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Closes #14753 ## What changed | File | Change | |---|---| | `pyproject.toml` | `requires-python` → `>=3.13,<3.15`; remove `strenum==0.4.15` | | `Dockerfile` | `uv python install 3.13`, `uv sync --python 3.13` | | `.github/workflows/tests.yml` | `uv sync --python 3.13` on both matrix legs | | `CLAUDE.md` | dev setup command + requirements note updated | | `deepdoc/parser/mineru_parser.py` | `from strenum import StrEnum` → `from enum import StrEnum` | | `agent/tools/code_exec.py` | same | `StrEnum` has been in the stdlib since Python 3.11 — the `strenum` backport package is no longer needed once the floor is 3.13. ## Why uv.lock is not regenerated `uv lock --python 3.13` fails because: 1. The infiniflow/graspologic fork pins `numpy>=1.26.4,<2.0.0` 2. `tensorflow-cpu>=2.20.0` (the first release with cp313 wheels) depends on `ml-dtypes>=0.5.1`, which requires `numpy>=2.1.0` 3. These two constraints are irreconcilable on Python 3.13 The lockfile regeneration requires loosening the `numpy` upper bound in the `infiniflow/graspologic` fork. Once that fork commit is updated and the SHA in `pyproject.toml:49` is bumped, `uv lock --python 3.13` will succeed. ## RFC corrections Two claims in the original RFC (#14753) did not hold up under code review: - **"graspologic hard-blocks 3.13"** — the infiniflow fork at the pinned commit has no `<3.13` Python constraint. The blocker is the transitive `numpy<2.0.0` conflict with tensorflow-cpu's test dependency, not a direct Python version cap. - **"free-threading throughput gains for I/O-bound workload"** — Python 3.13 free-threading requires a special `--disable-gil` build and provides no benefit for async I/O code (the GIL is already released during I/O). The real motivation is forward compatibility and improved error messages.
135 lines
3.9 KiB
Markdown
135 lines
3.9 KiB
Markdown
# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It's a full-stack application with:
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- Python backend (Flask-based API server)
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- React/TypeScript frontend (built with vitejs)
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- Microservices architecture with Docker deployment
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- Multiple data stores (MySQL, Elasticsearch/Infinity, Redis, MinIO)
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## Architecture
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### Backend (`/api/`)
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- **Main Server**: `api/ragflow_server.py` - Flask application entry point
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- **Apps**: Modular Flask blueprints in `api/apps/` for different functionalities:
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- `kb_app.py` - Knowledge base management
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- `dialog_app.py` - Chat/conversation handling
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- `document_app.py` - Document processing
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- `canvas_app.py` - Agent workflow canvas
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- `file_app.py` - File upload/management
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- **Services**: Business logic in `api/db/services/`
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- **Models**: Database models in `api/db/db_models.py`
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### Core Processing (`/rag/`)
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- **Document Processing**: `deepdoc/` - PDF parsing, OCR, layout analysis
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- **LLM Integration**: `rag/llm/` - Model abstractions for chat, embedding, reranking
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- **RAG Pipeline**: `rag/flow/` - Chunking, parsing, tokenization
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- **Graph RAG**: `rag/graphrag/` - Knowledge graph construction and querying
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### Agent System (`/agent/`)
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- **Components**: Modular workflow components (LLM, retrieval, categorize, etc.)
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- **Templates**: Pre-built agent workflows in `agent/templates/`
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- **Tools**: External API integrations (Tavily, Wikipedia, SQL execution, etc.)
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### Frontend (`/web/`)
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- React/TypeScript with vitejs framework
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- shadcn/ui components
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- State management with Zustand
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- Tailwind CSS for styling
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## Common Development Commands
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### Backend Development
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```bash
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# Install Python dependencies
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uv sync --python 3.13 --all-extras
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uv run python3 download_deps.py
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pre-commit install
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# Start dependent services
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docker compose -f docker/docker-compose-base.yml up -d
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# Run backend (requires services to be running)
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source .venv/bin/activate
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export PYTHONPATH=$(pwd)
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bash docker/launch_backend_service.sh
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# Run tests
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uv run pytest
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# Linting
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ruff check
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ruff format
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```
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### Frontend Development
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```bash
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cd web
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npm install
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npm run dev # Development server
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npm run build # Production build
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npm run lint # ESLint
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npm run test # Jest tests
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```
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### Docker Development
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```bash
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# Full stack with Docker
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cd docker
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docker compose -f docker-compose.yml up -d
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# Check server status
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docker logs -f ragflow-server
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# Rebuild images
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docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
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```
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## Key Configuration Files
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- `docker/.env` - Environment variables for Docker deployment
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- `docker/service_conf.yaml.template` - Backend service configuration
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- `pyproject.toml` - Python dependencies and project configuration
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- `web/package.json` - Frontend dependencies and scripts
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## Testing
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- **Python**: pytest with markers (p1/p2/p3 priority levels)
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- **Frontend**: Jest with React Testing Library
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- **API Tests**: HTTP API and SDK tests in `test/` and `sdk/python/test/`
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## Database Engines
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RAGFlow supports switching between Elasticsearch (default) and Infinity:
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- Set `DOC_ENGINE=infinity` in `docker/.env` to use Infinity
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- Requires container restart: `docker compose down -v && docker compose up -d`
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## Development Environment Requirements
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- Python 3.10-3.13
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- Node.js >=18.20.4
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- Docker & Docker Compose
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- uv package manager
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- 16GB+ RAM, 50GB+ disk space
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1. Think before acting. Read existing files before writing code.
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2. Be concise in output but thorough in reasoning.
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3. Prefer editing over rewriting whole files.
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4. Do not re-read files you have already read.
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5. Test your code before declaring done.
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6. No sycophantic openers or closing fluff.
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7. Keep solutions simple and direct.
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8. User instructions always override this file.
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