""" LightRAG FastAPI Server """ from fastapi import FastAPI, Depends, HTTPException, Request from fastapi.exceptions import RequestValidationError from fastapi.responses import JSONResponse import os import logging import logging.config import signal import sys import uvicorn import pipmaster as pm import inspect from fastapi.staticfiles import StaticFiles from fastapi.responses import RedirectResponse from pathlib import Path import configparser from ascii_colors import ASCIIColors from fastapi.middleware.cors import CORSMiddleware from contextlib import asynccontextmanager from dotenv import load_dotenv from lightrag.api.utils_api import ( get_combined_auth_dependency, display_splash_screen, check_env_file, ) from .config import ( global_args, update_uvicorn_mode_config, get_default_host, ) from lightrag.utils import get_env_value from lightrag import LightRAG, __version__ as core_version from lightrag.api import __api_version__ from lightrag.types import GPTKeywordExtractionFormat from lightrag.utils import EmbeddingFunc from lightrag.constants import ( DEFAULT_LOG_MAX_BYTES, DEFAULT_LOG_BACKUP_COUNT, DEFAULT_LOG_FILENAME, DEFAULT_LLM_TIMEOUT, DEFAULT_EMBEDDING_TIMEOUT, ) from lightrag.api.routers.document_routes import ( DocumentManager, create_document_routes, ) from lightrag.api.routers.query_routes import create_query_routes from lightrag.api.routers.graph_routes import create_graph_routes from lightrag.api.routers.ollama_api import OllamaAPI from lightrag.utils import logger, set_verbose_debug from lightrag.kg.shared_storage import ( get_namespace_data, initialize_pipeline_status, cleanup_keyed_lock, finalize_share_data, ) from fastapi.security import OAuth2PasswordRequestForm from lightrag.api.auth import auth_handler # 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) webui_title = os.getenv("WEBUI_TITLE") webui_description = os.getenv("WEBUI_DESCRIPTION") # Initialize config parser config = configparser.ConfigParser() config.read("config.ini") # Global authentication configuration auth_configured = bool(auth_handler.accounts) def setup_signal_handlers(): """Setup signal handlers for graceful shutdown""" def signal_handler(sig, frame): print(f"\n\nReceived signal {sig}, shutting down gracefully...") print(f"Process ID: {os.getpid()}") # Release shared resources finalize_share_data() # Exit with success status sys.exit(0) # Register signal handlers signal.signal(signal.SIGINT, signal_handler) # Ctrl+C signal.signal(signal.SIGTERM, signal_handler) # kill command class LLMConfigCache: """Smart LLM and Embedding configuration cache class""" def __init__(self, args): self.args = args # Initialize configurations based on binding conditions self.openai_llm_options = None self.ollama_llm_options = None self.ollama_embedding_options = None # Only initialize and log OpenAI options when using OpenAI-related bindings if args.llm_binding in ["openai", "azure_openai"]: from lightrag.llm.binding_options import OpenAILLMOptions self.openai_llm_options = OpenAILLMOptions.options_dict(args) logger.info(f"OpenAI LLM Options: {self.openai_llm_options}") # Only initialize and log Ollama LLM options when using Ollama LLM binding if args.llm_binding == "ollama": try: from lightrag.llm.binding_options import OllamaLLMOptions self.ollama_llm_options = OllamaLLMOptions.options_dict(args) logger.info(f"Ollama LLM Options: {self.ollama_llm_options}") except ImportError: logger.warning( "OllamaLLMOptions not available, using default configuration" ) self.ollama_llm_options = {} # Only initialize and log Ollama Embedding options when using Ollama Embedding binding if args.embedding_binding == "ollama": try: from lightrag.llm.binding_options import OllamaEmbeddingOptions self.ollama_embedding_options = OllamaEmbeddingOptions.options_dict( args ) logger.info( f"Ollama Embedding Options: {self.ollama_embedding_options}" ) except ImportError: logger.warning( "OllamaEmbeddingOptions not available, using default configuration" ) self.ollama_embedding_options = {} def check_frontend_build(): """Check if frontend is built and optionally check if source is up-to-date""" webui_dir = Path(__file__).parent / "webui" index_html = webui_dir / "index.html" # 1. Check if build files exist (required) if not index_html.exists(): ASCIIColors.red("\n" + "=" * 80) ASCIIColors.red("ERROR: Frontend Not Built") ASCIIColors.red("=" * 80) ASCIIColors.yellow("The WebUI frontend has not been built yet.") ASCIIColors.yellow( "Please build the frontend code first using the following commands:\n" ) ASCIIColors.cyan(" cd lightrag_webui") ASCIIColors.cyan(" bun install --frozen-lockfile") ASCIIColors.cyan(" bun run build") ASCIIColors.cyan(" cd ..") ASCIIColors.yellow("\nThen restart the service.\n") ASCIIColors.cyan( "Note: Make sure you have Bun installed. Visit https://bun.sh for installation." ) ASCIIColors.red("=" * 80 + "\n") sys.exit(1) # Exit immediately # 2. Check if this is a development environment (source directory exists) try: source_dir = Path(__file__).parent.parent.parent / "lightrag_webui" src_dir = source_dir / "src" # Determine if this is a development environment: source directory exists and contains src directory if not source_dir.exists() or not src_dir.exists(): # Production environment, skip source code check logger.debug( "Production environment detected, skipping source freshness check" ) return # Development environment, perform source code timestamp check logger.debug("Development environment detected, checking source freshness") # Source code file extensions (files to check) source_extensions = { ".ts", ".tsx", ".js", ".jsx", ".mjs", ".cjs", # TypeScript/JavaScript ".css", ".scss", ".sass", ".less", # Style files ".json", ".jsonc", # Configuration/data files ".html", ".htm", # Template files ".md", ".mdx", # Markdown } # Key configuration files (in lightrag_webui root directory) key_files = [ source_dir / "package.json", source_dir / "bun.lock", source_dir / "vite.config.ts", source_dir / "tsconfig.json", source_dir / "tailwind.config.js", source_dir / "index.html", ] # Get the latest modification time of source code latest_source_time = 0 # Check source code files in src directory for file_path in src_dir.rglob("*"): if file_path.is_file(): # Only check source code files, ignore temporary files and logs if file_path.suffix.lower() in source_extensions: mtime = file_path.stat().st_mtime latest_source_time = max(latest_source_time, mtime) # Check key configuration files for key_file in key_files: if key_file.exists(): mtime = key_file.stat().st_mtime latest_source_time = max(latest_source_time, mtime) # Get build time build_time = index_html.stat().st_mtime # Compare timestamps (5 second tolerance to avoid file system time precision issues) if latest_source_time > build_time + 5: ASCIIColors.yellow("\n" + "=" * 80) ASCIIColors.yellow("WARNING: Frontend Source Code Has Been Updated") ASCIIColors.yellow("=" * 80) ASCIIColors.yellow( "The frontend source code is newer than the current build." ) ASCIIColors.yellow( "This might happen after 'git pull' or manual code changes.\n" ) ASCIIColors.cyan( "Recommended: Rebuild the frontend to use the latest changes:" ) ASCIIColors.cyan(" cd lightrag_webui") ASCIIColors.cyan(" bun install --frozen-lockfile") ASCIIColors.cyan(" bun run build") ASCIIColors.cyan(" cd ..") ASCIIColors.yellow("\nThe server will continue with the current build.") ASCIIColors.yellow("=" * 80 + "\n") else: logger.info("Frontend build is up-to-date") except Exception as e: # If check fails, log warning but don't affect startup logger.warning(f"Failed to check frontend source freshness: {e}") def create_app(args): # Check frontend build first check_frontend_build() # Setup logging logger.setLevel(args.log_level) set_verbose_debug(args.verbose) # Create configuration cache (this will output configuration logs) config_cache = LLMConfigCache(args) # Verify that bindings are correctly setup if args.llm_binding not in [ "lollms", "ollama", "openai", "azure_openai", "aws_bedrock", ]: raise Exception("llm binding not supported") if args.embedding_binding not in [ "lollms", "ollama", "openai", "azure_openai", "aws_bedrock", "jina", ]: raise Exception("embedding binding not supported") # Set default hosts if not provided if args.llm_binding_host is None: args.llm_binding_host = get_default_host(args.llm_binding) if args.embedding_binding_host is None: args.embedding_binding_host = get_default_host(args.embedding_binding) # Add SSL validation if args.ssl: if not args.ssl_certfile or not args.ssl_keyfile: raise Exception( "SSL certificate and key files must be provided when SSL is enabled" ) if not os.path.exists(args.ssl_certfile): raise Exception(f"SSL certificate file not found: {args.ssl_certfile}") if not os.path.exists(args.ssl_keyfile): raise Exception(f"SSL key file not found: {args.ssl_keyfile}") # Check if API key is provided either through env var or args api_key = os.getenv("LIGHTRAG_API_KEY") or args.key # Initialize document manager with workspace support for data isolation doc_manager = DocumentManager(args.input_dir, workspace=args.workspace) @asynccontextmanager async def lifespan(app: FastAPI): """Lifespan context manager for startup and shutdown events""" # Store background tasks app.state.background_tasks = set() try: # Initialize database connections await rag.initialize_storages() await initialize_pipeline_status() # Data migration regardless of storage implementation await rag.check_and_migrate_data() ASCIIColors.green("\nServer is ready to accept connections! 🚀\n") yield finally: # Clean up database connections await rag.finalize_storages() # Clean up shared data finalize_share_data() # Initialize FastAPI app_kwargs = { "title": "LightRAG Server API", "description": ( "Providing API for LightRAG core, Web UI and Ollama Model Emulation" + "(With authentication)" if api_key else "" ), "version": __api_version__, "openapi_url": "/openapi.json", # Explicitly set OpenAPI schema URL "docs_url": "/docs", # Explicitly set docs URL "redoc_url": "/redoc", # Explicitly set redoc URL "lifespan": lifespan, } # Configure Swagger UI parameters # Enable persistAuthorization and tryItOutEnabled for better user experience app_kwargs["swagger_ui_parameters"] = { "persistAuthorization": True, "tryItOutEnabled": True, } app = FastAPI(**app_kwargs) # Add custom validation error handler for /query/data endpoint @app.exception_handler(RequestValidationError) async def validation_exception_handler( request: Request, exc: RequestValidationError ): # Check if this is a request to /query/data endpoint if request.url.path.endswith("/query/data"): # Extract error details error_details = [] for error in exc.errors(): field_path = " -> ".join(str(loc) for loc in error["loc"]) error_details.append(f"{field_path}: {error['msg']}") error_message = "; ".join(error_details) # Return in the expected format for /query/data return JSONResponse( status_code=400, content={ "status": "failure", "message": f"Validation error: {error_message}", "data": {}, "metadata": {}, }, ) else: # For other endpoints, return the default FastAPI validation error return JSONResponse(status_code=422, content={"detail": exc.errors()}) def get_cors_origins(): """Get allowed origins from global_args Returns a list of allowed origins, defaults to ["*"] if not set """ origins_str = global_args.cors_origins if origins_str == "*": return ["*"] return [origin.strip() for origin in origins_str.split(",")] # Add CORS middleware app.add_middleware( CORSMiddleware, allow_origins=get_cors_origins(), allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) # Create combined auth dependency for all endpoints combined_auth = get_combined_auth_dependency(api_key) # Create working directory if it doesn't exist Path(args.working_dir).mkdir(parents=True, exist_ok=True) def create_optimized_openai_llm_func( config_cache: LLMConfigCache, args, llm_timeout: int ): """Create optimized OpenAI LLM function with pre-processed configuration""" async def optimized_openai_alike_model_complete( prompt, system_prompt=None, history_messages=None, keyword_extraction=False, **kwargs, ) -> str: from lightrag.llm.openai import openai_complete_if_cache keyword_extraction = kwargs.pop("keyword_extraction", None) if keyword_extraction: kwargs["response_format"] = GPTKeywordExtractionFormat if history_messages is None: history_messages = [] # Use pre-processed configuration to avoid repeated parsing kwargs["timeout"] = llm_timeout if config_cache.openai_llm_options: kwargs.update(config_cache.openai_llm_options) return await openai_complete_if_cache( args.llm_model, prompt, system_prompt=system_prompt, history_messages=history_messages, base_url=args.llm_binding_host, api_key=args.llm_binding_api_key, **kwargs, ) return optimized_openai_alike_model_complete def create_optimized_azure_openai_llm_func( config_cache: LLMConfigCache, args, llm_timeout: int ): """Create optimized Azure OpenAI LLM function with pre-processed configuration""" async def optimized_azure_openai_model_complete( prompt, system_prompt=None, history_messages=None, keyword_extraction=False, **kwargs, ) -> str: from lightrag.llm.azure_openai import azure_openai_complete_if_cache keyword_extraction = kwargs.pop("keyword_extraction", None) if keyword_extraction: kwargs["response_format"] = GPTKeywordExtractionFormat if history_messages is None: history_messages = [] # Use pre-processed configuration to avoid repeated parsing kwargs["timeout"] = llm_timeout if config_cache.openai_llm_options: kwargs.update(config_cache.openai_llm_options) return await azure_openai_complete_if_cache( args.llm_model, prompt, system_prompt=system_prompt, history_messages=history_messages, base_url=args.llm_binding_host, api_key=os.getenv("AZURE_OPENAI_API_KEY", args.llm_binding_api_key), api_version=os.getenv("AZURE_OPENAI_API_VERSION", "2024-08-01-preview"), **kwargs, ) return optimized_azure_openai_model_complete def create_llm_model_func(binding: str): """ Create LLM model function based on binding type. Uses optimized functions for OpenAI bindings and lazy import for others. """ try: if binding == "lollms": from lightrag.llm.lollms import lollms_model_complete return lollms_model_complete elif binding == "ollama": from lightrag.llm.ollama import ollama_model_complete return ollama_model_complete elif binding == "aws_bedrock": return bedrock_model_complete # Already defined locally elif binding == "azure_openai": # Use optimized function with pre-processed configuration return create_optimized_azure_openai_llm_func( config_cache, args, llm_timeout ) else: # openai and compatible # Use optimized function with pre-processed configuration return create_optimized_openai_llm_func(config_cache, args, llm_timeout) except ImportError as e: raise Exception(f"Failed to import {binding} LLM binding: {e}") def create_llm_model_kwargs(binding: str, args, llm_timeout: int) -> dict: """ Create LLM model kwargs based on binding type. Uses lazy import for binding-specific options. """ if binding in ["lollms", "ollama"]: try: from lightrag.llm.binding_options import OllamaLLMOptions return { "host": args.llm_binding_host, "timeout": llm_timeout, "options": OllamaLLMOptions.options_dict(args), "api_key": args.llm_binding_api_key, } except ImportError as e: raise Exception(f"Failed to import {binding} options: {e}") return {} def create_optimized_embedding_function( config_cache: LLMConfigCache, binding, model, host, api_key, dimensions, args ): """ Create optimized embedding function with pre-processed configuration for applicable bindings. Uses lazy imports for all bindings and avoids repeated configuration parsing. """ async def optimized_embedding_function(texts): try: if binding == "lollms": from lightrag.llm.lollms import lollms_embed return await lollms_embed( texts, embed_model=model, host=host, api_key=api_key ) elif binding == "ollama": from lightrag.llm.ollama import ollama_embed # Use pre-processed configuration if available, otherwise fallback to dynamic parsing if config_cache.ollama_embedding_options is not None: ollama_options = config_cache.ollama_embedding_options else: # Fallback for cases where config cache wasn't initialized properly from lightrag.llm.binding_options import OllamaEmbeddingOptions ollama_options = OllamaEmbeddingOptions.options_dict(args) return await ollama_embed( texts, embed_model=model, host=host, api_key=api_key, options=ollama_options, ) elif binding == "azure_openai": from lightrag.llm.azure_openai import azure_openai_embed return await azure_openai_embed(texts, model=model, api_key=api_key) elif binding == "aws_bedrock": from lightrag.llm.bedrock import bedrock_embed return await bedrock_embed(texts, model=model) elif binding == "jina": from lightrag.llm.jina import jina_embed return await jina_embed( texts, dimensions=dimensions, base_url=host, api_key=api_key ) else: # openai and compatible from lightrag.llm.openai import openai_embed return await openai_embed( texts, model=model, base_url=host, api_key=api_key ) except ImportError as e: raise Exception(f"Failed to import {binding} embedding: {e}") return optimized_embedding_function llm_timeout = get_env_value("LLM_TIMEOUT", DEFAULT_LLM_TIMEOUT, int) embedding_timeout = get_env_value( "EMBEDDING_TIMEOUT", DEFAULT_EMBEDDING_TIMEOUT, int ) async def bedrock_model_complete( prompt, system_prompt=None, history_messages=None, keyword_extraction=False, **kwargs, ) -> str: # Lazy import from lightrag.llm.bedrock import bedrock_complete_if_cache keyword_extraction = kwargs.pop("keyword_extraction", None) if keyword_extraction: kwargs["response_format"] = GPTKeywordExtractionFormat if history_messages is None: history_messages = [] # Use global temperature for Bedrock kwargs["temperature"] = get_env_value("BEDROCK_LLM_TEMPERATURE", 1.0, float) return await bedrock_complete_if_cache( args.llm_model, prompt, system_prompt=system_prompt, history_messages=history_messages, **kwargs, ) # Create embedding function with optimized configuration embedding_func = EmbeddingFunc( embedding_dim=args.embedding_dim, func=create_optimized_embedding_function( config_cache=config_cache, binding=args.embedding_binding, model=args.embedding_model, host=args.embedding_binding_host, api_key=args.embedding_binding_api_key, dimensions=args.embedding_dim, args=args, # Pass args object for fallback option generation ), ) # Configure rerank function based on args.rerank_bindingparameter rerank_model_func = None if args.rerank_binding != "null": from lightrag.rerank import cohere_rerank, jina_rerank, ali_rerank # Map rerank binding to corresponding function rerank_functions = { "cohere": cohere_rerank, "jina": jina_rerank, "aliyun": ali_rerank, } # Select the appropriate rerank function based on binding selected_rerank_func = rerank_functions.get(args.rerank_binding) if not selected_rerank_func: logger.error(f"Unsupported rerank binding: {args.rerank_binding}") raise ValueError(f"Unsupported rerank binding: {args.rerank_binding}") # Get default values from selected_rerank_func if args values are None if args.rerank_model is None or args.rerank_binding_host is None: sig = inspect.signature(selected_rerank_func) # Set default model if args.rerank_model is None if args.rerank_model is None and "model" in sig.parameters: default_model = sig.parameters["model"].default if default_model != inspect.Parameter.empty: args.rerank_model = default_model # Set default base_url if args.rerank_binding_host is None if args.rerank_binding_host is None and "base_url" in sig.parameters: default_base_url = sig.parameters["base_url"].default if default_base_url != inspect.Parameter.empty: args.rerank_binding_host = default_base_url async def server_rerank_func( query: str, documents: list, top_n: int = None, extra_body: dict = None ): """Server rerank function with configuration from environment variables""" return await selected_rerank_func( query=query, documents=documents, top_n=top_n, api_key=args.rerank_binding_api_key, model=args.rerank_model, base_url=args.rerank_binding_host, extra_body=extra_body, ) rerank_model_func = server_rerank_func logger.info( f"Reranking is enabled: {args.rerank_model or 'default model'} using {args.rerank_binding} provider" ) else: logger.info("Reranking is disabled") # Create ollama_server_infos from command line arguments from lightrag.api.config import OllamaServerInfos ollama_server_infos = OllamaServerInfos( name=args.simulated_model_name, tag=args.simulated_model_tag ) # Initialize RAG with unified configuration try: rag = LightRAG( working_dir=args.working_dir, workspace=args.workspace, llm_model_func=create_llm_model_func(args.llm_binding), llm_model_name=args.llm_model, llm_model_max_async=args.max_async, summary_max_tokens=args.summary_max_tokens, summary_context_size=args.summary_context_size, chunk_token_size=int(args.chunk_size), chunk_overlap_token_size=int(args.chunk_overlap_size), llm_model_kwargs=create_llm_model_kwargs( args.llm_binding, args, llm_timeout ), embedding_func=embedding_func, default_llm_timeout=llm_timeout, default_embedding_timeout=embedding_timeout, kv_storage=args.kv_storage, graph_storage=args.graph_storage, vector_storage=args.vector_storage, doc_status_storage=args.doc_status_storage, vector_db_storage_cls_kwargs={ "cosine_better_than_threshold": args.cosine_threshold }, enable_llm_cache_for_entity_extract=args.enable_llm_cache_for_extract, enable_llm_cache=args.enable_llm_cache, rerank_model_func=rerank_model_func, max_parallel_insert=args.max_parallel_insert, max_graph_nodes=args.max_graph_nodes, addon_params={ "language": args.summary_language, "entity_types": args.entity_types, }, ollama_server_infos=ollama_server_infos, ) except Exception as e: logger.error(f"Failed to initialize LightRAG: {e}") raise # Add routes app.include_router( create_document_routes( rag, doc_manager, api_key, ) ) app.include_router(create_query_routes(rag, api_key, args.top_k)) app.include_router(create_graph_routes(rag, api_key)) # Add Ollama API routes ollama_api = OllamaAPI(rag, top_k=args.top_k, api_key=api_key) app.include_router(ollama_api.router, prefix="/api") @app.get("/") async def redirect_to_webui(): """Redirect root path to /webui""" return RedirectResponse(url="/webui") @app.get("/auth-status") async def get_auth_status(): """Get authentication status and guest token if auth is not configured""" if not auth_handler.accounts: # Authentication not configured, return guest token guest_token = auth_handler.create_token( username="guest", role="guest", metadata={"auth_mode": "disabled"} ) return { "auth_configured": False, "access_token": guest_token, "token_type": "bearer", "auth_mode": "disabled", "message": "Authentication is disabled. Using guest access.", "core_version": core_version, "api_version": __api_version__, "webui_title": webui_title, "webui_description": webui_description, } return { "auth_configured": True, "auth_mode": "enabled", "core_version": core_version, "api_version": __api_version__, "webui_title": webui_title, "webui_description": webui_description, } @app.post("/login") async def login(form_data: OAuth2PasswordRequestForm = Depends()): if not auth_handler.accounts: # Authentication not configured, return guest token guest_token = auth_handler.create_token( username="guest", role="guest", metadata={"auth_mode": "disabled"} ) return { "access_token": guest_token, "token_type": "bearer", "auth_mode": "disabled", "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: raise HTTPException(status_code=401, detail="Incorrect credentials") # Regular user login user_token = auth_handler.create_token( username=username, role="user", metadata={"auth_mode": "enabled"} ) return { "access_token": user_token, "token_type": "bearer", "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)]) async def get_status(): """Get current system status""" try: pipeline_status = await get_namespace_data("pipeline_status") if not auth_configured: auth_mode = "disabled" else: auth_mode = "enabled" # Cleanup expired keyed locks and get status keyed_lock_info = cleanup_keyed_lock() return { "status": "healthy", "working_directory": str(args.working_dir), "input_directory": str(args.input_dir), "configuration": { # LLM configuration binding/host address (if applicable)/model (if applicable) "llm_binding": args.llm_binding, "llm_binding_host": args.llm_binding_host, "llm_model": args.llm_model, # embedding model configuration binding/host address (if applicable)/model (if applicable) "embedding_binding": args.embedding_binding, "embedding_binding_host": args.embedding_binding_host, "embedding_model": args.embedding_model, "summary_max_tokens": args.summary_max_tokens, "summary_context_size": args.summary_context_size, "kv_storage": args.kv_storage, "doc_status_storage": args.doc_status_storage, "graph_storage": args.graph_storage, "vector_storage": args.vector_storage, "enable_llm_cache_for_extract": args.enable_llm_cache_for_extract, "enable_llm_cache": args.enable_llm_cache, "workspace": args.workspace, "max_graph_nodes": args.max_graph_nodes, # Rerank configuration "enable_rerank": rerank_model_func is not None, "rerank_binding": args.rerank_binding, "rerank_model": args.rerank_model if rerank_model_func else None, "rerank_binding_host": args.rerank_binding_host if rerank_model_func else None, # Environment variable status (requested configuration) "summary_language": args.summary_language, "force_llm_summary_on_merge": args.force_llm_summary_on_merge, "max_parallel_insert": args.max_parallel_insert, "cosine_threshold": args.cosine_threshold, "min_rerank_score": args.min_rerank_score, "related_chunk_number": args.related_chunk_number, "max_async": args.max_async, "embedding_func_max_async": args.embedding_func_max_async, "embedding_batch_num": args.embedding_batch_num, }, "auth_mode": auth_mode, "pipeline_busy": pipeline_status.get("busy", False), "keyed_locks": keyed_lock_info, "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)}") raise HTTPException(status_code=500, detail=str(e)) # Custom StaticFiles class for smart caching class SmartStaticFiles(StaticFiles): # Renamed from NoCacheStaticFiles async def get_response(self, path: str, scope): response = await super().get_response(path, scope) if path.endswith(".html"): response.headers["Cache-Control"] = ( "no-cache, no-store, must-revalidate" ) response.headers["Pragma"] = "no-cache" response.headers["Expires"] = "0" elif ( "/assets/" in path ): # Assets (JS, CSS, images, fonts) generated by Vite with hash in filename response.headers["Cache-Control"] = ( "public, max-age=31536000, immutable" ) # Add other rules here if needed for non-HTML, non-asset files # Ensure correct Content-Type if path.endswith(".js"): response.headers["Content-Type"] = "application/javascript" elif path.endswith(".css"): response.headers["Content-Type"] = "text/css" return response # Webui mount webui/index.html static_dir = Path(__file__).parent / "webui" static_dir.mkdir(exist_ok=True) app.mount( "/webui", SmartStaticFiles( directory=static_dir, html=True, check_dir=True ), # Use SmartStaticFiles name="webui", ) return app def get_application(args=None): """Factory function for creating the FastAPI application""" if args is None: args = global_args return create_app(args) def configure_logging(): """Configure logging for uvicorn startup""" # Reset any existing handlers to ensure clean configuration for logger_name in ["uvicorn", "uvicorn.access", "uvicorn.error", "lightrag"]: logger = logging.getLogger(logger_name) logger.handlers = [] logger.filters = [] # Get log directory path from environment variable log_dir = os.getenv("LOG_DIR", os.getcwd()) log_file_path = os.path.abspath(os.path.join(log_dir, DEFAULT_LOG_FILENAME)) print(f"\nLightRAG log file: {log_file_path}\n") os.makedirs(os.path.dirname(log_dir), exist_ok=True) # Get log file max size and backup count from environment variables log_max_bytes = get_env_value("LOG_MAX_BYTES", DEFAULT_LOG_MAX_BYTES, int) log_backup_count = get_env_value("LOG_BACKUP_COUNT", DEFAULT_LOG_BACKUP_COUNT, int) logging.config.dictConfig( { "version": 1, "disable_existing_loggers": False, "formatters": { "default": { "format": "%(levelname)s: %(message)s", }, "detailed": { "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s", }, }, "handlers": { "console": { "formatter": "default", "class": "logging.StreamHandler", "stream": "ext://sys.stderr", }, "file": { "formatter": "detailed", "class": "logging.handlers.RotatingFileHandler", "filename": log_file_path, "maxBytes": log_max_bytes, "backupCount": log_backup_count, "encoding": "utf-8", }, }, "loggers": { # Configure all uvicorn related loggers "uvicorn": { "handlers": ["console", "file"], "level": "INFO", "propagate": False, }, "uvicorn.access": { "handlers": ["console", "file"], "level": "INFO", "propagate": False, "filters": ["path_filter"], }, "uvicorn.error": { "handlers": ["console", "file"], "level": "INFO", "propagate": False, }, "lightrag": { "handlers": ["console", "file"], "level": "INFO", "propagate": False, "filters": ["path_filter"], }, }, "filters": { "path_filter": { "()": "lightrag.utils.LightragPathFilter", }, }, } ) def check_and_install_dependencies(): """Check and install required dependencies""" required_packages = [ "uvicorn", "tiktoken", "fastapi", # Add other required packages here ] for package in required_packages: if not pm.is_installed(package): print(f"Installing {package}...") pm.install(package) print(f"{package} installed successfully") def main(): # Check if running under Gunicorn if "GUNICORN_CMD_ARGS" in os.environ: # If started with Gunicorn, return directly as Gunicorn will call get_application print("Running under Gunicorn - worker management handled by Gunicorn") return # Check .env file if not check_env_file(): sys.exit(1) # Check and install dependencies check_and_install_dependencies() from multiprocessing import freeze_support freeze_support() # Configure logging before parsing args configure_logging() update_uvicorn_mode_config() display_splash_screen(global_args) # Setup signal handlers for graceful shutdown setup_signal_handlers() # Create application instance directly instead of using factory function app = create_app(global_args) # Start Uvicorn in single process mode uvicorn_config = { "app": app, # Pass application instance directly instead of string path "host": global_args.host, "port": global_args.port, "log_config": None, # Disable default config } if global_args.ssl: uvicorn_config.update( { "ssl_certfile": global_args.ssl_certfile, "ssl_keyfile": global_args.ssl_keyfile, } ) print( f"Starting Uvicorn server in single-process mode on {global_args.host}:{global_args.port}" ) uvicorn.run(**uvicorn_config) if __name__ == "__main__": main()