feat(config): Refactor configuration management

- Optimize JWT Auth module to load configuration via `global_args`.
- Decouple configuration-related code from `utils_api.py`, and add a new `config.py` file for unified configuration management.
- Adjust configuration import in `lightrag_server.py`, `auth.py`, and `document_routes.py` to be introduced through `global_args`.

env.example

@@ -160,7 +160,9 @@ REDIS_URI=redis://localhost:6379
 ### For JWT Auth
 AUTH_ACCOUNTS='admin:admin123,user1:pass456'     # username:password,username:password
 TOKEN_SECRET=Your-Key-For-LightRAG-API-Server    # JWT key
-TOKEN_EXPIRE_HOURS=4                             # expire duration
+#TOKEN_EXPIRE_HOURS=4                             # Expire duration, default 4
+#GUEST_TOKEN_EXPIRE_HOURS=2                       # Guest expire duration, default 2
+#JWT_ALGORITHM=HS256                              # JWT encode algorithm, default HS256
 
 ### API-Key to access LightRAG Server API
 # LIGHTRAG_API_KEY=your-secure-api-key-here

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
 
 load_dotenv()
 
@@ -17,13 +19,12 @@ class TokenPayload(BaseModel):
 
 class AuthHandler:
     def __init__(self):
-        self.secret = os.getenv("TOKEN_SECRET", "4f85ds4f56dsf46")
+        self.secret = global_args.token_secret
-        self.algorithm = "HS256"
+        self.algorithm = global_args.jwt_algorithm
-        self.expire_hours = int(os.getenv("TOKEN_EXPIRE_HOURS", 4))
+        self.expire_hours = global_args.token_expire_hours
-        self.guest_expire_hours = int(os.getenv("GUEST_TOKEN_EXPIRE_HOURS", 2))
+        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)

lightrag/api/config.py

@@ -0,0 +1,324 @@
+"""
+Configs for the LightRAG API.
+"""
+
+import os
+import argparse
+import logging
+
+
+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")
+
+    # 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", 4, int)
+    args.guest_token_expire_hours = get_env_value("GUEST_TOKEN_EXPIRE_HOURS", 2, 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()

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__
@@ -489,7 +492,7 @@ 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)
 
 
@@ -610,30 +613,31 @@ def main():
 
     # Configure logging before parsing args
     configure_logging()
-
+    update_uvicorn_mode_config()
-    args = parse_args(is_uvicorn_mode=True)
+    display_splash_screen(global_args)
-    display_splash_screen(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,
+        "host": global_args.host,
-        "port": args.port,
+        "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_certfile": global_args.ssl_certfile,
-                "ssl_keyfile": args.ssl_keyfile,
+                "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)
 
 

lightrag/api/routers/document_routes.py

@@ -16,10 +16,8 @@ from pydantic import BaseModel, Field, field_validator
 
 from lightrag import LightRAG
 from lightrag.base import DocProcessingStatus, DocStatus
-from lightrag.api.utils_api import (
+from lightrag.api.utils_api import get_combined_auth_dependency
-    get_combined_auth_dependency,
+from ..config import global_args
-    global_args,
-)
 
 router = APIRouter(
     prefix="/documents",
@@ -276,7 +274,7 @@ 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 +293,7 @@ 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 +313,7 @@ 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 +334,7 @@ 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
@@ -476,8 +474,8 @@ async def run_scanning_process(rag: LightRAG, doc_manager: DocumentManager):
         if not new_files:
             return
 
-        # Get MAX_PARALLEL_INSERT from global_args["main_args"]
+        # Get MAX_PARALLEL_INSERT from global_args
-        max_parallel = global_args["main_args"].max_parallel_insert
+        max_parallel = global_args.max_parallel_insert
         # Calculate batch size as 2 * MAX_PARALLEL_INSERT
         batch_size = 2 * max_parallel
 

lightrag/api/utils_api.py

@@ -2,18 +2,20 @@
 Utility functions for the LightRAG API.
 """
 
-import os
 import argparse
-from typing import Optional, List, Tuple
+import os
 import sys
+from typing import Optional, List, Tuple
+
 from ascii_colors import ASCIIColors
-import logging
-from lightrag.api import __api_version__
-from fastapi import HTTPException, Security, Request, status
 from dotenv import load_dotenv
+from fastapi import HTTPException, Security, Request, status
 from fastapi.security import APIKeyHeader, OAuth2PasswordBearer
 from starlette.status import HTTP_403_FORBIDDEN
+
+from lightrag.api import __api_version__
 from .auth import auth_handler
+from .config import ollama_server_infos
 from ..prompt import PROMPTS
 
 
@@ -40,8 +42,6 @@ def check_env_file():
 # Load environment variables
 load_dotenv()
 
-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(",")
@@ -62,19 +62,6 @@ 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
@@ -185,299 +172,6 @@ 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
@@ -518,8 +212,10 @@ 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:")
@@ -652,6 +348,11 @@ def display_splash_screen(args: argparse.Namespace) -> None:
         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()