Merge pull request #791 from ArnoChenFx/refactor-server

Refactor File Indexing for Background Asynchronous Processing

lightrag/api/lightrag_server.py

@@ -3,7 +3,6 @@ from fastapi import (
     HTTPException,
     File,
     UploadFile,
-    Form,
     BackgroundTasks,
 )
 import asyncio
@@ -14,7 +13,7 @@ import re
 from fastapi.staticfiles import StaticFiles
 import logging
 import argparse
-from typing import List, Any, Optional, Union, Dict
+from typing import List, Any, Optional, Dict
 from pydantic import BaseModel
 from lightrag import LightRAG, QueryParam
 from lightrag.types import GPTKeywordExtractionFormat
@@ -34,6 +33,9 @@ from starlette.status import HTTP_403_FORBIDDEN
 import pipmaster as pm
 from dotenv import load_dotenv
 import configparser
+import traceback
+from datetime import datetime
+
 from lightrag.utils import logger
 from .ollama_api import (
     OllamaAPI,
@@ -635,9 +637,47 @@ class SearchMode(str, Enum):
 
 class QueryRequest(BaseModel):
     query: str
+
+    """Specifies the retrieval mode"""
     mode: SearchMode = SearchMode.hybrid
-    stream: bool = False
-    only_need_context: bool = False
+
+    """If True, enables streaming output for real-time responses."""
+    stream: Optional[bool] = None
+
+    """If True, only returns the retrieved context without generating a response."""
+    only_need_context: Optional[bool] = None
+
+    """If True, only returns the generated prompt without producing a response."""
+    only_need_prompt: Optional[bool] = None
+
+    """Defines the response format. Examples: 'Multiple Paragraphs', 'Single Paragraph', 'Bullet Points'."""
+    response_type: Optional[str] = None
+
+    """Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode."""
+    top_k: Optional[int] = None
+
+    """Maximum number of tokens allowed for each retrieved text chunk."""
+    max_token_for_text_unit: Optional[int] = None
+
+    """Maximum number of tokens allocated for relationship descriptions in global retrieval."""
+    max_token_for_global_context: Optional[int] = None
+
+    """Maximum number of tokens allocated for entity descriptions in local retrieval."""
+    max_token_for_local_context: Optional[int] = None
+
+    """List of high-level keywords to prioritize in retrieval."""
+    hl_keywords: Optional[List[str]] = None
+
+    """List of low-level keywords to refine retrieval focus."""
+    ll_keywords: Optional[List[str]] = None
+
+    """Stores past conversation history to maintain context.
+    Format: [{"role": "user/assistant", "content": "message"}].
+    """
+    conversation_history: Optional[List[dict[str, Any]]] = None
+
+    """Number of complete conversation turns (user-assistant pairs) to consider in the response context."""
+    history_turns: Optional[int] = None
 
 
 class QueryResponse(BaseModel):
@@ -646,13 +686,38 @@ class QueryResponse(BaseModel):
 
 class InsertTextRequest(BaseModel):
     text: str
-    description: Optional[str] = None
 
 
 class InsertResponse(BaseModel):
     status: str
     message: str
-    document_count: int
+
+
+def QueryRequestToQueryParams(request: QueryRequest):
+    param = QueryParam(mode=request.mode, stream=request.stream)
+    if request.only_need_context is not None:
+        param.only_need_context = request.only_need_context
+    if request.only_need_prompt is not None:
+        param.only_need_prompt = request.only_need_prompt
+    if request.response_type is not None:
+        param.response_type = request.response_type
+    if request.top_k is not None:
+        param.top_k = request.top_k
+    if request.max_token_for_text_unit is not None:
+        param.max_token_for_text_unit = request.max_token_for_text_unit
+    if request.max_token_for_global_context is not None:
+        param.max_token_for_global_context = request.max_token_for_global_context
+    if request.max_token_for_local_context is not None:
+        param.max_token_for_local_context = request.max_token_for_local_context
+    if request.hl_keywords is not None:
+        param.hl_keywords = request.hl_keywords
+    if request.ll_keywords is not None:
+        param.ll_keywords = request.ll_keywords
+    if request.conversation_history is not None:
+        param.conversation_history = request.conversation_history
+    if request.history_turns is not None:
+        param.history_turns = request.history_turns
+    return param
 
 
 def get_api_key_dependency(api_key: Optional[str]):
@@ -666,7 +731,9 @@ def get_api_key_dependency(api_key: Optional[str]):
     # If API key is configured, use proper authentication
     api_key_header = APIKeyHeader(name="X-API-Key", auto_error=False)
 
-    async def api_key_auth(api_key_header_value: str | None = Security(api_key_header)):
+    async def api_key_auth(
+        api_key_header_value: Optional[str] = Security(api_key_header),
+    ):
         if not api_key_header_value:
             raise HTTPException(
                 status_code=HTTP_403_FORBIDDEN, detail="API Key required"
@@ -682,6 +749,7 @@ def get_api_key_dependency(api_key: Optional[str]):
 
 # Global configuration
 global_top_k = 60  # default value
+temp_prefix = "__tmp_"  # prefix for temporary files
 
 
 def create_app(args):
@@ -1132,79 +1200,162 @@ def create_app(args):
         ("llm_response_cache", rag.llm_response_cache),
     ]
 
-    async def index_file(file_path: Union[str, Path]) -> None:
-        """Index all files inside the folder with support for multiple file formats
+    async def pipeline_enqueue_file(file_path: Path) -> bool:
+        """Add a file to the queue for processing
 
         Args:
-            file_path: Path to the file to be indexed (str or Path object)
-
-        Raises:
-            ValueError: If file format is not supported
-            FileNotFoundError: If file doesn't exist
+            file_path: Path to the saved file
+        Returns:
+            bool: True if the file was successfully enqueued, False otherwise
         """
-        if not pm.is_installed("aiofiles"):
-            pm.install("aiofiles")
+        try:
+            content = ""
+            ext = file_path.suffix.lower()
 
-        # Convert to Path object if string
-        file_path = Path(file_path)
+            file = None
+            async with aiofiles.open(file_path, "rb") as f:
+                file = await f.read()
 
-        # Check if file exists
-        if not file_path.exists():
-            raise FileNotFoundError(f"File not found: {file_path}")
+            # Process based on file type
+            match ext:
+                case ".txt" | ".md":
+                    content = file.decode("utf-8")
+                case ".pdf":
+                    if not pm.is_installed("pypdf2"):
+                        pm.install("pypdf2")
+                    from PyPDF2 import PdfReader
+                    from io import BytesIO
+
+                    pdf_file = BytesIO(file)
+                    reader = PdfReader(pdf_file)
+                    for page in reader.pages:
+                        content += page.extract_text() + "\n"
+                case ".docx":
+                    if not pm.is_installed("docx"):
+                        pm.install("docx")
+                    from docx import Document
+                    from io import BytesIO
+
+                    docx_content = await file.read()
+                    docx_file = BytesIO(docx_content)
+                    doc = Document(docx_file)
+                    content = "\n".join(
+                        [paragraph.text for paragraph in doc.paragraphs]
+                    )
+                case ".pptx":
+                    if not pm.is_installed("pptx"):
+                        pm.install("pptx")
+                    from pptx import Presentation  # type: ignore
+                    from io import BytesIO
+
+                    pptx_content = await file.read()
+                    pptx_file = BytesIO(pptx_content)
+                    prs = Presentation(pptx_file)
+                    for slide in prs.slides:
+                        for shape in slide.shapes:
+                            if hasattr(shape, "text"):
+                                content += shape.text + "\n"
+                case _:
+                    logging.error(
+                        f"Unsupported file type: {file_path.name} (extension {ext})"
+                    )
+                    return False
+
+            # Insert into the RAG queue
+            if content:
+                await rag.apipeline_enqueue_documents(content)
+                logging.info(
+                    f"Successfully processed and enqueued file: {file_path.name}"
+                )
+                return True
+            else:
+                logging.error(
+                    f"No content could be extracted from file: {file_path.name}"
+                )
 
-        content = ""
-        # Get file extension in lowercase
-        ext = file_path.suffix.lower()
+        except Exception as e:
+            logging.error(
+                f"Error processing or enqueueing file {file_path.name}: {str(e)}"
+            )
+            logging.error(traceback.format_exc())
+        finally:
+            if file_path.name.startswith(temp_prefix):
+                # Clean up the temporary file after indexing
+                try:
+                    file_path.unlink()
+                except Exception as e:
+                    logging.error(f"Error deleting file {file_path}: {str(e)}")
+        return False
 
-        match ext:
-            case ".txt" | ".md":
-                # Text files handling
-                async with aiofiles.open(file_path, "r", encoding="utf-8") as f:
-                    content = await f.read()
+    async def pipeline_index_file(file_path: Path):
+        """Index a file
 
-            case ".pdf" | ".docx" | ".pptx" | ".xlsx":
-                if not pm.is_installed("docling"):
-                    pm.install("docling")
-                from docling.document_converter import DocumentConverter
+        Args:
+            file_path: Path to the saved file
+        """
+        try:
+            if await pipeline_enqueue_file(file_path):
+                await rag.apipeline_process_enqueue_documents()
 
-                async def convert_doc():
-                    def sync_convert():
-                        converter = DocumentConverter()
-                        result = converter.convert(file_path)
-                        return result.document.export_to_markdown()
+        except Exception as e:
+            logging.error(f"Error indexing file {file_path.name}: {str(e)}")
+            logging.error(traceback.format_exc())
 
-                    return await asyncio.to_thread(sync_convert)
+    async def pipeline_index_files(file_paths: List[Path]):
+        """Index multiple files concurrently
 
-                content = await convert_doc()
+        Args:
+            file_paths: Paths to the files to index
+        """
+        if not file_paths:
+            return
+        try:
+            enqueued = False
 
-            case _:
-                raise ValueError(f"Unsupported file format: {ext}")
+            if len(file_paths) == 1:
+                enqueued = await pipeline_enqueue_file(file_paths[0])
+            else:
+                tasks = [pipeline_enqueue_file(path) for path in file_paths]
+                enqueued = any(await asyncio.gather(*tasks))
 
-        # Insert content into RAG system
-        if content:
-            await rag.ainsert(content)
-            doc_manager.mark_as_indexed(file_path)
-            logging.info(f"Successfully indexed file: {file_path}")
-        else:
-            logging.warning(f"No content extracted from file: {file_path}")
+            if enqueued:
+                await rag.apipeline_process_enqueue_documents()
+        except Exception as e:
+            logging.error(f"Error indexing files: {str(e)}")
+            logging.error(traceback.format_exc())
 
-    @app.post("/documents/scan", dependencies=[Depends(optional_api_key)])
-    async def scan_for_new_documents(background_tasks: BackgroundTasks):
-        """Trigger the scanning process"""
-        global scan_progress
+    async def pipeline_index_texts(texts: List[str]):
+        """Index a list of texts
 
-        with progress_lock:
-            if scan_progress["is_scanning"]:
-                return {"status": "already_scanning"}
+        Args:
+            texts: The texts to index
+        """
+        if not texts:
+            return
+        await rag.apipeline_enqueue_documents(texts)
+        await rag.apipeline_process_enqueue_documents()
 
-            scan_progress["is_scanning"] = True
-            scan_progress["indexed_count"] = 0
-            scan_progress["progress"] = 0
+    async def save_temp_file(file: UploadFile = File(...)) -> Path:
+        """Save the uploaded file to a temporary location
 
-        # Start the scanning process in the background
-        background_tasks.add_task(run_scanning_process)
+        Args:
+            file: The uploaded file
 
-        return {"status": "scanning_started"}
+        Returns:
+            Path: The path to the saved file
+        """
+        # Generate unique filename to avoid conflicts
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        unique_filename = f"{temp_prefix}{timestamp}_{file.filename}"
+
+        # Create a temporary file to save the uploaded content
+        temp_path = doc_manager.input_dir / "temp" / unique_filename
+        temp_path.parent.mkdir(exist_ok=True)
+
+        # Save the file
+        with open(temp_path, "wb") as buffer:
+            shutil.copyfileobj(file.file, buffer)
+        return temp_path
 
     async def run_scanning_process():
         """Background task to scan and index documents"""
@@ -1220,7 +1371,7 @@ def create_app(args):
                     with progress_lock:
                         scan_progress["current_file"] = os.path.basename(file_path)
 
-                    await index_file(file_path)
+                    await pipeline_index_file(file_path)
 
                     with progress_lock:
                         scan_progress["indexed_count"] += 1
@@ -1238,6 +1389,24 @@ def create_app(args):
             with progress_lock:
                 scan_progress["is_scanning"] = False
 
+    @app.post("/documents/scan", dependencies=[Depends(optional_api_key)])
+    async def scan_for_new_documents(background_tasks: BackgroundTasks):
+        """Trigger the scanning process"""
+        global scan_progress
+
+        with progress_lock:
+            if scan_progress["is_scanning"]:
+                return {"status": "already_scanning"}
+
+            scan_progress["is_scanning"] = True
+            scan_progress["indexed_count"] = 0
+            scan_progress["progress"] = 0
+
+        # Start the scanning process in the background
+        background_tasks.add_task(run_scanning_process)
+
+        return {"status": "scanning_started"}
+
     @app.get("/documents/scan-progress")
     async def get_scan_progress():
         """Get the current scanning progress"""
@@ -1245,7 +1414,9 @@ def create_app(args):
             return scan_progress
 
     @app.post("/documents/upload", dependencies=[Depends(optional_api_key)])
-    async def upload_to_input_dir(file: UploadFile = File(...)):
+    async def upload_to_input_dir(
+        background_tasks: BackgroundTasks, file: UploadFile = File(...)
+    ):
         """
         Endpoint for uploading a file to the input directory and indexing it.
 
@@ -1254,6 +1425,7 @@ def create_app(args):
         indexes it for retrieval, and returns a success status with relevant details.
 
         Parameters:
+            background_tasks: FastAPI BackgroundTasks for async processing
             file (UploadFile): The file to be uploaded. It must have an allowed extension as per
                                `doc_manager.supported_extensions`.
 
@@ -1278,121 +1450,16 @@ def create_app(args):
             with open(file_path, "wb") as buffer:
                 shutil.copyfileobj(file.file, buffer)
 
-            # Immediately index the uploaded file
-            await index_file(file_path)
-
-            return {
-                "status": "success",
-                "message": f"File uploaded and indexed: {file.filename}",
-                "total_documents": len(doc_manager.indexed_files),
-            }
-        except Exception as e:
-            raise HTTPException(status_code=500, detail=str(e))
-
-    @app.post(
-        "/query", response_model=QueryResponse, dependencies=[Depends(optional_api_key)]
-    )
-    async def query_text(request: QueryRequest):
-        """
-        Handle a POST request at the /query endpoint to process user queries using RAG capabilities.
-
-        Parameters:
-            request (QueryRequest): A Pydantic model containing the following fields:
-                - query (str): The text of the user's query.
-                - mode (ModeEnum): Optional. Specifies the mode of retrieval augmentation.
-                - stream (bool): Optional. Determines if the response should be streamed.
-                - only_need_context (bool): Optional. If true, returns only the context without further processing.
-
-        Returns:
-            QueryResponse: A Pydantic model containing the result of the query processing.
-                           If a string is returned (e.g., cache hit), it's directly returned.
-                           Otherwise, an async generator may be used to build the response.
-
-        Raises:
-            HTTPException: Raised when an error occurs during the request handling process,
-                           with status code 500 and detail containing the exception message.
-        """
-        try:
-            response = await rag.aquery(
-                request.query,
-                param=QueryParam(
-                    mode=request.mode,
-                    stream=request.stream,
-                    only_need_context=request.only_need_context,
-                    top_k=global_top_k,
-                ),
-            )
-
-            # If response is a string (e.g. cache hit), return directly
-            if isinstance(response, str):
-                return QueryResponse(response=response)
-
-            # If it's an async generator, decide whether to stream based on stream parameter
-            if request.stream:
-                result = ""
-                async for chunk in response:
-                    result += chunk
-                return QueryResponse(response=result)
-            else:
-                result = ""
-                async for chunk in response:
-                    result += chunk
-                return QueryResponse(response=result)
-        except Exception as e:
-            trace_exception(e)
-            raise HTTPException(status_code=500, detail=str(e))
-
-    @app.post("/query/stream", dependencies=[Depends(optional_api_key)])
-    async def query_text_stream(request: QueryRequest):
-        """
-        This endpoint performs a retrieval-augmented generation (RAG) query and streams the response.
-
-        Args:
-            request (QueryRequest): The request object containing the query parameters.
-            optional_api_key (Optional[str], optional): An optional API key for authentication. Defaults to None.
-
-        Returns:
-            StreamingResponse: A streaming response containing the RAG query results.
-        """
-        try:
-            response = await rag.aquery(  # Use aquery instead of query, and add await
-                request.query,
-                param=QueryParam(
-                    mode=request.mode,
-                    stream=True,
-                    only_need_context=request.only_need_context,
-                    top_k=global_top_k,
-                ),
-            )
-
-            from fastapi.responses import StreamingResponse
-
-            async def stream_generator():
-                if isinstance(response, str):
-                    # If it's a string, send it all at once
-                    yield f"{json.dumps({'response': response})}\n"
-                else:
-                    # If it's an async generator, send chunks one by one
-                    try:
-                        async for chunk in response:
-                            if chunk:  # Only send non-empty content
-                                yield f"{json.dumps({'response': chunk})}\n"
-                    except Exception as e:
-                        logging.error(f"Streaming error: {str(e)}")
-                        yield f"{json.dumps({'error': str(e)})}\n"
+            # Add to background tasks
+            background_tasks.add_task(pipeline_index_file, file_path)
 
-            return StreamingResponse(
-                stream_generator(),
-                media_type="application/x-ndjson",
-                headers={
-                    "Cache-Control": "no-cache",
-                    "Connection": "keep-alive",
-                    "Content-Type": "application/x-ndjson",
-                    "X-Accel-Buffering": "no",  # 确保在Nginx代理时正确处理流式响应
-                },
+            return InsertResponse(
+                status="success",
+                message=f"File '{file.filename}' uploaded successfully. Processing will continue in background.",
             )
         except Exception as e:
-            trace_exception(e)
+            logging.error(f"Error /documents/upload: {file.filename}: {str(e)}")
+            logging.error(traceback.format_exc())
             raise HTTPException(status_code=500, detail=str(e))
 
     @app.post(
@@ -1400,7 +1467,9 @@ def create_app(args):
         response_model=InsertResponse,
         dependencies=[Depends(optional_api_key)],
     )
-    async def insert_text(request: InsertTextRequest):
+    async def insert_text(
+        request: InsertTextRequest, background_tasks: BackgroundTasks
+    ):
         """
         Insert text into the Retrieval-Augmented Generation (RAG) system.
 
@@ -1408,18 +1477,20 @@ def create_app(args):
 
         Args:
             request (InsertTextRequest): The request body containing the text to be inserted.
+            background_tasks: FastAPI BackgroundTasks for async processing
 
         Returns:
             InsertResponse: A response object containing the status of the operation, a message, and the number of documents inserted.
         """
         try:
-            await rag.ainsert(request.text)
+            background_tasks.add_task(pipeline_index_texts, [request.text])
             return InsertResponse(
                 status="success",
-                message="Text successfully inserted",
-                document_count=1,
+                message="Text successfully received. Processing will continue in background.",
             )
         except Exception as e:
+            logging.error(f"Error /documents/text: {str(e)}")
+            logging.error(traceback.format_exc())
             raise HTTPException(status_code=500, detail=str(e))
 
     @app.post(
@@ -1427,12 +1498,14 @@ def create_app(args):
         response_model=InsertResponse,
         dependencies=[Depends(optional_api_key)],
     )
-    async def insert_file(file: UploadFile = File(...), description: str = Form(None)):
+    async def insert_file(
+        background_tasks: BackgroundTasks, file: UploadFile = File(...)
+    ):
         """Insert a file directly into the RAG system
 
         Args:
+            background_tasks: FastAPI BackgroundTasks for async processing
             file: Uploaded file
-            description: Optional description of the file
 
         Returns:
             InsertResponse: Status of the insertion operation
@@ -1441,68 +1514,26 @@ def create_app(args):
             HTTPException: For unsupported file types or processing errors
         """
         try:
-            content = ""
-            # Get file extension in lowercase
-            ext = Path(file.filename).suffix.lower()
-
-            match ext:
-                case ".txt" | ".md":
-                    # Text files handling
-                    text_content = await file.read()
-                    content = text_content.decode("utf-8")
-
-                case ".pdf" | ".docx" | ".pptx" | ".xlsx":
-                    if not pm.is_installed("docling"):
-                        pm.install("docling")
-                    from docling.document_converter import DocumentConverter
-
-                    # Create a temporary file to save the uploaded content
-                    temp_path = Path("temp") / file.filename
-                    temp_path.parent.mkdir(exist_ok=True)
-
-                    # Save the uploaded file
-                    with temp_path.open("wb") as f:
-                        f.write(await file.read())
-
-                    try:
-
-                        async def convert_doc():
-                            def sync_convert():
-                                converter = DocumentConverter()
-                                result = converter.convert(str(temp_path))
-                                return result.document.export_to_markdown()
-
-                            return await asyncio.to_thread(sync_convert)
-
-                        content = await convert_doc()
-                    finally:
-                        # Clean up the temporary file
-                        temp_path.unlink()
-
-            # Insert content into RAG system
-            if content:
-                # Add description if provided
-                if description:
-                    content = f"{description}\n\n{content}"
-
-                await rag.ainsert(content)
-                logging.info(f"Successfully indexed file: {file.filename}")
-
-                return InsertResponse(
-                    status="success",
-                    message=f"File '{file.filename}' successfully inserted",
-                    document_count=1,
-                )
-            else:
+            if not doc_manager.is_supported_file(file.filename):
                 raise HTTPException(
                     status_code=400,
-                    detail="No content could be extracted from the file",
+                    detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}",
                 )
 
-        except UnicodeDecodeError:
-            raise HTTPException(status_code=400, detail="File encoding not supported")
+            # Create a temporary file to save the uploaded content
+            temp_path = save_temp_file(file)
+
+            # Add to background tasks
+            background_tasks.add_task(pipeline_index_file, temp_path)
+
+            return InsertResponse(
+                status="success",
+                message=f"File '{file.filename}' saved successfully. Processing will continue in background.",
+            )
+
         except Exception as e:
-            logging.error(f"Error processing file {file.filename}: {str(e)}")
+            logging.error(f"Error /documents/file: {str(e)}")
+            logging.error(traceback.format_exc())
             raise HTTPException(status_code=500, detail=str(e))
 
     @app.post(
@@ -1510,10 +1541,13 @@ def create_app(args):
         response_model=InsertResponse,
         dependencies=[Depends(optional_api_key)],
     )
-    async def insert_batch(files: List[UploadFile] = File(...)):
+    async def insert_batch(
+        background_tasks: BackgroundTasks, files: List[UploadFile] = File(...)
+    ):
         """Process multiple files in batch mode
 
         Args:
+            background_tasks: FastAPI BackgroundTasks for async processing
             files: List of files to process
 
         Returns:
@@ -1525,72 +1559,18 @@ def create_app(args):
         try:
             inserted_count = 0
             failed_files = []
+            temp_files = []
 
             for file in files:
-                try:
-                    content = ""
-                    ext = Path(file.filename).suffix.lower()
-
-                    match ext:
-                        case ".txt" | ".md":
-                            text_content = await file.read()
-                            content = text_content.decode("utf-8")
-
-                        case ".pdf":
-                            if not pm.is_installed("pypdf2"):
-                                pm.install("pypdf2")
-                            from PyPDF2 import PdfReader
-                            from io import BytesIO
-
-                            pdf_content = await file.read()
-                            pdf_file = BytesIO(pdf_content)
-                            reader = PdfReader(pdf_file)
-                            for page in reader.pages:
-                                content += page.extract_text() + "\n"
-
-                        case ".docx":
-                            if not pm.is_installed("docx"):
-                                pm.install("docx")
-                            from docx import Document
-                            from io import BytesIO
-
-                            docx_content = await file.read()
-                            docx_file = BytesIO(docx_content)
-                            doc = Document(docx_file)
-                            content = "\n".join(
-                                [paragraph.text for paragraph in doc.paragraphs]
-                            )
-
-                        case ".pptx":
-                            if not pm.is_installed("pptx"):
-                                pm.install("pptx")
-                            from pptx import Presentation  # type: ignore
-                            from io import BytesIO
-
-                            pptx_content = await file.read()
-                            pptx_file = BytesIO(pptx_content)
-                            prs = Presentation(pptx_file)
-                            for slide in prs.slides:
-                                for shape in slide.shapes:
-                                    if hasattr(shape, "text"):
-                                        content += shape.text + "\n"
-
-                        case _:
-                            failed_files.append(f"{file.filename} (unsupported type)")
-                            continue
-
-                    if content:
-                        await rag.ainsert(content)
-                        inserted_count += 1
-                        logging.info(f"Successfully indexed file: {file.filename}")
-                    else:
-                        failed_files.append(f"{file.filename} (no content extracted)")
+                if doc_manager.is_supported_file(file.filename):
+                    # Create a temporary file to save the uploaded content
+                    temp_files.append(save_temp_file(file))
+                    inserted_count += 1
+                else:
+                    failed_files.append(f"{file.filename} (unsupported type)")
 
-                except UnicodeDecodeError:
-                    failed_files.append(f"{file.filename} (encoding error)")
-                except Exception as e:
-                    failed_files.append(f"{file.filename} ({str(e)})")
-                    logging.error(f"Error processing file {file.filename}: {str(e)}")
+            if temp_files:
+                background_tasks.add_task(pipeline_index_files, temp_files)
 
             # Prepare status message
             if inserted_count == len(files):
@@ -1607,14 +1587,11 @@ def create_app(args):
                 if failed_files:
                     status_message += f". Failed files: {', '.join(failed_files)}"
 
-            return InsertResponse(
-                status=status,
-                message=status_message,
-                document_count=inserted_count,
-            )
+            return InsertResponse(status=status, message=status_message)
 
         except Exception as e:
-            logging.error(f"Batch processing error: {str(e)}")
+            logging.error(f"Error /documents/batch: {file.filename}: {str(e)}")
+            logging.error(traceback.format_exc())
             raise HTTPException(status_code=500, detail=str(e))
 
     @app.delete(
@@ -1637,11 +1614,103 @@ def create_app(args):
             rag.entities_vdb = None
             rag.relationships_vdb = None
             return InsertResponse(
-                status="success",
-                message="All documents cleared successfully",
-                document_count=0,
+                status="success", message="All documents cleared successfully"
+            )
+        except Exception as e:
+            logging.error(f"Error DELETE /documents: {str(e)}")
+            logging.error(traceback.format_exc())
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.post(
+        "/query", response_model=QueryResponse, dependencies=[Depends(optional_api_key)]
+    )
+    async def query_text(request: QueryRequest):
+        """
+        Handle a POST request at the /query endpoint to process user queries using RAG capabilities.
+
+        Parameters:
+            request (QueryRequest): The request object containing the query parameters.
+        Returns:
+            QueryResponse: A Pydantic model containing the result of the query processing.
+                           If a string is returned (e.g., cache hit), it's directly returned.
+                           Otherwise, an async generator may be used to build the response.
+
+        Raises:
+            HTTPException: Raised when an error occurs during the request handling process,
+                           with status code 500 and detail containing the exception message.
+        """
+        try:
+            response = await rag.aquery(
+                request.query, param=QueryRequestToQueryParams(request)
             )
+
+            # If response is a string (e.g. cache hit), return directly
+            if isinstance(response, str):
+                return QueryResponse(response=response)
+
+            # If it's an async generator, decide whether to stream based on stream parameter
+            if request.stream or hasattr(response, "__aiter__"):
+                result = ""
+                async for chunk in response:
+                    result += chunk
+                return QueryResponse(response=result)
+            elif isinstance(response, dict):
+                result = json.dumps(response, indent=2)
+                return QueryResponse(response=result)
+            else:
+                return QueryResponse(response=str(response))
         except Exception as e:
+            trace_exception(e)
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.post("/query/stream", dependencies=[Depends(optional_api_key)])
+    async def query_text_stream(request: QueryRequest):
+        """
+        This endpoint performs a retrieval-augmented generation (RAG) query and streams the response.
+
+        Args:
+            request (QueryRequest): The request object containing the query parameters.
+            optional_api_key (Optional[str], optional): An optional API key for authentication. Defaults to None.
+
+        Returns:
+            StreamingResponse: A streaming response containing the RAG query results.
+        """
+        try:
+            params = QueryRequestToQueryParams(request)
+
+            params.stream = True
+            response = await rag.aquery(  # Use aquery instead of query, and add await
+                request.query, param=params
+            )
+
+            from fastapi.responses import StreamingResponse
+
+            async def stream_generator():
+                if isinstance(response, str):
+                    # If it's a string, send it all at once
+                    yield f"{json.dumps({'response': response})}\n"
+                else:
+                    # If it's an async generator, send chunks one by one
+                    try:
+                        async for chunk in response:
+                            if chunk:  # Only send non-empty content
+                                yield f"{json.dumps({'response': chunk})}\n"
+                    except Exception as e:
+                        logging.error(f"Streaming error: {str(e)}")
+                        yield f"{json.dumps({'error': str(e)})}\n"
+
+            return StreamingResponse(
+                stream_generator(),
+                media_type="application/x-ndjson",
+                headers={
+                    "Cache-Control": "no-cache",
+                    "Connection": "keep-alive",
+                    "Content-Type": "application/x-ndjson",
+                    "X-Accel-Buffering": "no",  # 确保在Nginx代理时正确处理流式响应
+                },
+            )
+        except Exception as e:
+            trace_exception(e)
             raise HTTPException(status_code=500, detail=str(e))
 
     # query all graph labels

lightrag/base.py

@@ -249,20 +249,10 @@ class DocStatusStorage(BaseKVStorage):
         """Get counts of documents in each status"""
         raise NotImplementedError
 
-    async def get_failed_docs(self) -> dict[str, DocProcessingStatus]:
-        """Get all failed documents"""
-        raise NotImplementedError
-
-    async def get_pending_docs(self) -> dict[str, DocProcessingStatus]:
-        """Get all pending documents"""
-        raise NotImplementedError
-
-    async def get_processing_docs(self) -> dict[str, DocProcessingStatus]:
-        """Get all processing documents"""
-        raise NotImplementedError
-
-    async def get_processed_docs(self) -> dict[str, DocProcessingStatus]:
-        """Get all procesed documents"""
+    async def get_docs_by_status(
+        self, status: DocStatus
+    ) -> dict[str, DocProcessingStatus]:
+        """Get all documents with a specific status"""
         raise NotImplementedError
 
     async def update_doc_status(self, data: dict[str, Any]) -> None:

lightrag/kg/json_doc_status_impl.py

@@ -93,36 +93,14 @@ class JsonDocStatusStorage(DocStatusStorage):
             counts[doc["status"]] += 1
         return counts
 
-    async def get_failed_docs(self) -> dict[str, DocProcessingStatus]:
-        """Get all failed documents"""
+    async def get_docs_by_status(
+        self, status: DocStatus
+    ) -> dict[str, DocProcessingStatus]:
+        """all documents with a specific status"""
         return {
             k: DocProcessingStatus(**v)
             for k, v in self._data.items()
-            if v["status"] == DocStatus.FAILED
-        }
-
-    async def get_pending_docs(self) -> dict[str, DocProcessingStatus]:
-        """Get all pending documents"""
-        return {
-            k: DocProcessingStatus(**v)
-            for k, v in self._data.items()
-            if v["status"] == DocStatus.PENDING
-        }
-
-    async def get_processed_docs(self) -> dict[str, DocProcessingStatus]:
-        """Get all processed documents"""
-        return {
-            k: DocProcessingStatus(**v)
-            for k, v in self._data.items()
-            if v["status"] == DocStatus.PROCESSED
-        }
-
-    async def get_processing_docs(self) -> dict[str, DocProcessingStatus]:
-        """Get all processing documents"""
-        return {
-            k: DocProcessingStatus(**v)
-            for k, v in self._data.items()
-            if v["status"] == DocStatus.PROCESSING
+            if v["status"] == status
         }
 
     async def index_done_callback(self):

lightrag/kg/mongo_impl.py

@@ -175,7 +175,7 @@ class MongoDocStatusStorage(DocStatusStorage):
     async def get_docs_by_status(
         self, status: DocStatus
     ) -> dict[str, DocProcessingStatus]:
-        """Get all documents by status"""
+        """Get all documents with a specific status"""
         cursor = self._data.find({"status": status.value})
         result = await cursor.to_list()
         return {
@@ -191,22 +191,6 @@ class MongoDocStatusStorage(DocStatusStorage):
             for doc in result
         }
 
-    async def get_failed_docs(self) -> dict[str, DocProcessingStatus]:
-        """Get all failed documents"""
-        return await self.get_docs_by_status(DocStatus.FAILED)
-
-    async def get_pending_docs(self) -> dict[str, DocProcessingStatus]:
-        """Get all pending documents"""
-        return await self.get_docs_by_status(DocStatus.PENDING)
-
-    async def get_processing_docs(self) -> dict[str, DocProcessingStatus]:
-        """Get all processing documents"""
-        return await self.get_docs_by_status(DocStatus.PROCESSING)
-
-    async def get_processed_docs(self) -> dict[str, DocProcessingStatus]:
-        """Get all procesed documents"""
-        return await self.get_docs_by_status(DocStatus.PROCESSED)
-
 
 @dataclass
 class MongoGraphStorage(BaseGraphStorage):

lightrag/kg/postgres_impl.py

@@ -468,7 +468,7 @@ class PGDocStatusStorage(DocStatusStorage):
     async def get_docs_by_status(
         self, status: DocStatus
     ) -> Dict[str, DocProcessingStatus]:
-        """Get all documents by status"""
+        """all documents with a specific status"""
         sql = "select * from LIGHTRAG_DOC_STATUS where workspace=$1 and status=$2"
         params = {"workspace": self.db.workspace, "status": status}
         result = await self.db.query(sql, params, True)
@@ -485,22 +485,6 @@ class PGDocStatusStorage(DocStatusStorage):
             for element in result
         }
 
-    async def get_failed_docs(self) -> Dict[str, DocProcessingStatus]:
-        """Get all failed documents"""
-        return await self.get_docs_by_status(DocStatus.FAILED)
-
-    async def get_pending_docs(self) -> Dict[str, DocProcessingStatus]:
-        """Get all pending documents"""
-        return await self.get_docs_by_status(DocStatus.PENDING)
-
-    async def get_processing_docs(self) -> dict[str, DocProcessingStatus]:
-        """Get all processing documents"""
-        return await self.get_docs_by_status(DocStatus.PROCESSING)
-
-    async def get_processed_docs(self) -> dict[str, DocProcessingStatus]:
-        """Get all procesed documents"""
-        return await self.get_docs_by_status(DocStatus.PROCESSED)
-
     async def index_done_callback(self):
         """Save data after indexing, but for PostgreSQL, we already saved them during the upsert stage, so no action to take here"""
         logger.info("Doc status had been saved into postgresql db!")

lightrag/lightrag.py

@@ -89,7 +89,7 @@ STORAGE_IMPLEMENTATIONS = {
             "PGDocStatusStorage",
             "MongoDocStatusStorage",
         ],
-        "required_methods": ["get_pending_docs"],
+        "required_methods": ["get_docs_by_status"],
     },
 }
 
@@ -230,7 +230,7 @@ class LightRAG:
     """LightRAG: Simple and Fast Retrieval-Augmented Generation."""
 
     working_dir: str = field(
-        default_factory=lambda: f'./lightrag_cache_{datetime.now().strftime("%Y-%m-%d-%H:%M:%S")}'
+        default_factory=lambda: f"./lightrag_cache_{datetime.now().strftime('%Y-%m-%d-%H:%M:%S')}"
     )
     """Directory where cache and temporary files are stored."""
 
@@ -715,11 +715,11 @@ class LightRAG:
         # 1. Get all pending, failed, and abnormally terminated processing documents.
         to_process_docs: dict[str, DocProcessingStatus] = {}
 
-        processing_docs = await self.doc_status.get_processing_docs()
+        processing_docs = await self.doc_status.get_docs_by_status(DocStatus.PROCESSING)
         to_process_docs.update(processing_docs)
-        failed_docs = await self.doc_status.get_failed_docs()
+        failed_docs = await self.doc_status.get_docs_by_status(DocStatus.FAILED)
         to_process_docs.update(failed_docs)
-        pendings_docs = await self.doc_status.get_pending_docs()
+        pendings_docs = await self.doc_status.get_docs_by_status(DocStatus.PENDING)
         to_process_docs.update(pendings_docs)
 
         if not to_process_docs: