Fix linting

lightrag/api/routers/query_routes.py

@@ -198,7 +198,7 @@ def create_query_routes(rag, api_key: Optional[str] = None, top_k: int = 60):
     async def query_text_stream(request: QueryRequest):
         """
         This endpoint performs a retrieval-augmented generation (RAG) query and streams the response.
-        
+
         The streaming response includes:
         1. Reference list (sent first as a single message, if include_references=True)
         2. LLM response content (streamed as multiple chunks)
@@ -224,18 +224,22 @@ def create_query_routes(rag, api_key: Optional[str] = None, top_k: int = 60):
                 if request.include_references:
                     try:
                         # Use aquery_data to get reference list independently
-                        data_param = request.to_query_params(False)  # Non-streaming for data
+                        data_param = request.to_query_params(
-                        data_result = await rag.aquery_data(request.query, param=data_param)
+                            False
+                        )  # Non-streaming for data
+                        data_result = await rag.aquery_data(
+                            request.query, param=data_param
+                        )
                         if isinstance(data_result, dict) and "data" in data_result:
                             reference_list = data_result["data"].get("references", [])
                     except Exception as e:
                         logging.warning(f"Failed to get reference list: {str(e)}")
                         reference_list = []
-                
+
                 # Send reference list first (if requested)
                 if request.include_references:
                     yield f"{json.dumps({'references': reference_list})}\n"
-                
+
                 # Then stream the response content
                 if isinstance(response, str):
                     # If it's a string, send it all at once

lightrag/base.py

@@ -828,27 +828,29 @@ class DeletionResult:
 
 # Unified Query Result Data Structures for Reference List Support
 
+
 @dataclass
 class QueryResult:
     """
     Unified query result data structure for all query modes.
-    
+
     Attributes:
         content: Text content for non-streaming responses
         response_iterator: Streaming response iterator for streaming responses
         raw_data: Complete structured data including references and metadata
         is_streaming: Whether this is a streaming result
     """
+
     content: Optional[str] = None
     response_iterator: Optional[AsyncIterator[str]] = None
     raw_data: Optional[Dict[str, Any]] = None
     is_streaming: bool = False
-    
+
     @property
     def reference_list(self) -> List[Dict[str, str]]:
         """
         Convenient property to extract reference list from raw_data.
-        
+
         Returns:
             List[Dict[str, str]]: Reference list in format:
             [{"reference_id": "1", "file_path": "/path/to/file.pdf"}, ...]
@@ -856,12 +858,12 @@ class QueryResult:
         if self.raw_data:
             return self.raw_data.get("data", {}).get("references", [])
         return []
-    
+
     @property
     def metadata(self) -> Dict[str, Any]:
         """
         Convenient property to extract metadata from raw_data.
-        
+
         Returns:
             Dict[str, Any]: Query metadata including query_mode, keywords, etc.
         """
@@ -874,14 +876,15 @@ class QueryResult:
 class QueryContextResult:
     """
     Unified query context result data structure.
-    
+
     Attributes:
         context: LLM context string
         raw_data: Complete structured data including reference_list
     """
+
     context: str
     raw_data: Dict[str, Any]
-    
+
     @property
     def reference_list(self) -> List[Dict[str, str]]:
         """Convenient property to extract reference list from raw_data."""

lightrag/lightrag.py

@@ -2077,7 +2077,7 @@ class LightRAG:
         global_config = asdict(self)
 
         query_result = None
-        
+
         if param.mode in ["local", "global", "hybrid", "mix"]:
             query_result = await kg_query(
                 query.strip(),
@@ -2118,13 +2118,13 @@ class LightRAG:
             query_result = QueryResult(
                 content=response if not param.stream else None,
                 response_iterator=response if param.stream else None,
-                is_streaming=param.stream
+                is_streaming=param.stream,
             )
         else:
             raise ValueError(f"Unknown mode {param.mode}")
-        
+
         await self._query_done()
-        
+
         # Return appropriate response based on streaming mode
         if query_result.is_streaming:
             return query_result.response_iterator
@@ -2266,7 +2266,7 @@ class LightRAG:
         )
 
         query_result = None
-        
+
         if data_param.mode in ["local", "global", "hybrid", "mix"]:
             logger.debug(f"[aquery_data] Using kg_query for mode: {data_param.mode}")
             query_result = await kg_query(
@@ -2301,10 +2301,7 @@ class LightRAG:
                 [],  # no references
                 "bypass",
             )
-            query_result = QueryResult(
+            query_result = QueryResult(content="", raw_data=empty_raw_data)
-                content="",
-                raw_data=empty_raw_data
-            )
         else:
             raise ValueError(f"Unknown mode {data_param.mode}")
 

lightrag/operate.py

@@ -2282,7 +2282,7 @@ async def kg_query(
 ) -> QueryResult:
     """
     Execute knowledge graph query and return unified QueryResult object.
-    
+
     Args:
         query: Query string
         knowledge_graph_inst: Knowledge graph storage instance
@@ -2294,21 +2294,21 @@ async def kg_query(
         hashing_kv: Cache storage
         system_prompt: System prompt
         chunks_vdb: Document chunks vector database
-    
+
     Returns:
         QueryResult: Unified query result object containing:
             - content: Non-streaming response text content
             - response_iterator: Streaming response iterator
             - raw_data: Complete structured data (including references and metadata)
             - is_streaming: Whether this is a streaming result
-        
+
         Based on different query_param settings, different fields will be populated:
         - only_need_context=True: content contains context string
         - only_need_prompt=True: content contains complete prompt
         - stream=True: response_iterator contains streaming response, raw_data contains complete data
         - default: content contains LLM response text, raw_data contains complete data
     """
-    
+
     if not query:
         return QueryResult(content=PROMPTS["fail_response"])
 
@@ -2386,8 +2386,7 @@ async def kg_query(
     # Return different content based on query parameters
     if query_param.only_need_context and not query_param.only_need_prompt:
         return QueryResult(
-            content=context_result.context,
+            content=context_result.context, raw_data=context_result.raw_data
-            raw_data=context_result.raw_data
         )
 
     # Build system prompt
@@ -2405,10 +2404,7 @@ async def kg_query(
 
     if query_param.only_need_prompt:
         prompt_content = "\n\n".join([sys_prompt, "---User Query---", user_query])
-        return QueryResult(
+        return QueryResult(content=prompt_content, raw_data=context_result.raw_data)
-            content=prompt_content,
-            raw_data=context_result.raw_data
-        )
 
     # Call LLM
     tokenizer: Tokenizer = global_config["tokenizer"]
@@ -2466,16 +2462,13 @@ async def kg_query(
                 ),
             )
 
-        return QueryResult(
+        return QueryResult(content=response, raw_data=context_result.raw_data)
-            content=response,
-            raw_data=context_result.raw_data
-        )
     else:
         # Streaming response (AsyncIterator)
         return QueryResult(
             response_iterator=response,
             raw_data=context_result.raw_data,
-            is_streaming=True
+            is_streaming=True,
         )
 
 
@@ -3375,7 +3368,7 @@ async def _build_query_context(
     """
     Main query context building function using the new 4-stage architecture:
     1. Search -> 2. Truncate -> 3. Merge chunks -> 4. Build LLM context
-    
+
     Returns unified QueryContextResult containing both context and raw_data.
     """
 
@@ -3477,11 +3470,8 @@ async def _build_query_context(
     logger.debug(
         f"[_build_query_context] Raw data entities: {len(raw_data.get('data', {}).get('entities', []))}, relationships: {len(raw_data.get('data', {}).get('relationships', []))}, chunks: {len(raw_data.get('data', {}).get('chunks', []))}"
     )
-    
+
-    return QueryContextResult(
+    return QueryContextResult(context=context, raw_data=raw_data)
-        context=context,
-        raw_data=raw_data
-    )
 
 
 async def _get_node_data(
@@ -4090,7 +4080,7 @@ async def naive_query(
 ) -> QueryResult:
     """
     Execute naive query and return unified QueryResult object.
-    
+
     Args:
         query: Query string
         chunks_vdb: Document chunks vector database
@@ -4098,7 +4088,7 @@ async def naive_query(
         global_config: Global configuration
         hashing_kv: Cache storage
         system_prompt: System prompt
-    
+
     Returns:
         QueryResult: Unified query result object containing:
             - content: Non-streaming response text content
@@ -4106,7 +4096,7 @@ async def naive_query(
             - raw_data: Complete structured data (including references and metadata)
             - is_streaming: Whether this is a streaming result
     """
-    
+
     if not query:
         return QueryResult(content=PROMPTS["fail_response"])
 
@@ -4157,10 +4147,7 @@ async def naive_query(
             "naive",
         )
         empty_raw_data["message"] = "No relevant document chunks found."
-        return QueryResult(
+        return QueryResult(content=PROMPTS["fail_response"], raw_data=empty_raw_data)
-            content=PROMPTS["fail_response"],
-            raw_data=empty_raw_data
-        )
 
     # Calculate dynamic token limit for chunks
     max_total_tokens = getattr(
@@ -4275,10 +4262,7 @@ async def naive_query(
 """
 
     if query_param.only_need_context and not query_param.only_need_prompt:
-        return QueryResult(
+        return QueryResult(content=context_content, raw_data=raw_data)
-            content=context_content,
-            raw_data=raw_data
-        )
 
     user_query = (
         "\n\n".join([query, query_param.user_prompt])
@@ -4294,10 +4278,7 @@ async def naive_query(
 
     if query_param.only_need_prompt:
         prompt_content = "\n\n".join([sys_prompt, "---User Query---", user_query])
-        return QueryResult(
+        return QueryResult(content=prompt_content, raw_data=raw_data)
-            content=prompt_content,
-            raw_data=raw_data
-        )
 
     len_of_prompts = len(tokenizer.encode(query + sys_prompt))
     logger.debug(
@@ -4354,14 +4335,9 @@ async def naive_query(
                 ),
             )
 
-        return QueryResult(
+        return QueryResult(content=response, raw_data=raw_data)
-            content=response,
-            raw_data=raw_data
-        )
     else:
         # Streaming response (AsyncIterator)
         return QueryResult(
-            response_iterator=response,
+            response_iterator=response, raw_data=raw_data, is_streaming=True
-            raw_data=raw_data,
-            is_streaming=True
         )