diff --git a/README-zh.md b/README-zh.md index 784fd1f2..d345562f 100644 --- a/README-zh.md +++ b/README-zh.md @@ -11,7 +11,6 @@ - [X] [2024.12.31]🎯📢LightRAG现在支持[通过文档ID删除](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete)。 - [X] [2024.11.25]🎯📢LightRAG现在支持无缝集成[自定义知识图谱](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg)，使用户能够用自己的领域专业知识增强系统。 - [X] [2024.11.19]🎯📢LightRAG的综合指南现已在[LearnOpenCV](https://learnopencv.com/lightrag)上发布。非常感谢博客作者。 -- [X] [2024.11.12]🎯📢LightRAG现在支持[Oracle Database 23ai的所有存储类型（KV、向量和图）](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_oracle_demo.py)。 - [X] [2024.11.11]🎯📢LightRAG现在支持[通过实体名称删除实体](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete)。 - [X] [2024.11.09]🎯📢推出[LightRAG Gui](https://lightrag-gui.streamlit.app)，允许您插入、查询、可视化和下载LightRAG知识。 - [X] [2024.11.04]🎯📢现在您可以[使用Neo4J进行存储](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage)。 @@ -410,6 +409,54 @@ if __name__ == "__main__": +### Token统计功能 +
+ 概述和使用 + +LightRAG提供了TokenTracker工具来跟踪和管理大模型的token消耗。这个功能对于控制API成本和优化性能特别有用。 + +#### 使用方法 + +```python +from lightrag.utils import TokenTracker + +# 创建TokenTracker实例 +token_tracker = TokenTracker() + +# 方法1：使用上下文管理器（推荐） +# 适用于需要自动跟踪token使用的场景 +with token_tracker: + result1 = await llm_model_func("你的问题1") + result2 = await llm_model_func("你的问题2") + +# 方法2：手动添加token使用记录 +# 适用于需要更精细控制token统计的场景 +token_tracker.reset() + +rag.insert() + +rag.query("你的问题1", param=QueryParam(mode="naive")) +rag.query("你的问题2", param=QueryParam(mode="mix")) + +# 显示总token使用量（包含插入和查询操作） +print("Token usage:", token_tracker.get_usage()) +``` + +#### 使用建议 +- 在长会话或批量操作中使用上下文管理器，可以自动跟踪所有token消耗 +- 对于需要分段统计的场景，使用手动模式并适时调用reset() +- 定期检查token使用情况，有助于及时发现异常消耗 +- 在开发测试阶段积极使用此功能，以便优化生产环境的成本 + +#### 实际应用示例 +您可以参考以下示例来实现token统计： +- `examples/lightrag_gemini_track_token_demo.py`：使用Google Gemini模型的token统计示例 +- `examples/lightrag_siliconcloud_track_token_demo.py`：使用SiliconCloud模型的token统计示例 + +这些示例展示了如何在不同模型和场景下有效地使用TokenTracker功能。 + +
+ ### 对话历史 LightRAG现在通过对话历史功能支持多轮对话。以下是使用方法： @@ -1037,9 +1084,10 @@ rag.clear_cache(modes=["local"]) | **参数** | **类型** | **说明** | **默认值** | |--------------|----------|-----------------|-------------| | **working_dir** | `str` | 存储缓存的目录 | `lightrag_cache+timestamp` | -| **kv_storage** | `str` | 文档和文本块的存储类型。支持的类型：`JsonKVStorage`、`OracleKVStorage` | `JsonKVStorage` | -| **vector_storage** | `str` | 嵌入向量的存储类型。支持的类型：`NanoVectorDBStorage`、`OracleVectorDBStorage` | `NanoVectorDBStorage` | -| **graph_storage** | `str` | 图边和节点的存储类型。支持的类型：`NetworkXStorage`、`Neo4JStorage`、`OracleGraphStorage` | `NetworkXStorage` | +| **kv_storage** | `str` | Storage type for documents and text chunks. Supported types: `JsonKVStorage`,`PGKVStorage`,`RedisKVStorage`,`MongoKVStorage` | `JsonKVStorage` | +| **vector_storage** | `str` | Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`,`PGVectorStorage`,`MilvusVectorDBStorage`,`ChromaVectorDBStorage`,`FaissVectorDBStorage`,`MongoVectorDBStorage`,`QdrantVectorDBStorage` | `NanoVectorDBStorage` | +| **graph_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`,`Neo4JStorage`,`PGGraphStorage`,`AGEStorage` | `NetworkXStorage` | +| **doc_status_storage** | `str` | Storage type for documents process status. Supported types: `JsonDocStatusStorage`,`PGDocStatusStorage`,`MongoDocStatusStorage` | `JsonDocStatusStorage` | | **chunk_token_size** | `int` | 拆分文档时每个块的最大令牌大小 | `1200` | | **chunk_overlap_token_size** | `int` | 拆分文档时两个块之间的重叠令牌大小 | `100` | | **tiktoken_model_name** | `str` | 用于计算令牌数的Tiktoken编码器的模型名称 | `gpt-4o-mini` | diff --git a/README.md b/README.md index 6c8861a2..091b5b77 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,6 @@ - [X] [2024.12.31]🎯📢LightRAG now supports [deletion by document ID](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete). - [X] [2024.11.25]🎯📢LightRAG now supports seamless integration of [custom knowledge graphs](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg), empowering users to enhance the system with their own domain expertise. - [X] [2024.11.19]🎯📢A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). Many thanks to the blog author. -- [X] [2024.11.12]🎯📢LightRAG now supports [Oracle Database 23ai for all storage types (KV, vector, and graph)](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_oracle_demo.py). - [X] [2024.11.11]🎯📢LightRAG now supports [deleting entities by their names](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete). - [X] [2024.11.09]🎯📢Introducing the [LightRAG Gui](https://lightrag-gui.streamlit.app), which allows you to insert, query, visualize, and download LightRAG knowledge. - [X] [2024.11.04]🎯📢You can now [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage). @@ -443,6 +442,55 @@ if __name__ == "__main__": +### Token Usage Tracking + +
+ Overview and Usage + +LightRAG provides a TokenTracker tool to monitor and manage token consumption by large language models. This feature is particularly useful for controlling API costs and optimizing performance. + +#### Usage + +```python +from lightrag.utils import TokenTracker + +# Create TokenTracker instance +token_tracker = TokenTracker() + +# Method 1: Using context manager (Recommended) +# Suitable for scenarios requiring automatic token usage tracking +with token_tracker: + result1 = await llm_model_func("your question 1") + result2 = await llm_model_func("your question 2") + +# Method 2: Manually adding token usage records +# Suitable for scenarios requiring more granular control over token statistics +token_tracker.reset() + +rag.insert() + +rag.query("your question 1", param=QueryParam(mode="naive")) +rag.query("your question 2", param=QueryParam(mode="mix")) + +# Display total token usage (including insert and query operations) +print("Token usage:", token_tracker.get_usage()) +``` + +#### Usage Tips +- Use context managers for long sessions or batch operations to automatically track all token consumption +- For scenarios requiring segmented statistics, use manual mode and call reset() when appropriate +- Regular checking of token usage helps detect abnormal consumption early +- Actively use this feature during development and testing to optimize production costs + +#### Practical Examples +You can refer to these examples for implementing token tracking: +- `examples/lightrag_gemini_track_token_demo.py`: Token tracking example using Google Gemini model +- `examples/lightrag_siliconcloud_track_token_demo.py`: Token tracking example using SiliconCloud model + +These examples demonstrate how to effectively use the TokenTracker feature with different models and scenarios. + +
+ ### Conversation History Support @@ -607,7 +655,7 @@ The `apipeline_enqueue_documents` and `apipeline_process_enqueue_documents` func This is useful for scenarios where you want to process documents in the background while still allowing the main thread to continue executing. -And using a routine to process news documents. +And using a routine to process new documents. ```python rag = LightRAG(..) @@ -1096,9 +1144,10 @@ Valid modes are: | **Parameter** | **Type** | **Explanation** | **Default** | |--------------|----------|-----------------|-------------| | **working_dir** | `str` | Directory where the cache will be stored | `lightrag_cache+timestamp` | -| **kv_storage** | `str` | Storage type for documents and text chunks. Supported types: `JsonKVStorage`, `OracleKVStorage` | `JsonKVStorage` | -| **vector_storage** | `str` | Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`, `OracleVectorDBStorage` | `NanoVectorDBStorage` | -| **graph_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`, `Neo4JStorage`, `OracleGraphStorage` | `NetworkXStorage` | +| **kv_storage** | `str` | Storage type for documents and text chunks. Supported types: `JsonKVStorage`,`PGKVStorage`,`RedisKVStorage`,`MongoKVStorage` | `JsonKVStorage` | +| **vector_storage** | `str` | Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`,`PGVectorStorage`,`MilvusVectorDBStorage`,`ChromaVectorDBStorage`,`FaissVectorDBStorage`,`MongoVectorDBStorage`,`QdrantVectorDBStorage` | `NanoVectorDBStorage` | +| **graph_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`,`Neo4JStorage`,`PGGraphStorage`,`AGEStorage` | `NetworkXStorage` | +| **doc_status_storage** | `str` | Storage type for documents process status. Supported types: `JsonDocStatusStorage`,`PGDocStatusStorage`,`MongoDocStatusStorage` | `JsonDocStatusStorage` | | **chunk_token_size** | `int` | Maximum token size per chunk when splitting documents | `1200` | | **chunk_overlap_token_size** | `int` | Overlap token size between two chunks when splitting documents | `100` | | **tiktoken_model_name** | `str` | Model name for the Tiktoken encoder used to calculate token numbers | `gpt-4o-mini` | diff --git a/config.ini.example b/config.ini.example index 3041611e..5ff7cfbb 100644 --- a/config.ini.example +++ b/config.ini.example @@ -13,23 +13,6 @@ uri=redis://localhost:6379/1 [qdrant] uri = http://localhost:16333 -[oracle] -dsn = localhost:1521/XEPDB1 -user = your_username -password = your_password -config_dir = /path/to/oracle/config -wallet_location = /path/to/wallet # 可选 -wallet_password = your_wallet_password # 可选 -workspace = default # 可选,默认为default - -[tidb] -host = localhost -port = 4000 -user = your_username -password = your_password -database = your_database -workspace = default # 可选,默认为default - [postgres] host = localhost port = 5432 diff --git a/env.example b/env.example index 20d80d43..d21bbef6 100644 --- a/env.example +++ b/env.example @@ -4,11 +4,9 @@ # HOST=0.0.0.0 # PORT=9621 # WORKERS=2 -### separating data from difference Lightrag instances -# NAMESPACE_PREFIX=lightrag -### Max nodes return from grap retrieval -# MAX_GRAPH_NODES=1000 # CORS_ORIGINS=http://localhost:3000,http://localhost:8080 +WEBUI_TITLE='Graph RAG Engine' +WEBUI_DESCRIPTION="Simple and Fast Graph Based RAG System" ### Optional SSL Configuration # SSL=true @@ -22,6 +20,9 @@ ### Ollama Emulating Model Tag # OLLAMA_EMULATING_MODEL_TAG=latest +### Max nodes return from grap retrieval +# MAX_GRAPH_NODES=1000 + ### Logging level # LOG_LEVEL=INFO # VERBOSE=False @@ -110,24 +111,14 @@ LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage LIGHTRAG_GRAPH_STORAGE=NetworkXStorage LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage -### Oracle Database Configuration -ORACLE_DSN=localhost:1521/XEPDB1 -ORACLE_USER=your_username -ORACLE_PASSWORD='your_password' -ORACLE_CONFIG_DIR=/path/to/oracle/config -#ORACLE_WALLET_LOCATION=/path/to/wallet -#ORACLE_WALLET_PASSWORD='your_password' -### separating all data from difference Lightrag instances(deprecating, use NAMESPACE_PREFIX in future) -#ORACLE_WORKSPACE=default - -### TiDB Configuration -TIDB_HOST=localhost -TIDB_PORT=4000 -TIDB_USER=your_username -TIDB_PASSWORD='your_password' -TIDB_DATABASE=your_database -### separating all data from difference Lightrag instances(deprecating, use NAMESPACE_PREFIX in future) -#TIDB_WORKSPACE=default +### TiDB Configuration (Deprecated) +# TIDB_HOST=localhost +# TIDB_PORT=4000 +# TIDB_USER=your_username +# TIDB_PASSWORD='your_password' +# TIDB_DATABASE=your_database +### separating all data from difference Lightrag instances(deprecating) +# TIDB_WORKSPACE=default ### PostgreSQL Configuration POSTGRES_HOST=localhost @@ -135,8 +126,8 @@ POSTGRES_PORT=5432 POSTGRES_USER=your_username POSTGRES_PASSWORD='your_password' POSTGRES_DATABASE=your_database -### separating all data from difference Lightrag instances(deprecating, use NAMESPACE_PREFIX in future) -#POSTGRES_WORKSPACE=default +### separating all data from difference Lightrag instances(deprecating) +# POSTGRES_WORKSPACE=default ### Independent AGM Configuration(not for AMG embedded in PostreSQL) AGE_POSTGRES_DB= @@ -145,8 +136,8 @@ AGE_POSTGRES_PASSWORD= AGE_POSTGRES_HOST= # AGE_POSTGRES_PORT=8529 -### separating all data from difference Lightrag instances(deprecating, use NAMESPACE_PREFIX in future) # AGE Graph Name(apply to PostgreSQL and independent AGM) +### AGE_GRAPH_NAME is precated # AGE_GRAPH_NAME=lightrag ### Neo4j Configuration @@ -157,7 +148,7 @@ NEO4J_PASSWORD='your_password' ### MongoDB Configuration MONGO_URI=mongodb://root:root@localhost:27017/ MONGO_DATABASE=LightRAG -### separating all data from difference Lightrag instances(deprecating, use NAMESPACE_PREFIX in future) +### separating all data from difference Lightrag instances(deprecating) # MONGODB_GRAPH=false ### Milvus Configuration @@ -177,7 +168,9 @@ REDIS_URI=redis://localhost:6379 ### For JWT Auth # AUTH_ACCOUNTS='admin:admin123,user1:pass456' # TOKEN_SECRET=Your-Key-For-LightRAG-API-Server -# TOKEN_EXPIRE_HOURS=4 +# TOKEN_EXPIRE_HOURS=48 +# GUEST_TOKEN_EXPIRE_HOURS=24 +# JWT_ALGORITHM=HS256 ### API-Key to access LightRAG Server API # LIGHTRAG_API_KEY=your-secure-api-key-here diff --git a/examples/lightrag_api_ollama_demo.py b/examples/lightrag_api_ollama_demo.py deleted file mode 100644 index dad2a2e0..00000000 --- a/examples/lightrag_api_ollama_demo.py +++ /dev/null @@ -1,188 +0,0 @@ -from fastapi import FastAPI, HTTPException, File, UploadFile -from contextlib import asynccontextmanager -from pydantic import BaseModel -import os -from lightrag import LightRAG, QueryParam -from lightrag.llm.ollama import ollama_embed, ollama_model_complete -from lightrag.utils import EmbeddingFunc -from typing import Optional -import asyncio -import nest_asyncio -import aiofiles -from lightrag.kg.shared_storage import initialize_pipeline_status - -# Apply nest_asyncio to solve event loop issues -nest_asyncio.apply() - -DEFAULT_RAG_DIR = "index_default" - -DEFAULT_INPUT_FILE = "book.txt" -INPUT_FILE = os.environ.get("INPUT_FILE", f"{DEFAULT_INPUT_FILE}") -print(f"INPUT_FILE: {INPUT_FILE}") - -# Configure working directory -WORKING_DIR = os.environ.get("RAG_DIR", f"{DEFAULT_RAG_DIR}") -print(f"WORKING_DIR: {WORKING_DIR}") - - -if not os.path.exists(WORKING_DIR): - os.mkdir(WORKING_DIR) - - -async def init(): - rag = LightRAG( - working_dir=WORKING_DIR, - llm_model_func=ollama_model_complete, - llm_model_name="gemma2:9b", - llm_model_max_async=4, - llm_model_max_token_size=8192, - llm_model_kwargs={ - "host": "http://localhost:11434", - "options": {"num_ctx": 8192}, - }, - embedding_func=EmbeddingFunc( - embedding_dim=768, - max_token_size=8192, - func=lambda texts: ollama_embed( - texts, embed_model="nomic-embed-text", host="http://localhost:11434" - ), - ), - ) - - # Add initialization code - await rag.initialize_storages() - await initialize_pipeline_status() - - return rag - - -@asynccontextmanager -async def lifespan(app: FastAPI): - global rag - rag = await init() - print("done!") - yield - - -app = FastAPI( - title="LightRAG API", description="API for RAG operations", lifespan=lifespan -) - - -# Data models -class QueryRequest(BaseModel): - query: str - mode: str = "hybrid" - only_need_context: bool = False - - -class InsertRequest(BaseModel): - text: str - - -class Response(BaseModel): - status: str - data: Optional[str] = None - message: Optional[str] = None - - -# API routes -@app.post("/query", response_model=Response) -async def query_endpoint(request: QueryRequest): - try: - loop = asyncio.get_event_loop() - result = await loop.run_in_executor( - None, - lambda: rag.query( - request.query, - param=QueryParam( - mode=request.mode, only_need_context=request.only_need_context - ), - ), - ) - return Response(status="success", data=result) - except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) - - -# insert by text -@app.post("/insert", response_model=Response) -async def insert_endpoint(request: InsertRequest): - try: - loop = asyncio.get_event_loop() - await loop.run_in_executor(None, lambda: rag.insert(request.text)) - return Response(status="success", message="Text inserted successfully") - except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) - - -# insert by file in payload -@app.post("/insert_file", response_model=Response) -async def insert_file(file: UploadFile = File(...)): - try: - file_content = await file.read() - # Read file content - try: - content = file_content.decode("utf-8") - except UnicodeDecodeError: - # If UTF-8 decoding fails, try other encodings - content = file_content.decode("gbk") - # Insert file content - loop = asyncio.get_event_loop() - await loop.run_in_executor(None, lambda: rag.insert(content)) - - return Response( - status="success", - message=f"File content from {file.filename} inserted successfully", - ) - except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) - - -# insert by local default file -@app.post("/insert_default_file", response_model=Response) -@app.get("/insert_default_file", response_model=Response) -async def insert_default_file(): - try: - # Read file content from book.txt - async with aiofiles.open(INPUT_FILE, "r", encoding="utf-8") as file: - content = await file.read() - print(f"read input file {INPUT_FILE} successfully") - # Insert file content - loop = asyncio.get_event_loop() - await loop.run_in_executor(None, lambda: rag.insert(content)) - - return Response( - status="success", - message=f"File content from {INPUT_FILE} inserted successfully", - ) - except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) - - -@app.get("/health") -async def health_check(): - return {"status": "healthy"} - - -if __name__ == "__main__": - import uvicorn - - uvicorn.run(app, host="0.0.0.0", port=8020) - -# Usage example -# To run the server, use the following command in your terminal: -# python lightrag_api_openai_compatible_demo.py - -# Example requests: -# 1. Query: -# curl -X POST "http://127.0.0.1:8020/query" -H "Content-Type: application/json" -d '{"query": "your query here", "mode": "hybrid"}' - -# 2. Insert text: -# curl -X POST "http://127.0.0.1:8020/insert" -H "Content-Type: application/json" -d '{"text": "your text here"}' - -# 3. Insert file: -# curl -X POST "http://127.0.0.1:8020/insert_file" -H "Content-Type: multipart/form-data" -F "file=@path/to/your/file.txt" - -# 4. Health check: -# curl -X GET "http://127.0.0.1:8020/health" diff --git a/examples/lightrag_api_openai_compatible_demo.py b/examples/lightrag_api_openai_compatible_demo.py deleted file mode 100644 index 312be872..00000000 --- a/examples/lightrag_api_openai_compatible_demo.py +++ /dev/null @@ -1,204 +0,0 @@ -from fastapi import FastAPI, HTTPException, File, UploadFile -from contextlib import asynccontextmanager -from pydantic import BaseModel -import os -from lightrag import LightRAG, QueryParam -from lightrag.llm.openai import openai_complete_if_cache, openai_embed -from lightrag.utils import EmbeddingFunc -import numpy as np -from typing import Optional -import asyncio -import nest_asyncio -from lightrag.kg.shared_storage import initialize_pipeline_status - -# Apply nest_asyncio to solve event loop issues -nest_asyncio.apply() - -DEFAULT_RAG_DIR = "index_default" -app = FastAPI(title="LightRAG API", description="API for RAG operations") - -# Configure working directory -WORKING_DIR = os.environ.get("RAG_DIR", f"{DEFAULT_RAG_DIR}") -print(f"WORKING_DIR: {WORKING_DIR}") -LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-4o-mini") -print(f"LLM_MODEL: {LLM_MODEL}") -EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large") -print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}") -EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192)) -print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}") -BASE_URL = os.environ.get("BASE_URL", "https://api.openai.com/v1") -print(f"BASE_URL: {BASE_URL}") -API_KEY = os.environ.get("API_KEY", "xxxxxxxx") -print(f"API_KEY: {API_KEY}") - -if not os.path.exists(WORKING_DIR): - os.mkdir(WORKING_DIR) - - -# LLM model function - - -async def llm_model_func( - prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs -) -> str: - return await openai_complete_if_cache( - model=LLM_MODEL, - prompt=prompt, - system_prompt=system_prompt, - history_messages=history_messages, - base_url=BASE_URL, - api_key=API_KEY, - **kwargs, - ) - - -# Embedding function - - -async def embedding_func(texts: list[str]) -> np.ndarray: - return await openai_embed( - texts=texts, - model=EMBEDDING_MODEL, - base_url=BASE_URL, - api_key=API_KEY, - ) - - -async def get_embedding_dim(): - test_text = ["This is a test sentence."] - embedding = await embedding_func(test_text) - embedding_dim = embedding.shape[1] - print(f"{embedding_dim=}") - return embedding_dim - - -# Initialize RAG instance -async def init(): - embedding_dimension = await get_embedding_dim() - - rag = LightRAG( - working_dir=WORKING_DIR, - llm_model_func=llm_model_func, - embedding_func=EmbeddingFunc( - embedding_dim=embedding_dimension, - max_token_size=EMBEDDING_MAX_TOKEN_SIZE, - func=embedding_func, - ), - ) - - await rag.initialize_storages() - await initialize_pipeline_status() - - return rag - - -@asynccontextmanager -async def lifespan(app: FastAPI): - global rag - rag = await init() - print("done!") - yield - - -app = FastAPI( - title="LightRAG API", description="API for RAG operations", lifespan=lifespan -) - -# Data models - - -class QueryRequest(BaseModel): - query: str - mode: str = "hybrid" - only_need_context: bool = False - - -class InsertRequest(BaseModel): - text: str - - -class Response(BaseModel): - status: str - data: Optional[str] = None - message: Optional[str] = None - - -# API routes - - -@app.post("/query", response_model=Response) -async def query_endpoint(request: QueryRequest): - try: - loop = asyncio.get_event_loop() - result = await loop.run_in_executor( - None, - lambda: rag.query( - request.query, - param=QueryParam( - mode=request.mode, only_need_context=request.only_need_context - ), - ), - ) - return Response(status="success", data=result) - except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) - - -@app.post("/insert", response_model=Response) -async def insert_endpoint(request: InsertRequest): - try: - loop = asyncio.get_event_loop() - await loop.run_in_executor(None, lambda: rag.insert(request.text)) - return Response(status="success", message="Text inserted successfully") - except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) - - -@app.post("/insert_file", response_model=Response) -async def insert_file(file: UploadFile = File(...)): - try: - file_content = await file.read() - # Read file content - try: - content = file_content.decode("utf-8") - except UnicodeDecodeError: - # If UTF-8 decoding fails, try other encodings - content = file_content.decode("gbk") - # Insert file content - loop = asyncio.get_event_loop() - await loop.run_in_executor(None, lambda: rag.insert(content)) - - return Response( - status="success", - message=f"File content from {file.filename} inserted successfully", - ) - except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) - - -@app.get("/health") -async def health_check(): - return {"status": "healthy"} - - -if __name__ == "__main__": - import uvicorn - - uvicorn.run(app, host="0.0.0.0", port=8020) - -# Usage example -# To run the server, use the following command in your terminal: -# python lightrag_api_openai_compatible_demo.py - -# Example requests: -# 1. Query: -# curl -X POST "http://127.0.0.1:8020/query" -H "Content-Type: application/json" -d '{"query": "your query here", "mode": "hybrid"}' - -# 2. Insert text: -# curl -X POST "http://127.0.0.1:8020/insert" -H "Content-Type: application/json" -d '{"text": "your text here"}' - -# 3. Insert file: -# curl -X POST "http://127.0.0.1:8020/insert_file" -H "Content-Type: multipart/form-data" -F "file=@path/to/your/file.txt" - -# 4. Health check: -# curl -X GET "http://127.0.0.1:8020/health" diff --git a/examples/lightrag_api_oracle_demo.py b/examples/lightrag_api_oracle_demo.py deleted file mode 100644 index 3a82f479..00000000 --- a/examples/lightrag_api_oracle_demo.py +++ /dev/null @@ -1,267 +0,0 @@ -from fastapi import FastAPI, HTTPException, File, UploadFile -from fastapi import Query -from contextlib import asynccontextmanager -from pydantic import BaseModel -from typing import Optional, Any - -import sys -import os - - -from pathlib import Path - -import asyncio -import nest_asyncio -from lightrag import LightRAG, QueryParam -from lightrag.llm.openai import openai_complete_if_cache, openai_embed -from lightrag.utils import EmbeddingFunc -import numpy as np -from lightrag.kg.shared_storage import initialize_pipeline_status - - -print(os.getcwd()) -script_directory = Path(__file__).resolve().parent.parent -sys.path.append(os.path.abspath(script_directory)) - - -# Apply nest_asyncio to solve event loop issues -nest_asyncio.apply() - -DEFAULT_RAG_DIR = "index_default" - - -# We use OpenAI compatible API to call LLM on Oracle Cloud -# More docs here https://github.com/jin38324/OCI_GenAI_access_gateway -BASE_URL = "http://xxx.xxx.xxx.xxx:8088/v1/" -APIKEY = "ocigenerativeai" - -# Configure working directory -WORKING_DIR = os.environ.get("RAG_DIR", f"{DEFAULT_RAG_DIR}") -print(f"WORKING_DIR: {WORKING_DIR}") -LLM_MODEL = os.environ.get("LLM_MODEL", "cohere.command-r-plus-08-2024") -print(f"LLM_MODEL: {LLM_MODEL}") -EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "cohere.embed-multilingual-v3.0") -print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}") -EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 512)) -print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}") - -if not os.path.exists(WORKING_DIR): - os.mkdir(WORKING_DIR) - -os.environ["ORACLE_USER"] = "" -os.environ["ORACLE_PASSWORD"] = "" -os.environ["ORACLE_DSN"] = "" -os.environ["ORACLE_CONFIG_DIR"] = "path_to_config_dir" -os.environ["ORACLE_WALLET_LOCATION"] = "path_to_wallet_location" -os.environ["ORACLE_WALLET_PASSWORD"] = "wallet_password" -os.environ["ORACLE_WORKSPACE"] = "company" - - -async def llm_model_func( - prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs -) -> str: - return await openai_complete_if_cache( - LLM_MODEL, - prompt, - system_prompt=system_prompt, - history_messages=history_messages, - api_key=APIKEY, - base_url=BASE_URL, - **kwargs, - ) - - -async def embedding_func(texts: list[str]) -> np.ndarray: - return await openai_embed( - texts, - model=EMBEDDING_MODEL, - api_key=APIKEY, - base_url=BASE_URL, - ) - - -async def get_embedding_dim(): - test_text = ["This is a test sentence."] - embedding = await embedding_func(test_text) - embedding_dim = embedding.shape[1] - return embedding_dim - - -async def init(): - # Detect embedding dimension - embedding_dimension = await get_embedding_dim() - print(f"Detected embedding dimension: {embedding_dimension}") - # Create Oracle DB connection - # The `config` parameter is the connection configuration of Oracle DB - # More docs here https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html - # We storage data in unified tables, so we need to set a `workspace` parameter to specify which docs we want to store and query - # Below is an example of how to connect to Oracle Autonomous Database on Oracle Cloud - - # Initialize LightRAG - # We use Oracle DB as the KV/vector/graph storage - rag = LightRAG( - enable_llm_cache=False, - working_dir=WORKING_DIR, - chunk_token_size=512, - llm_model_func=llm_model_func, - embedding_func=EmbeddingFunc( - embedding_dim=embedding_dimension, - max_token_size=512, - func=embedding_func, - ), - graph_storage="OracleGraphStorage", - kv_storage="OracleKVStorage", - vector_storage="OracleVectorDBStorage", - ) - - await rag.initialize_storages() - await initialize_pipeline_status() - - return rag - - -# Extract and Insert into LightRAG storage -# with open("./dickens/book.txt", "r", encoding="utf-8") as f: -# await rag.ainsert(f.read()) - -# # Perform search in different modes -# modes = ["naive", "local", "global", "hybrid"] -# for mode in modes: -# print("="*20, mode, "="*20) -# print(await rag.aquery("这篇文档是关于什么内容的?", param=QueryParam(mode=mode))) -# print("-"*100, "\n") - -# Data models - - -class QueryRequest(BaseModel): - query: str - mode: str = "hybrid" - only_need_context: bool = False - only_need_prompt: bool = False - - -class DataRequest(BaseModel): - limit: int = 100 - - -class InsertRequest(BaseModel): - text: str - - -class Response(BaseModel): - status: str - data: Optional[Any] = None - message: Optional[str] = None - - -# API routes - -rag = None - - -@asynccontextmanager -async def lifespan(app: FastAPI): - global rag - rag = await init() - print("done!") - yield - - -app = FastAPI( - title="LightRAG API", description="API for RAG operations", lifespan=lifespan -) - - -@app.post("/query", response_model=Response) -async def query_endpoint(request: QueryRequest): - # try: - # loop = asyncio.get_event_loop() - if request.mode == "naive": - top_k = 3 - else: - top_k = 60 - result = await rag.aquery( - request.query, - param=QueryParam( - mode=request.mode, - only_need_context=request.only_need_context, - only_need_prompt=request.only_need_prompt, - top_k=top_k, - ), - ) - return Response(status="success", data=result) - # except Exception as e: - # raise HTTPException(status_code=500, detail=str(e)) - - -@app.get("/data", response_model=Response) -async def query_all_nodes(type: str = Query("nodes"), limit: int = Query(100)): - if type == "nodes": - result = await rag.chunk_entity_relation_graph.get_all_nodes(limit=limit) - elif type == "edges": - result = await rag.chunk_entity_relation_graph.get_all_edges(limit=limit) - elif type == "statistics": - result = await rag.chunk_entity_relation_graph.get_statistics() - return Response(status="success", data=result) - - -@app.post("/insert", response_model=Response) -async def insert_endpoint(request: InsertRequest): - try: - loop = asyncio.get_event_loop() - await loop.run_in_executor(None, lambda: rag.insert(request.text)) - return Response(status="success", message="Text inserted successfully") - except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) - - -@app.post("/insert_file", response_model=Response) -async def insert_file(file: UploadFile = File(...)): - try: - file_content = await file.read() - # Read file content - try: - content = file_content.decode("utf-8") - except UnicodeDecodeError: - # If UTF-8 decoding fails, try other encodings - content = file_content.decode("gbk") - # Insert file content - loop = asyncio.get_event_loop() - await loop.run_in_executor(None, lambda: rag.insert(content)) - - return Response( - status="success", - message=f"File content from {file.filename} inserted successfully", - ) - except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) - - -@app.get("/health") -async def health_check(): - return {"status": "healthy"} - - -if __name__ == "__main__": - import uvicorn - - uvicorn.run(app, host="127.0.0.1", port=8020) - -# Usage example -# To run the server, use the following command in your terminal: -# python lightrag_api_openai_compatible_demo.py - -# Example requests: -# 1. Query: -# curl -X POST "http://127.0.0.1:8020/query" -H "Content-Type: application/json" -d '{"query": "your query here", "mode": "hybrid"}' - -# 2. Insert text: -# curl -X POST "http://127.0.0.1:8020/insert" -H "Content-Type: application/json" -d '{"text": "your text here"}' - -# 3. Insert file: -# curl -X POST "http://127.0.0.1:8020/insert_file" -H "Content-Type: multipart/form-data" -F "file=@path/to/your/file.txt" - - -# 4. Health check: -# curl -X GET "http://127.0.0.1:8020/health" diff --git a/examples/lightrag_ollama_gremlin_demo.py b/examples/lightrag_ollama_gremlin_demo.py index 893b5606..7ae62810 100644 --- a/examples/lightrag_ollama_gremlin_demo.py +++ b/examples/lightrag_ollama_gremlin_demo.py @@ -1,3 +1,7 @@ +############################################## +# Gremlin storage implementation is deprecated +############################################## + import asyncio import inspect import os diff --git a/examples/lightrag_oracle_demo.py b/examples/lightrag_oracle_demo.py deleted file mode 100644 index 6663f6a1..00000000 --- a/examples/lightrag_oracle_demo.py +++ /dev/null @@ -1,141 +0,0 @@ -import sys -import os -from pathlib import Path -import asyncio -from lightrag import LightRAG, QueryParam -from lightrag.llm.openai import openai_complete_if_cache, openai_embed -from lightrag.utils import EmbeddingFunc -import numpy as np -from lightrag.kg.shared_storage import initialize_pipeline_status - -print(os.getcwd()) -script_directory = Path(__file__).resolve().parent.parent -sys.path.append(os.path.abspath(script_directory)) - -WORKING_DIR = "./dickens" - -# We use OpenAI compatible API to call LLM on Oracle Cloud -# More docs here https://github.com/jin38324/OCI_GenAI_access_gateway -BASE_URL = "http://xxx.xxx.xxx.xxx:8088/v1/" -APIKEY = "ocigenerativeai" -CHATMODEL = "cohere.command-r-plus" -EMBEDMODEL = "cohere.embed-multilingual-v3.0" -CHUNK_TOKEN_SIZE = 1024 -MAX_TOKENS = 4000 - -if not os.path.exists(WORKING_DIR): - os.mkdir(WORKING_DIR) - -os.environ["ORACLE_USER"] = "username" -os.environ["ORACLE_PASSWORD"] = "xxxxxxxxx" -os.environ["ORACLE_DSN"] = "xxxxxxx_medium" -os.environ["ORACLE_CONFIG_DIR"] = "path_to_config_dir" -os.environ["ORACLE_WALLET_LOCATION"] = "path_to_wallet_location" -os.environ["ORACLE_WALLET_PASSWORD"] = "wallet_password" -os.environ["ORACLE_WORKSPACE"] = "company" - - -async def llm_model_func( - prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs -) -> str: - return await openai_complete_if_cache( - CHATMODEL, - prompt, - system_prompt=system_prompt, - history_messages=history_messages, - api_key=APIKEY, - base_url=BASE_URL, - **kwargs, - ) - - -async def embedding_func(texts: list[str]) -> np.ndarray: - return await openai_embed( - texts, - model=EMBEDMODEL, - api_key=APIKEY, - base_url=BASE_URL, - ) - - -async def get_embedding_dim(): - test_text = ["This is a test sentence."] - embedding = await embedding_func(test_text) - embedding_dim = embedding.shape[1] - return embedding_dim - - -async def initialize_rag(): - # Detect embedding dimension - embedding_dimension = await get_embedding_dim() - print(f"Detected embedding dimension: {embedding_dimension}") - - # Initialize LightRAG - # We use Oracle DB as the KV/vector/graph storage - # You can add `addon_params={"example_number": 1, "language": "Simplfied Chinese"}` to control the prompt - rag = LightRAG( - # log_level="DEBUG", - working_dir=WORKING_DIR, - entity_extract_max_gleaning=1, - enable_llm_cache=True, - enable_llm_cache_for_entity_extract=True, - embedding_cache_config=None, # {"enabled": True,"similarity_threshold": 0.90}, - chunk_token_size=CHUNK_TOKEN_SIZE, - llm_model_max_token_size=MAX_TOKENS, - llm_model_func=llm_model_func, - embedding_func=EmbeddingFunc( - embedding_dim=embedding_dimension, - max_token_size=500, - func=embedding_func, - ), - graph_storage="OracleGraphStorage", - kv_storage="OracleKVStorage", - vector_storage="OracleVectorDBStorage", - addon_params={ - "example_number": 1, - "language": "Simplfied Chinese", - "entity_types": ["organization", "person", "geo", "event"], - "insert_batch_size": 2, - }, - ) - await rag.initialize_storages() - await initialize_pipeline_status() - - return rag - - -async def main(): - try: - # Initialize RAG instance - rag = await initialize_rag() - - # Extract and Insert into LightRAG storage - with open(WORKING_DIR + "/docs.txt", "r", encoding="utf-8") as f: - all_text = f.read() - texts = [x for x in all_text.split("\n") if x] - - # New mode use pipeline - await rag.apipeline_enqueue_documents(texts) - await rag.apipeline_process_enqueue_documents() - - # Old method use ainsert - # await rag.ainsert(texts) - - # Perform search in different modes - modes = ["naive", "local", "global", "hybrid"] - for mode in modes: - print("=" * 20, mode, "=" * 20) - print( - await rag.aquery( - "What are the top themes in this story?", - param=QueryParam(mode=mode), - ) - ) - print("-" * 100, "\n") - - except Exception as e: - print(f"An error occurred: {e}") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/examples/lightrag_tidb_demo.py b/examples/lightrag_tidb_demo.py index 52695560..50eac2ca 100644 --- a/examples/lightrag_tidb_demo.py +++ b/examples/lightrag_tidb_demo.py @@ -1,3 +1,7 @@ +########################################### +# TiDB storage implementation is deprecated +########################################### + import asyncio import os diff --git a/lightrag/api/README-zh.md b/lightrag/api/README-zh.md index 4bf31a61..03718656 100644 --- a/lightrag/api/README-zh.md +++ b/lightrag/api/README-zh.md @@ -291,11 +291,9 @@ LightRAG 使用 4 种类型的存储用于不同目的： ``` JsonKVStorage JsonFile(默认) -MongoKVStorage MogonDB -RedisKVStorage Redis -TiDBKVStorage TiDB PGKVStorage Postgres -OracleKVStorage Oracle +RedisKVStorage Redis +MongoKVStorage MogonDB ``` * GRAPH_STORAGE 支持的实现名称 @@ -303,25 +301,19 @@ OracleKVStorage Oracle ``` NetworkXStorage NetworkX(默认) Neo4JStorage Neo4J -MongoGraphStorage MongoDB -TiDBGraphStorage TiDB -AGEStorage AGE -GremlinStorage Gremlin PGGraphStorage Postgres -OracleGraphStorage Postgres +AGEStorage AGE ``` * VECTOR_STORAGE 支持的实现名称 ``` NanoVectorDBStorage NanoVector(默认) +PGVectorStorage Postgres MilvusVectorDBStorge Milvus ChromaVectorDBStorage Chroma -TiDBVectorDBStorage TiDB -PGVectorStorage Postgres FaissVectorDBStorage Faiss QdrantVectorDBStorage Qdrant -OracleVectorDBStorage Oracle MongoVectorDBStorage MongoDB ``` diff --git a/lightrag/api/README.md b/lightrag/api/README.md index 8b2e8177..27f3d14a 100644 --- a/lightrag/api/README.md +++ b/lightrag/api/README.md @@ -302,11 +302,9 @@ Each storage type have servals implementations: ``` JsonKVStorage JsonFile(default) -MongoKVStorage MogonDB -RedisKVStorage Redis -TiDBKVStorage TiDB PGKVStorage Postgres -OracleKVStorage Oracle +RedisKVStorage Redis +MongoKVStorage MogonDB ``` * GRAPH_STORAGE supported implement-name @@ -314,25 +312,19 @@ OracleKVStorage Oracle ``` NetworkXStorage NetworkX(defualt) Neo4JStorage Neo4J -MongoGraphStorage MongoDB -TiDBGraphStorage TiDB -AGEStorage AGE -GremlinStorage Gremlin PGGraphStorage Postgres -OracleGraphStorage Postgres +AGEStorage AGE ``` * VECTOR_STORAGE supported implement-name ``` NanoVectorDBStorage NanoVector(default) -MilvusVectorDBStorage Milvus -ChromaVectorDBStorage Chroma -TiDBVectorDBStorage TiDB PGVectorStorage Postgres +MilvusVectorDBStorge Milvus +ChromaVectorDBStorage Chroma FaissVectorDBStorage Faiss QdrantVectorDBStorage Qdrant -OracleVectorDBStorage Oracle MongoVectorDBStorage MongoDB ``` diff --git a/lightrag/api/__init__.py b/lightrag/api/__init__.py index ec1959de..75eb6b64 100644 --- a/lightrag/api/__init__.py +++ b/lightrag/api/__init__.py @@ -1 +1 @@ -__api_version__ = "1.2.8" +__api_version__ = "0136" diff --git a/lightrag/api/auth.py b/lightrag/api/auth.py index 58175b9d..0b61095d 100644 --- a/lightrag/api/auth.py +++ b/lightrag/api/auth.py @@ -1,9 +1,11 @@ -import os from datetime import datetime, timedelta + import jwt +from dotenv import load_dotenv from fastapi import HTTPException, status from pydantic import BaseModel -from dotenv import load_dotenv + +from .config import global_args # use the .env that is inside the current folder # allows to use different .env file for each lightrag instance @@ -20,13 +22,12 @@ class TokenPayload(BaseModel): class AuthHandler: def __init__(self): - self.secret = os.getenv("TOKEN_SECRET", "4f85ds4f56dsf46") - self.algorithm = "HS256" - self.expire_hours = int(os.getenv("TOKEN_EXPIRE_HOURS", 4)) - self.guest_expire_hours = int(os.getenv("GUEST_TOKEN_EXPIRE_HOURS", 2)) - + self.secret = global_args.token_secret + self.algorithm = global_args.jwt_algorithm + self.expire_hours = global_args.token_expire_hours + self.guest_expire_hours = global_args.guest_token_expire_hours self.accounts = {} - auth_accounts = os.getenv("AUTH_ACCOUNTS") + auth_accounts = global_args.auth_accounts if auth_accounts: for account in auth_accounts.split(","): username, password = account.split(":", 1) diff --git a/lightrag/api/config.py b/lightrag/api/config.py new file mode 100644 index 00000000..1bbdb1c9 --- /dev/null +++ b/lightrag/api/config.py @@ -0,0 +1,335 @@ +""" +Configs for the LightRAG API. +""" + +import os +import argparse +import logging +from dotenv import load_dotenv + +# use the .env that is inside the current folder +# allows to use different .env file for each lightrag instance +# the OS environment variables take precedence over the .env file +load_dotenv(dotenv_path=".env", override=False) + + +class OllamaServerInfos: + # Constants for emulated Ollama model information + LIGHTRAG_NAME = "lightrag" + LIGHTRAG_TAG = os.getenv("OLLAMA_EMULATING_MODEL_TAG", "latest") + LIGHTRAG_MODEL = f"{LIGHTRAG_NAME}:{LIGHTRAG_TAG}" + LIGHTRAG_SIZE = 7365960935 # it's a dummy value + LIGHTRAG_CREATED_AT = "2024-01-15T00:00:00Z" + LIGHTRAG_DIGEST = "sha256:lightrag" + + +ollama_server_infos = OllamaServerInfos() + + +class DefaultRAGStorageConfig: + KV_STORAGE = "JsonKVStorage" + VECTOR_STORAGE = "NanoVectorDBStorage" + GRAPH_STORAGE = "NetworkXStorage" + DOC_STATUS_STORAGE = "JsonDocStatusStorage" + + +def get_default_host(binding_type: str) -> str: + default_hosts = { + "ollama": os.getenv("LLM_BINDING_HOST", "http://localhost:11434"), + "lollms": os.getenv("LLM_BINDING_HOST", "http://localhost:9600"), + "azure_openai": os.getenv("AZURE_OPENAI_ENDPOINT", "https://api.openai.com/v1"), + "openai": os.getenv("LLM_BINDING_HOST", "https://api.openai.com/v1"), + } + return default_hosts.get( + binding_type, os.getenv("LLM_BINDING_HOST", "http://localhost:11434") + ) # fallback to ollama if unknown + + +def get_env_value(env_key: str, default: any, value_type: type = str) -> any: + """ + Get value from environment variable with type conversion + + Args: + env_key (str): Environment variable key + default (any): Default value if env variable is not set + value_type (type): Type to convert the value to + + Returns: + any: Converted value from environment or default + """ + value = os.getenv(env_key) + if value is None: + return default + + if value_type is bool: + return value.lower() in ("true", "1", "yes", "t", "on") + try: + return value_type(value) + except ValueError: + return default + + +def parse_args() -> argparse.Namespace: + """ + Parse command line arguments with environment variable fallback + + Args: + is_uvicorn_mode: Whether running under uvicorn mode + + Returns: + argparse.Namespace: Parsed arguments + """ + + parser = argparse.ArgumentParser( + description="LightRAG FastAPI Server with separate working and input directories" + ) + + # Server configuration + parser.add_argument( + "--host", + default=get_env_value("HOST", "0.0.0.0"), + help="Server host (default: from env or 0.0.0.0)", + ) + parser.add_argument( + "--port", + type=int, + default=get_env_value("PORT", 9621, int), + help="Server port (default: from env or 9621)", + ) + + # Directory configuration + parser.add_argument( + "--working-dir", + default=get_env_value("WORKING_DIR", "./rag_storage"), + help="Working directory for RAG storage (default: from env or ./rag_storage)", + ) + parser.add_argument( + "--input-dir", + default=get_env_value("INPUT_DIR", "./inputs"), + help="Directory containing input documents (default: from env or ./inputs)", + ) + + def timeout_type(value): + if value is None: + return 150 + if value is None or value == "None": + return None + return int(value) + + parser.add_argument( + "--timeout", + default=get_env_value("TIMEOUT", None, timeout_type), + type=timeout_type, + help="Timeout in seconds (useful when using slow AI). Use None for infinite timeout", + ) + + # RAG configuration + parser.add_argument( + "--max-async", + type=int, + default=get_env_value("MAX_ASYNC", 4, int), + help="Maximum async operations (default: from env or 4)", + ) + parser.add_argument( + "--max-tokens", + type=int, + default=get_env_value("MAX_TOKENS", 32768, int), + help="Maximum token size (default: from env or 32768)", + ) + + # Logging configuration + parser.add_argument( + "--log-level", + default=get_env_value("LOG_LEVEL", "INFO"), + choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"], + help="Logging level (default: from env or INFO)", + ) + parser.add_argument( + "--verbose", + action="store_true", + default=get_env_value("VERBOSE", False, bool), + help="Enable verbose debug output(only valid for DEBUG log-level)", + ) + + parser.add_argument( + "--key", + type=str, + default=get_env_value("LIGHTRAG_API_KEY", None), + help="API key for authentication. This protects lightrag server against unauthorized access", + ) + + # Optional https parameters + parser.add_argument( + "--ssl", + action="store_true", + default=get_env_value("SSL", False, bool), + help="Enable HTTPS (default: from env or False)", + ) + parser.add_argument( + "--ssl-certfile", + default=get_env_value("SSL_CERTFILE", None), + help="Path to SSL certificate file (required if --ssl is enabled)", + ) + parser.add_argument( + "--ssl-keyfile", + default=get_env_value("SSL_KEYFILE", None), + help="Path to SSL private key file (required if --ssl is enabled)", + ) + + parser.add_argument( + "--history-turns", + type=int, + default=get_env_value("HISTORY_TURNS", 3, int), + help="Number of conversation history turns to include (default: from env or 3)", + ) + + # Search parameters + parser.add_argument( + "--top-k", + type=int, + default=get_env_value("TOP_K", 60, int), + help="Number of most similar results to return (default: from env or 60)", + ) + parser.add_argument( + "--cosine-threshold", + type=float, + default=get_env_value("COSINE_THRESHOLD", 0.2, float), + help="Cosine similarity threshold (default: from env or 0.4)", + ) + + # Ollama model name + parser.add_argument( + "--simulated-model-name", + type=str, + default=get_env_value( + "SIMULATED_MODEL_NAME", ollama_server_infos.LIGHTRAG_MODEL + ), + help="Number of conversation history turns to include (default: from env or 3)", + ) + + # Namespace + parser.add_argument( + "--namespace-prefix", + type=str, + default=get_env_value("NAMESPACE_PREFIX", ""), + help="Prefix of the namespace", + ) + + parser.add_argument( + "--auto-scan-at-startup", + action="store_true", + default=False, + help="Enable automatic scanning when the program starts", + ) + + # Server workers configuration + parser.add_argument( + "--workers", + type=int, + default=get_env_value("WORKERS", 1, int), + help="Number of worker processes (default: from env or 1)", + ) + + # LLM and embedding bindings + parser.add_argument( + "--llm-binding", + type=str, + default=get_env_value("LLM_BINDING", "ollama"), + choices=["lollms", "ollama", "openai", "openai-ollama", "azure_openai"], + help="LLM binding type (default: from env or ollama)", + ) + parser.add_argument( + "--embedding-binding", + type=str, + default=get_env_value("EMBEDDING_BINDING", "ollama"), + choices=["lollms", "ollama", "openai", "azure_openai"], + help="Embedding binding type (default: from env or ollama)", + ) + + args = parser.parse_args() + + # convert relative path to absolute path + args.working_dir = os.path.abspath(args.working_dir) + args.input_dir = os.path.abspath(args.input_dir) + + # Inject storage configuration from environment variables + args.kv_storage = get_env_value( + "LIGHTRAG_KV_STORAGE", DefaultRAGStorageConfig.KV_STORAGE + ) + args.doc_status_storage = get_env_value( + "LIGHTRAG_DOC_STATUS_STORAGE", DefaultRAGStorageConfig.DOC_STATUS_STORAGE + ) + args.graph_storage = get_env_value( + "LIGHTRAG_GRAPH_STORAGE", DefaultRAGStorageConfig.GRAPH_STORAGE + ) + args.vector_storage = get_env_value( + "LIGHTRAG_VECTOR_STORAGE", DefaultRAGStorageConfig.VECTOR_STORAGE + ) + + # Get MAX_PARALLEL_INSERT from environment + args.max_parallel_insert = get_env_value("MAX_PARALLEL_INSERT", 2, int) + + # Handle openai-ollama special case + if args.llm_binding == "openai-ollama": + args.llm_binding = "openai" + args.embedding_binding = "ollama" + + args.llm_binding_host = get_env_value( + "LLM_BINDING_HOST", get_default_host(args.llm_binding) + ) + args.embedding_binding_host = get_env_value( + "EMBEDDING_BINDING_HOST", get_default_host(args.embedding_binding) + ) + args.llm_binding_api_key = get_env_value("LLM_BINDING_API_KEY", None) + args.embedding_binding_api_key = get_env_value("EMBEDDING_BINDING_API_KEY", "") + + # Inject model configuration + args.llm_model = get_env_value("LLM_MODEL", "mistral-nemo:latest") + args.embedding_model = get_env_value("EMBEDDING_MODEL", "bge-m3:latest") + args.embedding_dim = get_env_value("EMBEDDING_DIM", 1024, int) + args.max_embed_tokens = get_env_value("MAX_EMBED_TOKENS", 8192, int) + + # Inject chunk configuration + args.chunk_size = get_env_value("CHUNK_SIZE", 1200, int) + args.chunk_overlap_size = get_env_value("CHUNK_OVERLAP_SIZE", 100, int) + + # Inject LLM cache configuration + args.enable_llm_cache_for_extract = get_env_value( + "ENABLE_LLM_CACHE_FOR_EXTRACT", True, bool + ) + + # Inject LLM temperature configuration + args.temperature = get_env_value("TEMPERATURE", 0.5, float) + + # Select Document loading tool (DOCLING, DEFAULT) + args.document_loading_engine = get_env_value("DOCUMENT_LOADING_ENGINE", "DEFAULT") + + # Add environment variables that were previously read directly + args.cors_origins = get_env_value("CORS_ORIGINS", "*") + args.summary_language = get_env_value("SUMMARY_LANGUAGE", "en") + args.whitelist_paths = get_env_value("WHITELIST_PATHS", "/health,/api/*") + + # For JWT Auth + args.auth_accounts = get_env_value("AUTH_ACCOUNTS", "") + args.token_secret = get_env_value("TOKEN_SECRET", "lightrag-jwt-default-secret") + args.token_expire_hours = get_env_value("TOKEN_EXPIRE_HOURS", 48, int) + args.guest_token_expire_hours = get_env_value("GUEST_TOKEN_EXPIRE_HOURS", 24, int) + args.jwt_algorithm = get_env_value("JWT_ALGORITHM", "HS256") + + ollama_server_infos.LIGHTRAG_MODEL = args.simulated_model_name + + return args + + +def update_uvicorn_mode_config(): + # If in uvicorn mode and workers > 1, force it to 1 and log warning + if global_args.workers > 1: + original_workers = global_args.workers + global_args.workers = 1 + # Log warning directly here + logging.warning( + f"In uvicorn mode, workers parameter was set to {original_workers}. Forcing workers=1" + ) + + +global_args = parse_args() diff --git a/lightrag/api/lightrag_server.py b/lightrag/api/lightrag_server.py index 8110d6d4..84636bde 100644 --- a/lightrag/api/lightrag_server.py +++ b/lightrag/api/lightrag_server.py @@ -19,11 +19,14 @@ from contextlib import asynccontextmanager from dotenv import load_dotenv from lightrag.api.utils_api import ( get_combined_auth_dependency, - parse_args, - get_default_host, display_splash_screen, check_env_file, ) +from .config import ( + global_args, + update_uvicorn_mode_config, + get_default_host, +) import sys from lightrag import LightRAG, __version__ as core_version from lightrag.api import __api_version__ @@ -52,6 +55,10 @@ from lightrag.api.auth import auth_handler # the OS environment variables take precedence over the .env file load_dotenv(dotenv_path=".env", override=False) + +webui_title = os.getenv("WEBUI_TITLE") +webui_description = os.getenv("WEBUI_DESCRIPTION") + # Initialize config parser config = configparser.ConfigParser() config.read("config.ini") @@ -164,10 +171,10 @@ def create_app(args): app = FastAPI(**app_kwargs) def get_cors_origins(): - """Get allowed origins from environment variable + """Get allowed origins from global_args Returns a list of allowed origins, defaults to ["*"] if not set """ - origins_str = os.getenv("CORS_ORIGINS", "*") + origins_str = global_args.cors_origins if origins_str == "*": return ["*"] return [origin.strip() for origin in origins_str.split(",")] @@ -315,9 +322,10 @@ def create_app(args): "similarity_threshold": 0.95, "use_llm_check": False, }, - namespace_prefix=args.namespace_prefix, + # namespace_prefix=args.namespace_prefix, auto_manage_storages_states=False, max_parallel_insert=args.max_parallel_insert, + addon_params={"language": args.summary_language}, ) else: # azure_openai rag = LightRAG( @@ -345,9 +353,10 @@ def create_app(args): "similarity_threshold": 0.95, "use_llm_check": False, }, - namespace_prefix=args.namespace_prefix, + # namespace_prefix=args.namespace_prefix, auto_manage_storages_states=False, max_parallel_insert=args.max_parallel_insert, + addon_params={"language": args.summary_language}, ) # Add routes @@ -381,6 +390,8 @@ def create_app(args): "message": "Authentication is disabled. Using guest access.", "core_version": core_version, "api_version": __api_version__, + "webui_title": webui_title, + "webui_description": webui_description, } return { @@ -388,6 +399,8 @@ def create_app(args): "auth_mode": "enabled", "core_version": core_version, "api_version": __api_version__, + "webui_title": webui_title, + "webui_description": webui_description, } @app.post("/login") @@ -404,6 +417,8 @@ def create_app(args): "message": "Authentication is disabled. Using guest access.", "core_version": core_version, "api_version": __api_version__, + "webui_title": webui_title, + "webui_description": webui_description, } username = form_data.username if auth_handler.accounts.get(username) != form_data.password: @@ -421,6 +436,8 @@ def create_app(args): "auth_mode": "enabled", "core_version": core_version, "api_version": __api_version__, + "webui_title": webui_title, + "webui_description": webui_description, } @app.get("/health", dependencies=[Depends(combined_auth)]) @@ -454,10 +471,12 @@ def create_app(args): "vector_storage": args.vector_storage, "enable_llm_cache_for_extract": args.enable_llm_cache_for_extract, }, - "core_version": core_version, - "api_version": __api_version__, "auth_mode": auth_mode, "pipeline_busy": pipeline_status.get("busy", False), + "core_version": core_version, + "api_version": __api_version__, + "webui_title": webui_title, + "webui_description": webui_description, } except Exception as e: logger.error(f"Error getting health status: {str(e)}") @@ -490,7 +509,7 @@ def create_app(args): def get_application(args=None): """Factory function for creating the FastAPI application""" if args is None: - args = parse_args() + args = global_args return create_app(args) @@ -611,30 +630,31 @@ def main(): # Configure logging before parsing args configure_logging() - - args = parse_args(is_uvicorn_mode=True) - display_splash_screen(args) + update_uvicorn_mode_config() + display_splash_screen(global_args) # Create application instance directly instead of using factory function - app = create_app(args) + app = create_app(global_args) # Start Uvicorn in single process mode uvicorn_config = { "app": app, # Pass application instance directly instead of string path - "host": args.host, - "port": args.port, + "host": global_args.host, + "port": global_args.port, "log_config": None, # Disable default config } - if args.ssl: + if global_args.ssl: uvicorn_config.update( { - "ssl_certfile": args.ssl_certfile, - "ssl_keyfile": args.ssl_keyfile, + "ssl_certfile": global_args.ssl_certfile, + "ssl_keyfile": global_args.ssl_keyfile, } ) - print(f"Starting Uvicorn server in single-process mode on {args.host}:{args.port}") + print( + f"Starting Uvicorn server in single-process mode on {global_args.host}:{global_args.port}" + ) uvicorn.run(**uvicorn_config) diff --git a/lightrag/api/routers/document_routes.py b/lightrag/api/routers/document_routes.py index 445008ec..8e664006 100644 --- a/lightrag/api/routers/document_routes.py +++ b/lightrag/api/routers/document_routes.py @@ -10,16 +10,14 @@ import traceback import pipmaster as pm from datetime import datetime from pathlib import Path -from typing import Dict, List, Optional, Any +from typing import Dict, List, Optional, Any, Literal from fastapi import APIRouter, BackgroundTasks, Depends, File, HTTPException, UploadFile from pydantic import BaseModel, Field, field_validator from lightrag import LightRAG from lightrag.base import DocProcessingStatus, DocStatus -from lightrag.api.utils_api import ( - get_combined_auth_dependency, - global_args, -) +from lightrag.api.utils_api import get_combined_auth_dependency +from ..config import global_args router = APIRouter( prefix="/documents", @@ -30,7 +28,37 @@ router = APIRouter( temp_prefix = "__tmp__" +class ScanResponse(BaseModel): + """Response model for document scanning operation + + Attributes: + status: Status of the scanning operation + message: Optional message with additional details + """ + + status: Literal["scanning_started"] = Field( + description="Status of the scanning operation" + ) + message: Optional[str] = Field( + default=None, description="Additional details about the scanning operation" + ) + + class Config: + json_schema_extra = { + "example": { + "status": "scanning_started", + "message": "Scanning process has been initiated in the background", + } + } + + class InsertTextRequest(BaseModel): + """Request model for inserting a single text document + + Attributes: + text: The text content to be inserted into the RAG system + """ + text: str = Field( min_length=1, description="The text to insert", @@ -41,8 +69,21 @@ class InsertTextRequest(BaseModel): def strip_after(cls, text: str) -> str: return text.strip() + class Config: + json_schema_extra = { + "example": { + "text": "This is a sample text to be inserted into the RAG system." + } + } + class InsertTextsRequest(BaseModel): + """Request model for inserting multiple text documents + + Attributes: + texts: List of text contents to be inserted into the RAG system + """ + texts: list[str] = Field( min_length=1, description="The texts to insert", @@ -53,11 +94,116 @@ class InsertTextsRequest(BaseModel): def strip_after(cls, texts: list[str]) -> list[str]: return [text.strip() for text in texts] + class Config: + json_schema_extra = { + "example": { + "texts": [ + "This is the first text to be inserted.", + "This is the second text to be inserted.", + ] + } + } + class InsertResponse(BaseModel): - status: str = Field(description="Status of the operation") + """Response model for document insertion operations + + Attributes: + status: Status of the operation (success, duplicated, partial_success, failure) + message: Detailed message describing the operation result + """ + + status: Literal["success", "duplicated", "partial_success", "failure"] = Field( + description="Status of the operation" + ) message: str = Field(description="Message describing the operation result") + class Config: + json_schema_extra = { + "example": { + "status": "success", + "message": "File 'document.pdf' uploaded successfully. Processing will continue in background.", + } + } + + +class ClearDocumentsResponse(BaseModel): + """Response model for document clearing operation + + Attributes: + status: Status of the clear operation + message: Detailed message describing the operation result + """ + + status: Literal["success", "partial_success", "busy", "fail"] = Field( + description="Status of the clear operation" + ) + message: str = Field(description="Message describing the operation result") + + class Config: + json_schema_extra = { + "example": { + "status": "success", + "message": "All documents cleared successfully. Deleted 15 files.", + } + } + + +class ClearCacheRequest(BaseModel): + """Request model for clearing cache + + Attributes: + modes: Optional list of cache modes to clear + """ + + modes: Optional[ + List[Literal["default", "naive", "local", "global", "hybrid", "mix"]] + ] = Field( + default=None, + description="Modes of cache to clear. If None, clears all cache.", + ) + + class Config: + json_schema_extra = {"example": {"modes": ["default", "naive"]}} + + +class ClearCacheResponse(BaseModel): + """Response model for cache clearing operation + + Attributes: + status: Status of the clear operation + message: Detailed message describing the operation result + """ + + status: Literal["success", "fail"] = Field( + description="Status of the clear operation" + ) + message: str = Field(description="Message describing the operation result") + + class Config: + json_schema_extra = { + "example": { + "status": "success", + "message": "Successfully cleared cache for modes: ['default', 'naive']", + } + } + + +"""Response model for document status + +Attributes: + id: Document identifier + content_summary: Summary of document content + content_length: Length of document content + status: Current processing status + created_at: Creation timestamp (ISO format string) + updated_at: Last update timestamp (ISO format string) + chunks_count: Number of chunks (optional) + error: Error message if any (optional) + metadata: Additional metadata (optional) + file_path: Path to the document file +""" + class DocStatusResponse(BaseModel): @staticmethod @@ -68,34 +214,82 @@ class DocStatusResponse(BaseModel): return dt return dt.isoformat() - """Response model for document status + id: str = Field(description="Document identifier") + content_summary: str = Field(description="Summary of document content") + content_length: int = Field(description="Length of document content in characters") + status: DocStatus = Field(description="Current processing status") + created_at: str = Field(description="Creation timestamp (ISO format string)") + updated_at: str = Field(description="Last update timestamp (ISO format string)") + chunks_count: Optional[int] = Field( + default=None, description="Number of chunks the document was split into" + ) + error: Optional[str] = Field( + default=None, description="Error message if processing failed" + ) + metadata: Optional[dict[str, Any]] = Field( + default=None, description="Additional metadata about the document" + ) + file_path: str = Field(description="Path to the document file") - Attributes: - id: Document identifier - content_summary: Summary of document content - content_length: Length of document content - status: Current processing status - created_at: Creation timestamp (ISO format string) - updated_at: Last update timestamp (ISO format string) - chunks_count: Number of chunks (optional) - error: Error message if any (optional) - metadata: Additional metadata (optional) - """ - - id: str - content_summary: str - content_length: int - status: DocStatus - created_at: str - updated_at: str - chunks_count: Optional[int] = None - error: Optional[str] = None - metadata: Optional[dict[str, Any]] = None - file_path: str + class Config: + json_schema_extra = { + "example": { + "id": "doc_123456", + "content_summary": "Research paper on machine learning", + "content_length": 15240, + "status": "PROCESSED", + "created_at": "2025-03-31T12:34:56", + "updated_at": "2025-03-31T12:35:30", + "chunks_count": 12, + "error": None, + "metadata": {"author": "John Doe", "year": 2025}, + "file_path": "research_paper.pdf", + } + } class DocsStatusesResponse(BaseModel): - statuses: Dict[DocStatus, List[DocStatusResponse]] = {} + """Response model for document statuses + + Attributes: + statuses: Dictionary mapping document status to lists of document status responses + """ + + statuses: Dict[DocStatus, List[DocStatusResponse]] = Field( + default_factory=dict, + description="Dictionary mapping document status to lists of document status responses", + ) + + class Config: + json_schema_extra = { + "example": { + "statuses": { + "PENDING": [ + { + "id": "doc_123", + "content_summary": "Pending document", + "content_length": 5000, + "status": "PENDING", + "created_at": "2025-03-31T10:00:00", + "updated_at": "2025-03-31T10:00:00", + "file_path": "pending_doc.pdf", + } + ], + "PROCESSED": [ + { + "id": "doc_456", + "content_summary": "Processed document", + "content_length": 8000, + "status": "PROCESSED", + "created_at": "2025-03-31T09:00:00", + "updated_at": "2025-03-31T09:05:00", + "chunks_count": 8, + "file_path": "processed_doc.pdf", + } + ], + } + } + } class PipelineStatusResponse(BaseModel): @@ -276,7 +470,7 @@ async def pipeline_enqueue_file(rag: LightRAG, file_path: Path) -> bool: ) return False case ".pdf": - if global_args["main_args"].document_loading_engine == "DOCLING": + if global_args.document_loading_engine == "DOCLING": if not pm.is_installed("docling"): # type: ignore pm.install("docling") from docling.document_converter import DocumentConverter # type: ignore @@ -295,7 +489,7 @@ async def pipeline_enqueue_file(rag: LightRAG, file_path: Path) -> bool: for page in reader.pages: content += page.extract_text() + "\n" case ".docx": - if global_args["main_args"].document_loading_engine == "DOCLING": + if global_args.document_loading_engine == "DOCLING": if not pm.is_installed("docling"): # type: ignore pm.install("docling") from docling.document_converter import DocumentConverter # type: ignore @@ -315,7 +509,7 @@ async def pipeline_enqueue_file(rag: LightRAG, file_path: Path) -> bool: [paragraph.text for paragraph in doc.paragraphs] ) case ".pptx": - if global_args["main_args"].document_loading_engine == "DOCLING": + if global_args.document_loading_engine == "DOCLING": if not pm.is_installed("docling"): # type: ignore pm.install("docling") from docling.document_converter import DocumentConverter # type: ignore @@ -336,7 +530,7 @@ async def pipeline_enqueue_file(rag: LightRAG, file_path: Path) -> bool: if hasattr(shape, "text"): content += shape.text + "\n" case ".xlsx": - if global_args["main_args"].document_loading_engine == "DOCLING": + if global_args.document_loading_engine == "DOCLING": if not pm.is_installed("docling"): # type: ignore pm.install("docling") from docling.document_converter import DocumentConverter # type: ignore @@ -443,6 +637,7 @@ async def pipeline_index_texts(rag: LightRAG, texts: List[str]): await rag.apipeline_process_enqueue_documents() +# TODO: deprecate after /insert_file is removed async def save_temp_file(input_dir: Path, file: UploadFile = File(...)) -> Path: """Save the uploaded file to a temporary location @@ -476,8 +671,8 @@ async def run_scanning_process(rag: LightRAG, doc_manager: DocumentManager): if not new_files: return - # Get MAX_PARALLEL_INSERT from global_args["main_args"] - max_parallel = global_args["main_args"].max_parallel_insert + # Get MAX_PARALLEL_INSERT from global_args + max_parallel = global_args.max_parallel_insert # Calculate batch size as 2 * MAX_PARALLEL_INSERT batch_size = 2 * max_parallel @@ -509,7 +704,9 @@ def create_document_routes( # Create combined auth dependency for document routes combined_auth = get_combined_auth_dependency(api_key) - @router.post("/scan", dependencies=[Depends(combined_auth)]) + @router.post( + "/scan", response_model=ScanResponse, dependencies=[Depends(combined_auth)] + ) async def scan_for_new_documents(background_tasks: BackgroundTasks): """ Trigger the scanning process for new documents. @@ -519,13 +716,18 @@ def create_document_routes( that fact. Returns: - dict: A dictionary containing the scanning status + ScanResponse: A response object containing the scanning status """ # Start the scanning process in the background background_tasks.add_task(run_scanning_process, rag, doc_manager) - return {"status": "scanning_started"} + return ScanResponse( + status="scanning_started", + message="Scanning process has been initiated in the background", + ) - @router.post("/upload", dependencies=[Depends(combined_auth)]) + @router.post( + "/upload", response_model=InsertResponse, dependencies=[Depends(combined_auth)] + ) async def upload_to_input_dir( background_tasks: BackgroundTasks, file: UploadFile = File(...) ): @@ -645,6 +847,7 @@ def create_document_routes( logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) + # TODO: deprecated, use /upload instead @router.post( "/file", response_model=InsertResponse, dependencies=[Depends(combined_auth)] ) @@ -688,6 +891,7 @@ def create_document_routes( logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) + # TODO: deprecated, use /upload instead @router.post( "/file_batch", response_model=InsertResponse, @@ -752,32 +956,186 @@ def create_document_routes( raise HTTPException(status_code=500, detail=str(e)) @router.delete( - "", response_model=InsertResponse, dependencies=[Depends(combined_auth)] + "", response_model=ClearDocumentsResponse, dependencies=[Depends(combined_auth)] ) async def clear_documents(): """ Clear all documents from the RAG system. - This endpoint deletes all text chunks, entities vector database, and relationships - vector database, effectively clearing all documents from the RAG system. + This endpoint deletes all documents, entities, relationships, and files from the system. + It uses the storage drop methods to properly clean up all data and removes all files + from the input directory. Returns: - InsertResponse: A response object containing the status and message. + ClearDocumentsResponse: A response object containing the status and message. + - status="success": All documents and files were successfully cleared. + - status="partial_success": Document clear job exit with some errors. + - status="busy": Operation could not be completed because the pipeline is busy. + - status="fail": All storage drop operations failed, with message + - message: Detailed information about the operation results, including counts + of deleted files and any errors encountered. Raises: - HTTPException: If an error occurs during the clearing process (500). + HTTPException: Raised when a serious error occurs during the clearing process, + with status code 500 and error details in the detail field. """ - try: - rag.text_chunks = [] - rag.entities_vdb = None - rag.relationships_vdb = None - return InsertResponse( - status="success", message="All documents cleared successfully" + from lightrag.kg.shared_storage import ( + get_namespace_data, + get_pipeline_status_lock, + ) + + # Get pipeline status and lock + pipeline_status = await get_namespace_data("pipeline_status") + pipeline_status_lock = get_pipeline_status_lock() + + # Check and set status with lock + async with pipeline_status_lock: + if pipeline_status.get("busy", False): + return ClearDocumentsResponse( + status="busy", + message="Cannot clear documents while pipeline is busy", + ) + # Set busy to true + pipeline_status.update( + { + "busy": True, + "job_name": "Clearing Documents", + "job_start": datetime.now().isoformat(), + "docs": 0, + "batchs": 0, + "cur_batch": 0, + "request_pending": False, # Clear any previous request + "latest_message": "Starting document clearing process", + } ) + # Cleaning history_messages without breaking it as a shared list object + del pipeline_status["history_messages"][:] + pipeline_status["history_messages"].append( + "Starting document clearing process" + ) + + try: + # Use drop method to clear all data + drop_tasks = [] + storages = [ + rag.text_chunks, + rag.full_docs, + rag.entities_vdb, + rag.relationships_vdb, + rag.chunks_vdb, + rag.chunk_entity_relation_graph, + rag.doc_status, + ] + + # Log storage drop start + if "history_messages" in pipeline_status: + pipeline_status["history_messages"].append( + "Starting to drop storage components" + ) + + for storage in storages: + if storage is not None: + drop_tasks.append(storage.drop()) + + # Wait for all drop tasks to complete + drop_results = await asyncio.gather(*drop_tasks, return_exceptions=True) + + # Check for errors and log results + errors = [] + storage_success_count = 0 + storage_error_count = 0 + + for i, result in enumerate(drop_results): + storage_name = storages[i].__class__.__name__ + if isinstance(result, Exception): + error_msg = f"Error dropping {storage_name}: {str(result)}" + errors.append(error_msg) + logger.error(error_msg) + storage_error_count += 1 + else: + logger.info(f"Successfully dropped {storage_name}") + storage_success_count += 1 + + # Log storage drop results + if "history_messages" in pipeline_status: + if storage_error_count > 0: + pipeline_status["history_messages"].append( + f"Dropped {storage_success_count} storage components with {storage_error_count} errors" + ) + else: + pipeline_status["history_messages"].append( + f"Successfully dropped all {storage_success_count} storage components" + ) + + # If all storage operations failed, return error status and don't proceed with file deletion + if storage_success_count == 0 and storage_error_count > 0: + error_message = "All storage drop operations failed. Aborting document clearing process." + logger.error(error_message) + if "history_messages" in pipeline_status: + pipeline_status["history_messages"].append(error_message) + return ClearDocumentsResponse(status="fail", message=error_message) + + # Log file deletion start + if "history_messages" in pipeline_status: + pipeline_status["history_messages"].append( + "Starting to delete files in input directory" + ) + + # Delete all files in input_dir + deleted_files_count = 0 + file_errors_count = 0 + + for file_path in doc_manager.input_dir.glob("**/*"): + if file_path.is_file(): + try: + file_path.unlink() + deleted_files_count += 1 + except Exception as e: + logger.error(f"Error deleting file {file_path}: {str(e)}") + file_errors_count += 1 + + # Log file deletion results + if "history_messages" in pipeline_status: + if file_errors_count > 0: + pipeline_status["history_messages"].append( + f"Deleted {deleted_files_count} files with {file_errors_count} errors" + ) + errors.append(f"Failed to delete {file_errors_count} files") + else: + pipeline_status["history_messages"].append( + f"Successfully deleted {deleted_files_count} files" + ) + + # Prepare final result message + final_message = "" + if errors: + final_message = f"Cleared documents with some errors. Deleted {deleted_files_count} files." + status = "partial_success" + else: + final_message = f"All documents cleared successfully. Deleted {deleted_files_count} files." + status = "success" + + # Log final result + if "history_messages" in pipeline_status: + pipeline_status["history_messages"].append(final_message) + + # Return response based on results + return ClearDocumentsResponse(status=status, message=final_message) except Exception as e: - logger.error(f"Error DELETE /documents: {str(e)}") + error_msg = f"Error clearing documents: {str(e)}" + logger.error(error_msg) logger.error(traceback.format_exc()) + if "history_messages" in pipeline_status: + pipeline_status["history_messages"].append(error_msg) raise HTTPException(status_code=500, detail=str(e)) + finally: + # Reset busy status after completion + async with pipeline_status_lock: + pipeline_status["busy"] = False + completion_msg = "Document clearing process completed" + pipeline_status["latest_message"] = completion_msg + if "history_messages" in pipeline_status: + pipeline_status["history_messages"].append(completion_msg) @router.get( "/pipeline_status", @@ -850,7 +1208,9 @@ def create_document_routes( logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) - @router.get("", dependencies=[Depends(combined_auth)]) + @router.get( + "", response_model=DocsStatusesResponse, dependencies=[Depends(combined_auth)] + ) async def documents() -> DocsStatusesResponse: """ Get the status of all documents in the system. @@ -908,4 +1268,57 @@ def create_document_routes( logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) + @router.post( + "/clear_cache", + response_model=ClearCacheResponse, + dependencies=[Depends(combined_auth)], + ) + async def clear_cache(request: ClearCacheRequest): + """ + Clear cache data from the LLM response cache storage. + + This endpoint allows clearing specific modes of cache or all cache if no modes are specified. + Valid modes include: "default", "naive", "local", "global", "hybrid", "mix". + - "default" represents extraction cache. + - Other modes correspond to different query modes. + + Args: + request (ClearCacheRequest): The request body containing optional modes to clear. + + Returns: + ClearCacheResponse: A response object containing the status and message. + + Raises: + HTTPException: If an error occurs during cache clearing (400 for invalid modes, 500 for other errors). + """ + try: + # Validate modes if provided + valid_modes = ["default", "naive", "local", "global", "hybrid", "mix"] + if request.modes and not all(mode in valid_modes for mode in request.modes): + invalid_modes = [ + mode for mode in request.modes if mode not in valid_modes + ] + raise HTTPException( + status_code=400, + detail=f"Invalid mode(s): {invalid_modes}. Valid modes are: {valid_modes}", + ) + + # Call the aclear_cache method + await rag.aclear_cache(request.modes) + + # Prepare success message + if request.modes: + message = f"Successfully cleared cache for modes: {request.modes}" + else: + message = "Successfully cleared all cache" + + return ClearCacheResponse(status="success", message=message) + except HTTPException: + # Re-raise HTTP exceptions + raise + except Exception as e: + logger.error(f"Error clearing cache: {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 f9d77ff6..381df90b 100644 --- a/lightrag/api/routers/graph_routes.py +++ b/lightrag/api/routers/graph_routes.py @@ -3,7 +3,7 @@ This module contains all graph-related routes for the LightRAG API. """ from typing import Optional -from fastapi import APIRouter, Depends +from fastapi import APIRouter, Depends, Query from ..utils_api import get_combined_auth_dependency @@ -25,23 +25,20 @@ def create_graph_routes(rag, api_key: Optional[str] = None): @router.get("/graphs", dependencies=[Depends(combined_auth)]) async def get_knowledge_graph( - label: str, max_depth: int = 3, min_degree: int = 0, inclusive: bool = False + label: str = Query(..., description="Label to get knowledge graph for"), + max_depth: int = Query(3, description="Maximum depth of graph", ge=1), + max_nodes: int = Query(1000, description="Maximum nodes to return", ge=1), ): """ Retrieve a connected subgraph of nodes where the label includes the specified label. - Maximum number of nodes is constrained by the environment variable `MAX_GRAPH_NODES` (default: 1000). When reducing the number of nodes, the prioritization criteria are as follows: - 1. min_degree does not affect nodes directly connected to the matching nodes - 2. Label matching nodes take precedence - 3. Followed by nodes directly connected to the matching nodes - 4. Finally, the degree of the nodes - Maximum number of nodes is limited to env MAX_GRAPH_NODES(default: 1000) + 1. Hops(path) to the staring node take precedence + 2. Followed by the degree of the nodes Args: - label (str): Label to get knowledge graph for - max_depth (int, optional): Maximum depth of graph. Defaults to 3. - inclusive_search (bool, optional): If True, search for nodes that include the label. Defaults to False. - min_degree (int, optional): Minimum degree of nodes. Defaults to 0. + label (str): Label of the starting node + max_depth (int, optional): Maximum depth of the subgraph,Defaults to 3 + max_nodes: Maxiumu nodes to return Returns: Dict[str, List[str]]: Knowledge graph for label @@ -49,8 +46,7 @@ def create_graph_routes(rag, api_key: Optional[str] = None): return await rag.get_knowledge_graph( node_label=label, max_depth=max_depth, - inclusive=inclusive, - min_degree=min_degree, + max_nodes=max_nodes, ) return router diff --git a/lightrag/api/run_with_gunicorn.py b/lightrag/api/run_with_gunicorn.py index 065a12a1..cf902a8a 100644 --- a/lightrag/api/run_with_gunicorn.py +++ b/lightrag/api/run_with_gunicorn.py @@ -7,14 +7,9 @@ import os import sys import signal import pipmaster as pm -from lightrag.api.utils_api import parse_args, display_splash_screen, check_env_file +from lightrag.api.utils_api import display_splash_screen, check_env_file from lightrag.kg.shared_storage import initialize_share_data, finalize_share_data -from dotenv import load_dotenv - -# use the .env that is inside the current folder -# allows to use different .env file for each lightrag instance -# the OS environment variables take precedence over the .env file -load_dotenv(dotenv_path=".env", override=False) +from .config import global_args def check_and_install_dependencies(): @@ -59,20 +54,17 @@ def main(): signal.signal(signal.SIGINT, signal_handler) # Ctrl+C signal.signal(signal.SIGTERM, signal_handler) # kill command - # Parse all arguments using parse_args - args = parse_args(is_uvicorn_mode=False) - # Display startup information - display_splash_screen(args) + display_splash_screen(global_args) print("🚀 Starting LightRAG with Gunicorn") - print(f"🔄 Worker management: Gunicorn (workers={args.workers})") + print(f"🔄 Worker management: Gunicorn (workers={global_args.workers})") print("🔍 Preloading app: Enabled") print("📝 Note: Using Gunicorn's preload feature for shared data initialization") print("\n\n" + "=" * 80) print("MAIN PROCESS INITIALIZATION") print(f"Process ID: {os.getpid()}") - print(f"Workers setting: {args.workers}") + print(f"Workers setting: {global_args.workers}") print("=" * 80 + "\n") # Import Gunicorn's StandaloneApplication @@ -128,31 +120,43 @@ def main(): # Set configuration variables in gunicorn_config, prioritizing command line arguments gunicorn_config.workers = ( - args.workers if args.workers else int(os.getenv("WORKERS", 1)) + global_args.workers + if global_args.workers + else int(os.getenv("WORKERS", 1)) ) # Bind configuration prioritizes command line arguments - host = args.host if args.host != "0.0.0.0" else os.getenv("HOST", "0.0.0.0") - port = args.port if args.port != 9621 else int(os.getenv("PORT", 9621)) + host = ( + global_args.host + if global_args.host != "0.0.0.0" + else os.getenv("HOST", "0.0.0.0") + ) + port = ( + global_args.port + if global_args.port != 9621 + else int(os.getenv("PORT", 9621)) + ) gunicorn_config.bind = f"{host}:{port}" # Log level configuration prioritizes command line arguments gunicorn_config.loglevel = ( - args.log_level.lower() - if args.log_level + global_args.log_level.lower() + if global_args.log_level else os.getenv("LOG_LEVEL", "info") ) # Timeout configuration prioritizes command line arguments gunicorn_config.timeout = ( - args.timeout if args.timeout * 2 else int(os.getenv("TIMEOUT", 150 * 2)) + global_args.timeout + if global_args.timeout * 2 + else int(os.getenv("TIMEOUT", 150 * 2)) ) # Keepalive configuration gunicorn_config.keepalive = int(os.getenv("KEEPALIVE", 5)) # SSL configuration prioritizes command line arguments - if args.ssl or os.getenv("SSL", "").lower() in ( + if global_args.ssl or os.getenv("SSL", "").lower() in ( "true", "1", "yes", @@ -160,12 +164,14 @@ def main(): "on", ): gunicorn_config.certfile = ( - args.ssl_certfile - if args.ssl_certfile + global_args.ssl_certfile + if global_args.ssl_certfile else os.getenv("SSL_CERTFILE") ) gunicorn_config.keyfile = ( - args.ssl_keyfile if args.ssl_keyfile else os.getenv("SSL_KEYFILE") + global_args.ssl_keyfile + if global_args.ssl_keyfile + else os.getenv("SSL_KEYFILE") ) # Set configuration options from the module @@ -190,13 +196,13 @@ def main(): # Import the application from lightrag.api.lightrag_server import get_application - return get_application(args) + return get_application(global_args) # Create the application app = GunicornApp("") # Force workers to be an integer and greater than 1 for multi-process mode - workers_count = int(args.workers) + workers_count = int(global_args.workers) if workers_count > 1: # Set a flag to indicate we're in the main process os.environ["LIGHTRAG_MAIN_PROCESS"] = "1" diff --git a/lightrag/api/utils_api.py b/lightrag/api/utils_api.py index c01b7a37..ad75fd4e 100644 --- a/lightrag/api/utils_api.py +++ b/lightrag/api/utils_api.py @@ -7,15 +7,13 @@ import argparse from typing import Optional, List, Tuple import sys from ascii_colors import ASCIIColors -import logging from lightrag.api import __api_version__ as api_version from lightrag import __version__ as core_version from fastapi import HTTPException, Security, Request, status -from dotenv import load_dotenv from fastapi.security import APIKeyHeader, OAuth2PasswordBearer from starlette.status import HTTP_403_FORBIDDEN from .auth import auth_handler -from ..prompt import PROMPTS +from .config import ollama_server_infos, global_args def check_env_file(): @@ -36,16 +34,8 @@ def check_env_file(): return True -# use the .env that is inside the current folder -# allows to use different .env file for each lightrag instance -# the OS environment variables take precedence over the .env file -load_dotenv(dotenv_path=".env", override=False) - -global_args = {"main_args": None} - -# Get whitelist paths from environment variable, only once during initialization -default_whitelist = "/health,/api/*" -whitelist_paths = os.getenv("WHITELIST_PATHS", default_whitelist).split(",") +# Get whitelist paths from global_args, only once during initialization +whitelist_paths = global_args.whitelist_paths.split(",") # Pre-compile path matching patterns whitelist_patterns: List[Tuple[str, bool]] = [] @@ -63,19 +53,6 @@ for path in whitelist_paths: auth_configured = bool(auth_handler.accounts) -class OllamaServerInfos: - # Constants for emulated Ollama model information - LIGHTRAG_NAME = "lightrag" - LIGHTRAG_TAG = os.getenv("OLLAMA_EMULATING_MODEL_TAG", "latest") - LIGHTRAG_MODEL = f"{LIGHTRAG_NAME}:{LIGHTRAG_TAG}" - LIGHTRAG_SIZE = 7365960935 # it's a dummy value - LIGHTRAG_CREATED_AT = "2024-01-15T00:00:00Z" - LIGHTRAG_DIGEST = "sha256:lightrag" - - -ollama_server_infos = OllamaServerInfos() - - def get_combined_auth_dependency(api_key: Optional[str] = None): """ Create a combined authentication dependency that implements authentication logic @@ -186,299 +163,6 @@ def get_combined_auth_dependency(api_key: Optional[str] = None): return combined_dependency -class DefaultRAGStorageConfig: - KV_STORAGE = "JsonKVStorage" - VECTOR_STORAGE = "NanoVectorDBStorage" - GRAPH_STORAGE = "NetworkXStorage" - DOC_STATUS_STORAGE = "JsonDocStatusStorage" - - -def get_default_host(binding_type: str) -> str: - default_hosts = { - "ollama": os.getenv("LLM_BINDING_HOST", "http://localhost:11434"), - "lollms": os.getenv("LLM_BINDING_HOST", "http://localhost:9600"), - "azure_openai": os.getenv("AZURE_OPENAI_ENDPOINT", "https://api.openai.com/v1"), - "openai": os.getenv("LLM_BINDING_HOST", "https://api.openai.com/v1"), - } - return default_hosts.get( - binding_type, os.getenv("LLM_BINDING_HOST", "http://localhost:11434") - ) # fallback to ollama if unknown - - -def get_env_value(env_key: str, default: any, value_type: type = str) -> any: - """ - Get value from environment variable with type conversion - - Args: - env_key (str): Environment variable key - default (any): Default value if env variable is not set - value_type (type): Type to convert the value to - - Returns: - any: Converted value from environment or default - """ - value = os.getenv(env_key) - if value is None: - return default - - if value_type is bool: - return value.lower() in ("true", "1", "yes", "t", "on") - try: - return value_type(value) - except ValueError: - return default - - -def parse_args(is_uvicorn_mode: bool = False) -> argparse.Namespace: - """ - Parse command line arguments with environment variable fallback - - Args: - is_uvicorn_mode: Whether running under uvicorn mode - - Returns: - argparse.Namespace: Parsed arguments - """ - - parser = argparse.ArgumentParser( - description="LightRAG FastAPI Server with separate working and input directories" - ) - - # Server configuration - parser.add_argument( - "--host", - default=get_env_value("HOST", "0.0.0.0"), - help="Server host (default: from env or 0.0.0.0)", - ) - parser.add_argument( - "--port", - type=int, - default=get_env_value("PORT", 9621, int), - help="Server port (default: from env or 9621)", - ) - - # Directory configuration - parser.add_argument( - "--working-dir", - default=get_env_value("WORKING_DIR", "./rag_storage"), - help="Working directory for RAG storage (default: from env or ./rag_storage)", - ) - parser.add_argument( - "--input-dir", - default=get_env_value("INPUT_DIR", "./inputs"), - help="Directory containing input documents (default: from env or ./inputs)", - ) - - def timeout_type(value): - if value is None: - return 150 - if value is None or value == "None": - return None - return int(value) - - parser.add_argument( - "--timeout", - default=get_env_value("TIMEOUT", None, timeout_type), - type=timeout_type, - help="Timeout in seconds (useful when using slow AI). Use None for infinite timeout", - ) - - # RAG configuration - parser.add_argument( - "--max-async", - type=int, - default=get_env_value("MAX_ASYNC", 4, int), - help="Maximum async operations (default: from env or 4)", - ) - parser.add_argument( - "--max-tokens", - type=int, - default=get_env_value("MAX_TOKENS", 32768, int), - help="Maximum token size (default: from env or 32768)", - ) - - # Logging configuration - parser.add_argument( - "--log-level", - default=get_env_value("LOG_LEVEL", "INFO"), - choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"], - help="Logging level (default: from env or INFO)", - ) - parser.add_argument( - "--verbose", - action="store_true", - default=get_env_value("VERBOSE", False, bool), - help="Enable verbose debug output(only valid for DEBUG log-level)", - ) - - parser.add_argument( - "--key", - type=str, - default=get_env_value("LIGHTRAG_API_KEY", None), - help="API key for authentication. This protects lightrag server against unauthorized access", - ) - - # Optional https parameters - parser.add_argument( - "--ssl", - action="store_true", - default=get_env_value("SSL", False, bool), - help="Enable HTTPS (default: from env or False)", - ) - parser.add_argument( - "--ssl-certfile", - default=get_env_value("SSL_CERTFILE", None), - help="Path to SSL certificate file (required if --ssl is enabled)", - ) - parser.add_argument( - "--ssl-keyfile", - default=get_env_value("SSL_KEYFILE", None), - help="Path to SSL private key file (required if --ssl is enabled)", - ) - - parser.add_argument( - "--history-turns", - type=int, - default=get_env_value("HISTORY_TURNS", 3, int), - help="Number of conversation history turns to include (default: from env or 3)", - ) - - # Search parameters - parser.add_argument( - "--top-k", - type=int, - default=get_env_value("TOP_K", 60, int), - help="Number of most similar results to return (default: from env or 60)", - ) - parser.add_argument( - "--cosine-threshold", - type=float, - default=get_env_value("COSINE_THRESHOLD", 0.2, float), - help="Cosine similarity threshold (default: from env or 0.4)", - ) - - # Ollama model name - parser.add_argument( - "--simulated-model-name", - type=str, - default=get_env_value( - "SIMULATED_MODEL_NAME", ollama_server_infos.LIGHTRAG_MODEL - ), - help="Number of conversation history turns to include (default: from env or 3)", - ) - - # Namespace - parser.add_argument( - "--namespace-prefix", - type=str, - default=get_env_value("NAMESPACE_PREFIX", ""), - help="Prefix of the namespace", - ) - - parser.add_argument( - "--auto-scan-at-startup", - action="store_true", - default=False, - help="Enable automatic scanning when the program starts", - ) - - # Server workers configuration - parser.add_argument( - "--workers", - type=int, - default=get_env_value("WORKERS", 1, int), - help="Number of worker processes (default: from env or 1)", - ) - - # LLM and embedding bindings - parser.add_argument( - "--llm-binding", - type=str, - default=get_env_value("LLM_BINDING", "ollama"), - choices=["lollms", "ollama", "openai", "openai-ollama", "azure_openai"], - help="LLM binding type (default: from env or ollama)", - ) - parser.add_argument( - "--embedding-binding", - type=str, - default=get_env_value("EMBEDDING_BINDING", "ollama"), - choices=["lollms", "ollama", "openai", "azure_openai"], - help="Embedding binding type (default: from env or ollama)", - ) - - args = parser.parse_args() - - # If in uvicorn mode and workers > 1, force it to 1 and log warning - if is_uvicorn_mode and args.workers > 1: - original_workers = args.workers - args.workers = 1 - # Log warning directly here - logging.warning( - f"In uvicorn mode, workers parameter was set to {original_workers}. Forcing workers=1" - ) - - # convert relative path to absolute path - args.working_dir = os.path.abspath(args.working_dir) - args.input_dir = os.path.abspath(args.input_dir) - - # Inject storage configuration from environment variables - args.kv_storage = get_env_value( - "LIGHTRAG_KV_STORAGE", DefaultRAGStorageConfig.KV_STORAGE - ) - args.doc_status_storage = get_env_value( - "LIGHTRAG_DOC_STATUS_STORAGE", DefaultRAGStorageConfig.DOC_STATUS_STORAGE - ) - args.graph_storage = get_env_value( - "LIGHTRAG_GRAPH_STORAGE", DefaultRAGStorageConfig.GRAPH_STORAGE - ) - args.vector_storage = get_env_value( - "LIGHTRAG_VECTOR_STORAGE", DefaultRAGStorageConfig.VECTOR_STORAGE - ) - - # Get MAX_PARALLEL_INSERT from environment - args.max_parallel_insert = get_env_value("MAX_PARALLEL_INSERT", 2, int) - - # Handle openai-ollama special case - if args.llm_binding == "openai-ollama": - args.llm_binding = "openai" - args.embedding_binding = "ollama" - - args.llm_binding_host = get_env_value( - "LLM_BINDING_HOST", get_default_host(args.llm_binding) - ) - args.embedding_binding_host = get_env_value( - "EMBEDDING_BINDING_HOST", get_default_host(args.embedding_binding) - ) - args.llm_binding_api_key = get_env_value("LLM_BINDING_API_KEY", None) - args.embedding_binding_api_key = get_env_value("EMBEDDING_BINDING_API_KEY", "") - - # Inject model configuration - args.llm_model = get_env_value("LLM_MODEL", "mistral-nemo:latest") - args.embedding_model = get_env_value("EMBEDDING_MODEL", "bge-m3:latest") - args.embedding_dim = get_env_value("EMBEDDING_DIM", 1024, int) - args.max_embed_tokens = get_env_value("MAX_EMBED_TOKENS", 8192, int) - - # Inject chunk configuration - args.chunk_size = get_env_value("CHUNK_SIZE", 1200, int) - args.chunk_overlap_size = get_env_value("CHUNK_OVERLAP_SIZE", 100, int) - - # Inject LLM cache configuration - args.enable_llm_cache_for_extract = get_env_value( - "ENABLE_LLM_CACHE_FOR_EXTRACT", True, bool - ) - - # Inject LLM temperature configuration - args.temperature = get_env_value("TEMPERATURE", 0.5, float) - - # Select Document loading tool (DOCLING, DEFAULT) - args.document_loading_engine = get_env_value("DOCUMENT_LOADING_ENGINE", "DEFAULT") - - ollama_server_infos.LIGHTRAG_MODEL = args.simulated_model_name - - global_args["main_args"] = args - return args - - def display_splash_screen(args: argparse.Namespace) -> None: """ Display a colorful splash screen showing LightRAG server configuration @@ -489,7 +173,7 @@ def display_splash_screen(args: argparse.Namespace) -> None: # Banner ASCIIColors.cyan(f""" ╔══════════════════════════════════════════════════════════════╗ - ║ 🚀 LightRAG Server v{core_version}/{api_version} ║ + ║ 🚀 LightRAG Server v{core_version}/{api_version} ║ ║ Fast, Lightweight RAG Server Implementation ║ ╚══════════════════════════════════════════════════════════════╝ """) @@ -503,7 +187,7 @@ def display_splash_screen(args: argparse.Namespace) -> None: ASCIIColors.white(" ├─ Workers: ", end="") ASCIIColors.yellow(f"{args.workers}") ASCIIColors.white(" ├─ CORS Origins: ", end="") - ASCIIColors.yellow(f"{os.getenv('CORS_ORIGINS', '*')}") + ASCIIColors.yellow(f"{args.cors_origins}") ASCIIColors.white(" ├─ SSL Enabled: ", end="") ASCIIColors.yellow(f"{args.ssl}") if args.ssl: @@ -519,8 +203,10 @@ def display_splash_screen(args: argparse.Namespace) -> None: ASCIIColors.yellow(f"{args.verbose}") ASCIIColors.white(" ├─ History Turns: ", end="") ASCIIColors.yellow(f"{args.history_turns}") - ASCIIColors.white(" └─ API Key: ", end="") + ASCIIColors.white(" ├─ API Key: ", end="") ASCIIColors.yellow("Set" if args.key else "Not Set") + ASCIIColors.white(" └─ JWT Auth: ", end="") + ASCIIColors.yellow("Enabled" if args.auth_accounts else "Disabled") # Directory Configuration ASCIIColors.magenta("\n📂 Directory Configuration:") @@ -558,10 +244,9 @@ def display_splash_screen(args: argparse.Namespace) -> None: ASCIIColors.yellow(f"{args.embedding_dim}") # RAG Configuration - summary_language = os.getenv("SUMMARY_LANGUAGE", PROMPTS["DEFAULT_LANGUAGE"]) ASCIIColors.magenta("\n⚙️ RAG Configuration:") ASCIIColors.white(" ├─ Summary Language: ", end="") - ASCIIColors.yellow(f"{summary_language}") + ASCIIColors.yellow(f"{args.summary_language}") ASCIIColors.white(" ├─ Max Parallel Insert: ", end="") ASCIIColors.yellow(f"{args.max_parallel_insert}") ASCIIColors.white(" ├─ Max Embed Tokens: ", end="") @@ -595,19 +280,17 @@ def display_splash_screen(args: argparse.Namespace) -> None: protocol = "https" if args.ssl else "http" if args.host == "0.0.0.0": ASCIIColors.magenta("\n🌐 Server Access Information:") - ASCIIColors.white(" ├─ Local Access: ", end="") + ASCIIColors.white(" ├─ WebUI (local): ", end="") ASCIIColors.yellow(f"{protocol}://localhost:{args.port}") ASCIIColors.white(" ├─ Remote Access: ", end="") ASCIIColors.yellow(f"{protocol}://:{args.port}") ASCIIColors.white(" ├─ API Documentation (local): ", end="") ASCIIColors.yellow(f"{protocol}://localhost:{args.port}/docs") - ASCIIColors.white(" ├─ Alternative Documentation (local): ", end="") + ASCIIColors.white(" └─ Alternative Documentation (local): ", end="") ASCIIColors.yellow(f"{protocol}://localhost:{args.port}/redoc") - ASCIIColors.white(" └─ WebUI (local): ", end="") - ASCIIColors.yellow(f"{protocol}://localhost:{args.port}/webui") - ASCIIColors.yellow("\n📝 Note:") - ASCIIColors.white(""" Since the server is running on 0.0.0.0: + ASCIIColors.magenta("\n📝 Note:") + ASCIIColors.cyan(""" Since the server is running on 0.0.0.0: - Use 'localhost' or '127.0.0.1' for local access - Use your machine's IP address for remote access - To find your IP address: @@ -617,42 +300,24 @@ def display_splash_screen(args: argparse.Namespace) -> None: else: base_url = f"{protocol}://{args.host}:{args.port}" ASCIIColors.magenta("\n🌐 Server Access Information:") - ASCIIColors.white(" ├─ Base URL: ", end="") + ASCIIColors.white(" ├─ WebUI (local): ", end="") ASCIIColors.yellow(f"{base_url}") ASCIIColors.white(" ├─ API Documentation: ", end="") ASCIIColors.yellow(f"{base_url}/docs") ASCIIColors.white(" └─ Alternative Documentation: ", end="") ASCIIColors.yellow(f"{base_url}/redoc") - # Usage Examples - ASCIIColors.magenta("\n📚 Quick Start Guide:") - ASCIIColors.cyan(""" - 1. Access the Swagger UI: - Open your browser and navigate to the API documentation URL above - - 2. API Authentication:""") - if args.key: - ASCIIColors.cyan(""" Add the following header to your requests: - X-API-Key: - """) - else: - ASCIIColors.cyan(" No authentication required\n") - - ASCIIColors.cyan(""" 3. Basic Operations: - - POST /upload_document: Upload new documents to RAG - - POST /query: Query your document collection - - 4. Monitor the server: - - Check server logs for detailed operation information - - Use healthcheck endpoint: GET /health - """) - # Security Notice if args.key: ASCIIColors.yellow("\n⚠️ Security Notice:") ASCIIColors.white(""" API Key authentication is enabled. Make sure to include the X-API-Key header in all your requests. """) + if args.auth_accounts: + ASCIIColors.yellow("\n⚠️ Security Notice:") + ASCIIColors.white(""" JWT authentication is enabled. + Make sure to login before making the request, and include the 'Authorization' in the header. + """) # Ensure splash output flush to system log sys.stdout.flush() diff --git a/lightrag/api/webui/assets/index-CD5HxTy1.css b/lightrag/api/webui/assets/index-CD5HxTy1.css deleted file mode 100644 index a0ab321b..00000000 --- a/lightrag/api/webui/assets/index-CD5HxTy1.css +++ /dev/null @@ -1 +0,0 @@ -/*! tailwindcss v4.0.8 | MIT License | https://tailwindcss.com */@layer theme{:root,:host{--font-sans:ui-sans-serif,system-ui,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";--font-mono:ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;--color-red-100:oklch(.936 .032 17.717);--color-red-400:oklch(.704 .191 22.216);--color-red-500:oklch(.637 .237 25.331);--color-red-600:oklch(.577 .245 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\`/\`\` and will default to "${Wd}"`),null):e}function h9(e,t){let n,r,a,o,s;if(u9(e)){let u=e.getAttribute("action");r=u?Ba(u,t):null,n=e.getAttribute("method")||Vd,a=em(e.getAttribute("enctype"))||Wd,o=new FormData(e)}else if(l9(e)||c9(e)&&(e.type==="submit"||e.type==="image")){let u=e.form;if(u==null)throw new Error('Cannot submit a + +
+ {t('documentPanel.clearDocuments.warning')} +
+
+ {t('documentPanel.clearDocuments.confirm')} +
+ +
+
+ + ) => setConfirmText(e.target.value)} + placeholder={t('documentPanel.clearDocuments.confirmPlaceholder')} + className="w-full" + /> +
+ +
+ setClearCacheOption(checked === true)} + /> + +
+
+ + + + + ) diff --git a/lightrag_webui/src/components/documents/UploadDocumentsDialog.tsx b/lightrag_webui/src/components/documents/UploadDocumentsDialog.tsx index 977c4030..5785a7d3 100644 --- a/lightrag_webui/src/components/documents/UploadDocumentsDialog.tsx +++ b/lightrag_webui/src/components/documents/UploadDocumentsDialog.tsx @@ -17,7 +17,11 @@ import { uploadDocument } from '@/api/lightrag' import { UploadIcon } from 'lucide-react' import { useTranslation } from 'react-i18next' -export default function UploadDocumentsDialog() { +interface UploadDocumentsDialogProps { + onDocumentsUploaded?: () => Promise +} + +export default function UploadDocumentsDialog({ onDocumentsUploaded }: UploadDocumentsDialogProps) { const { t } = useTranslation() const [open, setOpen] = useState(false) const [isUploading, setIsUploading] = useState(false) @@ -55,6 +59,7 @@ export default function UploadDocumentsDialog() { const handleDocumentsUpload = useCallback( async (filesToUpload: File[]) => { setIsUploading(true) + let hasSuccessfulUpload = false // Only clear errors for files that are being uploaded, keep errors for rejected files setFileErrors(prev => { @@ -101,6 +106,9 @@ export default function UploadDocumentsDialog() { ...prev, [file.name]: result.message })) + } else { + // Mark that we had at least one successful upload + hasSuccessfulUpload = true } } catch (err) { console.error(`Upload failed for ${file.name}:`, err) @@ -142,6 +150,16 @@ export default function UploadDocumentsDialog() { } else { toast.success(t('documentPanel.uploadDocuments.batch.success'), { id: toastId }) } + + // Only update if at least one file was uploaded successfully + if (hasSuccessfulUpload) { + // Refresh document list + if (onDocumentsUploaded) { + onDocumentsUploaded().catch(err => { + console.error('Error refreshing documents:', err) + }) + } + } } catch (err) { console.error('Unexpected error during upload:', err) toast.error(t('documentPanel.uploadDocuments.generalError', { error: errorMessage(err) }), { id: toastId }) @@ -149,7 +167,7 @@ export default function UploadDocumentsDialog() { setIsUploading(false) } }, - [setIsUploading, setProgresses, setFileErrors, t] + [setIsUploading, setProgresses, setFileErrors, t, onDocumentsUploaded] ) return ( diff --git a/lightrag_webui/src/components/graph/GraphControl.tsx b/lightrag_webui/src/components/graph/GraphControl.tsx index baa98bfe..aca8a9c4 100644 --- a/lightrag_webui/src/components/graph/GraphControl.tsx +++ b/lightrag_webui/src/components/graph/GraphControl.tsx @@ -36,6 +36,8 @@ const GraphControl = ({ disableHoverEffect }: { disableHoverEffect?: boolean }) const enableEdgeEvents = useSettingsStore.use.enableEdgeEvents() const renderEdgeLabels = useSettingsStore.use.showEdgeLabel() const renderLabels = useSettingsStore.use.showNodeLabel() + const minEdgeSize = useSettingsStore.use.minEdgeSize() + const maxEdgeSize = useSettingsStore.use.maxEdgeSize() const selectedNode = useGraphStore.use.selectedNode() const focusedNode = useGraphStore.use.focusedNode() const selectedEdge = useGraphStore.use.selectedEdge() @@ -136,6 +138,51 @@ const GraphControl = ({ disableHoverEffect }: { disableHoverEffect?: boolean }) registerEvents(events) }, [registerEvents, enableEdgeEvents]) + /** + * When edge size settings change, recalculate edge sizes and refresh the sigma instance + * to ensure changes take effect immediately + */ + useEffect(() => { + if (sigma && sigmaGraph) { + // Get the graph from sigma + const graph = sigma.getGraph() + + // Find min and max weight values + let minWeight = Number.MAX_SAFE_INTEGER + let maxWeight = 0 + + graph.forEachEdge(edge => { + // Get original weight (before scaling) + const weight = graph.getEdgeAttribute(edge, 'originalWeight') || 1 + if (typeof weight === 'number') { + minWeight = Math.min(minWeight, weight) + maxWeight = Math.max(maxWeight, weight) + } + }) + + // Scale edge sizes based on weight range and current min/max edge size settings + const weightRange = maxWeight - minWeight + if (weightRange > 0) { + const sizeScale = maxEdgeSize - minEdgeSize + graph.forEachEdge(edge => { + const weight = graph.getEdgeAttribute(edge, 'originalWeight') || 1 + if (typeof weight === 'number') { + const scaledSize = minEdgeSize + sizeScale * Math.pow((weight - minWeight) / weightRange, 0.5) + graph.setEdgeAttribute(edge, 'size', scaledSize) + } + }) + } else { + // If all weights are the same, use default size + graph.forEachEdge(edge => { + graph.setEdgeAttribute(edge, 'size', minEdgeSize) + }) + } + + // Refresh the sigma instance to apply changes + sigma.refresh() + } + }, [sigma, sigmaGraph, minEdgeSize, maxEdgeSize]) + /** * When component mount or hovered node change * => Setting the sigma reducers diff --git a/lightrag_webui/src/components/graph/GraphLabels.tsx b/lightrag_webui/src/components/graph/GraphLabels.tsx index b89c2e74..f43bd955 100644 --- a/lightrag_webui/src/components/graph/GraphLabels.tsx +++ b/lightrag_webui/src/components/graph/GraphLabels.tsx @@ -1,4 +1,4 @@ -import { useCallback } from 'react' +import { useCallback, useEffect } from 'react' import { AsyncSelect } from '@/components/ui/AsyncSelect' import { useSettingsStore } from '@/stores/settings' import { useGraphStore } from '@/stores/graph' @@ -56,6 +56,23 @@ const GraphLabels = () => { [getSearchEngine] ) + // Validate if current queryLabel exists in allDatabaseLabels + useEffect(() => { + // Only update label when all conditions are met: + // 1. allDatabaseLabels is loaded (length > 1, as it has at least '*' by default) + // 2. Current label is not the default '*' + // 3. Current label doesn't exist in allDatabaseLabels + if ( + allDatabaseLabels.length > 1 && + label && + label !== '*' && + !allDatabaseLabels.includes(label) + ) { + console.log(`Label "${label}" not found in available labels, resetting to default`); + useSettingsStore.getState().setQueryLabel('*'); + } + }, [allDatabaseLabels, label]); + const handleRefresh = useCallback(() => { // Reset fetch status flags useGraphStore.getState().setLabelsFetchAttempted(false) diff --git a/lightrag_webui/src/components/graph/Legend.tsx b/lightrag_webui/src/components/graph/Legend.tsx new file mode 100644 index 00000000..b0b59de8 --- /dev/null +++ b/lightrag_webui/src/components/graph/Legend.tsx @@ -0,0 +1,41 @@ +import React from 'react' +import { useTranslation } from 'react-i18next' +import { useGraphStore } from '@/stores/graph' +import { Card } from '@/components/ui/Card' +import { ScrollArea } from '@/components/ui/ScrollArea' + +interface LegendProps { + className?: string +} + +const Legend: React.FC = ({ className }) => { + const { t } = useTranslation() + const typeColorMap = useGraphStore.use.typeColorMap() + + if (!typeColorMap || typeColorMap.size === 0) { + return null + } + + return ( + +

{t('graphPanel.legend')}

+ +
+ {Array.from(typeColorMap.entries()).map(([type, color]) => ( +
+
+ + {type} + +
+ ))} +
+ + + ) +} + +export default Legend diff --git a/lightrag_webui/src/components/graph/LegendButton.tsx b/lightrag_webui/src/components/graph/LegendButton.tsx new file mode 100644 index 00000000..cf036721 --- /dev/null +++ b/lightrag_webui/src/components/graph/LegendButton.tsx @@ -0,0 +1,32 @@ +import { useCallback } from 'react' +import { BookOpenIcon } from 'lucide-react' +import Button from '@/components/ui/Button' +import { controlButtonVariant } from '@/lib/constants' +import { useSettingsStore } from '@/stores/settings' +import { useTranslation } from 'react-i18next' + +/** + * Component that toggles legend visibility. + */ +const LegendButton = () => { + const { t } = useTranslation() + const showLegend = useSettingsStore.use.showLegend() + const setShowLegend = useSettingsStore.use.setShowLegend() + + const toggleLegend = useCallback(() => { + setShowLegend(!showLegend) + }, [showLegend, setShowLegend]) + + return ( + + ) +} + +export default LegendButton diff --git a/lightrag_webui/src/components/graph/Settings.tsx b/lightrag_webui/src/components/graph/Settings.tsx index 1989a01e..af585b83 100644 --- a/lightrag_webui/src/components/graph/Settings.tsx +++ b/lightrag_webui/src/components/graph/Settings.tsx @@ -8,7 +8,7 @@ import Input from '@/components/ui/Input' import { controlButtonVariant } from '@/lib/constants' import { useSettingsStore } from '@/stores/settings' -import { SettingsIcon } from 'lucide-react' +import { SettingsIcon, Undo2 } from 'lucide-react' import { useTranslation } from 'react-i18next'; /** @@ -44,14 +44,17 @@ const LabeledNumberInput = ({ onEditFinished, label, min, - max + max, + defaultValue }: { value: number onEditFinished: (value: number) => void label: string min: number max?: number + defaultValue?: number }) => { + const { t } = useTranslation(); const [currentValue, setCurrentValue] = useState(value) const onValueChange = useCallback( @@ -81,6 +84,13 @@ const LabeledNumberInput = ({ } }, [value, currentValue, onEditFinished]) + const handleReset = useCallback(() => { + if (defaultValue !== undefined && value !== defaultValue) { + setCurrentValue(defaultValue) + onEditFinished(defaultValue) + } + }, [defaultValue, value, onEditFinished]) + return (
- { - if (e.key === 'Enter') { - onBlur() - } - }} - /> +
+ { + if (e.key === 'Enter') { + onBlur() + } + }} + /> + {defaultValue !== undefined && ( + + )} +
) } @@ -120,8 +144,10 @@ export default function Settings() { const enableNodeDrag = useSettingsStore.use.enableNodeDrag() const enableHideUnselectedEdges = useSettingsStore.use.enableHideUnselectedEdges() const showEdgeLabel = useSettingsStore.use.showEdgeLabel() + const minEdgeSize = useSettingsStore.use.minEdgeSize() + const maxEdgeSize = useSettingsStore.use.maxEdgeSize() const graphQueryMaxDepth = useSettingsStore.use.graphQueryMaxDepth() - const graphMinDegree = useSettingsStore.use.graphMinDegree() + const graphMaxNodes = useSettingsStore.use.graphMaxNodes() const graphLayoutMaxIterations = useSettingsStore.use.graphLayoutMaxIterations() const enableHealthCheck = useSettingsStore.use.enableHealthCheck() @@ -180,15 +206,14 @@ export default function Settings() { }, 300) }, []) - const setGraphMinDegree = useCallback((degree: number) => { - if (degree < 0) return - useSettingsStore.setState({ graphMinDegree: degree }) + const setGraphMaxNodes = useCallback((nodes: number) => { + if (nodes < 1 || nodes > 1000) return + useSettingsStore.setState({ graphMaxNodes: nodes }) const currentLabel = useSettingsStore.getState().queryLabel useSettingsStore.getState().setQueryLabel('') setTimeout(() => { useSettingsStore.getState().setQueryLabel(currentLabel) }, 300) - }, []) const setGraphLayoutMaxIterations = useCallback((iterations: number) => { @@ -269,24 +294,75 @@ export default function Settings() { label={t('graphPanel.sideBar.settings.edgeEvents')} /> +
+ +
+ { + const newValue = Number(e.target.value); + if (!isNaN(newValue) && newValue >= 1 && newValue <= maxEdgeSize) { + useSettingsStore.setState({ minEdgeSize: newValue }); + } + }} + className="h-6 w-16 min-w-0 pr-1" + min={1} + max={Math.min(maxEdgeSize, 10)} + /> + - +
+ { + const newValue = Number(e.target.value); + if (!isNaN(newValue) && newValue >= minEdgeSize && newValue >= 1 && newValue <= 10) { + useSettingsStore.setState({ maxEdgeSize: newValue }); + } + }} + className="h-6 w-16 min-w-0 pr-1" + min={minEdgeSize} + max={10} + /> + +
+
+
+ diff --git a/lightrag_webui/src/components/graph/SettingsDisplay.tsx b/lightrag_webui/src/components/graph/SettingsDisplay.tsx index dec44c11..93fc0e01 100644 --- a/lightrag_webui/src/components/graph/SettingsDisplay.tsx +++ b/lightrag_webui/src/components/graph/SettingsDisplay.tsx @@ -8,12 +8,12 @@ import { useTranslation } from 'react-i18next' const SettingsDisplay = () => { const { t } = useTranslation() const graphQueryMaxDepth = useSettingsStore.use.graphQueryMaxDepth() - const graphMinDegree = useSettingsStore.use.graphMinDegree() + const graphMaxNodes = useSettingsStore.use.graphMaxNodes() return (
{t('graphPanel.sideBar.settings.depth')}: {graphQueryMaxDepth}
-
{t('graphPanel.sideBar.settings.degree')}: {graphMinDegree}
+
{t('graphPanel.sideBar.settings.max')}: {graphMaxNodes}
) } diff --git a/lightrag_webui/src/components/status/StatusCard.tsx b/lightrag_webui/src/components/status/StatusCard.tsx index e67cbd30..c9e64db9 100644 --- a/lightrag_webui/src/components/status/StatusCard.tsx +++ b/lightrag_webui/src/components/status/StatusCard.tsx @@ -4,14 +4,14 @@ import { useTranslation } from 'react-i18next' const StatusCard = ({ status }: { status: LightragStatus | null }) => { const { t } = useTranslation() if (!status) { - return
{t('graphPanel.statusCard.unavailable')}
+ return
{t('graphPanel.statusCard.unavailable')}
} return ( -
+

{t('graphPanel.statusCard.storageInfo')}

-
+
{t('graphPanel.statusCard.workingDirectory')}: {status.working_directory} {t('graphPanel.statusCard.inputDirectory')}: @@ -21,7 +21,7 @@ const StatusCard = ({ status }: { status: LightragStatus | null }) => {

{t('graphPanel.statusCard.llmConfig')}

-
+
{t('graphPanel.statusCard.llmBinding')}: {status.configuration.llm_binding} {t('graphPanel.statusCard.llmBindingHost')}: @@ -35,7 +35,7 @@ const StatusCard = ({ status }: { status: LightragStatus | null }) => {

{t('graphPanel.statusCard.embeddingConfig')}

-
+
{t('graphPanel.statusCard.embeddingBinding')}: {status.configuration.embedding_binding} {t('graphPanel.statusCard.embeddingBindingHost')}: @@ -47,7 +47,7 @@ const StatusCard = ({ status }: { status: LightragStatus | null }) => {

{t('graphPanel.statusCard.storageConfig')}

-
+
{t('graphPanel.statusCard.kvStorage')}: {status.configuration.kv_storage} {t('graphPanel.statusCard.docStatusStorage')}: diff --git a/lightrag_webui/src/components/status/StatusDialog.tsx b/lightrag_webui/src/components/status/StatusDialog.tsx new file mode 100644 index 00000000..48eaa4f7 --- /dev/null +++ b/lightrag_webui/src/components/status/StatusDialog.tsx @@ -0,0 +1,32 @@ +import { LightragStatus } from '@/api/lightrag' +import { useTranslation } from 'react-i18next' +import { + Dialog, + DialogContent, + DialogHeader, + DialogTitle, +} from '@/components/ui/Dialog' +import StatusCard from './StatusCard' + +interface StatusDialogProps { + open: boolean + onOpenChange: (open: boolean) => void + status: LightragStatus | null +} + +const StatusDialog = ({ open, onOpenChange, status }: StatusDialogProps) => { + const { t } = useTranslation() + + return ( + + + + {t('graphPanel.statusDialog.title')} + + + + + ) +} + +export default StatusDialog diff --git a/lightrag_webui/src/components/status/StatusIndicator.tsx b/lightrag_webui/src/components/status/StatusIndicator.tsx index 263bb99e..5a9fc751 100644 --- a/lightrag_webui/src/components/status/StatusIndicator.tsx +++ b/lightrag_webui/src/components/status/StatusIndicator.tsx @@ -1,8 +1,7 @@ import { cn } from '@/lib/utils' import { useBackendState } from '@/stores/state' import { useEffect, useState } from 'react' -import { Popover, PopoverContent, PopoverTrigger } from '@/components/ui/Popover' -import StatusCard from '@/components/status/StatusCard' +import StatusDialog from './StatusDialog' import { useTranslation } from 'react-i18next' const StatusIndicator = () => { @@ -11,6 +10,7 @@ const StatusIndicator = () => { const lastCheckTime = useBackendState.use.lastCheckTime() const status = useBackendState.use.status() const [animate, setAnimate] = useState(false) + const [dialogOpen, setDialogOpen] = useState(false) // listen to health change useEffect(() => { @@ -21,28 +21,30 @@ const StatusIndicator = () => { return (
- - -
-
- - {health ? t('graphPanel.statusIndicator.connected') : t('graphPanel.statusIndicator.disconnected')} - -
- - - - - +
setDialogOpen(true)} + > +
+ + {health ? t('graphPanel.statusIndicator.connected') : t('graphPanel.statusIndicator.disconnected')} + +
+ +
) } diff --git a/lightrag_webui/src/components/ui/Checkbox.tsx b/lightrag_webui/src/components/ui/Checkbox.tsx index 36ebe6e0..c9d4fafe 100644 --- a/lightrag_webui/src/components/ui/Checkbox.tsx +++ b/lightrag_webui/src/components/ui/Checkbox.tsx @@ -11,7 +11,7 @@ const Checkbox = React.forwardRef< >( { // Check if file_path exists and is a non-empty string if (!doc.file_path || typeof doc.file_path !== 'string' || doc.file_path.trim() === '') { @@ -148,6 +150,10 @@ export default function DocumentManager() { const [sortField, setSortField] = useState('updated_at') const [sortDirection, setSortDirection] = useState('desc') + // State for document status filter + const [statusFilter, setStatusFilter] = useState('all'); + + // Handle sort column click const handleSort = (field: SortField) => { if (sortField === field) { @@ -161,7 +167,7 @@ export default function DocumentManager() { } // Sort documents based on current sort field and direction - const sortDocuments = (documents: DocStatusResponse[]) => { + const sortDocuments = useCallback((documents: DocStatusResponse[]) => { return [...documents].sort((a, b) => { let valueA, valueB; @@ -188,7 +194,50 @@ export default function DocumentManager() { return sortMultiplier * (valueA > valueB ? 1 : valueA < valueB ? -1 : 0); } }); - } + }, [sortField, sortDirection, showFileName]); + + const filteredAndSortedDocs = useMemo(() => { + if (!docs) return null; + + let filteredDocs = { ...docs }; + + if (statusFilter !== 'all') { + filteredDocs = { + ...docs, + statuses: { + pending: [], + processing: [], + processed: [], + failed: [], + [statusFilter]: docs.statuses[statusFilter] || [] + } + }; + } + + if (!sortField || !sortDirection) return filteredDocs; + + const sortedStatuses = Object.entries(filteredDocs.statuses).reduce((acc, [status, documents]) => { + const sortedDocuments = sortDocuments(documents); + acc[status as DocStatus] = sortedDocuments; + return acc; + }, {} as DocsStatusesResponse['statuses']); + + return { ...filteredDocs, statuses: sortedStatuses }; + }, [docs, sortField, sortDirection, statusFilter, sortDocuments]); + + // Calculate document counts for each status + const documentCounts = useMemo(() => { + if (!docs) return { all: 0 } as Record; + + const counts: Record = { all: 0 }; + + Object.entries(docs.statuses).forEach(([status, documents]) => { + counts[status as DocStatus] = documents.length; + counts.all += documents.length; + }); + + return counts; + }, [docs]); // Store previous status counts const prevStatusCounts = useRef({ @@ -386,8 +435,8 @@ export default function DocumentManager() {
- - + +
{t('documentPanel.documentManager.uploadedTitle')} +
+ +
+ + + + + +
+
{t('documentPanel.documentManager.fileNameLabel')}
diff --git a/lightrag_webui/src/features/GraphViewer.tsx b/lightrag_webui/src/features/GraphViewer.tsx index 9d46af55..754101a7 100644 --- a/lightrag_webui/src/features/GraphViewer.tsx +++ b/lightrag_webui/src/features/GraphViewer.tsx @@ -18,6 +18,8 @@ import GraphSearch from '@/components/graph/GraphSearch' import GraphLabels from '@/components/graph/GraphLabels' import PropertiesView from '@/components/graph/PropertiesView' import SettingsDisplay from '@/components/graph/SettingsDisplay' +import Legend from '@/components/graph/Legend' +import LegendButton from '@/components/graph/LegendButton' import { useSettingsStore } from '@/stores/settings' import { useGraphStore } from '@/stores/graph' @@ -116,6 +118,7 @@ const GraphViewer = () => { const showPropertyPanel = useSettingsStore.use.showPropertyPanel() const showNodeSearchBar = useSettingsStore.use.showNodeSearchBar() const enableNodeDrag = useSettingsStore.use.enableNodeDrag() + const showLegend = useSettingsStore.use.showLegend() // Initialize sigma settings once on component mount // All dynamic settings will be updated in GraphControl using useSetSettings @@ -195,6 +198,7 @@ const GraphViewer = () => { + {/* */}
@@ -205,6 +209,12 @@ const GraphViewer = () => {
)} + {showLegend && ( +
+ +
+ )} + {/*
*/} diff --git a/lightrag_webui/src/features/LoginPage.tsx b/lightrag_webui/src/features/LoginPage.tsx index 847a4c9e..fc60370c 100644 --- a/lightrag_webui/src/features/LoginPage.tsx +++ b/lightrag_webui/src/features/LoginPage.tsx @@ -51,7 +51,7 @@ const LoginPage = () => { if (!status.auth_configured && status.access_token) { // If auth is not configured, use the guest token and redirect - login(status.access_token, true, status.core_version, status.api_version) + login(status.access_token, true, status.core_version, status.api_version, status.webui_title || null, status.webui_description || null) if (status.message) { toast.info(status.message) } @@ -96,7 +96,7 @@ const LoginPage = () => { // Check authentication mode const isGuestMode = response.auth_mode === 'disabled' - login(response.access_token, isGuestMode, response.core_version, response.api_version) + login(response.access_token, isGuestMode, response.core_version, response.api_version, response.webui_title || null, response.webui_description || null) // Set session flag for version check if (response.core_version || response.api_version) { diff --git a/lightrag_webui/src/features/SiteHeader.tsx b/lightrag_webui/src/features/SiteHeader.tsx index 8077d390..4881e4b6 100644 --- a/lightrag_webui/src/features/SiteHeader.tsx +++ b/lightrag_webui/src/features/SiteHeader.tsx @@ -8,6 +8,7 @@ import { cn } from '@/lib/utils' import { useTranslation } from 'react-i18next' import { navigationService } from '@/services/navigation' import { ZapIcon, GithubIcon, LogOutIcon } from 'lucide-react' +import { Tooltip, TooltipContent, TooltipProvider, TooltipTrigger } from '@/components/ui/Tooltip' interface NavigationTabProps { value: string @@ -55,7 +56,7 @@ function TabsNavigation() { export default function SiteHeader() { const { t } = useTranslation() - const { isGuestMode, coreVersion, apiVersion, username } = useAuthStore() + const { isGuestMode, coreVersion, apiVersion, username, webuiTitle, webuiDescription } = useAuthStore() const versionDisplay = (coreVersion && apiVersion) ? `${coreVersion}/${apiVersion}` @@ -67,17 +68,31 @@ export default function SiteHeader() { return (
-
+
+ {webuiTitle && ( +
+ | + + + + + {webuiTitle} + + + {webuiDescription && ( + + {webuiDescription} + + )} + + +
+ )}
@@ -91,6 +106,11 @@ export default function SiteHeader() {