""" LightRAG FastAPI Server """ from fastapi import FastAPI, Depends, HTTPException, status import asyncio import os import logging import logging.config import uvicorn import pipmaster as pm 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, ) import sys 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.api.routers.document_routes import ( DocumentManager, create_document_routes, run_scanning_process, ) 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, get_pipeline_status_lock, initialize_pipeline_status, ) 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) # Initialize config parser config = configparser.ConfigParser() config.read("config.ini") # Global authentication configuration auth_configured = bool(auth_handler.accounts) def create_app(args): # Setup logging logger.setLevel(args.log_level) set_verbose_debug(args.verbose) # Verify that bindings are correctly setup if args.llm_binding not in [ "lollms", "ollama", "openai", "openai-ollama", "azure_openai", ]: raise Exception("llm binding not supported") if args.embedding_binding not in ["lollms", "ollama", "openai", "azure_openai"]: 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 doc_manager = DocumentManager(args.input_dir) @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() pipeline_status = await get_namespace_data("pipeline_status") should_start_autoscan = False async with get_pipeline_status_lock(): # Auto scan documents if enabled if args.auto_scan_at_startup: if not pipeline_status.get("autoscanned", False): pipeline_status["autoscanned"] = True should_start_autoscan = True # Only run auto scan when no other process started it first if should_start_autoscan: # Create background task task = asyncio.create_task(run_scanning_process(rag, doc_manager)) app.state.background_tasks.add(task) task.add_done_callback(app.state.background_tasks.discard) logger.info(f"Process {os.getpid()} auto scan task started at startup.") ASCIIColors.green("\nServer is ready to accept connections! 🚀\n") yield finally: # Clean up database connections await rag.finalize_storages() # 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 "openapi_tags": [{"name": "api"}], "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) 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) if args.llm_binding == "lollms" or args.embedding_binding == "lollms": from lightrag.llm.lollms import lollms_model_complete, lollms_embed if args.llm_binding == "ollama" or args.embedding_binding == "ollama": from lightrag.llm.ollama import ollama_model_complete, ollama_embed if args.llm_binding == "openai" or args.embedding_binding == "openai": from lightrag.llm.openai import openai_complete_if_cache, openai_embed if args.llm_binding == "azure_openai" or args.embedding_binding == "azure_openai": from lightrag.llm.azure_openai import ( azure_openai_complete_if_cache, azure_openai_embed, ) if args.llm_binding_host == "openai-ollama" or args.embedding_binding == "ollama": from lightrag.llm.openai import openai_complete_if_cache from lightrag.llm.ollama import ollama_embed async def openai_alike_model_complete( prompt, system_prompt=None, history_messages=None, keyword_extraction=False, **kwargs, ) -> str: keyword_extraction = kwargs.pop("keyword_extraction", None) if keyword_extraction: kwargs["response_format"] = GPTKeywordExtractionFormat if history_messages is None: history_messages = [] kwargs["temperature"] = args.temperature 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, ) async def azure_openai_model_complete( prompt, system_prompt=None, history_messages=None, keyword_extraction=False, **kwargs, ) -> str: keyword_extraction = kwargs.pop("keyword_extraction", None) if keyword_extraction: kwargs["response_format"] = GPTKeywordExtractionFormat if history_messages is None: history_messages = [] kwargs["temperature"] = args.temperature 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"), api_version=os.getenv("AZURE_OPENAI_API_VERSION", "2024-08-01-preview"), **kwargs, ) embedding_func = EmbeddingFunc( embedding_dim=args.embedding_dim, max_token_size=args.max_embed_tokens, func=lambda texts: lollms_embed( texts, embed_model=args.embedding_model, host=args.embedding_binding_host, api_key=args.embedding_binding_api_key, ) if args.embedding_binding == "lollms" else ollama_embed( texts, embed_model=args.embedding_model, host=args.embedding_binding_host, api_key=args.embedding_binding_api_key, ) if args.embedding_binding == "ollama" else azure_openai_embed( texts, model=args.embedding_model, # no host is used for openai, api_key=args.embedding_binding_api_key, ) if args.embedding_binding == "azure_openai" else openai_embed( texts, model=args.embedding_model, base_url=args.embedding_binding_host, api_key=args.embedding_binding_api_key, ), ) # Initialize RAG if args.llm_binding in ["lollms", "ollama", "openai"]: rag = LightRAG( working_dir=args.working_dir, llm_model_func=lollms_model_complete if args.llm_binding == "lollms" else ollama_model_complete if args.llm_binding == "ollama" else openai_alike_model_complete, llm_model_name=args.llm_model, llm_model_max_async=args.max_async, llm_model_max_token_size=args.max_tokens, chunk_token_size=int(args.chunk_size), chunk_overlap_token_size=int(args.chunk_overlap_size), llm_model_kwargs={ "host": args.llm_binding_host, "timeout": args.timeout, "options": {"num_ctx": args.max_tokens}, "api_key": args.llm_binding_api_key, } if args.llm_binding == "lollms" or args.llm_binding == "ollama" else {}, embedding_func=embedding_func, 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, embedding_cache_config={ "enabled": True, "similarity_threshold": 0.95, "use_llm_check": False, }, # 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( working_dir=args.working_dir, llm_model_func=azure_openai_model_complete, chunk_token_size=int(args.chunk_size), chunk_overlap_token_size=int(args.chunk_overlap_size), llm_model_kwargs={ "timeout": args.timeout, }, llm_model_name=args.llm_model, llm_model_max_async=args.max_async, llm_model_max_token_size=args.max_tokens, embedding_func=embedding_func, 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, embedding_cache_config={ "enabled": True, "similarity_threshold": 0.95, "use_llm_check": False, }, # 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 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__, } return { "auth_configured": True, "auth_mode": "enabled", "core_version": core_version, "api_version": __api_version__, } @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__, } username = form_data.username if auth_handler.accounts.get(username) != form_data.password: raise HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, 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__, } @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" 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, "max_tokens": args.max_tokens, "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, }, "core_version": core_version, "api_version": __api_version__, "auth_mode": auth_mode, "pipeline_busy": pipeline_status.get("busy", False), } except Exception as e: logger.error(f"Error getting health status: {str(e)}") raise HTTPException(status_code=500, detail=str(e)) # Custom StaticFiles class to prevent caching of HTML files class NoCacheStaticFiles(StaticFiles): 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" return response # Webui mount webui/index.html static_dir = Path(__file__).parent / "webui" static_dir.mkdir(exist_ok=True) app.mount( "/webui", NoCacheStaticFiles(directory=static_dir, html=True, check_dir=True), 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, "lightrag.log")) 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 = int(os.getenv("LOG_MAX_BYTES", 10485760)) # Default 10MB log_backup_count = int(os.getenv("LOG_BACKUP_COUNT", 5)) # Default 5 backups 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) # 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()