fixed linting

extra/OpenWebuiTool/openwebui_tool.py

@@ -23,10 +23,6 @@ Requirements:
     - Lightrag
 """
 
-from pathlib import Path
-from typing import Optional, List, Dict, Union, Any
-from datetime import datetime
-
 # Tool version
 __version__ = "1.0.0"
 __author__ = "ParisNeo"

lightrag/api/lightrag_server.py

@@ -298,14 +298,12 @@ def parse_args() -> argparse.Namespace:
         help=f"llm server host URL (default: from env or {default_llm_host})",
     )
 
-    default_llm_api_key = get_env_value(
+    default_llm_api_key = get_env_value("LLM_BINDING_API_KEY", None)
-        "LLM_BINDING_API_KEY", None
-    )
 
     parser.add_argument(
         "--llm-binding-api-key",
         default=default_llm_api_key,
-        help=f"llm server API key (default: from env or empty string)",
+        help="llm server API key (default: from env or empty string)",
     )
 
     parser.add_argument(
@@ -324,13 +322,11 @@ def parse_args() -> argparse.Namespace:
         help=f"embedding server host URL (default: from env or {default_embedding_host})",
     )
 
-    default_embedding_api_key = get_env_value(
+    default_embedding_api_key = get_env_value("EMBEDDING_BINDING_API_KEY", "")
-        "EMBEDDING_BINDING_API_KEY", ""
-    )
     parser.add_argument(
         "--embedding-binding-api-key",
         default=default_embedding_api_key,
-        help=f"embedding server API key (default: from env or empty string)",
+        help="embedding server API key (default: from env or empty string)",
     )
 
     parser.add_argument(
@@ -649,26 +645,26 @@ def create_app(args):
             texts,
             embed_model=args.embedding_model,
             host=args.embedding_binding_host,
-            api_key = args.embedding_binding_api_key
+            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
+            api_key=args.embedding_binding_api_key,
         )
         if args.embedding_binding == "ollama"
         else azure_openai_embedding(
             texts,
             model=args.embedding_model,  # no host is used for openai,
-            api_key = args.embedding_binding_api_key
+            api_key=args.embedding_binding_api_key,
         )
         if args.embedding_binding == "azure_openai"
         else openai_embedding(
             texts,
             model=args.embedding_model,  # no host is used for openai,
-            api_key = args.embedding_binding_api_key
+            api_key=args.embedding_binding_api_key,
         ),
     )
 
@@ -686,7 +682,7 @@ def create_app(args):
                 "host": args.llm_binding_host,
                 "timeout": args.timeout,
                 "options": {"num_ctx": args.max_tokens},
-                "api_key": args.llm_binding_api_key
+                "api_key": args.llm_binding_api_key,
             },
             embedding_func=embedding_func,
         )

lightrag/llm.py

@@ -350,7 +350,7 @@ async def ollama_model_if_cache(
     timeout = kwargs.pop("timeout", None)
     kwargs.pop("hashing_kv", None)
     api_key = kwargs.pop("api_key", None)
-    headers={'Authorization': f'Bearer {api_key}'} if api_key else None
+    headers = {"Authorization": f"Bearer {api_key}"} if api_key else None
     ollama_client = ollama.AsyncClient(host=host, timeout=timeout, headers=headers)
     messages = []
     if system_prompt:
@@ -383,7 +383,7 @@ async def lollms_model_if_cache(
 
     stream = True if kwargs.get("stream") else False
     api_key = kwargs.pop("api_key", None)
-    headers={'Authorization': f'Bearer {api_key}'} if api_key else None
+    headers = {"Authorization": f"Bearer {api_key}"} if api_key else None
 
     # Extract lollms specific parameters
     request_data = {
@@ -626,7 +626,12 @@ async def nvidia_openai_complete(
 
 
 async def azure_openai_complete(
-    model: str = "gpt-4o-mini", prompt="", system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
+    model: str = "gpt-4o-mini",
+    prompt="",
+    system_prompt=None,
+    history_messages=[],
+    keyword_extraction=False,
+    **kwargs,
 ) -> str:
     keyword_extraction = kwargs.pop("keyword_extraction", None)
     result = await azure_openai_complete_if_cache(
@@ -1153,7 +1158,11 @@ async def ollama_embedding(texts: list[str], embed_model, **kwargs) -> np.ndarra
 
 async def ollama_embed(texts: list[str], embed_model, **kwargs) -> np.ndarray:
     api_key = kwargs.pop("api_key", None)
-    headers = {"Authorization": api_key, "Content-Type": "application/json"} if api_key else None
+    headers = (
+        {"Authorization": api_key, "Content-Type": "application/json"}
+        if api_key
+        else None
+    )
     kwargs["headers"] = headers
     ollama_client = ollama.Client(**kwargs)
     data = ollama_client.embed(model=embed_model, input=texts)
@@ -1176,14 +1185,19 @@ async def lollms_embed(
         np.ndarray: Array of embeddings
     """
     api_key = kwargs.pop("api_key", None)
-    headers = {"Authorization": api_key, "Content-Type": "application/json"} if api_key else None
+    headers = (
+        {"Authorization": api_key, "Content-Type": "application/json"}
+        if api_key
+        else None
+    )
     async with aiohttp.ClientSession(headers=headers) as session:
         embeddings = []
         for text in texts:
             request_data = {"text": text}
 
             async with session.post(
-                f"{base_url}/lollms_embed", json=request_data, 
+                f"{base_url}/lollms_embed",
+                json=request_data,
             ) as response:
                 result = await response.json()
                 embeddings.append(result["vector"])