            kbs = kbs.order_by(cls.model.getter_by(orderby).desc())
+        else:
+            kbs = kbs.order_by(cls.model.getter_by(orderby).asc())
+
+        kbs = kbs.paginate(page_number, items_per_page)
+
+        return list(kbs.dicts())
+
+    @classmethod
+    @DB.connection_context()
+    def get_by_tenant_ids_by_offset(cls, joined_tenant_ids, user_id, offset, count, orderby, desc):
+        kbs = cls.model.select().where(
+            ((cls.model.tenant_id.in_(joined_tenant_ids) & (cls.model.permission ==
+                                                            TenantPermission.TEAM.value)) | (
+                     cls.model.tenant_id == user_id))
+            & (cls.model.status == StatusEnum.VALID.value)
+        )
+        if desc:
+            kbs = kbs.order_by(cls.model.getter_by(orderby).desc())
+        else:
+            kbs = kbs.order_by(cls.model.getter_by(orderby).asc())
+
+        kbs = list(kbs.dicts())
+
+        kbs_length = len(kbs)
+        if offset < 0 or offset > kbs_length:
+            raise IndexError("Offset is out of the valid range.")
+
+        if count == -1:
+            return kbs[offset:]
+
+        return kbs[offset:offset+count]
+
+    @classmethod
+    @DB.connection_context()
+    def get_detail(cls, kb_id):
+        fields = [
+            cls.model.id,
+            #Tenant.embd_id,
+            cls.model.embd_id,
+            cls.model.avatar,
+            cls.model.name,
+            cls.model.language,
+            cls.model.description,
+            cls.model.permission,
+            cls.model.doc_num,
+            cls.model.token_num,
+            cls.model.chunk_num,
+            cls.model.parser_id,
+            cls.model.parser_config]
+        kbs = cls.model.select(*fields).join(Tenant, on=(
+                    (Tenant.id == cls.model.tenant_id) & (Tenant.status == StatusEnum.VALID.value))).where(
+            (cls.model.id == kb_id),
+            (cls.model.status == StatusEnum.VALID.value)
+        )
+        if not kbs:
+            return
+        d = kbs[0].to_dict()
+        #d["embd_id"] = kbs[0].tenant.embd_id
+        return d
+
+    @classmethod
+    @DB.connection_context()
+    def update_parser_config(cls, id, config):
+        e, m = cls.get_by_id(id)
+        if not e:
+            raise LookupError(f"knowledgebase({id}) not found.")
+
+        def dfs_update(old, new):
+            for k, v in new.items():
+                if k not in old:
+                    old[k] = v
+                    continue
+                if isinstance(v, dict):
+                    assert isinstance(old[k], dict)
+                    dfs_update(old[k], v)
+                elif isinstance(v, list):
+                    assert isinstance(old[k], list)
+                    old[k] = list(set(old[k] + v))
+                else:
+                    old[k] = v
+
+        dfs_update(m.parser_config, config)
+        cls.update_by_id(id, {"parser_config": m.parser_config})
+
+    @classmethod
+    @DB.connection_context()
+    def get_field_map(cls, ids):
+        conf = {}
+        for k in cls.get_by_ids(ids):
+            if k.parser_config and "field_map" in k.parser_config:
+                conf.update(k.parser_config["field_map"])
+        return conf
+
+    @classmethod
+    @DB.connection_context()
+    def get_by_name(cls, kb_name, tenant_id):
+        kb = cls.model.select().where(
+            (cls.model.name == kb_name)
+            & (cls.model.tenant_id == tenant_id)
+            & (cls.model.status == StatusEnum.VALID.value)
+        )
+        if kb:
+            return True, kb[0]
+        return False, None
+
+    @classmethod
+    @DB.connection_context()
+    def get_all_ids(cls):
+        return [m["id"] for m in cls.model.select(cls.model.id).dicts()]

api/db/services/llm_service.py

@@ -1,242 +1,271 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from api.db.services.user_service import TenantService
-from api.settings import database_logger
-from rag.llm import EmbeddingModel, CvModel, ChatModel, RerankModel, Seq2txtModel
-from api.db import LLMType
-from api.db.db_models import DB, UserTenant
-from api.db.db_models import LLMFactories, LLM, TenantLLM
-from api.db.services.common_service import CommonService
-
-
-class LLMFactoriesService(CommonService):
-    model = LLMFactories
-
-
-class LLMService(CommonService):
-    model = LLM
-
-
-class TenantLLMService(CommonService):
-    model = TenantLLM
-
-    @classmethod
-    @DB.connection_context()
-    def get_api_key(cls, tenant_id, model_name):
-        objs = cls.query(tenant_id=tenant_id, llm_name=model_name)
-        if not objs:
-            return
-        return objs[0]
-
-    @classmethod
-    @DB.connection_context()
-    def get_my_llms(cls, tenant_id):
-        fields = [
-            cls.model.llm_factory,
-            LLMFactories.logo,
-            LLMFactories.tags,
-            cls.model.model_type,
-            cls.model.llm_name,
-            cls.model.used_tokens
-        ]
-        objs = cls.model.select(*fields).join(LLMFactories, on=(cls.model.llm_factory == LLMFactories.name)).where(
-            cls.model.tenant_id == tenant_id, ~cls.model.api_key.is_null()).dicts()
-
-        return list(objs)
-
-    @classmethod
-    @DB.connection_context()
-    def model_instance(cls, tenant_id, llm_type,
-                       llm_name=None, lang="Chinese"):
-        e, tenant = TenantService.get_by_id(tenant_id)
-        if not e:
-            raise LookupError("Tenant not found")
-
-        if llm_type == LLMType.EMBEDDING.value:
-            mdlnm = tenant.embd_id if not llm_name else llm_name
-        elif llm_type == LLMType.SPEECH2TEXT.value:
-            mdlnm = tenant.asr_id
-        elif llm_type == LLMType.IMAGE2TEXT.value:
-            mdlnm = tenant.img2txt_id if not llm_name else llm_name
-        elif llm_type == LLMType.CHAT.value:
-            mdlnm = tenant.llm_id if not llm_name else llm_name
-        elif llm_type == LLMType.RERANK:
-            mdlnm = tenant.rerank_id if not llm_name else llm_name
-        else:
-            assert False, "LLM type error"
-
-        model_config = cls.get_api_key(tenant_id, mdlnm)
-        if model_config: model_config = model_config.to_dict()
-        if not model_config:
-            if llm_type in [LLMType.EMBEDDING, LLMType.RERANK]:
-                llm = LLMService.query(llm_name=llm_name if llm_name else mdlnm)
-                if llm and llm[0].fid in ["Youdao", "FastEmbed", "BAAI"]:
-                    model_config = {"llm_factory": llm[0].fid, "api_key":"", "llm_name": llm_name if llm_name else mdlnm, "api_base": ""}
-            if not model_config:
-                if llm_name == "flag-embedding":
-                    model_config = {"llm_factory": "Tongyi-Qianwen", "api_key": "",
-                                "llm_name": llm_name, "api_base": ""}
-                else:
-                    if not mdlnm:
-                        raise LookupError(f"Type of {llm_type} model is not set.")
-                    raise LookupError("Model({}) not authorized".format(mdlnm))
-
-        if llm_type == LLMType.EMBEDDING.value:
-            if model_config["llm_factory"] not in EmbeddingModel:
-                return
-            return EmbeddingModel[model_config["llm_factory"]](
-                model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
-
-        if llm_type == LLMType.RERANK:
-            if model_config["llm_factory"] not in RerankModel:
-                return
-            return RerankModel[model_config["llm_factory"]](
-                model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
-
-        if llm_type == LLMType.IMAGE2TEXT.value:
-            if model_config["llm_factory"] not in CvModel:
-                return
-            return CvModel[model_config["llm_factory"]](
-                model_config["api_key"], model_config["llm_name"], lang,
-                base_url=model_config["api_base"]
-            )
-
-        if llm_type == LLMType.CHAT.value:
-            if model_config["llm_factory"] not in ChatModel:
-                return
-            return ChatModel[model_config["llm_factory"]](
-                model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
-
-        if llm_type == LLMType.SPEECH2TEXT:
-            if model_config["llm_factory"] not in Seq2txtModel:
-                return
-            return Seq2txtModel[model_config["llm_factory"]](
-                model_config["api_key"], model_config["llm_name"], lang,
-                base_url=model_config["api_base"]
-            )
-
-    @classmethod
-    @DB.connection_context()
-    def increase_usage(cls, tenant_id, llm_type, used_tokens, llm_name=None):
-        e, tenant = TenantService.get_by_id(tenant_id)
-        if not e:
-            raise LookupError("Tenant not found")
-
-        if llm_type == LLMType.EMBEDDING.value:
-            mdlnm = tenant.embd_id
-        elif llm_type == LLMType.SPEECH2TEXT.value:
-            mdlnm = tenant.asr_id
-        elif llm_type == LLMType.IMAGE2TEXT.value:
-            mdlnm = tenant.img2txt_id
-        elif llm_type == LLMType.CHAT.value:
-            mdlnm = tenant.llm_id if not llm_name else llm_name
-        elif llm_type == LLMType.RERANK:
-            mdlnm = tenant.llm_id if not llm_name else llm_name
-        else:
-            assert False, "LLM type error"
-
-        num = 0
-        try:
-            for u in cls.query(tenant_id = tenant_id, llm_name=mdlnm):
-                num += cls.model.update(used_tokens = u.used_tokens + used_tokens)\
-                    .where(cls.model.tenant_id == tenant_id, cls.model.llm_name == mdlnm)\
-                    .execute()
-        except Exception as e:
-            pass
-        return num
-
-    @classmethod
-    @DB.connection_context()
-    def get_openai_models(cls):
-        objs = cls.model.select().where(
-            (cls.model.llm_factory == "OpenAI"),
-            ~(cls.model.llm_name == "text-embedding-3-small"),
-            ~(cls.model.llm_name == "text-embedding-3-large")
-        ).dicts()
-        return list(objs)
-
-
-class LLMBundle(object):
-    def __init__(self, tenant_id, llm_type, llm_name=None, lang="Chinese"):
-        self.tenant_id = tenant_id
-        self.llm_type = llm_type
-        self.llm_name = llm_name
-        self.mdl = TenantLLMService.model_instance(
-            tenant_id, llm_type, llm_name, lang=lang)
-        assert self.mdl, "Can't find mole for {}/{}/{}".format(
-            tenant_id, llm_type, llm_name)
-        self.max_length = 512
-        for lm in LLMService.query(llm_name=llm_name):
-            self.max_length = lm.max_tokens
-            break
-
-    def encode(self, texts: list, batch_size=32):
-        emd, used_tokens = self.mdl.encode(texts, batch_size)
-        if not TenantLLMService.increase_usage(
-                self.tenant_id, self.llm_type, used_tokens):
-            database_logger.error(
-                "Can't update token usage for {}/EMBEDDING".format(self.tenant_id))
-        return emd, used_tokens
-
-    def encode_queries(self, query: str):
-        emd, used_tokens = self.mdl.encode_queries(query)
-        if not TenantLLMService.increase_usage(
-                self.tenant_id, self.llm_type, used_tokens):
-            database_logger.error(
-                "Can't update token usage for {}/EMBEDDING".format(self.tenant_id))
-        return emd, used_tokens
-
-    def similarity(self, query: str, texts: list):
-        sim, used_tokens = self.mdl.similarity(query, texts)
-        if not TenantLLMService.increase_usage(
-                self.tenant_id, self.llm_type, used_tokens):
-            database_logger.error(
-                "Can't update token usage for {}/RERANK".format(self.tenant_id))
-        return sim, used_tokens
-
-    def describe(self, image, max_tokens=300):
-        txt, used_tokens = self.mdl.describe(image, max_tokens)
-        if not TenantLLMService.increase_usage(
-                self.tenant_id, self.llm_type, used_tokens):
-            database_logger.error(
-                "Can't update token usage for {}/IMAGE2TEXT".format(self.tenant_id))
-        return txt
-
-    def transcription(self, audio):
-        txt, used_tokens = self.mdl.transcription(audio)
-        if not TenantLLMService.increase_usage(
-                self.tenant_id, self.llm_type, used_tokens):
-            database_logger.error(
-                "Can't update token usage for {}/SEQUENCE2TXT".format(self.tenant_id))
-        return txt
-
-    def chat(self, system, history, gen_conf):
-        txt, used_tokens = self.mdl.chat(system, history, gen_conf)
-        if not TenantLLMService.increase_usage(
-                self.tenant_id, self.llm_type, used_tokens, self.llm_name):
-            database_logger.error(
-                "Can't update token usage for {}/CHAT".format(self.tenant_id))
-        return txt
-
-    def chat_streamly(self, system, history, gen_conf):
-        for txt in self.mdl.chat_streamly(system, history, gen_conf):
-            if isinstance(txt, int):
-                if not TenantLLMService.increase_usage(
-                        self.tenant_id, self.llm_type, txt, self.llm_name):
-                    database_logger.error(
-                        "Can't update token usage for {}/CHAT".format(self.tenant_id))
-                return
-            yield txt
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from api.db.services.user_service import TenantService
+from api.settings import database_logger
+from rag.llm import EmbeddingModel, CvModel, ChatModel, RerankModel, Seq2txtModel, TTSModel
+from api.db import LLMType
+from api.db.db_models import DB
+from api.db.db_models import LLMFactories, LLM, TenantLLM
+from api.db.services.common_service import CommonService
+
+
+class LLMFactoriesService(CommonService):
+    model = LLMFactories
+
+
+class LLMService(CommonService):
+    model = LLM
+
+
+class TenantLLMService(CommonService):
+    model = TenantLLM
+
+    @classmethod
+    @DB.connection_context()
+    def get_api_key(cls, tenant_id, model_name):
+        arr = model_name.split("@")
+        if len(arr) < 2:
+            objs = cls.query(tenant_id=tenant_id, llm_name=model_name)
+        else:
+            objs = cls.query(tenant_id=tenant_id, llm_name=arr[0], llm_factory=arr[1])
+        if not objs:
+            return
+        return objs[0]
+
+    @classmethod
+    @DB.connection_context()
+    def get_my_llms(cls, tenant_id):
+        fields = [
+            cls.model.llm_factory,
+            LLMFactories.logo,
+            LLMFactories.tags,
+            cls.model.model_type,
+            cls.model.llm_name,
+            cls.model.used_tokens
+        ]
+        objs = cls.model.select(*fields).join(LLMFactories, on=(cls.model.llm_factory == LLMFactories.name)).where(
+            cls.model.tenant_id == tenant_id, ~cls.model.api_key.is_null()).dicts()
+
+        return list(objs)
+
+    @classmethod
+    @DB.connection_context()
+    def model_instance(cls, tenant_id, llm_type,
+                       llm_name=None, lang="Chinese"):
+        e, tenant = TenantService.get_by_id(tenant_id)
+        if not e:
+            raise LookupError("Tenant not found")
+
+        if llm_type == LLMType.EMBEDDING.value:
+            mdlnm = tenant.embd_id if not llm_name else llm_name
+        elif llm_type == LLMType.SPEECH2TEXT.value:
+            mdlnm = tenant.asr_id
+        elif llm_type == LLMType.IMAGE2TEXT.value:
+            mdlnm = tenant.img2txt_id if not llm_name else llm_name
+        elif llm_type == LLMType.CHAT.value:
+            mdlnm = tenant.llm_id if not llm_name else llm_name
+        elif llm_type == LLMType.RERANK:
+            mdlnm = tenant.rerank_id if not llm_name else llm_name
+        elif llm_type == LLMType.TTS:
+            mdlnm = tenant.tts_id if not llm_name else llm_name
+        else:
+            assert False, "LLM type error"
+
+        model_config = cls.get_api_key(tenant_id, mdlnm)
+        tmp = mdlnm.split("@")
+        fid = None if len(tmp) < 2 else tmp[1]
+        mdlnm = tmp[0]
+        if model_config: model_config = model_config.to_dict()
+        if not model_config:
+            if llm_type in [LLMType.EMBEDDING, LLMType.RERANK]:
+                llm = LLMService.query(llm_name=mdlnm) if not fid else LLMService.query(llm_name=mdlnm, fid=fid)
+                if llm and llm[0].fid in ["Youdao", "FastEmbed", "BAAI"]:
+                    model_config = {"llm_factory": llm[0].fid, "api_key":"", "llm_name": mdlnm, "api_base": ""}
+            if not model_config:
+                if mdlnm == "flag-embedding":
+                    model_config = {"llm_factory": "Tongyi-Qianwen", "api_key": "",
+                                "llm_name": llm_name, "api_base": ""}
+                else:
+                    if not mdlnm:
+                        raise LookupError(f"Type of {llm_type} model is not set.")
+                    raise LookupError("Model({}) not authorized".format(mdlnm))
+
+        if llm_type == LLMType.EMBEDDING.value:
+            if model_config["llm_factory"] not in EmbeddingModel:
+                return
+            return EmbeddingModel[model_config["llm_factory"]](
+                model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
+
+        if llm_type == LLMType.RERANK:
+            if model_config["llm_factory"] not in RerankModel:
+                return
+            return RerankModel[model_config["llm_factory"]](
+                model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
+
+        if llm_type == LLMType.IMAGE2TEXT.value:
+            if model_config["llm_factory"] not in CvModel:
+                return
+            return CvModel[model_config["llm_factory"]](
+                model_config["api_key"], model_config["llm_name"], lang,
+                base_url=model_config["api_base"]
+            )
+
+        if llm_type == LLMType.CHAT.value:
+            if model_config["llm_factory"] not in ChatModel:
+                return
+            return ChatModel[model_config["llm_factory"]](
+                model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
+
+        if llm_type == LLMType.SPEECH2TEXT:
+            if model_config["llm_factory"] not in Seq2txtModel:
+                return
+            return Seq2txtModel[model_config["llm_factory"]](
+                model_config["api_key"], model_config["llm_name"], lang,
+                base_url=model_config["api_base"]
+            )
+        if llm_type == LLMType.TTS:
+            if model_config["llm_factory"] not in TTSModel:
+                return
+            return TTSModel[model_config["llm_factory"]](
+                model_config["api_key"],
+                model_config["llm_name"],
+                base_url=model_config["api_base"],
+            )
+
+    @classmethod
+    @DB.connection_context()
+    def increase_usage(cls, tenant_id, llm_type, used_tokens, llm_name=None):
+        e, tenant = TenantService.get_by_id(tenant_id)
+        if not e:
+            raise LookupError("Tenant not found")
+
+        if llm_type == LLMType.EMBEDDING.value:
+            mdlnm = tenant.embd_id
+        elif llm_type == LLMType.SPEECH2TEXT.value:
+            mdlnm = tenant.asr_id
+        elif llm_type == LLMType.IMAGE2TEXT.value:
+            mdlnm = tenant.img2txt_id
+        elif llm_type == LLMType.CHAT.value:
+            mdlnm = tenant.llm_id if not llm_name else llm_name
+        elif llm_type == LLMType.RERANK:
+            mdlnm = tenant.rerank_id if not llm_name else llm_name
+        elif llm_type == LLMType.TTS:
+            mdlnm = tenant.tts_id if not llm_name else llm_name
+        else:
+            assert False, "LLM type error"
+
+        num = 0
+        try:
+            for u in cls.query(tenant_id=tenant_id, llm_name=mdlnm):
+                num += cls.model.update(used_tokens=u.used_tokens + used_tokens)\
+                    .where(cls.model.tenant_id == tenant_id, cls.model.llm_name == mdlnm)\
+                    .execute()
+        except Exception as e:
+            pass
+        return num
+
+    @classmethod
+    @DB.connection_context()
+    def get_openai_models(cls):
+        objs = cls.model.select().where(
+            (cls.model.llm_factory == "OpenAI"),
+            ~(cls.model.llm_name == "text-embedding-3-small"),
+            ~(cls.model.llm_name == "text-embedding-3-large")
+        ).dicts()
+        return list(objs)
+
+
+class LLMBundle(object):
+    def __init__(self, tenant_id, llm_type, llm_name=None, lang="Chinese"):
+        self.tenant_id = tenant_id
+        self.llm_type = llm_type
+        self.llm_name = llm_name
+        self.mdl = TenantLLMService.model_instance(
+            tenant_id, llm_type, llm_name, lang=lang)
+        assert self.mdl, "Can't find mole for {}/{}/{}".format(
+            tenant_id, llm_type, llm_name)
+        self.max_length = 8192
+        for lm in LLMService.query(llm_name=llm_name):
+            self.max_length = lm.max_tokens
+            break
+    
+    def encode(self, texts: list, batch_size=32):
+        emd, used_tokens = self.mdl.encode(texts, batch_size)
+        if not TenantLLMService.increase_usage(
+                self.tenant_id, self.llm_type, used_tokens):
+            database_logger.error(
+                "Can't update token usage for {}/EMBEDDING".format(self.tenant_id))
+        return emd, used_tokens
+
+    def encode_queries(self, query: str):
+        emd, used_tokens = self.mdl.encode_queries(query)
+        if not TenantLLMService.increase_usage(
+                self.tenant_id, self.llm_type, used_tokens):
+            database_logger.error(
+                "Can't update token usage for {}/EMBEDDING".format(self.tenant_id))
+        return emd, used_tokens
+
+    def similarity(self, query: str, texts: list):
+        sim, used_tokens = self.mdl.similarity(query, texts)
+        if not TenantLLMService.increase_usage(
+                self.tenant_id, self.llm_type, used_tokens):
+            database_logger.error(
+                "Can't update token usage for {}/RERANK".format(self.tenant_id))
+        return sim, used_tokens
+
+    def describe(self, image, max_tokens=300):
+        txt, used_tokens = self.mdl.describe(image, max_tokens)
+        if not TenantLLMService.increase_usage(
+                self.tenant_id, self.llm_type, used_tokens):
+            database_logger.error(
+                "Can't update token usage for {}/IMAGE2TEXT".format(self.tenant_id))
+        return txt
+
+    def transcription(self, audio):
+        txt, used_tokens = self.mdl.transcription(audio)
+        if not TenantLLMService.increase_usage(
+                self.tenant_id, self.llm_type, used_tokens):
+            database_logger.error(
+                "Can't update token usage for {}/SEQUENCE2TXT".format(self.tenant_id))
+        return txt
+
+    def tts(self, text):
+        for chunk in self.mdl.tts(text):
+            if isinstance(chunk,int):
+                if not TenantLLMService.increase_usage(
+                    self.tenant_id, self.llm_type, chunk, self.llm_name):
+                        database_logger.error(
+                            "Can't update token usage for {}/TTS".format(self.tenant_id))
+                return
+            yield chunk     
+
+    def chat(self, system, history, gen_conf):
+        txt, used_tokens = self.mdl.chat(system, history, gen_conf)
+        if not TenantLLMService.increase_usage(
+                self.tenant_id, self.llm_type, used_tokens, self.llm_name):
+            database_logger.error(
+                "Can't update token usage for {}/CHAT".format(self.tenant_id))
+        return txt
+
+    def chat_streamly(self, system, history, gen_conf):
+        for txt in self.mdl.chat_streamly(system, history, gen_conf):
+            if isinstance(txt, int):
+                if not TenantLLMService.increase_usage(
+                        self.tenant_id, self.llm_type, txt, self.llm_name):
+                    database_logger.error(
+                        "Can't update token usage for {}/CHAT".format(self.tenant_id))
+                return
+            yield txt

api/db/services/task_service.py

@@ -1,175 +1,179 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import os
-import random
-
-from api.db.db_utils import bulk_insert_into_db
-from deepdoc.parser import PdfParser
-from peewee import JOIN
-from api.db.db_models import DB, File2Document, File
-from api.db import StatusEnum, FileType, TaskStatus
-from api.db.db_models import Task, Document, Knowledgebase, Tenant
-from api.db.services.common_service import CommonService
-from api.db.services.document_service import DocumentService
-from api.utils import current_timestamp, get_uuid
-from deepdoc.parser.excel_parser import RAGFlowExcelParser
-from rag.settings import SVR_QUEUE_NAME
-from rag.utils.minio_conn import MINIO
-from rag.utils.redis_conn import REDIS_CONN
-
-
-class TaskService(CommonService):
-    model = Task
-
-    @classmethod
-    @DB.connection_context()
-    def get_tasks(cls, task_id):
-        fields = [
-            cls.model.id,
-            cls.model.doc_id,
-            cls.model.from_page,
-            cls.model.to_page,
-            Document.kb_id,
-            Document.parser_id,
-            Document.parser_config,
-            Document.name,
-            Document.type,
-            Document.location,
-            Document.size,
-            Knowledgebase.tenant_id,
-            Knowledgebase.language,
-            Knowledgebase.embd_id,
-            Tenant.img2txt_id,
-            Tenant.asr_id,
-            Tenant.llm_id,
-            cls.model.update_time]
-        docs = cls.model.select(*fields) \
-            .join(Document, on=(cls.model.doc_id == Document.id)) \
-            .join(Knowledgebase, on=(Document.kb_id == Knowledgebase.id)) \
-            .join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id)) \
-            .where(cls.model.id == task_id)
-        docs = list(docs.dicts())
-        if not docs: return []
-
-        cls.model.update(progress_msg=cls.model.progress_msg + "\n" + "Task has been received.",
-                         progress=random.random() / 10.).where(
-            cls.model.id == docs[0]["id"]).execute()
-        return docs
-
-    @classmethod
-    @DB.connection_context()
-    def get_ongoing_doc_name(cls):
-        with DB.lock("get_task", -1):
-            docs = cls.model.select(*[Document.id, Document.kb_id, Document.location, File.parent_id]) \
-                .join(Document, on=(cls.model.doc_id == Document.id)) \
-                .join(File2Document, on=(File2Document.document_id == Document.id), join_type=JOIN.LEFT_OUTER) \
-                .join(File, on=(File2Document.file_id == File.id), join_type=JOIN.LEFT_OUTER) \
-                .where(
-                    Document.status == StatusEnum.VALID.value,
-                    Document.run == TaskStatus.RUNNING.value,
-                    ~(Document.type == FileType.VIRTUAL.value),
-                    cls.model.progress < 1,
-                    cls.model.create_time >= current_timestamp() - 1000 * 600
-                )
-            docs = list(docs.dicts())
-            if not docs: return []
-
-            return list(set([(d["parent_id"] if d["parent_id"] else d["kb_id"], d["location"]) for d in docs]))
-
-    @classmethod
-    @DB.connection_context()
-    def do_cancel(cls, id):
-        try:
-            task = cls.model.get_by_id(id)
-            _, doc = DocumentService.get_by_id(task.doc_id)
-            return doc.run == TaskStatus.CANCEL.value or doc.progress < 0
-        except Exception as e:
-            pass
-        return False
-
-    @classmethod
-    @DB.connection_context()
-    def update_progress(cls, id, info):
-        if os.environ.get("MACOS"):
-            if info["progress_msg"]:
-                cls.model.update(progress_msg=cls.model.progress_msg + "\n" + info["progress_msg"]).where(
-                    cls.model.id == id).execute()
-            if "progress" in info:
-                cls.model.update(progress=info["progress"]).where(
-                    cls.model.id == id).execute()
-            return
-
-        with DB.lock("update_progress", -1):
-            if info["progress_msg"]:
-                cls.model.update(progress_msg=cls.model.progress_msg + "\n" + info["progress_msg"]).where(
-                    cls.model.id == id).execute()
-            if "progress" in info:
-                cls.model.update(progress=info["progress"]).where(
-                    cls.model.id == id).execute()
-
-
-def queue_tasks(doc, bucket, name):
-    def new_task():
-        nonlocal doc
-        return {
-            "id": get_uuid(),
-            "doc_id": doc["id"]
-        }
-    tsks = []
-
-    if doc["type"] == FileType.PDF.value:
-        file_bin = MINIO.get(bucket, name)
-        do_layout = doc["parser_config"].get("layout_recognize", True)
-        pages = PdfParser.total_page_number(doc["name"], file_bin)
-        page_size = doc["parser_config"].get("task_page_size", 12)
-        if doc["parser_id"] == "paper":
-            page_size = doc["parser_config"].get("task_page_size", 22)
-        if doc["parser_id"] == "one":
-            page_size = 1000000000
-        if doc["parser_id"] == "knowledge_graph":
-            page_size = 1000000000
-        if not do_layout:
-            page_size = 1000000000
-        page_ranges = doc["parser_config"].get("pages")
-        if not page_ranges:
-            page_ranges = [(1, 100000)]
-        for s, e in page_ranges:
-            s -= 1
-            s = max(0, s)
-            e = min(e - 1, pages)
-            for p in range(s, e, page_size):
-                task = new_task()
-                task["from_page"] = p
-                task["to_page"] = min(p + page_size, e)
-                tsks.append(task)
-
-    elif doc["parser_id"] == "table":
-        file_bin = MINIO.get(bucket, name)
-        rn = RAGFlowExcelParser.row_number(
-            doc["name"], file_bin)
-        for i in range(0, rn, 3000):
-            task = new_task()
-            task["from_page"] = i
-            task["to_page"] = min(i + 3000, rn)
-            tsks.append(task)
-    else:
-        tsks.append(new_task())
-
-    bulk_insert_into_db(Task, tsks, True)
-    DocumentService.begin2parse(doc["id"])
-
-    for t in tsks:
-        assert REDIS_CONN.queue_product(SVR_QUEUE_NAME, message=t), "Can't access Redis. Please check the Redis' status."
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import os
+import random
+
+from api.db.db_utils import bulk_insert_into_db
+from deepdoc.parser import PdfParser
+from peewee import JOIN
+from api.db.db_models import DB, File2Document, File
+from api.db import StatusEnum, FileType, TaskStatus
+from api.db.db_models import Task, Document, Knowledgebase, Tenant
+from api.db.services.common_service import CommonService
+from api.db.services.document_service import DocumentService
+from api.utils import current_timestamp, get_uuid
+from deepdoc.parser.excel_parser import RAGFlowExcelParser
+from rag.settings import SVR_QUEUE_NAME
+from rag.utils.storage_factory import STORAGE_IMPL
+from rag.utils.redis_conn import REDIS_CONN
+
+
+class TaskService(CommonService):
+    model = Task
+
+    @classmethod
+    @DB.connection_context()
+    def get_tasks(cls, task_id):
+        fields = [
+            cls.model.id,
+            cls.model.doc_id,
+            cls.model.from_page,
+            cls.model.to_page,
+            cls.model.retry_count,
+            Document.kb_id,
+            Document.parser_id,
+            Document.parser_config,
+            Document.name,
+            Document.type,
+            Document.location,
+            Document.size,
+            Knowledgebase.tenant_id,
+            Knowledgebase.language,
+            Knowledgebase.embd_id,
+            Tenant.img2txt_id,
+            Tenant.asr_id,
+            Tenant.llm_id,
+            cls.model.update_time]
+        docs = cls.model.select(*fields) \
+            .join(Document, on=(cls.model.doc_id == Document.id)) \
+            .join(Knowledgebase, on=(Document.kb_id == Knowledgebase.id)) \
+            .join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id)) \
+            .where(cls.model.id == task_id)
+        docs = list(docs.dicts())
+        if not docs: return []
+
+        msg = "\nTask has been received."
+        prog = random.random() / 10.
+        if docs[0]["retry_count"] >= 3:
+            msg = "\nERROR: Task is abandoned after 3 times attempts."
+            prog = -1
+
+        cls.model.update(progress_msg=cls.model.progress_msg + msg,
+                         progress=prog,
+                         retry_count=docs[0]["retry_count"]+1
+                         ).where(
+            cls.model.id == docs[0]["id"]).execute()
+
+        if docs[0]["retry_count"] >= 3: return []
+
+        return docs
+
+    @classmethod
+    @DB.connection_context()
+    def get_ongoing_doc_name(cls):
+        with DB.lock("get_task", -1):
+            docs = cls.model.select(*[Document.id, Document.kb_id, Document.location, File.parent_id]) \
+                .join(Document, on=(cls.model.doc_id == Document.id)) \
+                .join(File2Document, on=(File2Document.document_id == Document.id), join_type=JOIN.LEFT_OUTER) \
+                .join(File, on=(File2Document.file_id == File.id), join_type=JOIN.LEFT_OUTER) \
+                .where(
+                    Document.status == StatusEnum.VALID.value,
+                    Document.run == TaskStatus.RUNNING.value,
+                    ~(Document.type == FileType.VIRTUAL.value),
+                    cls.model.progress < 1,
+                    cls.model.create_time >= current_timestamp() - 1000 * 600
+                )
+            docs = list(docs.dicts())
+            if not docs: return []
+
+            return list(set([(d["parent_id"] if d["parent_id"] else d["kb_id"], d["location"]) for d in docs]))
+
+    @classmethod
+    @DB.connection_context()
+    def do_cancel(cls, id):
+        try:
+            task = cls.model.get_by_id(id)
+            _, doc = DocumentService.get_by_id(task.doc_id)
+            return doc.run == TaskStatus.CANCEL.value or doc.progress < 0
+        except Exception as e:
+            pass
+        return False
+
+    @classmethod
+    @DB.connection_context()
+    def update_progress(cls, id, info):
+        if os.environ.get("MACOS"):
+            if info["progress_msg"]:
+                cls.model.update(progress_msg=cls.model.progress_msg + "\n" + info["progress_msg"]).where(
+                    cls.model.id == id).execute()
+            if "progress" in info:
+                cls.model.update(progress=info["progress"]).where(
+                    cls.model.id == id).execute()
+            return
+
+        with DB.lock("update_progress", -1):
+            if info["progress_msg"]:
+                cls.model.update(progress_msg=cls.model.progress_msg + "\n" + info["progress_msg"]).where(
+                    cls.model.id == id).execute()
+            if "progress" in info:
+                cls.model.update(progress=info["progress"]).where(
+                    cls.model.id == id).execute()
+
+
+def queue_tasks(doc: dict, bucket: str, name: str):
+    def new_task():
+        return {
+            "id": get_uuid(),
+            "doc_id": doc["id"]
+        }
+    tsks = []
+
+    if doc["type"] == FileType.PDF.value:
+        file_bin = STORAGE_IMPL.get(bucket, name)
+        do_layout = doc["parser_config"].get("layout_recognize", True)
+        pages = PdfParser.total_page_number(doc["name"], file_bin)
+        page_size = doc["parser_config"].get("task_page_size", 12)
+        if doc["parser_id"] == "paper":
+            page_size = doc["parser_config"].get("task_page_size", 22)
+        if doc["parser_id"] in ["one", "knowledge_graph"] or not do_layout:
+            page_size = 10 ** 9
+        page_ranges = doc["parser_config"].get("pages") or [(1, 10 ** 5)]
+        for s, e in page_ranges:
+            s -= 1
+            s = max(0, s)
+            e = min(e - 1, pages)
+            for p in range(s, e, page_size):
+                task = new_task()
+                task["from_page"] = p
+                task["to_page"] = min(p + page_size, e)
+                tsks.append(task)
+
+    elif doc["parser_id"] == "table":
+        file_bin = STORAGE_IMPL.get(bucket, name)
+        rn = RAGFlowExcelParser.row_number(doc["name"], file_bin)
+        for i in range(0, rn, 3000):
+            task = new_task()
+            task["from_page"] = i
+            task["to_page"] = min(i + 3000, rn)
+            tsks.append(task)
+    else:
+        tsks.append(new_task())
+
+    bulk_insert_into_db(Task, tsks, True)
+    DocumentService.begin2parse(doc["id"])
+
+    for t in tsks:
+        assert REDIS_CONN.queue_product(SVR_QUEUE_NAME, message=t), "Can't access Redis. Please check the Redis' status."

api/db/services/user_service.py

@@ -96,6 +96,7 @@ class TenantService(CommonService):
             cls.model.rerank_id,
             cls.model.asr_id,
             cls.model.img2txt_id,
+            cls.model.tts_id,
             cls.model.parser_ids,
             UserTenant.role]
         return list(cls.model.select(*fields)
@@ -136,3 +137,24 @@ class UserTenantService(CommonService):
             kwargs["id"] = get_uuid()
         obj = cls.model(**kwargs).save(force_insert=True)
         return obj
+
+    @classmethod
+    @DB.connection_context()
+    def get_by_tenant_id(cls, tenant_id):
+        fields = [
+            cls.model.user_id,
+            cls.model.tenant_id,
+            cls.model.role,
+            cls.model.status,
+            User.nickname,
+            User.email,
+            User.avatar,
+            User.is_authenticated,
+            User.is_active,
+            User.is_anonymous,
+            User.status,
+            User.is_superuser]
+        return list(cls.model.select(*fields)
+                    .join(User, on=((cls.model.user_id == User.id) & (cls.model.status == StatusEnum.VALID.value)))
+                    .where(cls.model.tenant_id == tenant_id)
+                    .dicts())

api/ragflow_server.py

@@ -1,100 +1,99 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-
-import logging
-import os
-import signal
-import sys
-import time
-import traceback
-from concurrent.futures import ThreadPoolExecutor
-
-from werkzeug.serving import run_simple
-from api.apps import app
-from api.db.runtime_config import RuntimeConfig
-from api.db.services.document_service import DocumentService
-from api.settings import (
-    HOST, HTTP_PORT, access_logger, database_logger, stat_logger,
-)
-from api import utils
-
-from api.db.db_models import init_database_tables as init_web_db
-from api.db.init_data import init_web_data
-from api.versions import get_versions
-
-
-def update_progress():
-    while True:
-        time.sleep(1)
-        try:
-            DocumentService.update_progress()
-        except Exception as e:
-            stat_logger.error("update_progress exception:" + str(e))
-
-
-if __name__ == '__main__':
-    print("""
-    ____                 ______ __               
-   / __ \ ____ _ ____ _ / ____// /____  _      __
-  / /_/ // __ `// __ `// /_   / // __ \| | /| / /
- / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ / 
-/_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/  
-              /____/                             
-
-    """, flush=True)
-    stat_logger.info(
-        f'project base: {utils.file_utils.get_project_base_directory()}'
-    )
-
-    # init db
-    init_web_db()
-    init_web_data()
-    # init runtime config
-    import argparse
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--version', default=False, help="rag flow version", action='store_true')
-    parser.add_argument('--debug', default=False, help="debug mode", action='store_true')
-    args = parser.parse_args()
-    if args.version:
-        print(get_versions())
-        sys.exit(0)
-
-    RuntimeConfig.DEBUG = args.debug
-    if RuntimeConfig.DEBUG:
-        stat_logger.info("run on debug mode")
-
-    RuntimeConfig.init_env()
-    RuntimeConfig.init_config(JOB_SERVER_HOST=HOST, HTTP_PORT=HTTP_PORT)
-
-    peewee_logger = logging.getLogger('peewee')
-    peewee_logger.propagate = False
-    # rag_arch.common.log.ROpenHandler
-    peewee_logger.addHandler(database_logger.handlers[0])
-    peewee_logger.setLevel(database_logger.level)
-
-    thr = ThreadPoolExecutor(max_workers=1)
-    thr.submit(update_progress)
-
-    # start http server
-    try:
-        stat_logger.info("RAG Flow http server start...")
-        werkzeug_logger = logging.getLogger("werkzeug")
-        for h in access_logger.handlers:
-            werkzeug_logger.addHandler(h)
-        run_simple(hostname=HOST, port=HTTP_PORT, application=app, threaded=True, use_reloader=RuntimeConfig.DEBUG, use_debugger=RuntimeConfig.DEBUG)
-    except Exception:
-        traceback.print_exc()
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+import logging
+import os
+import signal
+import sys
+import time
+import traceback
+from concurrent.futures import ThreadPoolExecutor
+
+from werkzeug.serving import run_simple
+from api.apps import app
+from api.db.runtime_config import RuntimeConfig
+from api.db.services.document_service import DocumentService
+from api.settings import (
+    HOST, HTTP_PORT, access_logger, database_logger, stat_logger,
+)
+from api import utils
+
+from api.db.db_models import init_database_tables as init_web_db
+from api.db.init_data import init_web_data
+from api.versions import get_versions
+
+
+def update_progress():
+    while True:
+        time.sleep(1)
+        try:
+            DocumentService.update_progress()
+        except Exception as e:
+            stat_logger.error("update_progress exception:" + str(e))
+
+
+if __name__ == '__main__':
+    print(r"""
+        ____   ___    ______ ______ __               
+       / __ \ /   |  / ____// ____// /____  _      __
+      / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+     / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ / 
+    /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/                             
+
+    """, flush=True)
+    stat_logger.info(
+        f'project base: {utils.file_utils.get_project_base_directory()}'
+    )
+
+    # init db
+    init_web_db()
+    init_web_data()
+    # init runtime config
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--version', default=False, help="rag flow version", action='store_true')
+    parser.add_argument('--debug', default=False, help="debug mode", action='store_true')
+    args = parser.parse_args()
+    if args.version:
+        print(get_versions())
+        sys.exit(0)
+
+    RuntimeConfig.DEBUG = args.debug
+    if RuntimeConfig.DEBUG:
+        stat_logger.info("run on debug mode")
+
+    RuntimeConfig.init_env()
+    RuntimeConfig.init_config(JOB_SERVER_HOST=HOST, HTTP_PORT=HTTP_PORT)
+
+    peewee_logger = logging.getLogger('peewee')
+    peewee_logger.propagate = False
+    # rag_arch.common.log.ROpenHandler
+    peewee_logger.addHandler(database_logger.handlers[0])
+    peewee_logger.setLevel(database_logger.level)
+
+    thr = ThreadPoolExecutor(max_workers=1)
+    thr.submit(update_progress)
+
+    # start http server
+    try:
+        stat_logger.info("RAG Flow http server start...")
+        werkzeug_logger = logging.getLogger("werkzeug")
+        for h in access_logger.handlers:
+            werkzeug_logger.addHandler(h)
+        run_simple(hostname=HOST, port=HTTP_PORT, application=app, threaded=True, use_reloader=RuntimeConfig.DEBUG, use_debugger=RuntimeConfig.DEBUG)
+    except Exception:
+        traceback.print_exc()
         os.kill(os.getpid(), signal.SIGKILL)

api/settings.py

@@ -1,251 +1,252 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import os
-from enum import IntEnum, Enum
-from api.utils.file_utils import get_project_base_directory
-from api.utils.log_utils import LoggerFactory, getLogger
-
-# Logger
-LoggerFactory.set_directory(
-    os.path.join(
-        get_project_base_directory(),
-        "logs",
-        "api"))
-# {CRITICAL: 50, FATAL:50, ERROR:40, WARNING:30, WARN:30, INFO:20, DEBUG:10, NOTSET:0}
-LoggerFactory.LEVEL = 30
-
-stat_logger = getLogger("stat")
-access_logger = getLogger("access")
-database_logger = getLogger("database")
-chat_logger = getLogger("chat")
-
-from rag.utils.es_conn import ELASTICSEARCH
-from rag.nlp import search
-from graphrag import search as kg_search
-from api.utils import get_base_config, decrypt_database_config
-
-API_VERSION = "v1"
-RAG_FLOW_SERVICE_NAME = "ragflow"
-SERVER_MODULE = "rag_flow_server.py"
-TEMP_DIRECTORY = os.path.join(get_project_base_directory(), "temp")
-RAG_FLOW_CONF_PATH = os.path.join(get_project_base_directory(), "conf")
-
-SUBPROCESS_STD_LOG_NAME = "std.log"
-
-ERROR_REPORT = True
-ERROR_REPORT_WITH_PATH = False
-
-MAX_TIMESTAMP_INTERVAL = 60
-SESSION_VALID_PERIOD = 7 * 24 * 60 * 60
-
-REQUEST_TRY_TIMES = 3
-REQUEST_WAIT_SEC = 2
-REQUEST_MAX_WAIT_SEC = 300
-
-USE_REGISTRY = get_base_config("use_registry")
-
-default_llm = {
-    "Tongyi-Qianwen": {
-        "chat_model": "qwen-plus",
-        "embedding_model": "text-embedding-v2",
-        "image2text_model": "qwen-vl-max",
-        "asr_model": "paraformer-realtime-8k-v1",
-    },
-    "OpenAI": {
-        "chat_model": "gpt-3.5-turbo",
-        "embedding_model": "text-embedding-ada-002",
-        "image2text_model": "gpt-4-vision-preview",
-        "asr_model": "whisper-1",
-    },
-    "Azure-OpenAI": {
-        "chat_model": "azure-gpt-35-turbo",
-        "embedding_model": "azure-text-embedding-ada-002",
-        "image2text_model": "azure-gpt-4-vision-preview",
-        "asr_model": "azure-whisper-1",
-    },
-    "ZHIPU-AI": {
-        "chat_model": "glm-3-turbo",
-        "embedding_model": "embedding-2",
-        "image2text_model": "glm-4v",
-        "asr_model": "",
-    },
-    "Ollama": {
-        "chat_model": "qwen-14B-chat",
-        "embedding_model": "flag-embedding",
-        "image2text_model": "",
-        "asr_model": "",
-    },
-    "Moonshot": {
-        "chat_model": "moonshot-v1-8k",
-        "embedding_model": "",
-        "image2text_model": "",
-        "asr_model": "",
-    },
-    "DeepSeek": {
-        "chat_model": "deepseek-chat",
-        "embedding_model": "",
-        "image2text_model": "",
-        "asr_model": "",
-    },
-    "VolcEngine": {
-        "chat_model": "",
-        "embedding_model": "",
-        "image2text_model": "",
-        "asr_model": "",
-    },
-    "BAAI": {
-        "chat_model": "",
-        "embedding_model": "BAAI/bge-large-zh-v1.5",
-        "image2text_model": "",
-        "asr_model": "",
-        "rerank_model": "BAAI/bge-reranker-v2-m3",
-    }
-}
-LLM = get_base_config("user_default_llm", {})
-LLM_FACTORY = LLM.get("factory", "Tongyi-Qianwen")
-LLM_BASE_URL = LLM.get("base_url")
-
-if LLM_FACTORY not in default_llm:
-    print(
-        "\33[91m【ERROR】\33[0m:",
-        f"LLM factory {LLM_FACTORY} has not supported yet, switch to 'Tongyi-Qianwen/QWen' automatically, and please check the API_KEY in service_conf.yaml.")
-    LLM_FACTORY = "Tongyi-Qianwen"
-CHAT_MDL = default_llm[LLM_FACTORY]["chat_model"]
-EMBEDDING_MDL = default_llm["BAAI"]["embedding_model"]
-RERANK_MDL = default_llm["BAAI"]["rerank_model"]
-ASR_MDL = default_llm[LLM_FACTORY]["asr_model"]
-IMAGE2TEXT_MDL = default_llm[LLM_FACTORY]["image2text_model"]
-
-API_KEY = LLM.get("api_key", "")
-PARSERS = LLM.get(
-    "parsers",
-    "naive:General,qa:Q&A,resume:Resume,manual:Manual,table:Table,paper:Paper,book:Book,laws:Laws,presentation:Presentation,picture:Picture,one:One,audio:Audio,knowledge_graph:Knowledge Graph")
-
-# distribution
-DEPENDENT_DISTRIBUTION = get_base_config("dependent_distribution", False)
-RAG_FLOW_UPDATE_CHECK = False
-
-HOST = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("host", "127.0.0.1")
-HTTP_PORT = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("http_port")
-
-SECRET_KEY = get_base_config(
-    RAG_FLOW_SERVICE_NAME,
-    {}).get(
-        "secret_key",
-    "infiniflow")
-TOKEN_EXPIRE_IN = get_base_config(
-    RAG_FLOW_SERVICE_NAME, {}).get(
-        "token_expires_in", 3600)
-
-NGINX_HOST = get_base_config(
-    RAG_FLOW_SERVICE_NAME, {}).get(
-        "nginx", {}).get("host") or HOST
-NGINX_HTTP_PORT = get_base_config(
-    RAG_FLOW_SERVICE_NAME, {}).get(
-        "nginx", {}).get("http_port") or HTTP_PORT
-
-RANDOM_INSTANCE_ID = get_base_config(
-    RAG_FLOW_SERVICE_NAME, {}).get(
-        "random_instance_id", False)
-
-PROXY = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("proxy")
-PROXY_PROTOCOL = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("protocol")
-
-DATABASE = decrypt_database_config(name="mysql")
-
-# Switch
-# upload
-UPLOAD_DATA_FROM_CLIENT = True
-
-# authentication
-AUTHENTICATION_CONF = get_base_config("authentication", {})
-
-# client
-CLIENT_AUTHENTICATION = AUTHENTICATION_CONF.get(
-    "client", {}).get(
-        "switch", False)
-HTTP_APP_KEY = AUTHENTICATION_CONF.get("client", {}).get("http_app_key")
-GITHUB_OAUTH = get_base_config("oauth", {}).get("github")
-FEISHU_OAUTH = get_base_config("oauth", {}).get("feishu")
-WECHAT_OAUTH = get_base_config("oauth", {}).get("wechat")
-
-# site
-SITE_AUTHENTICATION = AUTHENTICATION_CONF.get("site", {}).get("switch", False)
-
-# permission
-PERMISSION_CONF = get_base_config("permission", {})
-PERMISSION_SWITCH = PERMISSION_CONF.get("switch")
-COMPONENT_PERMISSION = PERMISSION_CONF.get("component")
-DATASET_PERMISSION = PERMISSION_CONF.get("dataset")
-
-HOOK_MODULE = get_base_config("hook_module")
-HOOK_SERVER_NAME = get_base_config("hook_server_name")
-
-ENABLE_MODEL_STORE = get_base_config('enable_model_store', False)
-# authentication
-USE_AUTHENTICATION = False
-USE_DATA_AUTHENTICATION = False
-AUTOMATIC_AUTHORIZATION_OUTPUT_DATA = True
-USE_DEFAULT_TIMEOUT = False
-AUTHENTICATION_DEFAULT_TIMEOUT = 7 * 24 * 60 * 60  # s
-PRIVILEGE_COMMAND_WHITELIST = []
-CHECK_NODES_IDENTITY = False
-
-retrievaler = search.Dealer(ELASTICSEARCH)
-kg_retrievaler = kg_search.KGSearch(ELASTICSEARCH)
-
-
-class CustomEnum(Enum):
-    @classmethod
-    def valid(cls, value):
-        try:
-            cls(value)
-            return True
-        except BaseException:
-            return False
-
-    @classmethod
-    def values(cls):
-        return [member.value for member in cls.__members__.values()]
-
-    @classmethod
-    def names(cls):
-        return [member.name for member in cls.__members__.values()]
-
-
-class PythonDependenceName(CustomEnum):
-    Rag_Source_Code = "python"
-    Python_Env = "miniconda"
-
-
-class ModelStorage(CustomEnum):
-    REDIS = "redis"
-    MYSQL = "mysql"
-
-
-class RetCode(IntEnum, CustomEnum):
-    SUCCESS = 0
-    NOT_EFFECTIVE = 10
-    EXCEPTION_ERROR = 100
-    ARGUMENT_ERROR = 101
-    DATA_ERROR = 102
-    OPERATING_ERROR = 103
-    CONNECTION_ERROR = 105
-    RUNNING = 106
-    PERMISSION_ERROR = 108
-    AUTHENTICATION_ERROR = 109
-    UNAUTHORIZED = 401
-    SERVER_ERROR = 500
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import os
+from enum import IntEnum, Enum
+from api.utils.file_utils import get_project_base_directory
+from api.utils.log_utils import LoggerFactory, getLogger
+
+# Logger
+LoggerFactory.set_directory(
+    os.path.join(
+        get_project_base_directory(),
+        "logs",
+        "api"))
+# {CRITICAL: 50, FATAL:50, ERROR:40, WARNING:30, WARN:30, INFO:20, DEBUG:10, NOTSET:0}
+LoggerFactory.LEVEL = 30
+
+stat_logger = getLogger("stat")
+access_logger = getLogger("access")
+database_logger = getLogger("database")
+chat_logger = getLogger("chat")
+
+from rag.utils.es_conn import ELASTICSEARCH
+from rag.nlp import search
+from graphrag import search as kg_search
+from api.utils import get_base_config, decrypt_database_config
+
+API_VERSION = "v1"
+RAG_FLOW_SERVICE_NAME = "ragflow"
+SERVER_MODULE = "rag_flow_server.py"
+TEMP_DIRECTORY = os.path.join(get_project_base_directory(), "temp")
+RAG_FLOW_CONF_PATH = os.path.join(get_project_base_directory(), "conf")
+LIGHTEN = os.environ.get('LIGHTEN')
+
+SUBPROCESS_STD_LOG_NAME = "std.log"
+
+ERROR_REPORT = True
+ERROR_REPORT_WITH_PATH = False
+
+MAX_TIMESTAMP_INTERVAL = 60
+SESSION_VALID_PERIOD = 7 * 24 * 60 * 60
+
+REQUEST_TRY_TIMES = 3
+REQUEST_WAIT_SEC = 2
+REQUEST_MAX_WAIT_SEC = 300
+
+USE_REGISTRY = get_base_config("use_registry")
+
+LLM = get_base_config("user_default_llm", {})
+LLM_FACTORY = LLM.get("factory", "Tongyi-Qianwen")
+LLM_BASE_URL = LLM.get("base_url")
+
+if not LIGHTEN:
+    default_llm = {
+        "Tongyi-Qianwen": {
+            "chat_model": "qwen-plus",
+            "embedding_model": "text-embedding-v2",
+            "image2text_model": "qwen-vl-max",
+            "asr_model": "paraformer-realtime-8k-v1",
+        },
+        "OpenAI": {
+            "chat_model": "gpt-3.5-turbo",
+            "embedding_model": "text-embedding-ada-002",
+            "image2text_model": "gpt-4-vision-preview",
+            "asr_model": "whisper-1",
+        },
+        "Azure-OpenAI": {
+            "chat_model": "gpt-35-turbo",
+            "embedding_model": "text-embedding-ada-002",
+            "image2text_model": "gpt-4-vision-preview",
+            "asr_model": "whisper-1",
+        },
+        "ZHIPU-AI": {
+            "chat_model": "glm-3-turbo",
+            "embedding_model": "embedding-2",
+            "image2text_model": "glm-4v",
+            "asr_model": "",
+        },
+        "Ollama": {
+            "chat_model": "qwen-14B-chat",
+            "embedding_model": "flag-embedding",
+            "image2text_model": "",
+            "asr_model": "",
+        },
+        "Moonshot": {
+            "chat_model": "moonshot-v1-8k",
+            "embedding_model": "",
+            "image2text_model": "",
+            "asr_model": "",
+        },
+        "DeepSeek": {
+            "chat_model": "deepseek-chat",
+            "embedding_model": "",
+            "image2text_model": "",
+            "asr_model": "",
+        },
+        "VolcEngine": {
+            "chat_model": "",
+            "embedding_model": "",
+            "image2text_model": "",
+            "asr_model": "",
+        },
+        "BAAI": {
+            "chat_model": "",
+            "embedding_model": "BAAI/bge-large-zh-v1.5",
+            "image2text_model": "",
+            "asr_model": "",
+            "rerank_model": "BAAI/bge-reranker-v2-m3",
+        }
+    }
+
+    CHAT_MDL = default_llm[LLM_FACTORY]["chat_model"]
+    EMBEDDING_MDL = default_llm["BAAI"]["embedding_model"]
+    RERANK_MDL = default_llm["BAAI"]["rerank_model"] if not LIGHTEN else ""
+    ASR_MDL = default_llm[LLM_FACTORY]["asr_model"]
+    IMAGE2TEXT_MDL = default_llm[LLM_FACTORY]["image2text_model"]
+else:
+    CHAT_MDL = EMBEDDING_MDL = RERANK_MDL = ASR_MDL = IMAGE2TEXT_MDL = ""
+
+API_KEY = LLM.get("api_key", "")
+PARSERS = LLM.get(
+    "parsers",
+    "naive:General,qa:Q&A,resume:Resume,manual:Manual,table:Table,paper:Paper,book:Book,laws:Laws,presentation:Presentation,picture:Picture,one:One,audio:Audio,knowledge_graph:Knowledge Graph,email:Email")
+
+# distribution
+DEPENDENT_DISTRIBUTION = get_base_config("dependent_distribution", False)
+RAG_FLOW_UPDATE_CHECK = False
+
+HOST = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("host", "127.0.0.1")
+HTTP_PORT = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("http_port")
+
+SECRET_KEY = get_base_config(
+    RAG_FLOW_SERVICE_NAME,
+    {}).get(
+        "secret_key",
+    "infiniflow")
+TOKEN_EXPIRE_IN = get_base_config(
+    RAG_FLOW_SERVICE_NAME, {}).get(
+        "token_expires_in", 3600)
+
+NGINX_HOST = get_base_config(
+    RAG_FLOW_SERVICE_NAME, {}).get(
+        "nginx", {}).get("host") or HOST
+NGINX_HTTP_PORT = get_base_config(
+    RAG_FLOW_SERVICE_NAME, {}).get(
+        "nginx", {}).get("http_port") or HTTP_PORT
+
+RANDOM_INSTANCE_ID = get_base_config(
+    RAG_FLOW_SERVICE_NAME, {}).get(
+        "random_instance_id", False)
+
+PROXY = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("proxy")
+PROXY_PROTOCOL = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("protocol")
+
+DATABASE_TYPE = os.getenv("DB_TYPE", 'mysql')
+DATABASE = decrypt_database_config(name=DATABASE_TYPE)
+
+# Switch
+# upload
+UPLOAD_DATA_FROM_CLIENT = True
+
+# authentication
+AUTHENTICATION_CONF = get_base_config("authentication", {})
+
+# client
+CLIENT_AUTHENTICATION = AUTHENTICATION_CONF.get(
+    "client", {}).get(
+        "switch", False)
+HTTP_APP_KEY = AUTHENTICATION_CONF.get("client", {}).get("http_app_key")
+GITHUB_OAUTH = get_base_config("oauth", {}).get("github")
+FEISHU_OAUTH = get_base_config("oauth", {}).get("feishu")
+WECHAT_OAUTH = get_base_config("oauth", {}).get("wechat")
+
+# site
+SITE_AUTHENTICATION = AUTHENTICATION_CONF.get("site", {}).get("switch", False)
+
+# permission
+PERMISSION_CONF = get_base_config("permission", {})
+PERMISSION_SWITCH = PERMISSION_CONF.get("switch")
+COMPONENT_PERMISSION = PERMISSION_CONF.get("component")
+DATASET_PERMISSION = PERMISSION_CONF.get("dataset")
+
+HOOK_MODULE = get_base_config("hook_module")
+HOOK_SERVER_NAME = get_base_config("hook_server_name")
+
+ENABLE_MODEL_STORE = get_base_config('enable_model_store', False)
+# authentication
+USE_AUTHENTICATION = False
+USE_DATA_AUTHENTICATION = False
+AUTOMATIC_AUTHORIZATION_OUTPUT_DATA = True
+USE_DEFAULT_TIMEOUT = False
+AUTHENTICATION_DEFAULT_TIMEOUT = 7 * 24 * 60 * 60  # s
+PRIVILEGE_COMMAND_WHITELIST = []
+CHECK_NODES_IDENTITY = False
+
+retrievaler = search.Dealer(ELASTICSEARCH)
+kg_retrievaler = kg_search.KGSearch(ELASTICSEARCH)
+
+
+class CustomEnum(Enum):
+    @classmethod
+    def valid(cls, value):
+        try:
+            cls(value)
+            return True
+        except BaseException:
+            return False
+
+    @classmethod
+    def values(cls):
+        return [member.value for member in cls.__members__.values()]
+
+    @classmethod
+    def names(cls):
+        return [member.name for member in cls.__members__.values()]
+
+
+class PythonDependenceName(CustomEnum):
+    Rag_Source_Code = "python"
+    Python_Env = "miniconda"
+
+
+class ModelStorage(CustomEnum):
+    REDIS = "redis"
+    MYSQL = "mysql"
+
+
+class RetCode(IntEnum, CustomEnum):
+    SUCCESS = 0
+    NOT_EFFECTIVE = 10
+    EXCEPTION_ERROR = 100
+    ARGUMENT_ERROR = 101
+    DATA_ERROR = 102
+    OPERATING_ERROR = 103
+    CONNECTION_ERROR = 105
+    RUNNING = 106
+    PERMISSION_ERROR = 108
+    AUTHENTICATION_ERROR = 109
+    UNAUTHORIZED = 401
+    SERVER_ERROR = 500

api/utils/__init__.py

@@ -1,346 +1,346 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import base64
-import datetime
-import io
-import json
-import os
-import pickle
-import socket
-import time
-import uuid
-import requests
-from enum import Enum, IntEnum
-import importlib
-from Cryptodome.PublicKey import RSA
-from Cryptodome.Cipher import PKCS1_v1_5 as Cipher_pkcs1_v1_5
-
-from filelock import FileLock
-
-from . import file_utils
-
-SERVICE_CONF = "service_conf.yaml"
-
-
-def conf_realpath(conf_name):
-    conf_path = f"conf/{conf_name}"
-    return os.path.join(file_utils.get_project_base_directory(), conf_path)
-
-
-def get_base_config(key, default=None, conf_name=SERVICE_CONF) -> dict:
-    local_config = {}
-    local_path = conf_realpath(f'local.{conf_name}')
-    if default is None:
-        default = os.environ.get(key.upper())
-
-    if os.path.exists(local_path):
-        local_config = file_utils.load_yaml_conf(local_path)
-        if not isinstance(local_config, dict):
-            raise ValueError(f'Invalid config file: "{local_path}".')
-
-        if key is not None and key in local_config:
-            return local_config[key]
-
-    config_path = conf_realpath(conf_name)
-    config = file_utils.load_yaml_conf(config_path)
-
-    if not isinstance(config, dict):
-        raise ValueError(f'Invalid config file: "{config_path}".')
-
-    config.update(local_config)
-    return config.get(key, default) if key is not None else config
-
-
-use_deserialize_safe_module = get_base_config(
-    'use_deserialize_safe_module', False)
-
-
-class CoordinationCommunicationProtocol(object):
-    HTTP = "http"
-    GRPC = "grpc"
-
-
-class BaseType:
-    def to_dict(self):
-        return dict([(k.lstrip("_"), v) for k, v in self.__dict__.items()])
-
-    def to_dict_with_type(self):
-        def _dict(obj):
-            module = None
-            if issubclass(obj.__class__, BaseType):
-                data = {}
-                for attr, v in obj.__dict__.items():
-                    k = attr.lstrip("_")
-                    data[k] = _dict(v)
-                module = obj.__module__
-            elif isinstance(obj, (list, tuple)):
-                data = []
-                for i, vv in enumerate(obj):
-                    data.append(_dict(vv))
-            elif isinstance(obj, dict):
-                data = {}
-                for _k, vv in obj.items():
-                    data[_k] = _dict(vv)
-            else:
-                data = obj
-            return {"type": obj.__class__.__name__,
-                    "data": data, "module": module}
-        return _dict(self)
-
-
-class CustomJSONEncoder(json.JSONEncoder):
-    def __init__(self, **kwargs):
-        self._with_type = kwargs.pop("with_type", False)
-        super().__init__(**kwargs)
-
-    def default(self, obj):
-        if isinstance(obj, datetime.datetime):
-            return obj.strftime('%Y-%m-%d %H:%M:%S')
-        elif isinstance(obj, datetime.date):
-            return obj.strftime('%Y-%m-%d')
-        elif isinstance(obj, datetime.timedelta):
-            return str(obj)
-        elif issubclass(type(obj), Enum) or issubclass(type(obj), IntEnum):
-            return obj.value
-        elif isinstance(obj, set):
-            return list(obj)
-        elif issubclass(type(obj), BaseType):
-            if not self._with_type:
-                return obj.to_dict()
-            else:
-                return obj.to_dict_with_type()
-        elif isinstance(obj, type):
-            return obj.__name__
-        else:
-            return json.JSONEncoder.default(self, obj)
-
-
-def rag_uuid():
-    return uuid.uuid1().hex
-
-
-def string_to_bytes(string):
-    return string if isinstance(
-        string, bytes) else string.encode(encoding="utf-8")
-
-
-def bytes_to_string(byte):
-    return byte.decode(encoding="utf-8")
-
-
-def json_dumps(src, byte=False, indent=None, with_type=False):
-    dest = json.dumps(
-        src,
-        indent=indent,
-        cls=CustomJSONEncoder,
-        with_type=with_type)
-    if byte:
-        dest = string_to_bytes(dest)
-    return dest
-
-
-def json_loads(src, object_hook=None, object_pairs_hook=None):
-    if isinstance(src, bytes):
-        src = bytes_to_string(src)
-    return json.loads(src, object_hook=object_hook,
-                      object_pairs_hook=object_pairs_hook)
-
-
-def current_timestamp():
-    return int(time.time() * 1000)
-
-
-def timestamp_to_date(timestamp, format_string="%Y-%m-%d %H:%M:%S"):
-    if not timestamp:
-        timestamp = time.time()
-    timestamp = int(timestamp) / 1000
-    time_array = time.localtime(timestamp)
-    str_date = time.strftime(format_string, time_array)
-    return str_date
-
-
-def date_string_to_timestamp(time_str, format_string="%Y-%m-%d %H:%M:%S"):
-    time_array = time.strptime(time_str, format_string)
-    time_stamp = int(time.mktime(time_array) * 1000)
-    return time_stamp
-
-
-def serialize_b64(src, to_str=False):
-    dest = base64.b64encode(pickle.dumps(src))
-    if not to_str:
-        return dest
-    else:
-        return bytes_to_string(dest)
-
-
-def deserialize_b64(src):
-    src = base64.b64decode(
-        string_to_bytes(src) if isinstance(
-            src, str) else src)
-    if use_deserialize_safe_module:
-        return restricted_loads(src)
-    return pickle.loads(src)
-
-
-safe_module = {
-    'numpy',
-    'rag_flow'
-}
-
-
-class RestrictedUnpickler(pickle.Unpickler):
-    def find_class(self, module, name):
-        import importlib
-        if module.split('.')[0] in safe_module:
-            _module = importlib.import_module(module)
-            return getattr(_module, name)
-        # Forbid everything else.
-        raise pickle.UnpicklingError("global '%s.%s' is forbidden" %
-                                     (module, name))
-
-
-def restricted_loads(src):
-    """Helper function analogous to pickle.loads()."""
-    return RestrictedUnpickler(io.BytesIO(src)).load()
-
-
-def get_lan_ip():
-    if os.name != "nt":
-        import fcntl
-        import struct
-
-        def get_interface_ip(ifname):
-            s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
-            return socket.inet_ntoa(
-                fcntl.ioctl(s.fileno(), 0x8915, struct.pack('256s', string_to_bytes(ifname[:15])))[20:24])
-
-    ip = socket.gethostbyname(socket.getfqdn())
-    if ip.startswith("127.") and os.name != "nt":
-        interfaces = [
-            "bond1",
-            "eth0",
-            "eth1",
-            "eth2",
-            "wlan0",
-            "wlan1",
-            "wifi0",
-            "ath0",
-            "ath1",
-            "ppp0",
-        ]
-        for ifname in interfaces:
-            try:
-                ip = get_interface_ip(ifname)
-                break
-            except IOError as e:
-                pass
-    return ip or ''
-
-
-def from_dict_hook(in_dict: dict):
-    if "type" in in_dict and "data" in in_dict:
-        if in_dict["module"] is None:
-            return in_dict["data"]
-        else:
-            return getattr(importlib.import_module(
-                in_dict["module"]), in_dict["type"])(**in_dict["data"])
-    else:
-        return in_dict
-
-
-def decrypt_database_password(password):
-    encrypt_password = get_base_config("encrypt_password", False)
-    encrypt_module = get_base_config("encrypt_module", False)
-    private_key = get_base_config("private_key", None)
-
-    if not password or not encrypt_password:
-        return password
-
-    if not private_key:
-        raise ValueError("No private key")
-
-    module_fun = encrypt_module.split("#")
-    pwdecrypt_fun = getattr(
-        importlib.import_module(
-            module_fun[0]),
-        module_fun[1])
-
-    return pwdecrypt_fun(private_key, password)
-
-
-def decrypt_database_config(
-        database=None, passwd_key="password", name="database"):
-    if not database:
-        database = get_base_config(name, {})
-
-    database[passwd_key] = decrypt_database_password(database[passwd_key])
-    return database
-
-
-def update_config(key, value, conf_name=SERVICE_CONF):
-    conf_path = conf_realpath(conf_name=conf_name)
-    if not os.path.isabs(conf_path):
-        conf_path = os.path.join(
-            file_utils.get_project_base_directory(), conf_path)
-
-    with FileLock(os.path.join(os.path.dirname(conf_path), ".lock")):
-        config = file_utils.load_yaml_conf(conf_path=conf_path) or {}
-        config[key] = value
-        file_utils.rewrite_yaml_conf(conf_path=conf_path, config=config)
-
-
-def get_uuid():
-    return uuid.uuid1().hex
-
-
-def datetime_format(date_time: datetime.datetime) -> datetime.datetime:
-    return datetime.datetime(date_time.year, date_time.month, date_time.day,
-                             date_time.hour, date_time.minute, date_time.second)
-
-
-def get_format_time() -> datetime.datetime:
-    return datetime_format(datetime.datetime.now())
-
-
-def str2date(date_time: str):
-    return datetime.datetime.strptime(date_time, '%Y-%m-%d')
-
-
-def elapsed2time(elapsed):
-    seconds = elapsed / 1000
-    minuter, second = divmod(seconds, 60)
-    hour, minuter = divmod(minuter, 60)
-    return '%02d:%02d:%02d' % (hour, minuter, second)
-
-
-def decrypt(line):
-    file_path = os.path.join(
-        file_utils.get_project_base_directory(),
-        "conf",
-        "private.pem")
-    rsa_key = RSA.importKey(open(file_path).read(), "Welcome")
-    cipher = Cipher_pkcs1_v1_5.new(rsa_key)
-    return cipher.decrypt(base64.b64decode(
-        line), "Fail to decrypt password!").decode('utf-8')
-
-
-def download_img(url):
-    if not url:
-        return ""
-    response = requests.get(url)
-    return "data:" + \
-           response.headers.get('Content-Type', 'image/jpg') + ";" + \
-           "base64," + base64.b64encode(response.content).decode("utf-8")
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import base64
+import datetime
+import io
+import json
+import os
+import pickle
+import socket
+import time
+import uuid
+import requests
+from enum import Enum, IntEnum
+import importlib
+from Cryptodome.PublicKey import RSA
+from Cryptodome.Cipher import PKCS1_v1_5 as Cipher_pkcs1_v1_5
+
+from filelock import FileLock
+
+from . import file_utils
+
+SERVICE_CONF = "service_conf.yaml"
+
+
+def conf_realpath(conf_name):
+    conf_path = f"conf/{conf_name}"
+    return os.path.join(file_utils.get_project_base_directory(), conf_path)
+
+
+def get_base_config(key, default=None, conf_name=SERVICE_CONF) -> dict:
+    local_config = {}
+    local_path = conf_realpath(f'local.{conf_name}')
+    if default is None:
+        default = os.environ.get(key.upper())
+
+    if os.path.exists(local_path):
+        local_config = file_utils.load_yaml_conf(local_path)
+        if not isinstance(local_config, dict):
+            raise ValueError(f'Invalid config file: "{local_path}".')
+
+        if key is not None and key in local_config:
+            return local_config[key]
+
+    config_path = conf_realpath(conf_name)
+    config = file_utils.load_yaml_conf(config_path)
+
+    if not isinstance(config, dict):
+        raise ValueError(f'Invalid config file: "{config_path}".')
+
+    config.update(local_config)
+    return config.get(key, default) if key is not None else config
+
+
+use_deserialize_safe_module = get_base_config(
+    'use_deserialize_safe_module', False)
+
+
+class CoordinationCommunicationProtocol(object):
+    HTTP = "http"
+    GRPC = "grpc"
+
+
+class BaseType:
+    def to_dict(self):
+        return dict([(k.lstrip("_"), v) for k, v in self.__dict__.items()])
+
+    def to_dict_with_type(self):
+        def _dict(obj):
+            module = None
+            if issubclass(obj.__class__, BaseType):
+                data = {}
+                for attr, v in obj.__dict__.items():
+                    k = attr.lstrip("_")
+                    data[k] = _dict(v)
+                module = obj.__module__
+            elif isinstance(obj, (list, tuple)):
+                data = []
+                for i, vv in enumerate(obj):
+                    data.append(_dict(vv))
+            elif isinstance(obj, dict):
+                data = {}
+                for _k, vv in obj.items():
+                    data[_k] = _dict(vv)
+            else:
+                data = obj
+            return {"type": obj.__class__.__name__,
+                    "data": data, "module": module}
+        return _dict(self)
+
+
+class CustomJSONEncoder(json.JSONEncoder):
+    def __init__(self, **kwargs):
+        self._with_type = kwargs.pop("with_type", False)
+        super().__init__(**kwargs)
+
+    def default(self, obj):
+        if isinstance(obj, datetime.datetime):
+            return obj.strftime('%Y-%m-%d %H:%M:%S')
+        elif isinstance(obj, datetime.date):
+            return obj.strftime('%Y-%m-%d')
+        elif isinstance(obj, datetime.timedelta):
+            return str(obj)
+        elif issubclass(type(obj), Enum) or issubclass(type(obj), IntEnum):
+            return obj.value
+        elif isinstance(obj, set):
+            return list(obj)
+        elif issubclass(type(obj), BaseType):
+            if not self._with_type:
+                return obj.to_dict()
+            else:
+                return obj.to_dict_with_type()
+        elif isinstance(obj, type):
+            return obj.__name__
+        else:
+            return json.JSONEncoder.default(self, obj)
+
+
+def rag_uuid():
+    return uuid.uuid1().hex
+
+
+def string_to_bytes(string):
+    return string if isinstance(
+        string, bytes) else string.encode(encoding="utf-8")
+
+
+def bytes_to_string(byte):
+    return byte.decode(encoding="utf-8")
+
+
+def json_dumps(src, byte=False, indent=None, with_type=False):
+    dest = json.dumps(
+        src,
+        indent=indent,
+        cls=CustomJSONEncoder,
+        with_type=with_type)
+    if byte:
+        dest = string_to_bytes(dest)
+    return dest
+
+
+def json_loads(src, object_hook=None, object_pairs_hook=None):
+    if isinstance(src, bytes):
+        src = bytes_to_string(src)
+    return json.loads(src, object_hook=object_hook,
+                      object_pairs_hook=object_pairs_hook)
+
+
+def current_timestamp():
+    return int(time.time() * 1000)
+
+
+def timestamp_to_date(timestamp, format_string="%Y-%m-%d %H:%M:%S"):
+    if not timestamp:
+        timestamp = time.time()
+    timestamp = int(timestamp) / 1000
+    time_array = time.localtime(timestamp)
+    str_date = time.strftime(format_string, time_array)
+    return str_date
+
+
+def date_string_to_timestamp(time_str, format_string="%Y-%m-%d %H:%M:%S"):
+    time_array = time.strptime(time_str, format_string)
+    time_stamp = int(time.mktime(time_array) * 1000)
+    return time_stamp
+
+
+def serialize_b64(src, to_str=False):
+    dest = base64.b64encode(pickle.dumps(src))
+    if not to_str:
+        return dest
+    else:
+        return bytes_to_string(dest)
+
+
+def deserialize_b64(src):
+    src = base64.b64decode(
+        string_to_bytes(src) if isinstance(
+            src, str) else src)
+    if use_deserialize_safe_module:
+        return restricted_loads(src)
+    return pickle.loads(src)
+
+
+safe_module = {
+    'numpy',
+    'rag_flow'
+}
+
+
+class RestrictedUnpickler(pickle.Unpickler):
+    def find_class(self, module, name):
+        import importlib
+        if module.split('.')[0] in safe_module:
+            _module = importlib.import_module(module)
+            return getattr(_module, name)
+        # Forbid everything else.
+        raise pickle.UnpicklingError("global '%s.%s' is forbidden" %
+                                     (module, name))
+
+
+def restricted_loads(src):
+    """Helper function analogous to pickle.loads()."""
+    return RestrictedUnpickler(io.BytesIO(src)).load()
+
+
+def get_lan_ip():
+    if os.name != "nt":
+        import fcntl
+        import struct
+
+        def get_interface_ip(ifname):
+            s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+            return socket.inet_ntoa(
+                fcntl.ioctl(s.fileno(), 0x8915, struct.pack('256s', string_to_bytes(ifname[:15])))[20:24])
+
+    ip = socket.gethostbyname(socket.getfqdn())
+    if ip.startswith("127.") and os.name != "nt":
+        interfaces = [
+            "bond1",
+            "eth0",
+            "eth1",
+            "eth2",
+            "wlan0",
+            "wlan1",
+            "wifi0",
+            "ath0",
+            "ath1",
+            "ppp0",
+        ]
+        for ifname in interfaces:
+            try:
+                ip = get_interface_ip(ifname)
+                break
+            except IOError as e:
+                pass
+    return ip or ''
+
+
+def from_dict_hook(in_dict: dict):
+    if "type" in in_dict and "data" in in_dict:
+        if in_dict["module"] is None:
+            return in_dict["data"]
+        else:
+            return getattr(importlib.import_module(
+                in_dict["module"]), in_dict["type"])(**in_dict["data"])
+    else:
+        return in_dict
+
+
+def decrypt_database_password(password):
+    encrypt_password = get_base_config("encrypt_password", False)
+    encrypt_module = get_base_config("encrypt_module", False)
+    private_key = get_base_config("private_key", None)
+
+    if not password or not encrypt_password:
+        return password
+
+    if not private_key:
+        raise ValueError("No private key")
+
+    module_fun = encrypt_module.split("#")
+    pwdecrypt_fun = getattr(
+        importlib.import_module(
+            module_fun[0]),
+        module_fun[1])
+
+    return pwdecrypt_fun(private_key, password)
+
+
+def decrypt_database_config(
+        database=None, passwd_key="password", name="database"):
+    if not database:
+        database = get_base_config(name, {})
+
+    database[passwd_key] = decrypt_database_password(database[passwd_key])
+    return database
+
+
+def update_config(key, value, conf_name=SERVICE_CONF):
+    conf_path = conf_realpath(conf_name=conf_name)
+    if not os.path.isabs(conf_path):
+        conf_path = os.path.join(
+            file_utils.get_project_base_directory(), conf_path)
+
+    with FileLock(os.path.join(os.path.dirname(conf_path), ".lock")):
+        config = file_utils.load_yaml_conf(conf_path=conf_path) or {}
+        config[key] = value
+        file_utils.rewrite_yaml_conf(conf_path=conf_path, config=config)
+
+
+def get_uuid():
+    return uuid.uuid1().hex
+
+
+def datetime_format(date_time: datetime.datetime) -> datetime.datetime:
+    return datetime.datetime(date_time.year, date_time.month, date_time.day,
+                             date_time.hour, date_time.minute, date_time.second)
+
+
+def get_format_time() -> datetime.datetime:
+    return datetime_format(datetime.datetime.now())
+
+
+def str2date(date_time: str):
+    return datetime.datetime.strptime(date_time, '%Y-%m-%d')
+
+
+def elapsed2time(elapsed):
+    seconds = elapsed / 1000
+    minuter, second = divmod(seconds, 60)
+    hour, minuter = divmod(minuter, 60)
+    return '%02d:%02d:%02d' % (hour, minuter, second)
+
+
+def decrypt(line):
+    file_path = os.path.join(
+        file_utils.get_project_base_directory(),
+        "conf",
+        "private.pem")
+    rsa_key = RSA.importKey(open(file_path).read(), "Welcome")
+    cipher = Cipher_pkcs1_v1_5.new(rsa_key)
+    return cipher.decrypt(base64.b64decode(
+        line), "Fail to decrypt password!").decode('utf-8')
+
+
+def download_img(url):
+    if not url:
+        return ""
+    response = requests.get(url)
+    return "data:" + \
+           response.headers.get('Content-Type', 'image/jpg') + ";" + \
+           "base64," + base64.b64encode(response.content).decode("utf-8")

api/utils/api_utils.py

@@ -1,269 +1,290 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import json
-import random
-import time
-from functools import wraps
-from io import BytesIO
-from flask import (
-    Response, jsonify, send_file, make_response,
-    request as flask_request,
-)
-from werkzeug.http import HTTP_STATUS_CODES
-
-from api.utils import json_dumps
-from api.settings import RetCode
-from api.settings import (
-    REQUEST_MAX_WAIT_SEC, REQUEST_WAIT_SEC,
-    stat_logger, CLIENT_AUTHENTICATION, HTTP_APP_KEY, SECRET_KEY
-)
-import requests
-import functools
-from api.utils import CustomJSONEncoder
-from uuid import uuid1
-from base64 import b64encode
-from hmac import HMAC
-from urllib.parse import quote, urlencode
-
-requests.models.complexjson.dumps = functools.partial(
-    json.dumps, cls=CustomJSONEncoder)
-
-
-def request(**kwargs):
-    sess = requests.Session()
-    stream = kwargs.pop('stream', sess.stream)
-    timeout = kwargs.pop('timeout', None)
-    kwargs['headers'] = {
-        k.replace(
-            '_',
-            '-').upper(): v for k,
-        v in kwargs.get(
-            'headers',
-            {}).items()}
-    prepped = requests.Request(**kwargs).prepare()
-
-    if CLIENT_AUTHENTICATION and HTTP_APP_KEY and SECRET_KEY:
-        timestamp = str(round(time() * 1000))
-        nonce = str(uuid1())
-        signature = b64encode(HMAC(SECRET_KEY.encode('ascii'), b'\n'.join([
-            timestamp.encode('ascii'),
-            nonce.encode('ascii'),
-            HTTP_APP_KEY.encode('ascii'),
-            prepped.path_url.encode('ascii'),
-            prepped.body if kwargs.get('json') else b'',
-            urlencode(
-                sorted(
-                    kwargs['data'].items()),
-                quote_via=quote,
-                safe='-._~').encode('ascii')
-            if kwargs.get('data') and isinstance(kwargs['data'], dict) else b'',
-        ]), 'sha1').digest()).decode('ascii')
-
-        prepped.headers.update({
-            'TIMESTAMP': timestamp,
-            'NONCE': nonce,
-            'APP-KEY': HTTP_APP_KEY,
-            'SIGNATURE': signature,
-        })
-
-    return sess.send(prepped, stream=stream, timeout=timeout)
-
-
-def get_exponential_backoff_interval(retries, full_jitter=False):
-    """Calculate the exponential backoff wait time."""
-    # Will be zero if factor equals 0
-    countdown = min(REQUEST_MAX_WAIT_SEC, REQUEST_WAIT_SEC * (2 ** retries))
-    # Full jitter according to
-    # https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/
-    if full_jitter:
-        countdown = random.randrange(countdown + 1)
-    # Adjust according to maximum wait time and account for negative values.
-    return max(0, countdown)
-
-
-def get_json_result(retcode=RetCode.SUCCESS, retmsg='success',
-                    data=None, job_id=None, meta=None):
-    import re
-    result_dict = {
-        "retcode": retcode,
-        "retmsg": retmsg,
-        # "retmsg": re.sub(r"rag", "seceum", retmsg, flags=re.IGNORECASE),
-        "data": data,
-        "jobId": job_id,
-        "meta": meta,
-    }
-
-    response = {}
-    for key, value in result_dict.items():
-        if value is None and key != "retcode":
-            continue
-        else:
-            response[key] = value
-    return jsonify(response)
-
-
-def get_data_error_result(retcode=RetCode.DATA_ERROR,
-                          retmsg='Sorry! Data missing!'):
-    import re
-    result_dict = {
-        "retcode": retcode,
-        "retmsg": re.sub(
-            r"rag",
-            "seceum",
-            retmsg,
-            flags=re.IGNORECASE)}
-    response = {}
-    for key, value in result_dict.items():
-        if value is None and key != "retcode":
-            continue
-        else:
-            response[key] = value
-    return jsonify(response)
-
-
-def server_error_response(e):
-    stat_logger.exception(e)
-    try:
-        if e.code == 401:
-            return get_json_result(retcode=401, retmsg=repr(e))
-    except BaseException:
-        pass
-    if len(e.args) > 1:
-        return get_json_result(
-            retcode=RetCode.EXCEPTION_ERROR, retmsg=repr(e.args[0]), data=e.args[1])
-    if repr(e).find("index_not_found_exception") >= 0:
-        return get_json_result(retcode=RetCode.EXCEPTION_ERROR, retmsg="No chunk found, please upload file and parse it.")
-
-    return get_json_result(retcode=RetCode.EXCEPTION_ERROR, retmsg=repr(e))
-
-
-def error_response(response_code, retmsg=None):
-    if retmsg is None:
-        retmsg = HTTP_STATUS_CODES.get(response_code, 'Unknown Error')
-
-    return Response(json.dumps({
-        'retmsg': retmsg,
-        'retcode': response_code,
-    }), status=response_code, mimetype='application/json')
-
-
-def validate_request(*args, **kwargs):
-    def wrapper(func):
-        @wraps(func)
-        def decorated_function(*_args, **_kwargs):
-            input_arguments = flask_request.json or flask_request.form.to_dict()
-            no_arguments = []
-            error_arguments = []
-            for arg in args:
-                if arg not in input_arguments:
-                    no_arguments.append(arg)
-            for k, v in kwargs.items():
-                config_value = input_arguments.get(k, None)
-                if config_value is None:
-                    no_arguments.append(k)
-                elif isinstance(v, (tuple, list)):
-                    if config_value not in v:
-                        error_arguments.append((k, set(v)))
-                elif config_value != v:
-                    error_arguments.append((k, v))
-            if no_arguments or error_arguments:
-                error_string = ""
-                if no_arguments:
-                    error_string += "required argument are missing: {}; ".format(
-                        ",".join(no_arguments))
-                if error_arguments:
-                    error_string += "required argument values: {}".format(
-                        ",".join(["{}={}".format(a[0], a[1]) for a in error_arguments]))
-                return get_json_result(
-                    retcode=RetCode.ARGUMENT_ERROR, retmsg=error_string)
-            return func(*_args, **_kwargs)
-        return decorated_function
-    return wrapper
-
-
-def is_localhost(ip):
-    return ip in {'127.0.0.1', '::1', '[::1]', 'localhost'}
-
-
-def send_file_in_mem(data, filename):
-    if not isinstance(data, (str, bytes)):
-        data = json_dumps(data)
-    if isinstance(data, str):
-        data = data.encode('utf-8')
-
-    f = BytesIO()
-    f.write(data)
-    f.seek(0)
-
-    return send_file(f, as_attachment=True, attachment_filename=filename)
-
-
-def get_json_result(retcode=RetCode.SUCCESS, retmsg='success', data=None):
-    response = {"retcode": retcode, "retmsg": retmsg, "data": data}
-    return jsonify(response)
-
-
-def cors_reponse(retcode=RetCode.SUCCESS,
-                 retmsg='success', data=None, auth=None):
-    result_dict = {"retcode": retcode, "retmsg": retmsg, "data": data}
-    response_dict = {}
-    for key, value in result_dict.items():
-        if value is None and key != "retcode":
-            continue
-        else:
-            response_dict[key] = value
-    response = make_response(jsonify(response_dict))
-    if auth:
-        response.headers["Authorization"] = auth
-    response.headers["Access-Control-Allow-Origin"] = "*"
-    response.headers["Access-Control-Allow-Method"] = "*"
-    response.headers["Access-Control-Allow-Headers"] = "*"
-    response.headers["Access-Control-Allow-Headers"] = "*"
-    response.headers["Access-Control-Expose-Headers"] = "Authorization"
-    return response
-
-def construct_result(code=RetCode.DATA_ERROR, message='data is missing'):
-    import re
-    result_dict = {"code": code, "message": re.sub(r"rag", "seceum", message, flags=re.IGNORECASE)}
-    response = {}
-    for key, value in result_dict.items():
-        if value is None and key != "code":
-            continue
-        else:
-            response[key] = value
-    return jsonify(response)
-
-
-def construct_json_result(code=RetCode.SUCCESS, message='success', data=None):
-    if data is None:
-        return jsonify({"code": code, "message": message})
-    else:
-        return jsonify({"code": code, "message": message, "data": data})
-
-
-def construct_error_response(e):
-    stat_logger.exception(e)
-    try:
-        if e.code == 401:
-            return construct_json_result(code=RetCode.UNAUTHORIZED, message=repr(e))
-    except BaseException:
-        pass
-    if len(e.args) > 1:
-        return construct_json_result(code=RetCode.EXCEPTION_ERROR, message=repr(e.args[0]), data=e.args[1])
-    if repr(e).find("index_not_found_exception") >=0:
-        return construct_json_result(code=RetCode.EXCEPTION_ERROR, message="No chunk found, please upload file and parse it.")
-
-    return construct_json_result(code=RetCode.EXCEPTION_ERROR, message=repr(e))
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import functools
+import json
+import random
+import time
+from base64 import b64encode
+from functools import wraps
+from hmac import HMAC
+from io import BytesIO
+from urllib.parse import quote, urlencode
+from uuid import uuid1
+
+import requests
+from flask import (
+    Response, jsonify, send_file, make_response,
+    request as flask_request,
+)
+from werkzeug.http import HTTP_STATUS_CODES
+
+from api.db.db_models import APIToken
+from api.settings import (
+    REQUEST_MAX_WAIT_SEC, REQUEST_WAIT_SEC,
+    stat_logger, CLIENT_AUTHENTICATION, HTTP_APP_KEY, SECRET_KEY
+)
+from api.settings import RetCode
+from api.utils import CustomJSONEncoder
+from api.utils import json_dumps
+
+requests.models.complexjson.dumps = functools.partial(
+    json.dumps, cls=CustomJSONEncoder)
+
+
+def request(**kwargs):
+    sess = requests.Session()
+    stream = kwargs.pop('stream', sess.stream)
+    timeout = kwargs.pop('timeout', None)
+    kwargs['headers'] = {
+        k.replace(
+            '_',
+            '-').upper(): v for k,
+        v in kwargs.get(
+            'headers',
+            {}).items()}
+    prepped = requests.Request(**kwargs).prepare()
+
+    if CLIENT_AUTHENTICATION and HTTP_APP_KEY and SECRET_KEY:
+        timestamp = str(round(time() * 1000))
+        nonce = str(uuid1())
+        signature = b64encode(HMAC(SECRET_KEY.encode('ascii'), b'\n'.join([
+            timestamp.encode('ascii'),
+            nonce.encode('ascii'),
+            HTTP_APP_KEY.encode('ascii'),
+            prepped.path_url.encode('ascii'),
+            prepped.body if kwargs.get('json') else b'',
+            urlencode(
+                sorted(
+                    kwargs['data'].items()),
+                quote_via=quote,
+                safe='-._~').encode('ascii')
+            if kwargs.get('data') and isinstance(kwargs['data'], dict) else b'',
+        ]), 'sha1').digest()).decode('ascii')
+
+        prepped.headers.update({
+            'TIMESTAMP': timestamp,
+            'NONCE': nonce,
+            'APP-KEY': HTTP_APP_KEY,
+            'SIGNATURE': signature,
+        })
+
+    return sess.send(prepped, stream=stream, timeout=timeout)
+
+
+def get_exponential_backoff_interval(retries, full_jitter=False):
+    """Calculate the exponential backoff wait time."""
+    # Will be zero if factor equals 0
+    countdown = min(REQUEST_MAX_WAIT_SEC, REQUEST_WAIT_SEC * (2 ** retries))
+    # Full jitter according to
+    # https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/
+    if full_jitter:
+        countdown = random.randrange(countdown + 1)
+    # Adjust according to maximum wait time and account for negative values.
+    return max(0, countdown)
+
+
+def get_json_result(retcode=RetCode.SUCCESS, retmsg='success',
+                    data=None, job_id=None, meta=None):
+    result_dict = {
+        "retcode": retcode,
+        "retmsg": retmsg,
+        # "retmsg": re.sub(r"rag", "seceum", retmsg, flags=re.IGNORECASE),
+        "data": data,
+        "jobId": job_id,
+        "meta": meta,
+    }
+
+    response = {}
+    for key, value in result_dict.items():
+        if value is None and key != "retcode":
+            continue
+        else:
+            response[key] = value
+    return jsonify(response)
+
+
+def get_data_error_result(retcode=RetCode.DATA_ERROR,
+                          retmsg='Sorry! Data missing!'):
+    import re
+    result_dict = {
+        "retcode": retcode,
+        "retmsg": re.sub(
+            r"rag",
+            "seceum",
+            retmsg,
+            flags=re.IGNORECASE)}
+    response = {}
+    for key, value in result_dict.items():
+        if value is None and key != "retcode":
+            continue
+        else:
+            response[key] = value
+    return jsonify(response)
+
+
+def server_error_response(e):
+    stat_logger.exception(e)
+    try:
+        if e.code == 401:
+            return get_json_result(retcode=401, retmsg=repr(e))
+    except BaseException:
+        pass
+    if len(e.args) > 1:
+        return get_json_result(
+            retcode=RetCode.EXCEPTION_ERROR, retmsg=repr(e.args[0]), data=e.args[1])
+    if repr(e).find("index_not_found_exception") >= 0:
+        return get_json_result(retcode=RetCode.EXCEPTION_ERROR,
+                               retmsg="No chunk found, please upload file and parse it.")
+
+    return get_json_result(retcode=RetCode.EXCEPTION_ERROR, retmsg=repr(e))
+
+
+def error_response(response_code, retmsg=None):
+    if retmsg is None:
+        retmsg = HTTP_STATUS_CODES.get(response_code, 'Unknown Error')
+
+    return Response(json.dumps({
+        'retmsg': retmsg,
+        'retcode': response_code,
+    }), status=response_code, mimetype='application/json')
+
+
+def validate_request(*args, **kwargs):
+    def wrapper(func):
+        @wraps(func)
+        def decorated_function(*_args, **_kwargs):
+            input_arguments = flask_request.json or flask_request.form.to_dict()
+            no_arguments = []
+            error_arguments = []
+            for arg in args:
+                if arg not in input_arguments:
+                    no_arguments.append(arg)
+            for k, v in kwargs.items():
+                config_value = input_arguments.get(k, None)
+                if config_value is None:
+                    no_arguments.append(k)
+                elif isinstance(v, (tuple, list)):
+                    if config_value not in v:
+                        error_arguments.append((k, set(v)))
+                elif config_value != v:
+                    error_arguments.append((k, v))
+            if no_arguments or error_arguments:
+                error_string = ""
+                if no_arguments:
+                    error_string += "required argument are missing: {}; ".format(
+                        ",".join(no_arguments))
+                if error_arguments:
+                    error_string += "required argument values: {}".format(
+                        ",".join(["{}={}".format(a[0], a[1]) for a in error_arguments]))
+                return get_json_result(
+                    retcode=RetCode.ARGUMENT_ERROR, retmsg=error_string)
+            return func(*_args, **_kwargs)
+
+        return decorated_function
+
+    return wrapper
+
+
+def is_localhost(ip):
+    return ip in {'127.0.0.1', '::1', '[::1]', 'localhost'}
+
+
+def send_file_in_mem(data, filename):
+    if not isinstance(data, (str, bytes)):
+        data = json_dumps(data)
+    if isinstance(data, str):
+        data = data.encode('utf-8')
+
+    f = BytesIO()
+    f.write(data)
+    f.seek(0)
+
+    return send_file(f, as_attachment=True, attachment_filename=filename)
+
+
+def get_json_result(retcode=RetCode.SUCCESS, retmsg='success', data=None):
+    response = {"retcode": retcode, "retmsg": retmsg, "data": data}
+    return jsonify(response)
+
+
+def construct_response(retcode=RetCode.SUCCESS,
+                       retmsg='success', data=None, auth=None):
+    result_dict = {"retcode": retcode, "retmsg": retmsg, "data": data}
+    response_dict = {}
+    for key, value in result_dict.items():
+        if value is None and key != "retcode":
+            continue
+        else:
+            response_dict[key] = value
+    response = make_response(jsonify(response_dict))
+    if auth:
+        response.headers["Authorization"] = auth
+    response.headers["Access-Control-Allow-Origin"] = "*"
+    response.headers["Access-Control-Allow-Method"] = "*"
+    response.headers["Access-Control-Allow-Headers"] = "*"
+    response.headers["Access-Control-Allow-Headers"] = "*"
+    response.headers["Access-Control-Expose-Headers"] = "Authorization"
+    return response
+
+
+def construct_result(code=RetCode.DATA_ERROR, message='data is missing'):
+    import re
+    result_dict = {"code": code, "message": re.sub(r"rag", "seceum", message, flags=re.IGNORECASE)}
+    response = {}
+    for key, value in result_dict.items():
+        if value is None and key != "code":
+            continue
+        else:
+            response[key] = value
+    return jsonify(response)
+
+
+def construct_json_result(code=RetCode.SUCCESS, message='success', data=None):
+    if data is None:
+        return jsonify({"code": code, "message": message})
+    else:
+        return jsonify({"code": code, "message": message, "data": data})
+
+
+def construct_error_response(e):
+    stat_logger.exception(e)
+    try:
+        if e.code == 401:
+            return construct_json_result(code=RetCode.UNAUTHORIZED, message=repr(e))
+    except BaseException:
+        pass
+    if len(e.args) > 1:
+        return construct_json_result(code=RetCode.EXCEPTION_ERROR, message=repr(e.args[0]), data=e.args[1])
+    if repr(e).find("index_not_found_exception") >= 0:
+        return construct_json_result(code=RetCode.EXCEPTION_ERROR,
+                                     message="No chunk found, please upload file and parse it.")
+
+    return construct_json_result(code=RetCode.EXCEPTION_ERROR, message=repr(e))
+
+
+def token_required(func):
+    @wraps(func)
+    def decorated_function(*args, **kwargs):
+        token = flask_request.headers.get('Authorization').split()[1]
+        objs = APIToken.query(token=token)
+        if not objs:
+            return get_json_result(
+                data=False, retmsg='Token is not valid!', retcode=RetCode.AUTHENTICATION_ERROR
+            )
+        kwargs['tenant_id'] = objs[0].tenant_id
+        return func(*args, **kwargs)
+
+    return decorated_function

api/utils/commands.py

@@ -1,78 +1,78 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-
-import base64
-import click
-import re
-
-from flask import Flask
-from werkzeug.security import generate_password_hash
-
-from api.db.services import UserService
-
-
-@click.command('reset-password', help='Reset the account password.')
-@click.option('--email', prompt=True, help='The email address of the account whose password you need to reset')
-@click.option('--new-password', prompt=True, help='the new password.')
-@click.option('--password-confirm', prompt=True, help='the new password confirm.')
-def reset_password(email, new_password, password_confirm):
-    if str(new_password).strip() != str(password_confirm).strip():
-        click.echo(click.style('sorry. The two passwords do not match.', fg='red'))
-        return
-    user = UserService.query(email=email)
-    if not user:
-        click.echo(click.style('sorry. The Email is not registered!.', fg='red'))
-        return
-    encode_password = base64.b64encode(new_password.encode('utf-8')).decode('utf-8')
-    password_hash = generate_password_hash(encode_password)
-    user_dict = {
-        'password': password_hash
-    }
-    UserService.update_user(user[0].id,user_dict)
-    click.echo(click.style('Congratulations! Password has been reset.', fg='green'))
-
-
-@click.command('reset-email', help='Reset the account email.')
-@click.option('--email', prompt=True, help='The old email address of the account whose email you need to reset')
-@click.option('--new-email', prompt=True, help='the new email.')
-@click.option('--email-confirm', prompt=True, help='the new email confirm.')
-def reset_email(email, new_email, email_confirm):
-    if str(new_email).strip() != str(email_confirm).strip():
-        click.echo(click.style('Sorry, new email and confirm email do not match.', fg='red'))
-        return
-    if str(new_email).strip() == str(email).strip():
-        click.echo(click.style('Sorry, new email and old email are the same.', fg='red'))
-        return
-    user = UserService.query(email=email)
-    if not user:
-        click.echo(click.style('sorry. the account: [{}] not exist .'.format(email), fg='red'))
-        return
-    if not re.match(r"^[\w\._-]+@([\w_-]+\.)+[\w-]{2,4}$", new_email):
-        click.echo(click.style('sorry. {} is not a valid email. '.format(new_email), fg='red'))
-        return
-    new_user = UserService.query(email=new_email)
-    if new_user:
-        click.echo(click.style('sorry. the account: [{}] is exist .'.format(new_email), fg='red'))
-        return
-    user_dict = {
-        'email': new_email
-    }
-    UserService.update_user(user[0].id,user_dict)
-    click.echo(click.style('Congratulations!, email has been reset.', fg='green'))
-
-def register_commands(app: Flask):
-    app.cli.add_command(reset_password)
-    app.cli.add_command(reset_email)
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+import base64
+import click
+import re
+
+from flask import Flask
+from werkzeug.security import generate_password_hash
+
+from api.db.services import UserService
+
+
+@click.command('reset-password', help='Reset the account password.')
+@click.option('--email', prompt=True, help='The email address of the account whose password you need to reset')
+@click.option('--new-password', prompt=True, help='the new password.')
+@click.option('--password-confirm', prompt=True, help='the new password confirm.')
+def reset_password(email, new_password, password_confirm):
+    if str(new_password).strip() != str(password_confirm).strip():
+        click.echo(click.style('sorry. The two passwords do not match.', fg='red'))
+        return
+    user = UserService.query(email=email)
+    if not user:
+        click.echo(click.style('sorry. The Email is not registered!.', fg='red'))
+        return
+    encode_password = base64.b64encode(new_password.encode('utf-8')).decode('utf-8')
+    password_hash = generate_password_hash(encode_password)
+    user_dict = {
+        'password': password_hash
+    }
+    UserService.update_user(user[0].id,user_dict)
+    click.echo(click.style('Congratulations! Password has been reset.', fg='green'))
+
+
+@click.command('reset-email', help='Reset the account email.')
+@click.option('--email', prompt=True, help='The old email address of the account whose email you need to reset')
+@click.option('--new-email', prompt=True, help='the new email.')
+@click.option('--email-confirm', prompt=True, help='the new email confirm.')
+def reset_email(email, new_email, email_confirm):
+    if str(new_email).strip() != str(email_confirm).strip():
+        click.echo(click.style('Sorry, new email and confirm email do not match.', fg='red'))
+        return
+    if str(new_email).strip() == str(email).strip():
+        click.echo(click.style('Sorry, new email and old email are the same.', fg='red'))
+        return
+    user = UserService.query(email=email)
+    if not user:
+        click.echo(click.style('sorry. the account: [{}] not exist .'.format(email), fg='red'))
+        return
+    if not re.match(r"^[\w\._-]+@([\w_-]+\.)+[\w-]{2,4}$", new_email):
+        click.echo(click.style('sorry. {} is not a valid email. '.format(new_email), fg='red'))
+        return
+    new_user = UserService.query(email=new_email)
+    if new_user:
+        click.echo(click.style('sorry. the account: [{}] is exist .'.format(new_email), fg='red'))
+        return
+    user_dict = {
+        'email': new_email
+    }
+    UserService.update_user(user[0].id,user_dict)
+    click.echo(click.style('Congratulations!, email has been reset.', fg='green'))
+
+def register_commands(app: Flask):
+    app.cli.add_command(reset_password)
+    app.cli.add_command(reset_email)

api/utils/file_utils.py

@@ -1,207 +1,207 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import base64
-import json
-import os
-import re
-from io import BytesIO
-
-import pdfplumber
-from PIL import Image
-from cachetools import LRUCache, cached
-from ruamel.yaml import YAML
-
-from api.db import FileType
-
-PROJECT_BASE = os.getenv("RAG_PROJECT_BASE") or os.getenv("RAG_DEPLOY_BASE")
-RAG_BASE = os.getenv("RAG_BASE")
-
-
-def get_project_base_directory(*args):
-    global PROJECT_BASE
-    if PROJECT_BASE is None:
-        PROJECT_BASE = os.path.abspath(
-            os.path.join(
-                os.path.dirname(os.path.realpath(__file__)),
-                os.pardir,
-                os.pardir,
-            )
-        )
-
-    if args:
-        return os.path.join(PROJECT_BASE, *args)
-    return PROJECT_BASE
-
-
-def get_rag_directory(*args):
-    global RAG_BASE
-    if RAG_BASE is None:
-        RAG_BASE = os.path.abspath(
-            os.path.join(
-                os.path.dirname(os.path.realpath(__file__)),
-                os.pardir,
-                os.pardir,
-                os.pardir,
-            )
-        )
-    if args:
-        return os.path.join(RAG_BASE, *args)
-    return RAG_BASE
-
-
-def get_rag_python_directory(*args):
-    return get_rag_directory("python", *args)
-
-
-def get_home_cache_dir():
-    dir = os.path.join(os.path.expanduser('~'), ".ragflow")
-    try:
-        os.mkdir(dir)
-    except OSError as error:
-        pass
-    return dir
-
-
-@cached(cache=LRUCache(maxsize=10))
-def load_json_conf(conf_path):
-    if os.path.isabs(conf_path):
-        json_conf_path = conf_path
-    else:
-        json_conf_path = os.path.join(get_project_base_directory(), conf_path)
-    try:
-        with open(json_conf_path) as f:
-            return json.load(f)
-    except BaseException:
-        raise EnvironmentError(
-            "loading json file config from '{}' failed!".format(json_conf_path)
-        )
-
-
-def dump_json_conf(config_data, conf_path):
-    if os.path.isabs(conf_path):
-        json_conf_path = conf_path
-    else:
-        json_conf_path = os.path.join(get_project_base_directory(), conf_path)
-    try:
-        with open(json_conf_path, "w") as f:
-            json.dump(config_data, f, indent=4)
-    except BaseException:
-        raise EnvironmentError(
-            "loading json file config from '{}' failed!".format(json_conf_path)
-        )
-
-
-def load_json_conf_real_time(conf_path):
-    if os.path.isabs(conf_path):
-        json_conf_path = conf_path
-    else:
-        json_conf_path = os.path.join(get_project_base_directory(), conf_path)
-    try:
-        with open(json_conf_path) as f:
-            return json.load(f)
-    except BaseException:
-        raise EnvironmentError(
-            "loading json file config from '{}' failed!".format(json_conf_path)
-        )
-
-
-def load_yaml_conf(conf_path):
-    if not os.path.isabs(conf_path):
-        conf_path = os.path.join(get_project_base_directory(), conf_path)
-    try:
-        with open(conf_path) as f:
-            yaml = YAML(typ='safe', pure=True)
-            return yaml.load(f)
-    except Exception as e:
-        raise EnvironmentError(
-            "loading yaml file config from {} failed:".format(conf_path), e
-        )
-
-
-def rewrite_yaml_conf(conf_path, config):
-    if not os.path.isabs(conf_path):
-        conf_path = os.path.join(get_project_base_directory(), conf_path)
-    try:
-        with open(conf_path, "w") as f:
-            yaml = YAML(typ="safe")
-            yaml.dump(config, f)
-    except Exception as e:
-        raise EnvironmentError(
-            "rewrite yaml file config {} failed:".format(conf_path), e
-        )
-
-
-def rewrite_json_file(filepath, json_data):
-    with open(filepath, "w") as f:
-        json.dump(json_data, f, indent=4, separators=(",", ": "))
-    f.close()
-
-
-def filename_type(filename):
-    filename = filename.lower()
-    if re.match(r".*\.pdf$", filename):
-        return FileType.PDF.value
-
-    if re.match(
-             r".*\.(doc|docx|ppt|pptx|yml|xml|htm|json|csv|txt|ini|xls|xlsx|wps|rtf|hlp|pages|numbers|key|md|py|js|java|c|cpp|h|php|go|ts|sh|cs|kt|html)$", filename):
-        return FileType.DOC.value
-
-    if re.match(
-            r".*\.(wav|flac|ape|alac|wavpack|wv|mp3|aac|ogg|vorbis|opus|mp3)$", filename):
-        return FileType.AURAL.value
-
-    if re.match(r".*\.(jpg|jpeg|png|tif|gif|pcx|tga|exif|fpx|svg|psd|cdr|pcd|dxf|ufo|eps|ai|raw|WMF|webp|avif|apng|icon|ico|mpg|mpeg|avi|rm|rmvb|mov|wmv|asf|dat|asx|wvx|mpe|mpa|mp4)$", filename):
-        return FileType.VISUAL.value
-
-    return FileType.OTHER.value
-
-
-def thumbnail(filename, blob):
-    filename = filename.lower()
-    if re.match(r".*\.pdf$", filename):
-        pdf = pdfplumber.open(BytesIO(blob))
-        buffered = BytesIO()
-        pdf.pages[0].to_image(resolution=32).annotated.save(buffered, format="png")
-        return "data:image/png;base64," + \
-            base64.b64encode(buffered.getvalue()).decode("utf-8")
-
-    if re.match(r".*\.(jpg|jpeg|png|tif|gif|icon|ico|webp)$", filename):
-        image = Image.open(BytesIO(blob))
-        image.thumbnail((30, 30))
-        buffered = BytesIO()
-        image.save(buffered, format="png")
-        return "data:image/png;base64," + \
-            base64.b64encode(buffered.getvalue()).decode("utf-8")
-
-    if re.match(r".*\.(ppt|pptx)$", filename):
-        import aspose.slides as slides
-        import aspose.pydrawing as drawing
-        try:
-            with slides.Presentation(BytesIO(blob)) as presentation:
-                buffered = BytesIO()
-                presentation.slides[0].get_thumbnail(0.03, 0.03).save(
-                    buffered, drawing.imaging.ImageFormat.png)
-                return "data:image/png;base64," + \
-                    base64.b64encode(buffered.getvalue()).decode("utf-8")
-        except Exception as e:
-            pass
-
-
-def traversal_files(base):
-    for root, ds, fs in os.walk(base):
-        for f in fs:
-            fullname = os.path.join(root, f)
-            yield fullname
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import base64
+import json
+import os
+import re
+from io import BytesIO
+
+import pdfplumber
+from PIL import Image
+from cachetools import LRUCache, cached
+from ruamel.yaml import YAML
+
+from api.db import FileType
+
+PROJECT_BASE = os.getenv("RAG_PROJECT_BASE") or os.getenv("RAG_DEPLOY_BASE")
+RAG_BASE = os.getenv("RAG_BASE")
+
+
+def get_project_base_directory(*args):
+    global PROJECT_BASE
+    if PROJECT_BASE is None:
+        PROJECT_BASE = os.path.abspath(
+            os.path.join(
+                os.path.dirname(os.path.realpath(__file__)),
+                os.pardir,
+                os.pardir,
+            )
+        )
+
+    if args:
+        return os.path.join(PROJECT_BASE, *args)
+    return PROJECT_BASE
+
+
+def get_rag_directory(*args):
+    global RAG_BASE
+    if RAG_BASE is None:
+        RAG_BASE = os.path.abspath(
+            os.path.join(
+                os.path.dirname(os.path.realpath(__file__)),
+                os.pardir,
+                os.pardir,
+                os.pardir,
+            )
+        )
+    if args:
+        return os.path.join(RAG_BASE, *args)
+    return RAG_BASE
+
+
+def get_rag_python_directory(*args):
+    return get_rag_directory("python", *args)
+
+
+def get_home_cache_dir():
+    dir = os.path.join(os.path.expanduser('~'), ".ragflow")
+    try:
+        os.mkdir(dir)
+    except OSError as error:
+        pass
+    return dir
+
+
+@cached(cache=LRUCache(maxsize=10))
+def load_json_conf(conf_path):
+    if os.path.isabs(conf_path):
+        json_conf_path = conf_path
+    else:
+        json_conf_path = os.path.join(get_project_base_directory(), conf_path)
+    try:
+        with open(json_conf_path) as f:
+            return json.load(f)
+    except BaseException:
+        raise EnvironmentError(
+            "loading json file config from '{}' failed!".format(json_conf_path)
+        )
+
+
+def dump_json_conf(config_data, conf_path):
+    if os.path.isabs(conf_path):
+        json_conf_path = conf_path
+    else:
+        json_conf_path = os.path.join(get_project_base_directory(), conf_path)
+    try:
+        with open(json_conf_path, "w") as f:
+            json.dump(config_data, f, indent=4)
+    except BaseException:
+        raise EnvironmentError(
+            "loading json file config from '{}' failed!".format(json_conf_path)
+        )
+
+
+def load_json_conf_real_time(conf_path):
+    if os.path.isabs(conf_path):
+        json_conf_path = conf_path
+    else:
+        json_conf_path = os.path.join(get_project_base_directory(), conf_path)
+    try:
+        with open(json_conf_path) as f:
+            return json.load(f)
+    except BaseException:
+        raise EnvironmentError(
+            "loading json file config from '{}' failed!".format(json_conf_path)
+        )
+
+
+def load_yaml_conf(conf_path):
+    if not os.path.isabs(conf_path):
+        conf_path = os.path.join(get_project_base_directory(), conf_path)
+    try:
+        with open(conf_path) as f:
+            yaml = YAML(typ='safe', pure=True)
+            return yaml.load(f)
+    except Exception as e:
+        raise EnvironmentError(
+            "loading yaml file config from {} failed:".format(conf_path), e
+        )
+
+
+def rewrite_yaml_conf(conf_path, config):
+    if not os.path.isabs(conf_path):
+        conf_path = os.path.join(get_project_base_directory(), conf_path)
+    try:
+        with open(conf_path, "w") as f:
+            yaml = YAML(typ="safe")
+            yaml.dump(config, f)
+    except Exception as e:
+        raise EnvironmentError(
+            "rewrite yaml file config {} failed:".format(conf_path), e
+        )
+
+
+def rewrite_json_file(filepath, json_data):
+    with open(filepath, "w") as f:
+        json.dump(json_data, f, indent=4, separators=(",", ": "))
+    f.close()
+
+
+def filename_type(filename):
+    filename = filename.lower()
+    if re.match(r".*\.pdf$", filename):
+        return FileType.PDF.value
+
+    if re.match(
+             r".*\.(eml|doc|docx|ppt|pptx|yml|xml|htm|json|csv|txt|ini|xls|xlsx|wps|rtf|hlp|pages|numbers|key|md|py|js|java|c|cpp|h|php|go|ts|sh|cs|kt|html|sql)$", filename):
+        return FileType.DOC.value
+
+    if re.match(
+            r".*\.(wav|flac|ape|alac|wavpack|wv|mp3|aac|ogg|vorbis|opus|mp3)$", filename):
+        return FileType.AURAL.value
+
+    if re.match(r".*\.(jpg|jpeg|png|tif|gif|pcx|tga|exif|fpx|svg|psd|cdr|pcd|dxf|ufo|eps|ai|raw|WMF|webp|avif|apng|icon|ico|mpg|mpeg|avi|rm|rmvb|mov|wmv|asf|dat|asx|wvx|mpe|mpa|mp4)$", filename):
+        return FileType.VISUAL.value
+
+    return FileType.OTHER.value
+
+
+def thumbnail(filename, blob):
+    filename = filename.lower()
+    if re.match(r".*\.pdf$", filename):
+        pdf = pdfplumber.open(BytesIO(blob))
+        buffered = BytesIO()
+        pdf.pages[0].to_image(resolution=32).annotated.save(buffered, format="png")
+        return "data:image/png;base64," + \
+            base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+    if re.match(r".*\.(jpg|jpeg|png|tif|gif|icon|ico|webp)$", filename):
+        image = Image.open(BytesIO(blob))
+        image.thumbnail((30, 30))
+        buffered = BytesIO()
+        image.save(buffered, format="png")
+        return "data:image/png;base64," + \
+            base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+    if re.match(r".*\.(ppt|pptx)$", filename):
+        import aspose.slides as slides
+        import aspose.pydrawing as drawing
+        try:
+            with slides.Presentation(BytesIO(blob)) as presentation:
+                buffered = BytesIO()
+                presentation.slides[0].get_thumbnail(0.03, 0.03).save(
+                    buffered, drawing.imaging.ImageFormat.png)
+                return "data:image/png;base64," + \
+                    base64.b64encode(buffered.getvalue()).decode("utf-8")
+        except Exception as e:
+            pass
+
+
+def traversal_files(base):
+    for root, ds, fs in os.walk(base):
+        for f in fs:
+            fullname = os.path.join(root, f)
+            yield fullname

api/utils/log_utils.py

@@ -1,313 +1,313 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import os
-import typing
-import traceback
-import logging
-import inspect
-from logging.handlers import TimedRotatingFileHandler
-from threading import RLock
-
-from api.utils import file_utils
-
-
-class LoggerFactory(object):
-    TYPE = "FILE"
-    LOG_FORMAT = "[%(levelname)s] [%(asctime)s] [%(module)s.%(funcName)s] [line:%(lineno)d]: %(message)s"
-    logging.basicConfig(format=LOG_FORMAT)
-    LEVEL = logging.DEBUG
-    logger_dict = {}
-    global_handler_dict = {}
-
-    LOG_DIR = None
-    PARENT_LOG_DIR = None
-    log_share = True
-
-    append_to_parent_log = None
-
-    lock = RLock()
-    # CRITICAL = 50
-    # FATAL = CRITICAL
-    # ERROR = 40
-    # WARNING = 30
-    # WARN = WARNING
-    # INFO = 20
-    # DEBUG = 10
-    # NOTSET = 0
-    levels = (10, 20, 30, 40)
-    schedule_logger_dict = {}
-
-    @staticmethod
-    def set_directory(directory=None, parent_log_dir=None,
-                      append_to_parent_log=None, force=False):
-        if parent_log_dir:
-            LoggerFactory.PARENT_LOG_DIR = parent_log_dir
-        if append_to_parent_log:
-            LoggerFactory.append_to_parent_log = append_to_parent_log
-        with LoggerFactory.lock:
-            if not directory:
-                directory = file_utils.get_project_base_directory("logs")
-            if not LoggerFactory.LOG_DIR or force:
-                LoggerFactory.LOG_DIR = directory
-            if LoggerFactory.log_share:
-                oldmask = os.umask(000)
-                os.makedirs(LoggerFactory.LOG_DIR, exist_ok=True)
-                os.umask(oldmask)
-            else:
-                os.makedirs(LoggerFactory.LOG_DIR, exist_ok=True)
-            for loggerName, ghandler in LoggerFactory.global_handler_dict.items():
-                for className, (logger,
-                                handler) in LoggerFactory.logger_dict.items():
-                    logger.removeHandler(ghandler)
-                ghandler.close()
-            LoggerFactory.global_handler_dict = {}
-            for className, (logger,
-                            handler) in LoggerFactory.logger_dict.items():
-                logger.removeHandler(handler)
-                _handler = None
-                if handler:
-                    handler.close()
-                if className != "default":
-                    _handler = LoggerFactory.get_handler(className)
-                    logger.addHandler(_handler)
-                LoggerFactory.assemble_global_handler(logger)
-                LoggerFactory.logger_dict[className] = logger, _handler
-
-    @staticmethod
-    def new_logger(name):
-        logger = logging.getLogger(name)
-        logger.propagate = False
-        logger.setLevel(LoggerFactory.LEVEL)
-        return logger
-
-    @staticmethod
-    def get_logger(class_name=None):
-        with LoggerFactory.lock:
-            if class_name in LoggerFactory.logger_dict.keys():
-                logger, handler = LoggerFactory.logger_dict[class_name]
-                if not logger:
-                    logger, handler = LoggerFactory.init_logger(class_name)
-            else:
-                logger, handler = LoggerFactory.init_logger(class_name)
-            return logger
-
-    @staticmethod
-    def get_global_handler(logger_name, level=None, log_dir=None):
-        if not LoggerFactory.LOG_DIR:
-            return logging.StreamHandler()
-        if log_dir:
-            logger_name_key = logger_name + "_" + log_dir
-        else:
-            logger_name_key = logger_name + "_" + LoggerFactory.LOG_DIR
-        # if loggerName not in LoggerFactory.globalHandlerDict:
-        if logger_name_key not in LoggerFactory.global_handler_dict:
-            with LoggerFactory.lock:
-                if logger_name_key not in LoggerFactory.global_handler_dict:
-                    handler = LoggerFactory.get_handler(
-                        logger_name, level, log_dir)
-                    LoggerFactory.global_handler_dict[logger_name_key] = handler
-        return LoggerFactory.global_handler_dict[logger_name_key]
-
-    @staticmethod
-    def get_handler(class_name, level=None, log_dir=None,
-                    log_type=None, job_id=None):
-        if not log_type:
-            if not LoggerFactory.LOG_DIR or not class_name:
-                return logging.StreamHandler()
-                # return Diy_StreamHandler()
-
-            if not log_dir:
-                log_file = os.path.join(
-                    LoggerFactory.LOG_DIR,
-                    "{}.log".format(class_name))
-            else:
-                log_file = os.path.join(log_dir, "{}.log".format(class_name))
-        else:
-            log_file = os.path.join(log_dir, "rag_flow_{}.log".format(
-                log_type) if level == LoggerFactory.LEVEL else 'rag_flow_{}_error.log'.format(log_type))
-
-        os.makedirs(os.path.dirname(log_file), exist_ok=True)
-        if LoggerFactory.log_share:
-            handler = ROpenHandler(log_file,
-                                   when='D',
-                                   interval=1,
-                                   backupCount=14,
-                                   delay=True)
-        else:
-            handler = TimedRotatingFileHandler(log_file,
-                                               when='D',
-                                               interval=1,
-                                               backupCount=14,
-                                               delay=True)
-        if level:
-            handler.level = level
-
-        return handler
-
-    @staticmethod
-    def init_logger(class_name):
-        with LoggerFactory.lock:
-            logger = LoggerFactory.new_logger(class_name)
-            handler = None
-            if class_name:
-                handler = LoggerFactory.get_handler(class_name)
-                logger.addHandler(handler)
-                LoggerFactory.logger_dict[class_name] = logger, handler
-
-            else:
-                LoggerFactory.logger_dict["default"] = logger, handler
-
-            LoggerFactory.assemble_global_handler(logger)
-            return logger, handler
-
-    @staticmethod
-    def assemble_global_handler(logger):
-        if LoggerFactory.LOG_DIR:
-            for level in LoggerFactory.levels:
-                if level >= LoggerFactory.LEVEL:
-                    level_logger_name = logging._levelToName[level]
-                    logger.addHandler(
-                        LoggerFactory.get_global_handler(
-                            level_logger_name, level))
-        if LoggerFactory.append_to_parent_log and LoggerFactory.PARENT_LOG_DIR:
-            for level in LoggerFactory.levels:
-                if level >= LoggerFactory.LEVEL:
-                    level_logger_name = logging._levelToName[level]
-                    logger.addHandler(
-                        LoggerFactory.get_global_handler(level_logger_name, level, LoggerFactory.PARENT_LOG_DIR))
-
-
-def setDirectory(directory=None):
-    LoggerFactory.set_directory(directory)
-
-
-def setLevel(level):
-    LoggerFactory.LEVEL = level
-
-
-def getLogger(className=None, useLevelFile=False):
-    if className is None:
-        frame = inspect.stack()[1]
-        module = inspect.getmodule(frame[0])
-        className = 'stat'
-    return LoggerFactory.get_logger(className)
-
-
-def exception_to_trace_string(ex):
-    return "".join(traceback.TracebackException.from_exception(ex).format())
-
-
-class ROpenHandler(TimedRotatingFileHandler):
-    def _open(self):
-        prevumask = os.umask(000)
-        rtv = TimedRotatingFileHandler._open(self)
-        os.umask(prevumask)
-        return rtv
-
-
-def sql_logger(job_id='', log_type='sql'):
-    key = job_id + log_type
-    if key in LoggerFactory.schedule_logger_dict.keys():
-        return LoggerFactory.schedule_logger_dict[key]
-    return get_job_logger(job_id=job_id, log_type=log_type)
-
-
-def ready_log(msg, job=None, task=None, role=None, party_id=None, detail=None):
-    prefix, suffix = base_msg(job, task, role, party_id, detail)
-    return f"{prefix}{msg} ready{suffix}"
-
-
-def start_log(msg, job=None, task=None, role=None, party_id=None, detail=None):
-    prefix, suffix = base_msg(job, task, role, party_id, detail)
-    return f"{prefix}start to {msg}{suffix}"
-
-
-def successful_log(msg, job=None, task=None, role=None,
-                   party_id=None, detail=None):
-    prefix, suffix = base_msg(job, task, role, party_id, detail)
-    return f"{prefix}{msg} successfully{suffix}"
-
-
-def warning_log(msg, job=None, task=None, role=None,
-                party_id=None, detail=None):
-    prefix, suffix = base_msg(job, task, role, party_id, detail)
-    return f"{prefix}{msg} is not effective{suffix}"
-
-
-def failed_log(msg, job=None, task=None, role=None,
-               party_id=None, detail=None):
-    prefix, suffix = base_msg(job, task, role, party_id, detail)
-    return f"{prefix}failed to {msg}{suffix}"
-
-
-def base_msg(job=None, task=None, role: str = None,
-             party_id: typing.Union[str, int] = None, detail=None):
-    if detail:
-        detail_msg = f" detail: \n{detail}"
-    else:
-        detail_msg = ""
-    if task is not None:
-        return f"task {task.f_task_id} {task.f_task_version} ", f" on {task.f_role} {task.f_party_id}{detail_msg}"
-    elif job is not None:
-        return "", f" on {job.f_role} {job.f_party_id}{detail_msg}"
-    elif role and party_id:
-        return "", f" on {role} {party_id}{detail_msg}"
-    else:
-        return "", f"{detail_msg}"
-
-
-def exception_to_trace_string(ex):
-    return "".join(traceback.TracebackException.from_exception(ex).format())
-
-
-def get_logger_base_dir():
-    job_log_dir = file_utils.get_rag_flow_directory('logs')
-    return job_log_dir
-
-
-def get_job_logger(job_id, log_type):
-    rag_flow_log_dir = file_utils.get_rag_flow_directory('logs', 'rag_flow')
-    job_log_dir = file_utils.get_rag_flow_directory('logs', job_id)
-    if not job_id:
-        log_dirs = [rag_flow_log_dir]
-    else:
-        if log_type == 'audit':
-            log_dirs = [job_log_dir, rag_flow_log_dir]
-        else:
-            log_dirs = [job_log_dir]
-    if LoggerFactory.log_share:
-        oldmask = os.umask(000)
-        os.makedirs(job_log_dir, exist_ok=True)
-        os.makedirs(rag_flow_log_dir, exist_ok=True)
-        os.umask(oldmask)
-    else:
-        os.makedirs(job_log_dir, exist_ok=True)
-        os.makedirs(rag_flow_log_dir, exist_ok=True)
-    logger = LoggerFactory.new_logger(f"{job_id}_{log_type}")
-    for job_log_dir in log_dirs:
-        handler = LoggerFactory.get_handler(class_name=None, level=LoggerFactory.LEVEL,
-                                            log_dir=job_log_dir, log_type=log_type, job_id=job_id)
-        error_handler = LoggerFactory.get_handler(
-            class_name=None,
-            level=logging.ERROR,
-            log_dir=job_log_dir,
-            log_type=log_type,
-            job_id=job_id)
-        logger.addHandler(handler)
-        logger.addHandler(error_handler)
-    with LoggerFactory.lock:
-        LoggerFactory.schedule_logger_dict[job_id + log_type] = logger
-    return logger
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import os
+import typing
+import traceback
+import logging
+import inspect
+from logging.handlers import TimedRotatingFileHandler
+from threading import RLock
+
+from api.utils import file_utils
+
+
+class LoggerFactory(object):
+    TYPE = "FILE"
+    LOG_FORMAT = "[%(levelname)s] [%(asctime)s] [%(module)s.%(funcName)s] [line:%(lineno)d]: %(message)s"
+    logging.basicConfig(format=LOG_FORMAT)
+    LEVEL = logging.DEBUG
+    logger_dict = {}
+    global_handler_dict = {}
+
+    LOG_DIR = None
+    PARENT_LOG_DIR = None
+    log_share = True
+
+    append_to_parent_log = None
+
+    lock = RLock()
+    # CRITICAL = 50
+    # FATAL = CRITICAL
+    # ERROR = 40
+    # WARNING = 30
+    # WARN = WARNING
+    # INFO = 20
+    # DEBUG = 10
+    # NOTSET = 0
+    levels = (10, 20, 30, 40)
+    schedule_logger_dict = {}
+
+    @staticmethod
+    def set_directory(directory=None, parent_log_dir=None,
+                      append_to_parent_log=None, force=False):
+        if parent_log_dir:
+            LoggerFactory.PARENT_LOG_DIR = parent_log_dir
+        if append_to_parent_log:
+            LoggerFactory.append_to_parent_log = append_to_parent_log
+        with LoggerFactory.lock:
+            if not directory:
+                directory = file_utils.get_project_base_directory("logs")
+            if not LoggerFactory.LOG_DIR or force:
+                LoggerFactory.LOG_DIR = directory
+            if LoggerFactory.log_share:
+                oldmask = os.umask(000)
+                os.makedirs(LoggerFactory.LOG_DIR, exist_ok=True)
+                os.umask(oldmask)
+            else:
+                os.makedirs(LoggerFactory.LOG_DIR, exist_ok=True)
+            for loggerName, ghandler in LoggerFactory.global_handler_dict.items():
+                for className, (logger,
+                                handler) in LoggerFactory.logger_dict.items():
+                    logger.removeHandler(ghandler)
+                ghandler.close()
+            LoggerFactory.global_handler_dict = {}
+            for className, (logger,
+                            handler) in LoggerFactory.logger_dict.items():
+                logger.removeHandler(handler)
+                _handler = None
+                if handler:
+                    handler.close()
+                if className != "default":
+                    _handler = LoggerFactory.get_handler(className)
+                    logger.addHandler(_handler)
+                LoggerFactory.assemble_global_handler(logger)
+                LoggerFactory.logger_dict[className] = logger, _handler
+
+    @staticmethod
+    def new_logger(name):
+        logger = logging.getLogger(name)
+        logger.propagate = False
+        logger.setLevel(LoggerFactory.LEVEL)
+        return logger
+
+    @staticmethod
+    def get_logger(class_name=None):
+        with LoggerFactory.lock:
+            if class_name in LoggerFactory.logger_dict.keys():
+                logger, handler = LoggerFactory.logger_dict[class_name]
+                if not logger:
+                    logger, handler = LoggerFactory.init_logger(class_name)
+            else:
+                logger, handler = LoggerFactory.init_logger(class_name)
+            return logger
+
+    @staticmethod
+    def get_global_handler(logger_name, level=None, log_dir=None):
+        if not LoggerFactory.LOG_DIR:
+            return logging.StreamHandler()
+        if log_dir:
+            logger_name_key = logger_name + "_" + log_dir
+        else:
+            logger_name_key = logger_name + "_" + LoggerFactory.LOG_DIR
+        # if loggerName not in LoggerFactory.globalHandlerDict:
+        if logger_name_key not in LoggerFactory.global_handler_dict:
+            with LoggerFactory.lock:
+                if logger_name_key not in LoggerFactory.global_handler_dict:
+                    handler = LoggerFactory.get_handler(
+                        logger_name, level, log_dir)
+                    LoggerFactory.global_handler_dict[logger_name_key] = handler
+        return LoggerFactory.global_handler_dict[logger_name_key]
+
+    @staticmethod
+    def get_handler(class_name, level=None, log_dir=None,
+                    log_type=None, job_id=None):
+        if not log_type:
+            if not LoggerFactory.LOG_DIR or not class_name:
+                return logging.StreamHandler()
+                # return Diy_StreamHandler()
+
+            if not log_dir:
+                log_file = os.path.join(
+                    LoggerFactory.LOG_DIR,
+                    "{}.log".format(class_name))
+            else:
+                log_file = os.path.join(log_dir, "{}.log".format(class_name))
+        else:
+            log_file = os.path.join(log_dir, "rag_flow_{}.log".format(
+                log_type) if level == LoggerFactory.LEVEL else 'rag_flow_{}_error.log'.format(log_type))
+
+        os.makedirs(os.path.dirname(log_file), exist_ok=True)
+        if LoggerFactory.log_share:
+            handler = ROpenHandler(log_file,
+                                   when='D',
+                                   interval=1,
+                                   backupCount=14,
+                                   delay=True)
+        else:
+            handler = TimedRotatingFileHandler(log_file,
+                                               when='D',
+                                               interval=1,
+                                               backupCount=14,
+                                               delay=True)
+        if level:
+            handler.level = level
+
+        return handler
+
+    @staticmethod
+    def init_logger(class_name):
+        with LoggerFactory.lock:
+            logger = LoggerFactory.new_logger(class_name)
+            handler = None
+            if class_name:
+                handler = LoggerFactory.get_handler(class_name)
+                logger.addHandler(handler)
+                LoggerFactory.logger_dict[class_name] = logger, handler
+
+            else:
+                LoggerFactory.logger_dict["default"] = logger, handler
+
+            LoggerFactory.assemble_global_handler(logger)
+            return logger, handler
+
+    @staticmethod
+    def assemble_global_handler(logger):
+        if LoggerFactory.LOG_DIR:
+            for level in LoggerFactory.levels:
+                if level >= LoggerFactory.LEVEL:
+                    level_logger_name = logging._levelToName[level]
+                    logger.addHandler(
+                        LoggerFactory.get_global_handler(
+                            level_logger_name, level))
+        if LoggerFactory.append_to_parent_log and LoggerFactory.PARENT_LOG_DIR:
+            for level in LoggerFactory.levels:
+                if level >= LoggerFactory.LEVEL:
+                    level_logger_name = logging._levelToName[level]
+                    logger.addHandler(
+                        LoggerFactory.get_global_handler(level_logger_name, level, LoggerFactory.PARENT_LOG_DIR))
+
+
+def setDirectory(directory=None):
+    LoggerFactory.set_directory(directory)
+
+
+def setLevel(level):
+    LoggerFactory.LEVEL = level
+
+
+def getLogger(className=None, useLevelFile=False):
+    if className is None:
+        frame = inspect.stack()[1]
+        module = inspect.getmodule(frame[0])
+        className = 'stat'
+    return LoggerFactory.get_logger(className)
+
+
+def exception_to_trace_string(ex):
+    return "".join(traceback.TracebackException.from_exception(ex).format())
+
+
+class ROpenHandler(TimedRotatingFileHandler):
+    def _open(self):
+        prevumask = os.umask(000)
+        rtv = TimedRotatingFileHandler._open(self)
+        os.umask(prevumask)
+        return rtv
+
+
+def sql_logger(job_id='', log_type='sql'):
+    key = job_id + log_type
+    if key in LoggerFactory.schedule_logger_dict.keys():
+        return LoggerFactory.schedule_logger_dict[key]
+    return get_job_logger(job_id=job_id, log_type=log_type)
+
+
+def ready_log(msg, job=None, task=None, role=None, party_id=None, detail=None):
+    prefix, suffix = base_msg(job, task, role, party_id, detail)
+    return f"{prefix}{msg} ready{suffix}"
+
+
+def start_log(msg, job=None, task=None, role=None, party_id=None, detail=None):
+    prefix, suffix = base_msg(job, task, role, party_id, detail)
+    return f"{prefix}start to {msg}{suffix}"
+
+
+def successful_log(msg, job=None, task=None, role=None,
+                   party_id=None, detail=None):
+    prefix, suffix = base_msg(job, task, role, party_id, detail)
+    return f"{prefix}{msg} successfully{suffix}"
+
+
+def warning_log(msg, job=None, task=None, role=None,
+                party_id=None, detail=None):
+    prefix, suffix = base_msg(job, task, role, party_id, detail)
+    return f"{prefix}{msg} is not effective{suffix}"
+
+
+def failed_log(msg, job=None, task=None, role=None,
+               party_id=None, detail=None):
+    prefix, suffix = base_msg(job, task, role, party_id, detail)
+    return f"{prefix}failed to {msg}{suffix}"
+
+
+def base_msg(job=None, task=None, role: str = None,
+             party_id: typing.Union[str, int] = None, detail=None):
+    if detail:
+        detail_msg = f" detail: \n{detail}"
+    else:
+        detail_msg = ""
+    if task is not None:
+        return f"task {task.f_task_id} {task.f_task_version} ", f" on {task.f_role} {task.f_party_id}{detail_msg}"
+    elif job is not None:
+        return "", f" on {job.f_role} {job.f_party_id}{detail_msg}"
+    elif role and party_id:
+        return "", f" on {role} {party_id}{detail_msg}"
+    else:
+        return "", f"{detail_msg}"
+
+
+def exception_to_trace_string(ex):
+    return "".join(traceback.TracebackException.from_exception(ex).format())
+
+
+def get_logger_base_dir():
+    job_log_dir = file_utils.get_rag_flow_directory('logs')
+    return job_log_dir
+
+
+def get_job_logger(job_id, log_type):
+    rag_flow_log_dir = file_utils.get_rag_flow_directory('logs', 'rag_flow')
+    job_log_dir = file_utils.get_rag_flow_directory('logs', job_id)
+    if not job_id:
+        log_dirs = [rag_flow_log_dir]
+    else:
+        if log_type == 'audit':
+            log_dirs = [job_log_dir, rag_flow_log_dir]
+        else:
+            log_dirs = [job_log_dir]
+    if LoggerFactory.log_share:
+        oldmask = os.umask(000)
+        os.makedirs(job_log_dir, exist_ok=True)
+        os.makedirs(rag_flow_log_dir, exist_ok=True)
+        os.umask(oldmask)
+    else:
+        os.makedirs(job_log_dir, exist_ok=True)
+        os.makedirs(rag_flow_log_dir, exist_ok=True)
+    logger = LoggerFactory.new_logger(f"{job_id}_{log_type}")
+    for job_log_dir in log_dirs:
+        handler = LoggerFactory.get_handler(class_name=None, level=LoggerFactory.LEVEL,
+                                            log_dir=job_log_dir, log_type=log_type, job_id=job_id)
+        error_handler = LoggerFactory.get_handler(
+            class_name=None,
+            level=logging.ERROR,
+            log_dir=job_log_dir,
+            log_type=log_type,
+            job_id=job_id)
+        logger.addHandler(handler)
+        logger.addHandler(error_handler)
+    with LoggerFactory.lock:
+        LoggerFactory.schedule_logger_dict[job_id + log_type] = logger
+    return logger

api/utils/t_crypt.py

@@ -1,24 +1,24 @@
-import base64
-import os
-import sys
-from Cryptodome.PublicKey import RSA
-from Cryptodome.Cipher import PKCS1_v1_5 as Cipher_pkcs1_v1_5
-from api.utils import decrypt, file_utils
-
-
-def crypt(line):
-    file_path = os.path.join(
-        file_utils.get_project_base_directory(),
-        "conf",
-        "public.pem")
-    rsa_key = RSA.importKey(open(file_path).read(),"Welcome")
-    cipher = Cipher_pkcs1_v1_5.new(rsa_key)
-    password_base64 = base64.b64encode(line.encode('utf-8')).decode("utf-8")
-    encrypted_password = cipher.encrypt(password_base64.encode())
-    return base64.b64encode(encrypted_password).decode('utf-8')
-
-
-if __name__ == "__main__":
-    pswd = crypt(sys.argv[1])
-    print(pswd)
-    print(decrypt(pswd))
+import base64
+import os
+import sys
+from Cryptodome.PublicKey import RSA
+from Cryptodome.Cipher import PKCS1_v1_5 as Cipher_pkcs1_v1_5
+from api.utils import decrypt, file_utils
+
+
+def crypt(line):
+    file_path = os.path.join(
+        file_utils.get_project_base_directory(),
+        "conf",
+        "public.pem")
+    rsa_key = RSA.importKey(open(file_path).read(),"Welcome")
+    cipher = Cipher_pkcs1_v1_5.new(rsa_key)
+    password_base64 = base64.b64encode(line.encode('utf-8')).decode("utf-8")
+    encrypted_password = cipher.encrypt(password_base64.encode())
+    return base64.b64encode(encrypted_password).decode('utf-8')
+
+
+if __name__ == "__main__":
+    pswd = crypt(sys.argv[1])
+    print(pswd)
+    print(decrypt(pswd))

api/versions.py

@@ -1,28 +1,26 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import os
-import dotenv
-import typing
-from api.utils.file_utils import get_project_base_directory
-
-
-def get_versions() -> typing.Mapping[str, typing.Any]:
-    dotenv.load_dotenv(dotenv.find_dotenv())
-    return dotenv.dotenv_values()
-
-
-def get_rag_version() -> typing.Optional[str]:
-    return get_versions().get("RAGFLOW_VERSION", "dev")
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import dotenv
+import typing
+
+
+def get_versions() -> typing.Mapping[str, typing.Any]:
+    dotenv.load_dotenv(dotenv.find_dotenv())
+    return dotenv.dotenv_values()
+
+
+def get_rag_version() -> typing.Optional[str]:
+    return get_versions().get("RAGFLOW_IMAGE", "infiniflow/ragflow:dev").split(":")[-1]

conf/service_conf.yaml

@@ -1,49 +0,0 @@
-ragflow:
-  host: 0.0.0.0
-  http_port: 9380
-mysql:
-  name: 'rag_flow'
-  user: 'root'
-  password: 'infini_rag_flow'
-  host: 'mysql'
-  port: 3306
-  max_connections: 100
-  stale_timeout: 30
-minio:
-  user: 'rag_flow'
-  password: 'infini_rag_flow'
-  host: 'minio:9000'
-es:
-  hosts: 'http://es01:9200'
-  username: 'elastic'
-  password: 'infini_rag_flow'
-redis:
-  db: 1
-  password: 'infini_rag_flow'
-  host: 'redis:6379'
-user_default_llm:
-  factory: 'Tongyi-Qianwen'
-  api_key: 'sk-xxxxxxxxxxxxx'
-  base_url: ''
-oauth:
-  github:
-    client_id: xxxxxxxxxxxxxxxxxxxxxxxxx
-    secret_key: xxxxxxxxxxxxxxxxxxxxxxxxxxxx
-    url: https://github.com/login/oauth/access_token
-  feishu:
-    app_id: cli_xxxxxxxxxxxxxxxxxxx
-    app_secret: xxxxxxxxxxxxxxxxxxxxxxxxxxxx
-    app_access_token_url: https://open.feishu.cn/open-apis/auth/v3/app_access_token/internal
-    user_access_token_url: https://open.feishu.cn/open-apis/authen/v1/oidc/access_token
-    grant_type: 'authorization_code'
-authentication:
-  client:
-    switch: false
-    http_app_key:
-    http_secret_key:
-  site:
-    switch: false
-permission:
-  switch: false
-  component: false
-  dataset: false

conf/service_conf.yaml

@@ -0,0 +1 @@
+../docker/service_conf.yaml

deepdoc/README.md

@@ -1,122 +1,122 @@
-English | [简体中文](./README_zh.md)
-
-# *Deep*Doc
-
-- [1. Introduction](#1)
-- [2. Vision](#2)
-- [3. Parser](#3)
-
-<a name="1"></a>
-## 1. Introduction
-
-With a bunch of documents from various domains with various formats and along with diverse retrieval requirements, 
-an accurate analysis becomes a very challenge task. *Deep*Doc is born for that purpose.
-There are 2 parts in *Deep*Doc so far: vision and parser. 
-You can run the flowing test programs if you're interested in our results of OCR, layout recognition and TSR.
-```bash
-python deepdoc/vision/t_ocr.py -h
-usage: t_ocr.py [-h] --inputs INPUTS [--output_dir OUTPUT_DIR]
-
-options:
-  -h, --help            show this help message and exit
-  --inputs INPUTS       Directory where to store images or PDFs, or a file path to a single image or PDF
-  --output_dir OUTPUT_DIR
-                        Directory where to store the output images. Default: './ocr_outputs'
-```
-```bash
-python deepdoc/vision/t_recognizer.py -h
-usage: t_recognizer.py [-h] --inputs INPUTS [--output_dir OUTPUT_DIR] [--threshold THRESHOLD] [--mode {layout,tsr}]
-
-options:
-  -h, --help            show this help message and exit
-  --inputs INPUTS       Directory where to store images or PDFs, or a file path to a single image or PDF
-  --output_dir OUTPUT_DIR
-                        Directory where to store the output images. Default: './layouts_outputs'
-  --threshold THRESHOLD
-                        A threshold to filter out detections. Default: 0.5
-  --mode {layout,tsr}   Task mode: layout recognition or table structure recognition
-```
-
-Our models are served on HuggingFace. If you have trouble downloading HuggingFace models, this might help!!
-```bash
-export HF_ENDPOINT=https://hf-mirror.com
-```
-
-<a name="2"></a>
-## 2. Vision
-
-We use vision information to resolve problems as human being.
-  - OCR. Since a lot of documents presented as images or at least be able to transform to image, 
-    OCR is a very essential and fundamental or even universal solution for text extraction.
-    ```bash
-        python deepdoc/vision/t_ocr.py --inputs=path_to_images_or_pdfs --output_dir=path_to_store_result
-     ```
-    The inputs could be directory to images or PDF, or a image or PDF. 
-    You can look into the folder 'path_to_store_result' where has images which demonstrate the positions of results,
-    txt files which contain the OCR text.
-    <div align="center" style="margin-top:20px;margin-bottom:20px;">
-    <img src="https://github.com/infiniflow/ragflow/assets/12318111/f25bee3d-aaf7-4102-baf5-d5208361d110" width="900"/>
-    </div>
-
-  - Layout recognition. Documents from different domain may have various layouts, 
-    like, newspaper, magazine, book and résumé are distinct in terms of layout. 
-    Only when machine have an accurate layout analysis, it can decide if these text parts are successive or not, 
-    or this part needs Table Structure Recognition(TSR) to process, or this part is a figure and described with this caption.
-    We have 10 basic layout components which covers most cases:
-      - Text
-      - Title
-      - Figure
-      - Figure caption
-      - Table
-      - Table caption
-      - Header
-      - Footer
-      - Reference
-      - Equation
-      
-     Have a try on the following command to see the layout detection results.
-     ```bash
-        python deepdoc/vision/t_recognizer.py --inputs=path_to_images_or_pdfs --threshold=0.2 --mode=layout --output_dir=path_to_store_result
-     ```
-    The inputs could be directory to images or PDF, or a image or PDF. 
-    You can look into the folder 'path_to_store_result' where has images which demonstrate the detection results as following:
-    <div align="center" style="margin-top:20px;margin-bottom:20px;">
-    <img src="https://github.com/infiniflow/ragflow/assets/12318111/07e0f625-9b28-43d0-9fbb-5bf586cd286f" width="1000"/>
-    </div>
-  
-  - Table Structure Recognition(TSR). Data table is a frequently used structure to present data including numbers or text.
-    And the structure of a table might be very complex, like hierarchy headers, spanning cells and projected row headers.
-    Along with TSR, we also reassemble the content into sentences which could be well comprehended by LLM. 
-    We have five labels for TSR task:
-      - Column
-      - Row
-      - Column header
-      - Projected row header
-      - Spanning cell
-      
-    Have a try on the following command to see the layout detection results.
-     ```bash
-        python deepdoc/vision/t_recognizer.py --inputs=path_to_images_or_pdfs --threshold=0.2 --mode=tsr --output_dir=path_to_store_result
-     ```
-    The inputs could be directory to images or PDF, or a image or PDF. 
-    You can look into the folder 'path_to_store_result' where has both images and html pages which demonstrate the detection results as following:
-    <div align="center" style="margin-top:20px;margin-bottom:20px;">
-    <img src="https://github.com/infiniflow/ragflow/assets/12318111/cb24e81b-f2ba-49f3-ac09-883d75606f4c" width="1000"/>
-    </div>
-        
-<a name="3"></a>
-## 3. Parser
-
-Four kinds of document formats as PDF, DOCX, EXCEL and PPT have their corresponding parser. 
-The most complex one is PDF parser since PDF's flexibility. The output of PDF parser includes:
-  - Text chunks with their own positions in PDF(page number and rectangular positions).
-  - Tables with cropped image from the PDF, and contents which has already translated into natural language sentences.
-  - Figures with caption and text in the figures.
-  
-### Résumé
-
-The résumé is a very complicated kind of document. A résumé which is composed of unstructured text 
-with various layouts could be resolved into structured data composed of nearly a hundred of fields.
-We haven't opened the parser yet, as we open the processing method after parsing procedure.
-
+English | [简体中文](./README_zh.md)
+
+# *Deep*Doc
+
+- [1. Introduction](#1)
+- [2. Vision](#2)
+- [3. Parser](#3)
+
+<a name="1"></a>
+## 1. Introduction
+
+With a bunch of documents from various domains with various formats and along with diverse retrieval requirements, 
+an accurate analysis becomes a very challenge task. *Deep*Doc is born for that purpose.
+There are 2 parts in *Deep*Doc so far: vision and parser. 
+You can run the flowing test programs if you're interested in our results of OCR, layout recognition and TSR.
+```bash
+python deepdoc/vision/t_ocr.py -h
+usage: t_ocr.py [-h] --inputs INPUTS [--output_dir OUTPUT_DIR]
+
+options:
+  -h, --help            show this help message and exit
+  --inputs INPUTS       Directory where to store images or PDFs, or a file path to a single image or PDF
+  --output_dir OUTPUT_DIR
+                        Directory where to store the output images. Default: './ocr_outputs'
+```
+```bash
+python deepdoc/vision/t_recognizer.py -h
+usage: t_recognizer.py [-h] --inputs INPUTS [--output_dir OUTPUT_DIR] [--threshold THRESHOLD] [--mode {layout,tsr}]
+
+options:
+  -h, --help            show this help message and exit
+  --inputs INPUTS       Directory where to store images or PDFs, or a file path to a single image or PDF
+  --output_dir OUTPUT_DIR
+                        Directory where to store the output images. Default: './layouts_outputs'
+  --threshold THRESHOLD
+                        A threshold to filter out detections. Default: 0.5
+  --mode {layout,tsr}   Task mode: layout recognition or table structure recognition
+```
+
+Our models are served on HuggingFace. If you have trouble downloading HuggingFace models, this might help!!
+```bash
+export HF_ENDPOINT=https://hf-mirror.com
+```
+
+<a name="2"></a>
+## 2. Vision
+
+We use vision information to resolve problems as human being.
+  - OCR. Since a lot of documents presented as images or at least be able to transform to image, 
+    OCR is a very essential and fundamental or even universal solution for text extraction.
+    ```bash
+        python deepdoc/vision/t_ocr.py --inputs=path_to_images_or_pdfs --output_dir=path_to_store_result
+     ```
+    The inputs could be directory to images or PDF, or a image or PDF. 
+    You can look into the folder 'path_to_store_result' where has images which demonstrate the positions of results,
+    txt files which contain the OCR text.
+    <div align="center" style="margin-top:20px;margin-bottom:20px;">
+    <img src="https://github.com/infiniflow/ragflow/assets/12318111/f25bee3d-aaf7-4102-baf5-d5208361d110" width="900"/>
+    </div>
+
+  - Layout recognition. Documents from different domain may have various layouts, 
+    like, newspaper, magazine, book and résumé are distinct in terms of layout. 
+    Only when machine have an accurate layout analysis, it can decide if these text parts are successive or not, 
+    or this part needs Table Structure Recognition(TSR) to process, or this part is a figure and described with this caption.
+    We have 10 basic layout components which covers most cases:
+      - Text
+      - Title
+      - Figure
+      - Figure caption
+      - Table
+      - Table caption
+      - Header
+      - Footer
+      - Reference
+      - Equation
+      
+     Have a try on the following command to see the layout detection results.
+     ```bash
+        python deepdoc/vision/t_recognizer.py --inputs=path_to_images_or_pdfs --threshold=0.2 --mode=layout --output_dir=path_to_store_result
+     ```
+    The inputs could be directory to images or PDF, or a image or PDF. 
+    You can look into the folder 'path_to_store_result' where has images which demonstrate the detection results as following:
+    <div align="center" style="margin-top:20px;margin-bottom:20px;">
+    <img src="https://github.com/infiniflow/ragflow/assets/12318111/07e0f625-9b28-43d0-9fbb-5bf586cd286f" width="1000"/>
+    </div>
+  
+  - Table Structure Recognition(TSR). Data table is a frequently used structure to present data including numbers or text.
+    And the structure of a table might be very complex, like hierarchy headers, spanning cells and projected row headers.
+    Along with TSR, we also reassemble the content into sentences which could be well comprehended by LLM. 
+    We have five labels for TSR task:
+      - Column
+      - Row
+      - Column header
+      - Projected row header
+      - Spanning cell
+      
+    Have a try on the following command to see the layout detection results.
+     ```bash
+        python deepdoc/vision/t_recognizer.py --inputs=path_to_images_or_pdfs --threshold=0.2 --mode=tsr --output_dir=path_to_store_result
+     ```
+    The inputs could be directory to images or PDF, or a image or PDF. 
+    You can look into the folder 'path_to_store_result' where has both images and html pages which demonstrate the detection results as following:
+    <div align="center" style="margin-top:20px;margin-bottom:20px;">
+    <img src="https://github.com/infiniflow/ragflow/assets/12318111/cb24e81b-f2ba-49f3-ac09-883d75606f4c" width="1000"/>
+    </div>
+        
+<a name="3"></a>
+## 3. Parser
+
+Four kinds of document formats as PDF, DOCX, EXCEL and PPT have their corresponding parser. 
+The most complex one is PDF parser since PDF's flexibility. The output of PDF parser includes:
+  - Text chunks with their own positions in PDF(page number and rectangular positions).
+  - Tables with cropped image from the PDF, and contents which has already translated into natural language sentences.
+  - Figures with caption and text in the figures.
+  
+### Résumé
+
+The résumé is a very complicated kind of document. A résumé which is composed of unstructured text 
+with various layouts could be resolved into structured data composed of nearly a hundred of fields.
+We haven't opened the parser yet, as we open the processing method after parsing procedure.
+