""" Configs for the LightRAG API. """ import os import argparse import logging from dotenv import load_dotenv from lightrag.utils import get_env_value from lightrag.llm.binding_options import ( OllamaEmbeddingOptions, OllamaLLMOptions, OpenAILLMOptions, ) from lightrag.base import OllamaServerInfos import sys from lightrag.constants import ( DEFAULT_WOKERS, DEFAULT_TIMEOUT, DEFAULT_TOP_K, DEFAULT_CHUNK_TOP_K, DEFAULT_HISTORY_TURNS, DEFAULT_MAX_ENTITY_TOKENS, DEFAULT_MAX_RELATION_TOKENS, DEFAULT_MAX_TOTAL_TOKENS, DEFAULT_COSINE_THRESHOLD, DEFAULT_RELATED_CHUNK_NUMBER, DEFAULT_MIN_RERANK_SCORE, DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE, DEFAULT_MAX_ASYNC, DEFAULT_SUMMARY_MAX_TOKENS, DEFAULT_SUMMARY_LANGUAGE, DEFAULT_EMBEDDING_FUNC_MAX_ASYNC, DEFAULT_EMBEDDING_BATCH_NUM, DEFAULT_OLLAMA_MODEL_NAME, DEFAULT_OLLAMA_MODEL_TAG, DEFAULT_TEMPERATURE, ) # 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) 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 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)", ) parser.add_argument( "--timeout", default=get_env_value("TIMEOUT", DEFAULT_TIMEOUT, int, special_none=True), type=int, 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", DEFAULT_MAX_ASYNC, int), help=f"Maximum async operations (default: from env or {DEFAULT_MAX_ASYNC})", ) parser.add_argument( "--max-tokens", type=int, default=get_env_value("MAX_TOKENS", DEFAULT_SUMMARY_MAX_TOKENS, int), help=f"Maximum token size (default: from env or {DEFAULT_SUMMARY_MAX_TOKENS})", ) # 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)", ) # Ollama model configuration parser.add_argument( "--simulated-model-name", type=str, default=get_env_value("OLLAMA_EMULATING_MODEL_NAME", DEFAULT_OLLAMA_MODEL_NAME), help="Name for the simulated Ollama model (default: from env or lightrag)", ) parser.add_argument( "--simulated-model-tag", type=str, default=get_env_value("OLLAMA_EMULATING_MODEL_TAG", DEFAULT_OLLAMA_MODEL_TAG), help="Tag for the simulated Ollama model (default: from env or latest)", ) # Namespace parser.add_argument( "--workspace", type=str, default=get_env_value("WORKSPACE", ""), help="Default workspace for all storage", ) 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", DEFAULT_WOKERS, 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", "aws_bedrock"], 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", "aws_bedrock", "jina"], help="Embedding binding type (default: from env or ollama)", ) # Conditionally add binding options defined in binding_options module # This will add command line arguments for all binding options (e.g., --ollama-embedding-num_ctx) # and corresponding environment variables (e.g., OLLAMA_EMBEDDING_NUM_CTX) if "--llm-binding" in sys.argv: try: idx = sys.argv.index("--llm-binding") if idx + 1 < len(sys.argv) and sys.argv[idx + 1] == "ollama": OllamaLLMOptions.add_args(parser) except IndexError: pass elif os.environ.get("LLM_BINDING") == "ollama": OllamaLLMOptions.add_args(parser) if "--embedding-binding" in sys.argv: try: idx = sys.argv.index("--embedding-binding") if idx + 1 < len(sys.argv) and sys.argv[idx + 1] == "ollama": OllamaEmbeddingOptions.add_args(parser) except IndexError: pass elif os.environ.get("EMBEDDING_BINDING") == "ollama": OllamaEmbeddingOptions.add_args(parser) # Add OpenAI LLM options when llm-binding is openai or azure_openai if "--llm-binding" in sys.argv: try: idx = sys.argv.index("--llm-binding") if idx + 1 < len(sys.argv) and sys.argv[idx + 1] in [ "openai", "azure_openai", ]: OpenAILLMOptions.add_args(parser) except IndexError: pass elif os.environ.get("LLM_BINDING") in ["openai", "azure_openai"]: OpenAILLMOptions.add_args(parser) # Add global temperature command line argument parser.add_argument( "--temperature", type=float, default=get_env_value("TEMPERATURE", DEFAULT_TEMPERATURE, float), help="Global temperature setting for LLM (default: from env TEMPERATURE or 0.1)", ) 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) # Get MAX_GRAPH_NODES from environment args.max_graph_nodes = get_env_value("MAX_GRAPH_NODES", 1000, int) # Handle openai-ollama special case if args.llm_binding == "openai-ollama": args.llm_binding = "openai" args.embedding_binding = "ollama" # Ollama ctx_num args.ollama_num_ctx = get_env_value("OLLAMA_NUM_CTX", 32768, int) 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) # 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) args.enable_llm_cache = get_env_value("ENABLE_LLM_CACHE", True, bool) # Handle Ollama LLM temperature with priority cascade when llm-binding is ollama if args.llm_binding == "ollama": # Priority order (highest to lowest): # 1. --ollama-llm-temperature command argument # 2. OLLAMA_LLM_TEMPERATURE environment variable # 3. --temperature command argument # 4. TEMPERATURE environment variable # Check if --ollama-llm-temperature was explicitly provided in command line if "--ollama-llm-temperature" not in sys.argv: # Use args.temperature which handles --temperature command arg and TEMPERATURE env var priority args.ollama_llm_temperature = args.temperature # Handle OpenAI LLM temperature with priority cascade when llm-binding is openai or azure_openai if args.llm_binding in ["openai", "azure_openai"]: # Priority order (highest to lowest): # 1. --openai-llm-temperature command argument # 2. OPENAI_LLM_TEMPERATURE environment variable # 3. --temperature command argument # 4. TEMPERATURE environment variable # Check if --openai-llm-temperature was explicitly provided in command line if "--openai-llm-temperature" not in sys.argv: # Use args.temperature which handles --temperature command arg and TEMPERATURE env var priority args.openai_llm_temperature = args.temperature # 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", DEFAULT_SUMMARY_LANGUAGE) 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") # Rerank model configuration args.rerank_model = get_env_value("RERANK_MODEL", "BAAI/bge-reranker-v2-m3") args.rerank_binding_host = get_env_value("RERANK_BINDING_HOST", None) args.rerank_binding_api_key = get_env_value("RERANK_BINDING_API_KEY", None) # Min rerank score configuration args.min_rerank_score = get_env_value("MIN_RERANK_SCORE", DEFAULT_MIN_RERANK_SCORE, float) # Query configuration args.history_turns = get_env_value("HISTORY_TURNS", DEFAULT_HISTORY_TURNS, int) args.top_k = get_env_value("TOP_K", DEFAULT_TOP_K, int) args.chunk_top_k = get_env_value("CHUNK_TOP_K", DEFAULT_CHUNK_TOP_K, int) args.max_entity_tokens = get_env_value("MAX_ENTITY_TOKENS", DEFAULT_MAX_ENTITY_TOKENS, int) args.max_relation_tokens = get_env_value("MAX_RELATION_TOKENS", DEFAULT_MAX_RELATION_TOKENS, int) args.max_total_tokens = get_env_value("MAX_TOTAL_TOKENS", DEFAULT_MAX_TOTAL_TOKENS, int) args.cosine_threshold = get_env_value("COSINE_THRESHOLD", DEFAULT_COSINE_THRESHOLD, float) args.related_chunk_number = get_env_value("RELATED_CHUNK_NUMBER", DEFAULT_RELATED_CHUNK_NUMBER, int) # Add missing environment variables for health endpoint args.force_llm_summary_on_merge = get_env_value( "FORCE_LLM_SUMMARY_ON_MERGE", DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE, int ) args.embedding_func_max_async = get_env_value("EMBEDDING_FUNC_MAX_ASYNC", DEFAULT_EMBEDDING_FUNC_MAX_ASYNC, int) args.embedding_batch_num = get_env_value("EMBEDDING_BATCH_NUM", DEFAULT_EMBEDDING_BATCH_NUM, int) ollama_server_infos.LIGHTRAG_NAME = args.simulated_model_name ollama_server_infos.LIGHTRAG_TAG = args.simulated_model_tag 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()