refactor file indexing for background async processing

lightrag/api/lightrag_server.py

@@ -14,7 +14,7 @@ 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 +34,9 @@ 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,
@@ -645,7 +648,6 @@ class InsertTextRequest(BaseModel):
 class InsertResponse(BaseModel):
     status: str
     message: str
-    document_count: int
 
 
 def get_api_key_dependency(api_key: Optional[str]):
@@ -675,6 +677,7 @@ 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):
@@ -1116,79 +1119,122 @@ def create_app(args):
         ("llm_response_cache", rag.llm_response_cache),
     ]
 
-    async def index_file(file_path: Union[str, Path]) -> None:
+    async def index_file(file_path: Path, description: Optional[str] = None):
-        """Index all files inside the folder with support for multiple file formats
+        """Index a file
 
         Args:
-            file_path: Path to the file to be indexed (str or Path object)
+            file_path: Path to the saved file
-
+            description: Optional description of the file
-        Raises:
-            ValueError: If file format is not supported
-            FileNotFoundError: If file doesn't exist
         """
-        if not pm.is_installed("aiofiles"):
+        try:
-            pm.install("aiofiles")
+            content = ""
+            ext = file_path.suffix.lower()
 
-        # Convert to Path object if string
+            file = None
-        file_path = Path(file_path)
+            async with aiofiles.open(file_path, "rb") as f:
+                file = await f.read()
 
-        # Check if file exists
+            # Process based on file type
-        if not file_path.exists():
+            match ext:
-            raise FileNotFoundError(f"File not found: {file_path}")
+                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
 
-        content = ""
+                    pdf_file = BytesIO(file)
-        # Get file extension in lowercase
+                    reader = PdfReader(pdf_file)
-        ext = file_path.suffix.lower()
+                    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
 
-        match ext:
+                    docx_content = await file.read()
-            case ".txt" | ".md":
+                    docx_file = BytesIO(docx_content)
-                # Text files handling
+                    doc = Document(docx_file)
-                async with aiofiles.open(file_path, "r", encoding="utf-8") as f:
+                    content = "\n".join(
-                    content = await f.read()
+                        [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
 
-            case ".pdf" | ".docx" | ".pptx" | ".xlsx":
+                    pptx_content = await file.read()
-                if not pm.is_installed("docling"):
+                    pptx_file = BytesIO(pptx_content)
-                    pm.install("docling")
+                    prs = Presentation(pptx_file)
-                from docling.document_converter import DocumentConverter
+                    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
 
-                async def convert_doc():
+            # Add description if provided
-                    def sync_convert():
+            if description:
-                        converter = DocumentConverter()
+                content = f"{description}\n\n{content}"
-                        result = converter.convert(file_path)
-                        return result.document.export_to_markdown()
 
-                    return await asyncio.to_thread(sync_convert)
+            # Insert into RAG system
+            if content:
+                await rag.ainsert(content)
+                logging.info(
+                    f"Successfully processed and indexed file: {file_path.name}"
+                )
+            else:
+                logging.error(
+                    f"No content could be extracted from file: {file_path.name}"
+                )
 
-                content = await convert_doc()
+        except Exception as e:
+            logging.error(f"Error indexing 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)}")
 
-            case _:
+    async def batch_index_files(file_paths: List[Path]):
-                raise ValueError(f"Unsupported file format: {ext}")
+        """Index multiple files
 
-        # Insert content into RAG system
+        Args:
-        if content:
+            file_paths: Paths to the files to index
-            await rag.ainsert(content)
+        """
-            doc_manager.mark_as_indexed(file_path)
+        for file_path in file_paths:
-            logging.info(f"Successfully indexed file: {file_path}")
+            await index_file(file_path)
-        else:
-            logging.warning(f"No content extracted from file: {file_path}")
 
-    @app.post("/documents/scan", dependencies=[Depends(optional_api_key)])
+    async def save_temp_file(file: UploadFile = File(...)) -> Path:
-    async def scan_for_new_documents(background_tasks: BackgroundTasks):
+        """Save the uploaded file to a temporary location
-        """Trigger the scanning process"""
-        global scan_progress
 
-        with progress_lock:
+        Args:
-            if scan_progress["is_scanning"]:
+            file: The uploaded file
-                return {"status": "already_scanning"}
 
-            scan_progress["is_scanning"] = True
+        Returns:
-            scan_progress["indexed_count"] = 0
+            Path: The path to the saved file
-            scan_progress["progress"] = 0
+        """
+        # Generate unique filename to avoid conflicts
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        unique_filename = f"{temp_prefix}{timestamp}_{file.filename}"
 
-        # Start the scanning process in the background
+        # Create a temporary file to save the uploaded content
-        background_tasks.add_task(run_scanning_process)
+        temp_path = doc_manager.input_dir / "temp" / unique_filename
+        temp_path.parent.mkdir(exist_ok=True)
 
-        return {"status": "scanning_started"}
+        # 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"""
@@ -1221,6 +1267,24 @@ 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"""
@@ -1228,7 +1292,9 @@ 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.
 
@@ -1237,6 +1303,7 @@ 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`.
 
@@ -1261,15 +1328,178 @@ def create_app(args):
             with open(file_path, "wb") as buffer:
                 shutil.copyfileobj(file.file, buffer)
 
-            # Immediately index the uploaded file
+            # Add to background tasks
-            await index_file(file_path)
+            background_tasks.add_task(index_file, file_path)
 
-            return {
+            return InsertResponse(
-                "status": "success",
+                status="success",
-                "message": f"File uploaded and indexed: {file.filename}",
+                message=f"File '{file.filename}' uploaded successfully. Processing will continue in background.",
-                "total_documents": len(doc_manager.indexed_files),
+            )
-            }
         except Exception as 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(
+        "/documents/text",
+        response_model=InsertResponse,
+        dependencies=[Depends(optional_api_key)],
+    )
+    async def insert_text(
+        request: InsertTextRequest, background_tasks: BackgroundTasks
+    ):
+        """
+        Insert text into the Retrieval-Augmented Generation (RAG) system.
+
+        This endpoint allows you to insert text data into the RAG system for later retrieval and use in generating responses.
+
+        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:
+            background_tasks.add_task(rag.ainsert, request.text)
+            return InsertResponse(
+                status="success",
+                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(
+        "/documents/file",
+        response_model=InsertResponse,
+        dependencies=[Depends(optional_api_key)],
+    )
+    async def insert_file(
+        background_tasks: BackgroundTasks,
+        file: UploadFile = File(...),
+        description: str = Form(None),
+    ):
+        """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
+
+        Raises:
+            HTTPException: For unsupported file types or processing errors
+        """
+        try:
+            if not doc_manager.is_supported_file(file.filename):
+                raise HTTPException(
+                    status_code=400,
+                    detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}",
+                )
+
+            # Create a temporary file to save the uploaded content
+            temp_path = save_temp_file(file)
+
+            # Add to background tasks
+            background_tasks.add_task(index_file, temp_path, description)
+
+            return InsertResponse(
+                status="success",
+                message=f"File '{file.filename}' saved successfully. Processing will continue in background.",
+            )
+
+        except Exception as e:
+            logging.error(f"Error /documents/file: {str(e)}")
+            logging.error(traceback.format_exc())
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.post(
+        "/documents/batch",
+        response_model=InsertResponse,
+        dependencies=[Depends(optional_api_key)],
+    )
+    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:
+            InsertResponse: Status of the batch insertion operation
+
+        Raises:
+            HTTPException: For processing errors
+        """
+        try:
+            inserted_count = 0
+            failed_files = []
+            temp_files = []
+
+            for file in files:
+                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)")
+
+            if temp_files:
+                background_tasks.add_task(batch_index_files, temp_files)
+
+            # Prepare status message
+            if inserted_count == len(files):
+                status = "success"
+                status_message = f"Successfully inserted all {inserted_count} documents"
+            elif inserted_count > 0:
+                status = "partial_success"
+                status_message = f"Successfully inserted {inserted_count} out of {len(files)} documents"
+                if failed_files:
+                    status_message += f". Failed files: {', '.join(failed_files)}"
+            else:
+                status = "failure"
+                status_message = "No documents were successfully inserted"
+                if failed_files:
+                    status_message += f". Failed files: {', '.join(failed_files)}"
+
+            return InsertResponse(status=status, message=status_message)
+
+        except Exception as 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(
+        "/documents",
+        response_model=InsertResponse,
+        dependencies=[Depends(optional_api_key)],
+    )
+    async def clear_documents():
+        """
+        Clear all documents from the LightRAG system.
+
+        This endpoint deletes all text chunks, entities vector database, and relationships vector database,
+        effectively clearing all documents from the LightRAG system.
+
+        Returns:
+            InsertResponse: A response object containing the status, message, and the new document count (0 in this case).
+        """
+        try:
+            rag.text_chunks = []
+            rag.entities_vdb = None
+            rag.relationships_vdb = None
+            return InsertResponse(
+                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(
@@ -1381,255 +1611,6 @@ def create_app(args):
             trace_exception(e)
             raise HTTPException(status_code=500, detail=str(e))
 
-    @app.post(
-        "/documents/text",
-        response_model=InsertResponse,
-        dependencies=[Depends(optional_api_key)],
-    )
-    async def insert_text(request: InsertTextRequest):
-        """
-        Insert text into the Retrieval-Augmented Generation (RAG) system.
-
-        This endpoint allows you to insert text data into the RAG system for later retrieval and use in generating responses.
-
-        Args:
-            request (InsertTextRequest): The request body containing the text to be inserted.
-
-        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)
-            return InsertResponse(
-                status="success",
-                message="Text successfully inserted",
-                document_count=1,
-            )
-        except Exception as e:
-            raise HTTPException(status_code=500, detail=str(e))
-
-    @app.post(
-        "/documents/file",
-        response_model=InsertResponse,
-        dependencies=[Depends(optional_api_key)],
-    )
-    async def insert_file(file: UploadFile = File(...), description: str = Form(None)):
-        """Insert a file directly into the RAG system
-
-        Args:
-            file: Uploaded file
-            description: Optional description of the file
-
-        Returns:
-            InsertResponse: Status of the insertion operation
-
-        Raises:
-            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:
-                raise HTTPException(
-                    status_code=400,
-                    detail="No content could be extracted from the file",
-                )
-
-        except UnicodeDecodeError:
-            raise HTTPException(status_code=400, detail="File encoding not supported")
-        except Exception as e:
-            logging.error(f"Error processing file {file.filename}: {str(e)}")
-            raise HTTPException(status_code=500, detail=str(e))
-
-    @app.post(
-        "/documents/batch",
-        response_model=InsertResponse,
-        dependencies=[Depends(optional_api_key)],
-    )
-    async def insert_batch(files: List[UploadFile] = File(...)):
-        """Process multiple files in batch mode
-
-        Args:
-            files: List of files to process
-
-        Returns:
-            InsertResponse: Status of the batch insertion operation
-
-        Raises:
-            HTTPException: For processing errors
-        """
-        try:
-            inserted_count = 0
-            failed_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)")
-
-                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)}")
-
-            # Prepare status message
-            if inserted_count == len(files):
-                status = "success"
-                status_message = f"Successfully inserted all {inserted_count} documents"
-            elif inserted_count > 0:
-                status = "partial_success"
-                status_message = f"Successfully inserted {inserted_count} out of {len(files)} documents"
-                if failed_files:
-                    status_message += f". Failed files: {', '.join(failed_files)}"
-            else:
-                status = "failure"
-                status_message = "No documents were successfully inserted"
-                if failed_files:
-                    status_message += f". Failed files: {', '.join(failed_files)}"
-
-            return InsertResponse(
-                status=status,
-                message=status_message,
-                document_count=inserted_count,
-            )
-
-        except Exception as e:
-            logging.error(f"Batch processing error: {str(e)}")
-            raise HTTPException(status_code=500, detail=str(e))
-
-    @app.delete(
-        "/documents",
-        response_model=InsertResponse,
-        dependencies=[Depends(optional_api_key)],
-    )
-    async def clear_documents():
-        """
-        Clear all documents from the LightRAG system.
-
-        This endpoint deletes all text chunks, entities vector database, and relationships vector database,
-        effectively clearing all documents from the LightRAG system.
-
-        Returns:
-            InsertResponse: A response object containing the status, message, and the new document count (0 in this case).
-        """
-        try:
-            rag.text_chunks = []
-            rag.entities_vdb = None
-            rag.relationships_vdb = None
-            return InsertResponse(
-                status="success",
-                message="All documents cleared successfully",
-                document_count=0,
-            )
-        except Exception as e:
-            raise HTTPException(status_code=500, detail=str(e))
-
     # query all graph labels
     @app.get("/graph/label/list")
     async def get_graph_labels():