run precommit to fix linting issues

lightrag/api/lightrag_server.py

@@ -23,21 +23,25 @@ from fastapi.middleware.cors import CORSMiddleware
 
 from starlette.status import HTTP_403_FORBIDDEN
 
+
 def get_default_host(binding_type: str) -> str:
     default_hosts = {
         "ollama": "http://localhost:11434",
         "lollms": "http://localhost:9600",
         "azure_openai": "https://api.openai.com/v1",
-        "openai": "https://api.openai.com/v1"
+        "openai": "https://api.openai.com/v1",
     }
-    return default_hosts.get(binding_type, "http://localhost:11434")  # fallback to ollama if unknown
+    return default_hosts.get(
+        binding_type, "http://localhost:11434"
+    )  # fallback to ollama if unknown
+
 
 def parse_args():
     parser = argparse.ArgumentParser(
         description="LightRAG FastAPI Server with separate working and input directories"
     )
 
-    #Start by the bindings
+    # Start by the bindings
     parser.add_argument(
         "--llm-binding",
         default="ollama",
@@ -48,7 +52,7 @@ def parse_args():
         default="ollama",
         help="Embedding binding to be used. Supported: lollms, ollama, openai (default: ollama)",
     )
-    
+
     # Parse just these arguments first
     temp_args, _ = parser.parse_known_args()
 
@@ -152,19 +156,17 @@ def parse_args():
 
     # Optional https parameters
     parser.add_argument(
-        "--ssl",
+        "--ssl", action="store_true", help="Enable HTTPS (default: False)"
-        action="store_true",
-        help="Enable HTTPS (default: False)"
     )
     parser.add_argument(
         "--ssl-certfile",
         default=None,
-        help="Path to SSL certificate file (required if --ssl is enabled)"
+        help="Path to SSL certificate file (required if --ssl is enabled)",
     )
     parser.add_argument(
         "--ssl-keyfile",
-        default=None, 
+        default=None,
-        help="Path to SSL private key file (required if --ssl is enabled)"
+        help="Path to SSL private key file (required if --ssl is enabled)",
     )
     return parser.parse_args()
 
@@ -261,17 +263,17 @@ def create_app(args):
     if args.embedding_binding not in ["lollms", "ollama", "openai"]:
         raise Exception("embedding binding not supported")
 
-
     # 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")
+            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}")
-        
+
-        
     # Setup logging
     logging.basicConfig(
         format="%(levelname)s:%(message)s", level=getattr(logging, args.log_level)
@@ -309,33 +311,48 @@ def create_app(args):
     # Initialize document manager
     doc_manager = DocumentManager(args.input_dir)
 
-
-
     # Initialize RAG
     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 azure_openai_complete_if_cache if args.llm_binding=="azure_openai" else openai_complete_if_cache,
+        llm_model_func=lollms_model_complete
+        if args.llm_binding == "lollms"
+        else ollama_model_complete
+        if args.llm_binding == "ollama"
+        else azure_openai_complete_if_cache
+        if args.llm_binding == "azure_openai"
+        else openai_complete_if_cache,
         llm_model_name=args.llm_model,
         llm_model_max_async=args.max_async,
         llm_model_max_token_size=args.max_tokens,
         llm_model_kwargs={
             "host": args.llm_binding_host,
-            "timeout":args.timeout,
+            "timeout": args.timeout,
             "options": {"num_ctx": args.max_tokens},
         },
         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
+                texts,
-            ) if args.llm_binding=="lollms" else ollama_embed(
+                embed_model=args.embedding_model,
-                texts, embed_model=args.embedding_model, host=args.embedding_binding_host
+                host=args.embedding_binding_host,
-            ) if args.llm_binding=="ollama" else azure_openai_embedding(
-                texts, model=args.embedding_model # no host is used for openai
-            ) if args.llm_binding=="azure_openai" else openai_embedding(
-                texts, model=args.embedding_model # no host is used for openai
             )
-            
+            if args.llm_binding == "lollms"
+            else ollama_embed(
+                texts,
+                embed_model=args.embedding_model,
+                host=args.embedding_binding_host,
+            )
+            if args.llm_binding == "ollama"
+            else azure_openai_embedding(
+                texts,
+                model=args.embedding_model,  # no host is used for openai
+            )
+            if args.llm_binding == "azure_openai"
+            else openai_embedding(
+                texts,
+                model=args.embedding_model,  # no host is used for openai
+            ),
         ),
     )
 
@@ -568,12 +585,10 @@ def create_app(args):
                 "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,
             },
         }
@@ -590,12 +605,14 @@ def main():
         "app": app,
         "host": args.host,
         "port": args.port,
-    }    
+    }
     if args.ssl:
-        uvicorn_config.update({
+        uvicorn_config.update(
-            "ssl_certfile": args.ssl_certfile,
+            {
-            "ssl_keyfile": args.ssl_keyfile,
+                "ssl_certfile": args.ssl_certfile,
-        })    
+                "ssl_keyfile": args.ssl_keyfile,
+            }
+        )
     uvicorn.run(**uvicorn_config)
 
 

lightrag/llm.py

@@ -336,7 +336,6 @@ async def hf_model_if_cache(
         (RateLimitError, APIConnectionError, APITimeoutError)
     ),
 )
-
 async def ollama_model_if_cache(
     model,
     prompt,
@@ -411,6 +410,7 @@ async def lollms_model_if_cache(
 
     async with aiohttp.ClientSession(timeout=timeout) as session:
         if stream:
+
             async def inner():
                 async with session.post(
                     f"{base_url}/lollms_generate", json=request_data