diff --git a/MANIFEST.in b/MANIFEST.in index 3c771107..cf12c1e9 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,3 +1,4 @@ include requirements.txt include lightrag/api/requirements.txt recursive-include lightrag/api/webui * +recursive-include lightrag/api/static * diff --git a/README-zh.md b/README-zh.md index 867478bd..552d566a 100644 --- a/README-zh.md +++ b/README-zh.md @@ -104,18 +104,26 @@ lightrag-server git clone https://github.com/HKUDS/LightRAG.git cd LightRAG # 如有必要，创建Python虚拟环境 -# 以可编辑模式安装并支持API +# 以可开发（编辑）模式安装LightRAG服务器 pip install -e ".[api]" -cp env.example .env + +cp env.example .env # 使用你的LLM和Embedding模型访问参数更新.env文件 + +# 构建前端代码 +cd lightrag_webui +bun install --frozen-lockfile +bun run build +cd .. + lightrag-server ``` * 使用 Docker Compose 启动 LightRAG 服务器 -``` +```bash git clone https://github.com/HKUDS/LightRAG.git cd LightRAG -cp env.example .env +cp env.example .env # 使用你的LLM和Embedding模型访问参数更新.env文件 # modify LLM and Embedding settings in .env docker compose up ``` diff --git a/README.md b/README.md index 52b9008c..b844a18d 100644 --- a/README.md +++ b/README.md @@ -103,19 +103,27 @@ lightrag-server ```bash git clone https://github.com/HKUDS/LightRAG.git cd LightRAG -# create a Python virtual enviroment if neccesary +# Create a Python virtual enviroment if neccesary # Install in editable mode with API support pip install -e ".[api]" -cp env.example .env + +cp env.example .env # Update the .env with your LLM and embedding configurations + +# Build front-end artifacts +cd lightrag_webui +bun install --frozen-lockfile +bun run build +cd .. + lightrag-server ``` * Launching the LightRAG Server with Docker Compose -``` +```bash git clone https://github.com/HKUDS/LightRAG.git cd LightRAG -cp env.example .env +cp env.example .env # Update the .env with your LLM and embedding configurations # modify LLM and Embedding settings in .env docker compose up ``` @@ -1539,7 +1547,7 @@ The dataset used in LightRAG can be downloaded from [TommyChien/UltraDomain](htt ### Generate Query -LightRAG uses the following prompt to generate high-level queries, with the corresponding code in `example/generate_query.py`. +LightRAG uses the following prompt to generate high-level queries, with the corresponding code in `examples/generate_query.py`.
Prompt diff --git a/env.example b/env.example index 828f962e..3c5113ff 100644 --- a/env.example +++ b/env.example @@ -208,6 +208,7 @@ OPENAI_LLM_MAX_COMPLETION_TOKENS=9000 # OPENAI_LLM_EXTRA_BODY='{"chat_template_kwargs": {"enable_thinking": false}}' ### use the following command to see all support options for Ollama LLM +### If LightRAG deployed in Docker uses host.docker.internal instead of localhost in LLM_BINDING_HOST ### lightrag-server --llm-binding ollama --help ### Ollama Server Specific Parameters ### OLLAMA_LLM_NUM_CTX must be provided, and should at least larger than MAX_TOTAL_TOKENS + 2000 @@ -229,7 +230,7 @@ EMBEDDING_BINDING=ollama EMBEDDING_MODEL=bge-m3:latest EMBEDDING_DIM=1024 EMBEDDING_BINDING_API_KEY=your_api_key -# If the embedding service is deployed within the same Docker stack, use host.docker.internal instead of localhost +# If LightRAG deployed in Docker uses host.docker.internal instead of localhost EMBEDDING_BINDING_HOST=http://localhost:11434 ### OpenAI compatible (VoyageAI embedding openai compatible) diff --git a/examples/generate_query.py b/examples/generate_query.py new file mode 100644 index 00000000..705b23d3 --- /dev/null +++ b/examples/generate_query.py @@ -0,0 +1,55 @@ +from openai import OpenAI + +# os.environ["OPENAI_API_KEY"] = "" + + +def openai_complete_if_cache( + model="gpt-4o-mini", prompt=None, system_prompt=None, history_messages=[], **kwargs +) -> str: + openai_client = OpenAI() + + messages = [] + if system_prompt: + messages.append({"role": "system", "content": system_prompt}) + messages.extend(history_messages) + messages.append({"role": "user", "content": prompt}) + + response = openai_client.chat.completions.create( + model=model, messages=messages, **kwargs + ) + return response.choices[0].message.content + + +if __name__ == "__main__": + description = "" + prompt = f""" + Given the following description of a dataset: + + {description} + + Please identify 5 potential users who would engage with this dataset. For each user, list 5 tasks they would perform with this dataset. Then, for each (user, task) combination, generate 5 questions that require a high-level understanding of the entire dataset. + + Output the results in the following structure: + - User 1: [user description] + - Task 1: [task description] + - Question 1: + - Question 2: + - Question 3: + - Question 4: + - Question 5: + - Task 2: [task description] + ... + - Task 5: [task description] + - User 2: [user description] + ... + - User 5: [user description] + ... + """ + + result = openai_complete_if_cache(model="gpt-4o-mini", prompt=prompt) + + file_path = "./queries.txt" + with open(file_path, "w") as file: + file.write(result) + + print(f"Queries written to {file_path}") diff --git a/lightrag-api b/lightrag-api deleted file mode 100644 index 89c814fb..00000000 --- a/lightrag-api +++ /dev/null @@ -1,4 +0,0 @@ -#!/bin/bash - -source /home/netman/lightrag-xyj/venv/bin/activate -lightrag-server diff --git a/lightrag.service.example b/lightrag.service.example index 3c96e0b4..e99198d1 100644 --- a/lightrag.service.example +++ b/lightrag.service.example @@ -1,5 +1,5 @@ [Unit] -Description=LightRAG XYJ Ollama Service +Description=LightRAG XYJ Service After=network.target [Service] @@ -8,10 +8,23 @@ User=netman # Memory settings MemoryHigh=8G MemoryMax=12G -WorkingDirectory=/home/netman/lightrag-xyj -ExecStart=/home/netman/lightrag-xyj/lightrag-api + +# Set the LightRAG installation directory (change this to match your installation path) +Environment="LIGHTRAG_HOME=/home/netman/lightrag-xyj" + +# Set Environment to your Python virtual environment +Environment="PATH=${LIGHTRAG_HOME}/.venv/bin" +WorkingDirectory=${LIGHTRAG_HOME} +ExecStart=${LIGHTRAG_HOME}/.venv/bin/lightrag-server +# ExecStart=${LIGHTRAG_HOME}/.venv/bin/lightrag-gunicorn + +# Kill mode require ExecStart must be gunicorn or unvicorn main process +KillMode=process +ExecStop=/bin/kill -s TERM $MAINPID +TimeoutStopSec=60 + Restart=always -RestartSec=10 +RestartSec=30 [Install] WantedBy=multi-user.target diff --git a/lightrag/__init__.py b/lightrag/__init__.py index c38b09e5..41531149 100644 --- a/lightrag/__init__.py +++ b/lightrag/__init__.py @@ -1,5 +1,5 @@ from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam -__version__ = "1.4.9.4" +__version__ = "1.4.9.6" __author__ = "Zirui Guo" __url__ = "https://github.com/HKUDS/LightRAG" diff --git a/lightrag/api/README-zh.md b/lightrag/api/README-zh.md index 692c589d..bd2bbd62 100644 --- a/lightrag/api/README-zh.md +++ b/lightrag/api/README-zh.md @@ -184,24 +184,16 @@ MAX_ASYNC=4 ### 将 Lightrag 安装为 Linux 服务 -从示例文件 `lightrag.service.example` 创建您的服务文件 `lightrag.service`。修改服务文件中的 WorkingDirectory 和 ExecStart： +从示例文件 `lightrag.service.example` 创建您的服务文件 `lightrag.service`。修改服务文件中的服务启动定义： ```text -Description=LightRAG Ollama Service -WorkingDirectory= -ExecStart=/lightrag/api/lightrag-api -``` - -修改您的服务启动脚本：`lightrag-api`。根据需要更改 python 虚拟环境激活命令： - -```shell -#!/bin/bash - -# 您的 python 虚拟环境激活命令 -source /home/netman/lightrag-xyj/venv/bin/activate -# 启动 lightrag api 服务器 -lightrag-server +# Set Enviroment to your Python virtual enviroment +Environment="PATH=/home/netman/lightrag-xyj/venv/bin" +WorkingDirectory=/home/netman/lightrag-xyj +# ExecStart=/home/netman/lightrag-xyj/venv/bin/lightrag-server +ExecStart=/home/netman/lightrag-xyj/venv/bin/lightrag-gunicorn ``` +> ExecStart命令必须是 lightrag-gunicorn 或 lightrag-server 中的一个，不能使用其它脚本包裹它们。因为停止服务必须要求主进程必须是这两个进程。 安装 LightRAG 服务。如果您的系统是 Ubuntu，以下命令将生效： diff --git a/lightrag/api/README.md b/lightrag/api/README.md index a16d9023..339500da 100644 --- a/lightrag/api/README.md +++ b/lightrag/api/README.md @@ -189,24 +189,18 @@ MAX_ASYNC=4 ### Install LightRAG as a Linux Service -Create your service file `lightrag.service` from the sample file: `lightrag.service.example`. Modify the `WorkingDirectory` and `ExecStart` in the service file: +Create your service file `lightrag.service` from the sample file: `lightrag.service.example`. Modify the start options the service file: ```text -Description=LightRAG Ollama Service -WorkingDirectory= -ExecStart=/lightrag/api/lightrag-api +# Set Enviroment to your Python virtual enviroment +Environment="PATH=/home/netman/lightrag-xyj/venv/bin" +WorkingDirectory=/home/netman/lightrag-xyj +# ExecStart=/home/netman/lightrag-xyj/venv/bin/lightrag-server +ExecStart=/home/netman/lightrag-xyj/venv/bin/lightrag-gunicorn + ``` -Modify your service startup script: `lightrag-api`. Change your Python virtual environment activation command as needed: - -```shell -#!/bin/bash - -# your python virtual environment activation -source /home/netman/lightrag-xyj/venv/bin/activate -# start lightrag api server -lightrag-server -``` +> The ExecStart command must be either `lightrag-gunicorn` or `lightrag-server`; no wrapper scripts are allowed. This is because service termination requires the main process to be one of these two executables. Install LightRAG service. If your system is Ubuntu, the following commands will work: diff --git a/lightrag/api/__init__.py b/lightrag/api/__init__.py index 6268052f..a3f452af 100644 --- a/lightrag/api/__init__.py +++ b/lightrag/api/__init__.py @@ -1 +1 @@ -__api_version__ = "0243" +__api_version__ = "0248" diff --git a/lightrag/api/gunicorn_config.py b/lightrag/api/gunicorn_config.py index 7b25b5b9..e000b46c 100644 --- a/lightrag/api/gunicorn_config.py +++ b/lightrag/api/gunicorn_config.py @@ -129,11 +129,13 @@ def on_exit(server): print("=" * 80) print("GUNICORN MASTER PROCESS: Shutting down") print(f"Process ID: {os.getpid()}") - print("=" * 80) - # Release shared resources + print("Finalizing shared storage...") finalize_share_data() + print("Gunicorn shutdown complete") + print("=" * 80) + print("=" * 80) print("Gunicorn shutdown complete") print("=" * 80) diff --git a/lightrag/api/lightrag_server.py b/lightrag/api/lightrag_server.py index 19bc549a..3269fbb5 100644 --- a/lightrag/api/lightrag_server.py +++ b/lightrag/api/lightrag_server.py @@ -5,10 +5,13 @@ LightRAG FastAPI Server from fastapi import FastAPI, Depends, HTTPException, Request from fastapi.exceptions import RequestValidationError from fastapi.responses import JSONResponse +from fastapi.openapi.docs import ( + get_swagger_ui_html, + get_swagger_ui_oauth2_redirect_html, +) import os import logging import logging.config -import signal import sys import uvicorn import pipmaster as pm @@ -78,24 +81,6 @@ config.read("config.ini") auth_configured = bool(auth_handler.accounts) -def setup_signal_handlers(): - """Setup signal handlers for graceful shutdown""" - - def signal_handler(sig, frame): - print(f"\n\nReceived signal {sig}, shutting down gracefully...") - print(f"Process ID: {os.getpid()}") - - # Release shared resources - finalize_share_data() - - # Exit with success status - sys.exit(0) - - # Register signal handlers - signal.signal(signal.SIGINT, signal_handler) # Ctrl+C - signal.signal(signal.SIGTERM, signal_handler) # kill command - - class LLMConfigCache: """Smart LLM and Embedding configuration cache class""" @@ -341,8 +326,15 @@ def create_app(args): # Clean up database connections await rag.finalize_storages() - # Clean up shared data - finalize_share_data() + if "LIGHTRAG_GUNICORN_MODE" not in os.environ: + # Only perform cleanup in Uvicorn single-process mode + logger.debug("Unvicorn Mode: finalizing shared storage...") + finalize_share_data() + else: + # In Gunicorn mode with preload_app=True, cleanup is handled by on_exit hooks + logger.debug( + "Gunicorn Mode: postpone shared storage finalization to master process" + ) # Initialize FastAPI base_description = ( @@ -358,7 +350,7 @@ def create_app(args): "description": swagger_description, "version": __api_version__, "openapi_url": "/openapi.json", # Explicitly set OpenAPI schema URL - "docs_url": "/docs", # Explicitly set docs URL + "docs_url": None, # Disable default docs, we'll create custom endpoint "redoc_url": "/redoc", # Explicitly set redoc URL "lifespan": lifespan, } @@ -769,6 +761,25 @@ def create_app(args): ollama_api = OllamaAPI(rag, top_k=args.top_k, api_key=api_key) app.include_router(ollama_api.router, prefix="/api") + # Custom Swagger UI endpoint for offline support + @app.get("/docs", include_in_schema=False) + async def custom_swagger_ui_html(): + """Custom Swagger UI HTML with local static files""" + return get_swagger_ui_html( + openapi_url=app.openapi_url, + title=app.title + " - Swagger UI", + oauth2_redirect_url="/docs/oauth2-redirect", + swagger_js_url="/static/swagger-ui/swagger-ui-bundle.js", + swagger_css_url="/static/swagger-ui/swagger-ui.css", + swagger_favicon_url="/static/swagger-ui/favicon-32x32.png", + swagger_ui_parameters=app.swagger_ui_parameters, + ) + + @app.get("/docs/oauth2-redirect", include_in_schema=False) + async def swagger_ui_redirect(): + """OAuth2 redirect for Swagger UI""" + return get_swagger_ui_oauth2_redirect_html() + @app.get("/") async def redirect_to_webui(): """Redirect root path to /webui""" @@ -935,6 +946,15 @@ def create_app(args): return response + # Mount Swagger UI static files for offline support + swagger_static_dir = Path(__file__).parent / "static" / "swagger-ui" + if swagger_static_dir.exists(): + app.mount( + "/static/swagger-ui", + StaticFiles(directory=swagger_static_dir), + name="swagger-ui-static", + ) + # Webui mount webui/index.html static_dir = Path(__file__).parent / "webui" static_dir.mkdir(exist_ok=True) @@ -1076,8 +1096,10 @@ def main(): update_uvicorn_mode_config() display_splash_screen(global_args) - # Setup signal handlers for graceful shutdown - setup_signal_handlers() + # Note: Signal handlers are NOT registered here because: + # - Uvicorn has built-in signal handling that properly calls lifespan shutdown + # - Custom signal handlers can interfere with uvicorn's graceful shutdown + # - Cleanup is handled by the lifespan context manager's finally block # Create application instance directly instead of using factory function app = create_app(global_args) diff --git a/lightrag/api/routers/document_routes.py b/lightrag/api/routers/document_routes.py index 848d5eb8..54e6477d 100644 --- a/lightrag/api/routers/document_routes.py +++ b/lightrag/api/routers/document_routes.py @@ -161,6 +161,28 @@ class ReprocessResponse(BaseModel): } +class CancelPipelineResponse(BaseModel): + """Response model for pipeline cancellation operation + + Attributes: + status: Status of the cancellation request + message: Message describing the operation result + """ + + status: Literal["cancellation_requested", "not_busy"] = Field( + description="Status of the cancellation request" + ) + message: str = Field(description="Human-readable message describing the operation") + + class Config: + json_schema_extra = { + "example": { + "status": "cancellation_requested", + "message": "Pipeline cancellation has been requested. Documents will be marked as FAILED.", + } + } + + class InsertTextRequest(BaseModel): """Request model for inserting a single text document @@ -458,7 +480,7 @@ class DocsStatusesResponse(BaseModel): "id": "doc_789", "content_summary": "Document pending final indexing", "content_length": 7200, - "status": "multimodal_processed", + "status": "preprocessed", "created_at": "2025-03-31T09:30:00", "updated_at": "2025-03-31T09:35:00", "track_id": "upload_20250331_093000_xyz789", @@ -1534,7 +1556,19 @@ async def background_delete_documents( try: # Loop through each document ID and delete them one by one for i, doc_id in enumerate(doc_ids, 1): + # Check for cancellation at the start of each document deletion async with pipeline_status_lock: + if pipeline_status.get("cancellation_requested", False): + cancel_msg = f"Deletion cancelled by user at document {i}/{total_docs}. {len(successful_deletions)} deleted, {total_docs - i + 1} remaining." + logger.info(cancel_msg) + pipeline_status["latest_message"] = cancel_msg + pipeline_status["history_messages"].append(cancel_msg) + # Add remaining documents to failed list with cancellation reason + failed_deletions.extend( + doc_ids[i - 1 :] + ) # i-1 because enumerate starts at 1 + break # Exit the loop, remaining documents unchanged + start_msg = f"Deleting document {i}/{total_docs}: {doc_id}" logger.info(start_msg) pipeline_status["cur_batch"] = i @@ -1697,6 +1731,10 @@ async def background_delete_documents( # Final summary and check for pending requests async with pipeline_status_lock: pipeline_status["busy"] = False + pipeline_status["pending_requests"] = False # Reset pending requests flag + pipeline_status["cancellation_requested"] = ( + False # Always reset cancellation flag + ) completion_msg = f"Deletion completed: {len(successful_deletions)} successful, {len(failed_deletions)} failed" pipeline_status["latest_message"] = completion_msg pipeline_status["history_messages"].append(completion_msg) @@ -2230,7 +2268,7 @@ def create_document_routes( logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) - # TODO: Deprecated + # TODO: Deprecated, use /documents/paginated instead @router.get( "", response_model=DocsStatusesResponse, dependencies=[Depends(combined_auth)] ) @@ -2754,4 +2792,63 @@ def create_document_routes( logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) + @router.post( + "/cancel_pipeline", + response_model=CancelPipelineResponse, + dependencies=[Depends(combined_auth)], + ) + async def cancel_pipeline(): + """ + Request cancellation of the currently running pipeline. + + This endpoint sets a cancellation flag in the pipeline status. The pipeline will: + 1. Check this flag at key processing points + 2. Stop processing new documents + 3. Cancel all running document processing tasks + 4. Mark all PROCESSING documents as FAILED with reason "User cancelled" + + The cancellation is graceful and ensures data consistency. Documents that have + completed processing will remain in PROCESSED status. + + Returns: + CancelPipelineResponse: Response with status and message + - status="cancellation_requested": Cancellation flag has been set + - status="not_busy": Pipeline is not currently running + + Raises: + HTTPException: If an error occurs while setting cancellation flag (500). + """ + try: + from lightrag.kg.shared_storage import ( + get_namespace_data, + get_pipeline_status_lock, + ) + + pipeline_status = await get_namespace_data("pipeline_status") + pipeline_status_lock = get_pipeline_status_lock() + + async with pipeline_status_lock: + if not pipeline_status.get("busy", False): + return CancelPipelineResponse( + status="not_busy", + message="Pipeline is not currently running. No cancellation needed.", + ) + + # Set cancellation flag + pipeline_status["cancellation_requested"] = True + cancel_msg = "Pipeline cancellation requested by user" + logger.info(cancel_msg) + pipeline_status["latest_message"] = cancel_msg + pipeline_status["history_messages"].append(cancel_msg) + + return CancelPipelineResponse( + status="cancellation_requested", + message="Pipeline cancellation has been requested. Documents will be marked as FAILED.", + ) + + except Exception as e: + logger.error(f"Error requesting pipeline cancellation: {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=str(e)) + return router diff --git a/lightrag/api/routers/graph_routes.py b/lightrag/api/routers/graph_routes.py index dbf4527e..e892ff01 100644 --- a/lightrag/api/routers/graph_routes.py +++ b/lightrag/api/routers/graph_routes.py @@ -17,6 +17,7 @@ class EntityUpdateRequest(BaseModel): entity_name: str updated_data: Dict[str, Any] allow_rename: bool = False + allow_merge: bool = False class RelationUpdateRequest(BaseModel): @@ -221,22 +222,178 @@ def create_graph_routes(rag, api_key: Optional[str] = None): """ Update an entity's properties in the knowledge graph + This endpoint allows updating entity properties, including renaming entities. + When renaming to an existing entity name, the behavior depends on allow_merge: + Args: - request (EntityUpdateRequest): Request containing entity name, updated data, and rename flag + request (EntityUpdateRequest): Request containing: + - entity_name (str): Name of the entity to update + - updated_data (Dict[str, Any]): Dictionary of properties to update + - allow_rename (bool): Whether to allow entity renaming (default: False) + - allow_merge (bool): Whether to merge into existing entity when renaming + causes name conflict (default: False) Returns: - Dict: Updated entity information + Dict with the following structure: + { + "status": "success", + "message": "Entity updated successfully" | "Entity merged successfully into 'target_name'", + "data": { + "entity_name": str, # Final entity name + "description": str, # Entity description + "entity_type": str, # Entity type + "source_id": str, # Source chunk IDs + ... # Other entity properties + }, + "operation_summary": { + "merged": bool, # Whether entity was merged into another + "merge_status": str, # "success" | "failed" | "not_attempted" + "merge_error": str | None, # Error message if merge failed + "operation_status": str, # "success" | "partial_success" | "failure" + "target_entity": str | None, # Target entity name if renaming/merging + "final_entity": str, # Final entity name after operation + "renamed": bool # Whether entity was renamed + } + } + + operation_status values explained: + - "success": All operations completed successfully + * For simple updates: entity properties updated + * For renames: entity renamed successfully + * For merges: non-name updates applied AND merge completed + + - "partial_success": Update succeeded but merge failed + * Non-name property updates were applied successfully + * Merge operation failed (entity not merged) + * Original entity still exists with updated properties + * Use merge_error for failure details + + - "failure": Operation failed completely + * If merge_status == "failed": Merge attempted but both update and merge failed + * If merge_status == "not_attempted": Regular update failed + * No changes were applied to the entity + + merge_status values explained: + - "success": Entity successfully merged into target entity + - "failed": Merge operation was attempted but failed + - "not_attempted": No merge was attempted (normal update/rename) + + Behavior when renaming to an existing entity: + - If allow_merge=False: Raises ValueError with 400 status (default behavior) + - If allow_merge=True: Automatically merges the source entity into the existing target entity, + preserving all relationships and applying non-name updates first + + Example Request (simple update): + POST /graph/entity/edit + { + "entity_name": "Tesla", + "updated_data": {"description": "Updated description"}, + "allow_rename": false, + "allow_merge": false + } + + Example Response (simple update success): + { + "status": "success", + "message": "Entity updated successfully", + "data": { ... }, + "operation_summary": { + "merged": false, + "merge_status": "not_attempted", + "merge_error": null, + "operation_status": "success", + "target_entity": null, + "final_entity": "Tesla", + "renamed": false + } + } + + Example Request (rename with auto-merge): + POST /graph/entity/edit + { + "entity_name": "Elon Msk", + "updated_data": { + "entity_name": "Elon Musk", + "description": "Corrected description" + }, + "allow_rename": true, + "allow_merge": true + } + + Example Response (merge success): + { + "status": "success", + "message": "Entity merged successfully into 'Elon Musk'", + "data": { ... }, + "operation_summary": { + "merged": true, + "merge_status": "success", + "merge_error": null, + "operation_status": "success", + "target_entity": "Elon Musk", + "final_entity": "Elon Musk", + "renamed": true + } + } + + Example Response (partial success - update succeeded but merge failed): + { + "status": "success", + "message": "Entity updated successfully", + "data": { ... }, # Data reflects updated "Elon Msk" entity + "operation_summary": { + "merged": false, + "merge_status": "failed", + "merge_error": "Target entity locked by another operation", + "operation_status": "partial_success", + "target_entity": "Elon Musk", + "final_entity": "Elon Msk", # Original entity still exists + "renamed": true + } + } """ try: result = await rag.aedit_entity( entity_name=request.entity_name, updated_data=request.updated_data, allow_rename=request.allow_rename, + allow_merge=request.allow_merge, + ) + + # Extract operation_summary from result, with fallback for backward compatibility + operation_summary = result.get( + "operation_summary", + { + "merged": False, + "merge_status": "not_attempted", + "merge_error": None, + "operation_status": "success", + "target_entity": None, + "final_entity": request.updated_data.get( + "entity_name", request.entity_name + ), + "renamed": request.updated_data.get( + "entity_name", request.entity_name + ) + != request.entity_name, + }, + ) + + # Separate entity data from operation_summary for clean response + entity_data = dict(result) + entity_data.pop("operation_summary", None) + + # Generate appropriate response message based on merge status + response_message = ( + f"Entity merged successfully into '{operation_summary['final_entity']}'" + if operation_summary.get("merged") + else "Entity updated successfully" ) return { "status": "success", - "message": "Entity updated successfully", - "data": result, + "message": response_message, + "data": entity_data, + "operation_summary": operation_summary, } except ValueError as ve: logger.error( @@ -299,6 +456,7 @@ def create_graph_routes(rag, api_key: Optional[str] = None): entity_data (dict): Entity properties including: - description (str): Textual description of the entity - entity_type (str): Category/type of the entity (e.g., PERSON, ORGANIZATION, LOCATION) + - source_id (str): Related chunk_id from which the description originates - Additional custom properties as needed Response Schema: @@ -309,6 +467,7 @@ def create_graph_routes(rag, api_key: Optional[str] = None): "entity_name": "Tesla", "description": "Electric vehicle manufacturer", "entity_type": "ORGANIZATION", + "source_id": "chunk-123chunk-456" ... (other entity properties) } } @@ -361,10 +520,11 @@ def create_graph_routes(rag, api_key: Optional[str] = None): """ Create a new relationship between two entities in the knowledge graph - This endpoint establishes a directed relationship between two existing entities. - Both the source and target entities must already exist in the knowledge graph. - The system automatically generates vector embeddings for the relationship to - enable semantic search and graph traversal. + This endpoint establishes an undirected relationship between two existing entities. + The provided source/target order is accepted for convenience, but the backend + stored edge is undirected and may be returned with the entities swapped. + Both entities must already exist in the knowledge graph. The system automatically + generates vector embeddings for the relationship to enable semantic search and graph traversal. Prerequisites: - Both source_entity and target_entity must exist in the knowledge graph @@ -376,6 +536,7 @@ def create_graph_routes(rag, api_key: Optional[str] = None): relation_data (dict): Relationship properties including: - description (str): Textual description of the relationship - keywords (str): Comma-separated keywords describing the relationship type + - source_id (str): Related chunk_id from which the description originates - weight (float): Relationship strength/importance (default: 1.0) - Additional custom properties as needed @@ -388,6 +549,7 @@ def create_graph_routes(rag, api_key: Optional[str] = None): "tgt_id": "Tesla", "description": "Elon Musk is the CEO of Tesla", "keywords": "CEO, founder", + "source_id": "chunk-123chunk-456" "weight": 1.0, ... (other relationship properties) } diff --git a/lightrag/api/routers/query_routes.py b/lightrag/api/routers/query_routes.py index 53cc41c0..f0ee0e98 100644 --- a/lightrag/api/routers/query_routes.py +++ b/lightrag/api/routers/query_routes.py @@ -73,6 +73,16 @@ class QueryRequest(BaseModel): ge=1, ) + hl_keywords: list[str] = Field( + default_factory=list, + description="List of high-level keywords to prioritize in retrieval. Leave empty to use the LLM to generate the keywords.", + ) + + ll_keywords: list[str] = Field( + default_factory=list, + description="List of low-level keywords to refine retrieval focus. Leave empty to use the LLM to generate the keywords.", + ) + conversation_history: Optional[List[Dict[str, Any]]] = Field( default=None, description="Stores past conversation history to maintain context. Format: [{'role': 'user/assistant', 'content': 'message'}].", @@ -294,6 +304,16 @@ def create_query_routes(rag, api_key: Optional[str] = None, top_k: int = 60): } ``` + Bypass initial LLM call by providing high-level and low-level keywords: + ```json + { + "query": "What is Retrieval-Augmented-Generation?", + "hl_keywords": ["machine learning", "information retrieval", "natural language processing"], + "ll_keywords": ["retrieval augmented generation", "RAG", "knowledge base"], + "mode": "mix" + } + ``` + Advanced query with references: ```json { @@ -482,6 +502,16 @@ def create_query_routes(rag, api_key: Optional[str] = None, top_k: int = 60): } ``` + Bypass initial LLM call by providing high-level and low-level keywords: + ```json + { + "query": "What is Retrieval-Augmented-Generation?", + "hl_keywords": ["machine learning", "information retrieval", "natural language processing"], + "ll_keywords": ["retrieval augmented generation", "RAG", "knowledge base"], + "mode": "mix" + } + ``` + Complete response query: ```json { @@ -968,6 +998,16 @@ def create_query_routes(rag, api_key: Optional[str] = None, top_k: int = 60): } ``` + Bypass initial LLM call by providing high-level and low-level keywords: + ```json + { + "query": "What is Retrieval-Augmented-Generation?", + "hl_keywords": ["machine learning", "information retrieval", "natural language processing"], + "ll_keywords": ["retrieval augmented generation", "RAG", "knowledge base"], + "mode": "mix" + } + ``` + **Response Analysis:** - **Empty arrays**: Normal for certain modes (e.g., naive mode has no entities/relationships) - **Processing info**: Shows retrieval statistics and token usage diff --git a/lightrag/api/run_with_gunicorn.py b/lightrag/api/run_with_gunicorn.py index 929db019..f2d4d859 100644 --- a/lightrag/api/run_with_gunicorn.py +++ b/lightrag/api/run_with_gunicorn.py @@ -5,12 +5,11 @@ Start LightRAG server with Gunicorn import os import sys -import signal import pipmaster as pm from lightrag.api.utils_api import display_splash_screen, check_env_file from lightrag.api.config import global_args from lightrag.utils import get_env_value -from lightrag.kg.shared_storage import initialize_share_data, finalize_share_data +from lightrag.kg.shared_storage import initialize_share_data from lightrag.constants import ( DEFAULT_WOKERS, @@ -34,21 +33,10 @@ def check_and_install_dependencies(): print(f"{package} installed successfully") -# Signal handler for graceful shutdown -def signal_handler(sig, frame): - print("\n\n" + "=" * 80) - print("RECEIVED TERMINATION SIGNAL") - print(f"Process ID: {os.getpid()}") - print("=" * 80 + "\n") - - # Release shared resources - finalize_share_data() - - # Exit with success status - sys.exit(0) - - def main(): + # Set Gunicorn mode flag for lifespan cleanup detection + os.environ["LIGHTRAG_GUNICORN_MODE"] = "1" + # Check .env file if not check_env_file(): sys.exit(1) @@ -56,9 +44,8 @@ def main(): # Check and install dependencies check_and_install_dependencies() - # Register signal handlers for graceful shutdown - signal.signal(signal.SIGINT, signal_handler) # Ctrl+C - signal.signal(signal.SIGTERM, signal_handler) # kill command + # Note: Signal handlers are NOT registered here because: + # - Master cleanup already handled by gunicorn_config.on_exit() # Display startup information display_splash_screen(global_args) diff --git a/lightrag/api/static/swagger-ui/favicon-32x32.png b/lightrag/api/static/swagger-ui/favicon-32x32.png new file mode 100644 index 00000000..249737fe Binary files /dev/null and b/lightrag/api/static/swagger-ui/favicon-32x32.png differ diff --git a/lightrag/api/static/swagger-ui/swagger-ui-bundle.js b/lightrag/api/static/swagger-ui/swagger-ui-bundle.js new file mode 100644 index 00000000..ed21274f --- /dev/null +++ b/lightrag/api/static/swagger-ui/swagger-ui-bundle.js @@ -0,0 +1,2 @@ +/*! 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if(x>u||V>>L&C;if(ae!==V>>>L&C)break;ae&&($+=(1<u&&(B=B.removeBefore(a,L,x-$)),B&&Vu&&(u=x.size),isIterable(w)||(x=x.map((function(s){return fromJS(s)}))),a.push(x)}return u>s.size&&(s=s.setSize(u)),mergeIntoCollectionWith(s,o,a)}function getTailOffset(s){return s>>w<=x&&w.size>=2*_.size?(a=(u=w.filter((function(s,o){return void 0!==s&&C!==o}))).toKeyedSeq().map((function(s){return s[0]})).flip().toMap(),s.__ownerID&&(a.__ownerID=u.__ownerID=s.__ownerID)):(a=_.remove(o),u=C===w.size-1?w.pop():w.set(C,void 0))}else if(L){if(i===w.get(C)[1])return s;a=_,u=w.set(C,[o,i])}else a=_.set(o,w.size),u=w.set(w.size,[o,i]);return s.__ownerID?(s.size=a.size,s._map=a,s._list=u,s.__hash=void 0,s):makeOrderedMap(a,u)}function ToKeyedSequence(s,o){this._iter=s,this._useKeys=o,this.size=s.size}function ToIndexedSequence(s){this._iter=s,this.size=s.size}function ToSetSequence(s){this._iter=s,this.size=s.size}function FromEntriesSequence(s){this._iter=s,this.size=s.size}function flipFactory(s){var 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