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#
# Copyright 2025 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 typing import Any
Feat/configurable metadata display (#13464) ### What problem does this PR solve? Currently, RAGFlow's Search and Chat interfaces display only raw vectorized text chunks during retrieval, without contextual information about their source documents. Users cannot see document titles, page numbers, upload dates, or custom metadata fields that would help them understand and trust the retrieved results. This PR introduces an **optional metadata display feature** that enriches retrieved chunks with document-level metadata in both the Search tab and Chatbot interface. **Key improvements:** - **Search results**: Display document metadata as styled badges beneath chunk snippets - **Chat citations**: Show metadata in citation popovers and reference lists for better source context - **LLM context**: Metadata is injected into the LLM prompt to enable more accurate, citation-aware responses - **External API support**: Applications using RAGFlow's SDK retrieval endpoints (`/v1/retrieval`, `/v1/searchbots/retrieval_test`) can opt-in via request parameters - **User control**: Multi-select dropdown UI allows users to choose which metadata fields to display **Implementation approach:** - ✅ Reuses existing `DocMetadataService` infrastructure (no new database tables or indices) - ✅ Settings stored in existing JSON configuration fields (`search_config.reference_metadata`, `prompt_config.reference_metadata`) - ✅ No database migrations required - ✅ Disabled by default (fully opt-in and backward-compatible) - ✅ Dynamic metadata field selection populated from actual document metadata keys - ✅ Fixed critical bug where Python's builtin `set()` was shadowed by a route handler function **Modified endpoints (all backward-compatible):** - `POST /v1/retrieval` (Public SDK) - `POST /v1/searchbots/retrieval_test` (Searchbots) - `POST /v1/chunk/retrieval_test` (UI/Internal) - Chat completions endpoints (via `extra_body.reference_metadata` or `prompt_config`) ### Type of change - [x] New Feature (non-breaking change which adds functionality) ###Images - <img width="879" height="1275" alt="image" src="https://github.com/user-attachments/assets/95b2d731-31ae-45a1-b081-bf5893f52aeb" /> <br><br> <br><br> <img width="1532" height="362" alt="image" src="https://github.com/user-attachments/assets/9cebc65b-b7a7-459f-b25e-3b13fa9b638e" /> <br><br> <br><br> <img width="2586" height="1320" alt="image" src="https://github.com/user-attachments/assets/2153d493-d899-461f-a7a9-041391e07776" /> --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Attili-sys <Attili-sys@users.noreply.github.com> Co-authored-by: Ahmad Intisar <ahmadintisar@Ahmads-MacBook-M4-Pro.local>
2026-04-30 18:13:27 +03:00
from .base import Base
from .document import Document
class DataSet(Base):
class ParserConfig(Base):
def __init__(self, rag, res_dict):
super().__init__(rag, res_dict)
def __init__(self, rag, res_dict):
self.id = ""
self.name = ""
self.avatar = ""
self.tenant_id = None
self.description = ""
self.embedding_model = ""
self.permission = "me"
self.document_count = 0
self.chunk_count = 0
self.chunk_method = "naive"
self.parser_config = None
self.pagerank = 0
for k in list(res_dict.keys()):
if k not in self.__dict__:
res_dict.pop(k)
super().__init__(rag, res_dict)
def update(self, update_message: dict):
res = self.put(f"/datasets/{self.id}", update_message)
res = res.json()
if res.get("code") != 0:
raise Exception(res["message"])
self._update_from_dict(self.rag, res.get("data", {}))
return self
def upload_documents(self, document_list: list[dict]):
url = f"/datasets/{self.id}/documents"
files = [("file", (ele["display_name"], ele["blob"])) for ele in document_list]
res = self.post(path=url, json=None, files=files)
res = res.json()
if res.get("code") == 0:
doc_list = []
for doc in res["data"]:
document = Document(self.rag, doc)
doc_list.append(document)
return doc_list
raise Exception(res.get("message"))
def list_documents(
self,
id: str | None = None,
ids: list[str] | None = None,
name: str | None = None,
keywords: str | None = None,
page: int = 1,
page_size: int = 30,
orderby: str = "create_time",
desc: bool = True,
create_time_from: int = 0,
create_time_to: int = 0,
):
# Validate that id and ids are not used together
if id and ids:
raise ValueError("Cannot use both 'id' and 'ids' parameters at the same time.")
Feat/configurable metadata display (#13464) ### What problem does this PR solve? Currently, RAGFlow's Search and Chat interfaces display only raw vectorized text chunks during retrieval, without contextual information about their source documents. Users cannot see document titles, page numbers, upload dates, or custom metadata fields that would help them understand and trust the retrieved results. This PR introduces an **optional metadata display feature** that enriches retrieved chunks with document-level metadata in both the Search tab and Chatbot interface. **Key improvements:** - **Search results**: Display document metadata as styled badges beneath chunk snippets - **Chat citations**: Show metadata in citation popovers and reference lists for better source context - **LLM context**: Metadata is injected into the LLM prompt to enable more accurate, citation-aware responses - **External API support**: Applications using RAGFlow's SDK retrieval endpoints (`/v1/retrieval`, `/v1/searchbots/retrieval_test`) can opt-in via request parameters - **User control**: Multi-select dropdown UI allows users to choose which metadata fields to display **Implementation approach:** - ✅ Reuses existing `DocMetadataService` infrastructure (no new database tables or indices) - ✅ Settings stored in existing JSON configuration fields (`search_config.reference_metadata`, `prompt_config.reference_metadata`) - ✅ No database migrations required - ✅ Disabled by default (fully opt-in and backward-compatible) - ✅ Dynamic metadata field selection populated from actual document metadata keys - ✅ Fixed critical bug where Python's builtin `set()` was shadowed by a route handler function **Modified endpoints (all backward-compatible):** - `POST /v1/retrieval` (Public SDK) - `POST /v1/searchbots/retrieval_test` (Searchbots) - `POST /v1/chunk/retrieval_test` (UI/Internal) - Chat completions endpoints (via `extra_body.reference_metadata` or `prompt_config`) ### Type of change - [x] New Feature (non-breaking change which adds functionality) ###Images - <img width="879" height="1275" alt="image" src="https://github.com/user-attachments/assets/95b2d731-31ae-45a1-b081-bf5893f52aeb" /> <br><br> <br><br> <img width="1532" height="362" alt="image" src="https://github.com/user-attachments/assets/9cebc65b-b7a7-459f-b25e-3b13fa9b638e" /> <br><br> <br><br> <img width="2586" height="1320" alt="image" src="https://github.com/user-attachments/assets/2153d493-d899-461f-a7a9-041391e07776" /> --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Attili-sys <Attili-sys@users.noreply.github.com> Co-authored-by: Ahmad Intisar <ahmadintisar@Ahmads-MacBook-M4-Pro.local>
2026-04-30 18:13:27 +03:00
params = {
"id": id,
"name": name,
"keywords": keywords,
"page": page,
"page_size": page_size,
"orderby": orderby,
"desc": desc,
"create_time_from": create_time_from,
"create_time_to": create_time_to,
}
# Handle ids parameter - convert to multiple query params
if ids:
for doc_id in ids:
params.append(("ids", doc_id))
res = self.get(f"/datasets/{self.id}/documents", params=params)
res = res.json()
documents = []
if res.get("code") == 0:
for document in res["data"].get("docs"):
documents.append(Document(self.rag, document))
return documents
raise Exception(res["message"])
def delete_documents(self, ids: list[str] | None = None, delete_all: bool = False):
res = self.rm(f"/datasets/{self.id}/documents", {"ids": ids, "delete_all": delete_all})
res = res.json()
if res.get("code") != 0:
raise Exception(res["message"])
Feat/configurable metadata display (#13464) ### What problem does this PR solve? Currently, RAGFlow's Search and Chat interfaces display only raw vectorized text chunks during retrieval, without contextual information about their source documents. Users cannot see document titles, page numbers, upload dates, or custom metadata fields that would help them understand and trust the retrieved results. This PR introduces an **optional metadata display feature** that enriches retrieved chunks with document-level metadata in both the Search tab and Chatbot interface. **Key improvements:** - **Search results**: Display document metadata as styled badges beneath chunk snippets - **Chat citations**: Show metadata in citation popovers and reference lists for better source context - **LLM context**: Metadata is injected into the LLM prompt to enable more accurate, citation-aware responses - **External API support**: Applications using RAGFlow's SDK retrieval endpoints (`/v1/retrieval`, `/v1/searchbots/retrieval_test`) can opt-in via request parameters - **User control**: Multi-select dropdown UI allows users to choose which metadata fields to display **Implementation approach:** - ✅ Reuses existing `DocMetadataService` infrastructure (no new database tables or indices) - ✅ Settings stored in existing JSON configuration fields (`search_config.reference_metadata`, `prompt_config.reference_metadata`) - ✅ No database migrations required - ✅ Disabled by default (fully opt-in and backward-compatible) - ✅ Dynamic metadata field selection populated from actual document metadata keys - ✅ Fixed critical bug where Python's builtin `set()` was shadowed by a route handler function **Modified endpoints (all backward-compatible):** - `POST /v1/retrieval` (Public SDK) - `POST /v1/searchbots/retrieval_test` (Searchbots) - `POST /v1/chunk/retrieval_test` (UI/Internal) - Chat completions endpoints (via `extra_body.reference_metadata` or `prompt_config`) ### Type of change - [x] New Feature (non-breaking change which adds functionality) ###Images - <img width="879" height="1275" alt="image" src="https://github.com/user-attachments/assets/95b2d731-31ae-45a1-b081-bf5893f52aeb" /> <br><br> <br><br> <img width="1532" height="362" alt="image" src="https://github.com/user-attachments/assets/9cebc65b-b7a7-459f-b25e-3b13fa9b638e" /> <br><br> <br><br> <img width="2586" height="1320" alt="image" src="https://github.com/user-attachments/assets/2153d493-d899-461f-a7a9-041391e07776" /> --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Attili-sys <Attili-sys@users.noreply.github.com> Co-authored-by: Ahmad Intisar <ahmadintisar@Ahmads-MacBook-M4-Pro.local>
2026-04-30 18:13:27 +03:00
def _get_documents_status(self, document_ids):
import time
terminal_states = {"DONE", "FAIL", "CANCEL"}
interval_sec = 1
pending = set(document_ids)
finished = []
while pending:
for doc_id in list(pending):
def fetch_doc(doc_id: str) -> Document | None:
try:
docs = self.list_documents(id=doc_id)
return docs[0] if docs else None
except Exception:
return None
doc = fetch_doc(doc_id)
if doc is None:
continue
if isinstance(doc.run, str) and doc.run.upper() in terminal_states:
finished.append((doc_id, doc.run, doc.chunk_count, doc.token_count))
pending.discard(doc_id)
elif float(doc.progress or 0.0) >= 1.0:
finished.append((doc_id, "DONE", doc.chunk_count, doc.token_count))
pending.discard(doc_id)
if pending:
time.sleep(interval_sec)
return finished
def async_parse_documents(self, document_ids):
res = self.post(f"/datasets/{self.id}/chunks", {"document_ids": document_ids})
res = res.json()
if res.get("code") != 0:
raise Exception(res.get("message"))
def parse_documents(self, document_ids):
try:
self.async_parse_documents(document_ids)
self._get_documents_status(document_ids)
except KeyboardInterrupt:
self.async_cancel_parse_documents(document_ids)
return self._get_documents_status(document_ids)
def async_cancel_parse_documents(self, document_ids):
res = self.rm(f"/datasets/{self.id}/chunks", {"document_ids": document_ids})
res = res.json()
if res.get("code") != 0:
raise Exception(res.get("message"))
def get_auto_metadata(self) -> dict[str, Any]:
"""
Retrieve auto-metadata configuration for a dataset via SDK.
"""
feat(api): add unified index API and dataset management endpoints (#14222) ### What problem does this PR solve? ## Summary Refactor the dataset API layer into a clean service/REST separation pattern, add a unified `/index` API for graph/raptor/mindmap operations, and introduce several new dataset management endpoints with full test coverage. ## Changes ### Service Layer (`dataset_api_service.py`) - Added `trace_index(dataset_id, tenant_id, index_type)` — unified trace function for all index types - Added `run_index`, `delete_index` service functions - Added `get_dataset`, `get_ingestion_summary`, `list_ingestion_logs`, `get_ingestion_log` - Added `run_embedding`, `list_tags`, `aggregate_tags`, `delete_tags`, `rename_tag` - Added `get_flattened_metadata`, `get_auto_metadata`, `update_auto_metadata` ### REST API Layer (`dataset_api.py`) **New unified routes:** | Method | Route | Description | |--------|-------|-------------| | POST | `/datasets/<id>/index?type=graph\|raptor\|mindmap` | Run index task | | GET | `/datasets/<id>/index?type=graph\|raptor\|mindmap` | Trace index task | | DELETE | `/datasets/<id>/<index_type>` | Delete index | | GET | `/datasets/<id>` | Get dataset details | | GET | `/datasets/<id>/ingestions/summary` | Ingestion summary | | GET | `/datasets/<id>/ingestions` | List ingestion logs | | GET | `/datasets/<id>/ingestions/<log_id>` | Get single ingestion log | | POST | `/datasets/<id>/embedding` | Run embedding | | GET | `/datasets/<id>/tags` | List tags | | GET | `/datasets/tags/aggregation` | Aggregate tags across datasets | | DELETE | `/datasets/<id>/tags` | Delete tags | | PUT | `/datasets/<id>/tags` | Rename tag | | GET | `/datasets/metadata/flattened` | Get flattened metadata | | GET/PUT | `/datasets/<id>/metadata/config` | New metadata config path | **Removed routes (replaced by unified `/index`):** - `POST /datasets/<id>/mindmap` - `GET /datasets/<id>/mindmap` **Preserved legacy routes (backward compatibility):** - `/run_graphrag`, `/trace_graphrag`, `/run_raptor`, `/trace_raptor` - `/auto_metadata` GET/PUT ### Test Suite - Updated `common.py` helpers: added `trace_index`, removed `run_mindmap`/`trace_mindmap` - Added 7 new test files with 39 test cases total: | Test File | Cases | |-----------|-------| | `test_get_dataset.py` | 4 | | `test_ingestion_summary.py` | 2 | | `test_ingestion_logs.py` | 5 | | `test_index_api.py` | 14 | | `test_embedding.py` | 2 | | `test_tags.py` | 8 | | `test_flattened_metadata.py` | 4 | - Deleted `test_mindmap_tasks.py` (covered by unified index tests) ## Design Decisions 1. **Unified `/index?type=...`** — single endpoint replaces 3 separate route pairs for graph/raptor/mindmap 2. **Backward compatibility** — old routes (`/run_graphrag`, `/run_raptor`, `/auto_metadata`) preserved alongside new paths 3. **`_VALID_INDEX_TYPES = {"graph", "raptor", "mindmap"}`** — input validation via constant set 4. **`_INDEX_TYPE_TO_TASK_ID_FIELD`** — maps index type to KB model task ID field for clean dispatch ## Files Changed - `api/apps/restful_apis/dataset_api.py` - `api/apps/services/dataset_api_service.py` - `sdk/python/ragflow_sdk/modules/dataset.py` - `test/testcases/test_http_api/common.py` - `test/testcases/test_http_api/test_dataset_management/` (7 new files) ### Type of change - [x] New Feature (non-breaking change which adds functionality) - [x] Refactoring --------- Signed-off-by: noob <yixiao121314@outlook.com>
2026-04-27 01:38:01 +00:00
res = self.get(f"/datasets/{self.id}/metadata/config")
res = res.json()
if res.get("code") == 0:
return res["data"]
raise Exception(res["message"])
def update_auto_metadata(self, **config: Any) -> dict[str, Any]:
"""
Update auto-metadata configuration for a dataset via SDK.
"""
feat(api): add unified index API and dataset management endpoints (#14222) ### What problem does this PR solve? ## Summary Refactor the dataset API layer into a clean service/REST separation pattern, add a unified `/index` API for graph/raptor/mindmap operations, and introduce several new dataset management endpoints with full test coverage. ## Changes ### Service Layer (`dataset_api_service.py`) - Added `trace_index(dataset_id, tenant_id, index_type)` — unified trace function for all index types - Added `run_index`, `delete_index` service functions - Added `get_dataset`, `get_ingestion_summary`, `list_ingestion_logs`, `get_ingestion_log` - Added `run_embedding`, `list_tags`, `aggregate_tags`, `delete_tags`, `rename_tag` - Added `get_flattened_metadata`, `get_auto_metadata`, `update_auto_metadata` ### REST API Layer (`dataset_api.py`) **New unified routes:** | Method | Route | Description | |--------|-------|-------------| | POST | `/datasets/<id>/index?type=graph\|raptor\|mindmap` | Run index task | | GET | `/datasets/<id>/index?type=graph\|raptor\|mindmap` | Trace index task | | DELETE | `/datasets/<id>/<index_type>` | Delete index | | GET | `/datasets/<id>` | Get dataset details | | GET | `/datasets/<id>/ingestions/summary` | Ingestion summary | | GET | `/datasets/<id>/ingestions` | List ingestion logs | | GET | `/datasets/<id>/ingestions/<log_id>` | Get single ingestion log | | POST | `/datasets/<id>/embedding` | Run embedding | | GET | `/datasets/<id>/tags` | List tags | | GET | `/datasets/tags/aggregation` | Aggregate tags across datasets | | DELETE | `/datasets/<id>/tags` | Delete tags | | PUT | `/datasets/<id>/tags` | Rename tag | | GET | `/datasets/metadata/flattened` | Get flattened metadata | | GET/PUT | `/datasets/<id>/metadata/config` | New metadata config path | **Removed routes (replaced by unified `/index`):** - `POST /datasets/<id>/mindmap` - `GET /datasets/<id>/mindmap` **Preserved legacy routes (backward compatibility):** - `/run_graphrag`, `/trace_graphrag`, `/run_raptor`, `/trace_raptor` - `/auto_metadata` GET/PUT ### Test Suite - Updated `common.py` helpers: added `trace_index`, removed `run_mindmap`/`trace_mindmap` - Added 7 new test files with 39 test cases total: | Test File | Cases | |-----------|-------| | `test_get_dataset.py` | 4 | | `test_ingestion_summary.py` | 2 | | `test_ingestion_logs.py` | 5 | | `test_index_api.py` | 14 | | `test_embedding.py` | 2 | | `test_tags.py` | 8 | | `test_flattened_metadata.py` | 4 | - Deleted `test_mindmap_tasks.py` (covered by unified index tests) ## Design Decisions 1. **Unified `/index?type=...`** — single endpoint replaces 3 separate route pairs for graph/raptor/mindmap 2. **Backward compatibility** — old routes (`/run_graphrag`, `/run_raptor`, `/auto_metadata`) preserved alongside new paths 3. **`_VALID_INDEX_TYPES = {"graph", "raptor", "mindmap"}`** — input validation via constant set 4. **`_INDEX_TYPE_TO_TASK_ID_FIELD`** — maps index type to KB model task ID field for clean dispatch ## Files Changed - `api/apps/restful_apis/dataset_api.py` - `api/apps/services/dataset_api_service.py` - `sdk/python/ragflow_sdk/modules/dataset.py` - `test/testcases/test_http_api/common.py` - `test/testcases/test_http_api/test_dataset_management/` (7 new files) ### Type of change - [x] New Feature (non-breaking change which adds functionality) - [x] Refactoring --------- Signed-off-by: noob <yixiao121314@outlook.com>
2026-04-27 01:38:01 +00:00
res = self.put(f"/datasets/{self.id}/metadata/config", config)
res = res.json()
if res.get("code") == 0:
return res["data"]
raise Exception(res["message"])