""" This module contains all document-related routes for the LightRAG API. """ import asyncio from lightrag.utils import logger import aiofiles import shutil import traceback import pipmaster as pm from datetime import datetime from pathlib import Path from typing import Dict, List, Optional, Any from fastapi import APIRouter, BackgroundTasks, Depends, File, HTTPException, UploadFile from pydantic import BaseModel, Field, field_validator from lightrag import LightRAG from lightrag.base import DocProcessingStatus, DocStatus from lightrag.api.utils_api import ( get_combined_auth_dependency, global_args, ) router = APIRouter( prefix="/documents", tags=["documents"], ) # Temporary file prefix temp_prefix = "__tmp__" class InsertTextRequest(BaseModel): text: str = Field( min_length=1, description="The text to insert", ) @field_validator("text", mode="after") @classmethod def strip_after(cls, text: str) -> str: return text.strip() class InsertTextsRequest(BaseModel): texts: list[str] = Field( min_length=1, description="The texts to insert", ) @field_validator("texts", mode="after") @classmethod def strip_after(cls, texts: list[str]) -> list[str]: return [text.strip() for text in texts] class InsertResponse(BaseModel): status: str = Field(description="Status of the operation") message: str = Field(description="Message describing the operation result") class DocStatusResponse(BaseModel): @staticmethod def format_datetime(dt: Any) -> Optional[str]: if dt is None: return None if isinstance(dt, str): return dt return dt.isoformat() """Response model for document status Attributes: id: Document identifier content_summary: Summary of document content content_length: Length of document content status: Current processing status created_at: Creation timestamp (ISO format string) updated_at: Last update timestamp (ISO format string) chunks_count: Number of chunks (optional) error: Error message if any (optional) metadata: Additional metadata (optional) """ id: str content_summary: str content_length: int status: DocStatus created_at: str updated_at: str chunks_count: Optional[int] = None error: Optional[str] = None metadata: Optional[dict[str, Any]] = None class DocsStatusesResponse(BaseModel): statuses: Dict[DocStatus, List[DocStatusResponse]] = {} class PipelineStatusResponse(BaseModel): """Response model for pipeline status Attributes: autoscanned: Whether auto-scan has started busy: Whether the pipeline is currently busy job_name: Current job name (e.g., indexing files/indexing texts) job_start: Job start time as ISO format string (optional) docs: Total number of documents to be indexed batchs: Number of batches for processing documents cur_batch: Current processing batch request_pending: Flag for pending request for processing latest_message: Latest message from pipeline processing history_messages: List of history messages update_status: Status of update flags for all namespaces """ autoscanned: bool = False busy: bool = False job_name: str = "Default Job" job_start: Optional[str] = None docs: int = 0 batchs: int = 0 cur_batch: int = 0 request_pending: bool = False latest_message: str = "" history_messages: Optional[List[str]] = None update_status: Optional[dict] = None class Config: extra = "allow" # Allow additional fields from the pipeline status class DocumentManager: def __init__( self, input_dir: str, supported_extensions: tuple = ( ".txt", ".md", ".pdf", ".docx", ".pptx", ".xlsx", ".rtf", # Rich Text Format ".odt", # OpenDocument Text ".tex", # LaTeX ".epub", # Electronic Publication ".html", # HyperText Markup Language ".htm", # HyperText Markup Language ".csv", # Comma-Separated Values ".json", # JavaScript Object Notation ".xml", # eXtensible Markup Language ".yaml", # YAML Ain't Markup Language ".yml", # YAML ".log", # Log files ".conf", # Configuration files ".ini", # Initialization files ".properties", # Java properties files ".sql", # SQL scripts ".bat", # Batch files ".sh", # Shell scripts ".c", # C source code ".cpp", # C++ source code ".py", # Python source code ".java", # Java source code ".js", # JavaScript source code ".ts", # TypeScript source code ".swift", # Swift source code ".go", # Go source code ".rb", # Ruby source code ".php", # PHP source code ".css", # Cascading Style Sheets ".scss", # Sassy CSS ".less", # LESS CSS ), ): self.input_dir = Path(input_dir) self.supported_extensions = supported_extensions self.indexed_files = set() # Create input directory if it doesn't exist self.input_dir.mkdir(parents=True, exist_ok=True) def scan_directory_for_new_files(self) -> List[Path]: """Scan input directory for new files""" new_files = [] for ext in self.supported_extensions: logger.debug(f"Scanning for {ext} files in {self.input_dir}") for file_path in self.input_dir.rglob(f"*{ext}"): if file_path not in self.indexed_files: new_files.append(file_path) return new_files def mark_as_indexed(self, file_path: Path): self.indexed_files.add(file_path) def is_supported_file(self, filename: str) -> bool: return any(filename.lower().endswith(ext) for ext in self.supported_extensions) async def pipeline_enqueue_file(rag: LightRAG, file_path: Path) -> bool: """Add a file to the queue for processing Args: rag: LightRAG instance file_path: Path to the saved file Returns: bool: True if the file was successfully enqueued, False otherwise """ try: content = "" ext = file_path.suffix.lower() file = None async with aiofiles.open(file_path, "rb") as f: file = await f.read() # Process based on file type match ext: case ( ".txt" | ".md" | ".html" | ".htm" | ".tex" | ".json" | ".xml" | ".yaml" | ".yml" | ".rtf" | ".odt" | ".epub" | ".csv" | ".log" | ".conf" | ".ini" | ".properties" | ".sql" | ".bat" | ".sh" | ".c" | ".cpp" | ".py" | ".java" | ".js" | ".ts" | ".swift" | ".go" | ".rb" | ".php" | ".css" | ".scss" | ".less" ): try: # Try to decode as UTF-8 content = file.decode("utf-8") # Validate content if not content or len(content.strip()) == 0: logger.error(f"Empty content in file: {file_path.name}") return False # Check if content looks like binary data string representation if content.startswith("b'") or content.startswith('b"'): logger.error( f"File {file_path.name} appears to contain binary data representation instead of text" ) return False except UnicodeDecodeError: logger.error( f"File {file_path.name} is not valid UTF-8 encoded text. Please convert it to UTF-8 before processing." ) return False case ".pdf": if global_args["main_args"].document_loading_engine == "DOCLING": if not pm.is_installed("docling"): # type: ignore pm.install("docling") from docling.document_converter import DocumentConverter # type: ignore converter = DocumentConverter() result = converter.convert(file_path) content = result.document.export_to_markdown() else: if not pm.is_installed("pypdf2"): # type: ignore pm.install("pypdf2") from PyPDF2 import PdfReader # type: ignore 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 global_args["main_args"].document_loading_engine == "DOCLING": if not pm.is_installed("docling"): # type: ignore pm.install("docling") from docling.document_converter import DocumentConverter # type: ignore converter = DocumentConverter() result = converter.convert(file_path) content = result.document.export_to_markdown() else: if not pm.is_installed("python-docx"): # type: ignore pm.install("docx") from docx import Document # type: ignore from io import BytesIO docx_file = BytesIO(file) doc = Document(docx_file) content = "\n".join( [paragraph.text for paragraph in doc.paragraphs] ) case ".pptx": if global_args["main_args"].document_loading_engine == "DOCLING": if not pm.is_installed("docling"): # type: ignore pm.install("docling") from docling.document_converter import DocumentConverter # type: ignore converter = DocumentConverter() result = converter.convert(file_path) content = result.document.export_to_markdown() else: if not pm.is_installed("python-pptx"): # type: ignore pm.install("pptx") from pptx import Presentation # type: ignore from io import BytesIO pptx_file = BytesIO(file) prs = Presentation(pptx_file) for slide in prs.slides: for shape in slide.shapes: if hasattr(shape, "text"): content += shape.text + "\n" case ".xlsx": if global_args["main_args"].document_loading_engine == "DOCLING": if not pm.is_installed("docling"): # type: ignore pm.install("docling") from docling.document_converter import DocumentConverter # type: ignore converter = DocumentConverter() result = converter.convert(file_path) content = result.document.export_to_markdown() else: if not pm.is_installed("openpyxl"): # type: ignore pm.install("openpyxl") from openpyxl import load_workbook # type: ignore from io import BytesIO xlsx_file = BytesIO(file) wb = load_workbook(xlsx_file) for sheet in wb: content += f"Sheet: {sheet.title}\n" for row in sheet.iter_rows(values_only=True): content += ( "\t".join( str(cell) if cell is not None else "" for cell in row ) + "\n" ) content += "\n" case _: logger.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, file_paths=file_path.name) logger.info(f"Successfully fetched and enqueued file: {file_path.name}") return True else: logger.error(f"No content could be extracted from file: {file_path.name}") except Exception as e: logger.error(f"Error processing or enqueueing file {file_path.name}: {str(e)}") logger.error(traceback.format_exc()) finally: if file_path.name.startswith(temp_prefix): try: file_path.unlink() except Exception as e: logger.error(f"Error deleting file {file_path}: {str(e)}") return False async def pipeline_index_file(rag: LightRAG, file_path: Path): """Index a file Args: rag: LightRAG instance file_path: Path to the saved file """ try: if await pipeline_enqueue_file(rag, file_path): await rag.apipeline_process_enqueue_documents() except Exception as e: logger.error(f"Error indexing file {file_path.name}: {str(e)}") logger.error(traceback.format_exc()) async def pipeline_index_files(rag: LightRAG, file_paths: List[Path]): """Index multiple files sequentially to avoid high CPU load Args: rag: LightRAG instance file_paths: Paths to the files to index """ if not file_paths: return try: enqueued = False # Process files sequentially for file_path in file_paths: if await pipeline_enqueue_file(rag, file_path): enqueued = True # Process the queue only if at least one file was successfully enqueued if enqueued: await rag.apipeline_process_enqueue_documents() except Exception as e: logger.error(f"Error indexing files: {str(e)}") logger.error(traceback.format_exc()) async def pipeline_index_texts(rag: LightRAG, texts: List[str]): """Index a list of texts Args: rag: LightRAG instance texts: The texts to index """ if not texts: return await rag.apipeline_enqueue_documents(texts) await rag.apipeline_process_enqueue_documents() async def save_temp_file(input_dir: Path, file: UploadFile = File(...)) -> Path: """Save the uploaded file to a temporary location Args: file: The uploaded file 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 = 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(rag: LightRAG, doc_manager: DocumentManager): """Background task to scan and index documents""" try: new_files = doc_manager.scan_directory_for_new_files() total_files = len(new_files) logger.info(f"Found {total_files} new files to index.") if not new_files: return # Get MAX_PARALLEL_INSERT from global_args["main_args"] max_parallel = global_args["main_args"].max_parallel_insert # Calculate batch size as 2 * MAX_PARALLEL_INSERT batch_size = 2 * max_parallel # Process files in batches for i in range(0, total_files, batch_size): batch_files = new_files[i : i + batch_size] batch_num = i // batch_size + 1 total_batches = (total_files + batch_size - 1) // batch_size logger.info( f"Processing batch {batch_num}/{total_batches} with {len(batch_files)} files" ) await pipeline_index_files(rag, batch_files) # Log progress processed = min(i + batch_size, total_files) logger.info( f"Processed {processed}/{total_files} files ({processed/total_files*100:.1f}%)" ) except Exception as e: logger.error(f"Error during scanning process: {str(e)}") logger.error(traceback.format_exc()) def create_document_routes( rag: LightRAG, doc_manager: DocumentManager, api_key: Optional[str] = None ): # Create combined auth dependency for document routes combined_auth = get_combined_auth_dependency(api_key) @router.post("/scan", dependencies=[Depends(combined_auth)]) async def scan_for_new_documents(background_tasks: BackgroundTasks): """ Trigger the scanning process for new documents. This endpoint initiates a background task that scans the input directory for new documents and processes them. If a scanning process is already running, it returns a status indicating that fact. Returns: dict: A dictionary containing the scanning status """ # Start the scanning process in the background background_tasks.add_task(run_scanning_process, rag, doc_manager) return {"status": "scanning_started"} @router.post("/upload", dependencies=[Depends(combined_auth)]) async def upload_to_input_dir( background_tasks: BackgroundTasks, file: UploadFile = File(...) ): """ Upload a file to the input directory and index it. This API endpoint accepts a file through an HTTP POST request, checks if the uploaded file is of a supported type, saves it in the specified input directory, indexes it for retrieval, and returns a success status with relevant details. Args: background_tasks: FastAPI BackgroundTasks for async processing file (UploadFile): The file to be uploaded. It must have an allowed extension. Returns: InsertResponse: A response object containing the upload status and a message. Raises: HTTPException: If the file type is not supported (400) or other errors occur (500). """ 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}", ) file_path = doc_manager.input_dir / file.filename with open(file_path, "wb") as buffer: shutil.copyfileobj(file.file, buffer) # Add to background tasks background_tasks.add_task(pipeline_index_file, rag, file_path) return InsertResponse( status="success", message=f"File '{file.filename}' uploaded successfully. Processing will continue in background.", ) except Exception as e: logger.error(f"Error /documents/upload: {file.filename}: {str(e)}") logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) @router.post( "/text", response_model=InsertResponse, dependencies=[Depends(combined_auth)] ) async def insert_text( request: InsertTextRequest, background_tasks: BackgroundTasks ): """ Insert text into the 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. Raises: HTTPException: If an error occurs during text processing (500). """ try: background_tasks.add_task(pipeline_index_texts, rag, [request.text]) return InsertResponse( status="success", message="Text successfully received. Processing will continue in background.", ) except Exception as e: logger.error(f"Error /documents/text: {str(e)}") logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) @router.post( "/texts", response_model=InsertResponse, dependencies=[Depends(combined_auth)], ) async def insert_texts( request: InsertTextsRequest, background_tasks: BackgroundTasks ): """ Insert multiple texts into the RAG system. This endpoint allows you to insert multiple text entries into the RAG system in a single request. Args: request (InsertTextsRequest): The request body containing the list of texts. background_tasks: FastAPI BackgroundTasks for async processing Returns: InsertResponse: A response object containing the status of the operation. Raises: HTTPException: If an error occurs during text processing (500). """ try: background_tasks.add_task(pipeline_index_texts, rag, request.texts) return InsertResponse( status="success", message="Text successfully received. Processing will continue in background.", ) except Exception as e: logger.error(f"Error /documents/text: {str(e)}") logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) @router.post( "/file", response_model=InsertResponse, dependencies=[Depends(combined_auth)] ) async def insert_file( background_tasks: BackgroundTasks, file: UploadFile = File(...) ): """ Insert a file directly into the RAG system. This endpoint accepts a file upload and processes it for inclusion in the RAG system. The file is saved temporarily and processed in the background. Args: background_tasks: FastAPI BackgroundTasks for async processing file (UploadFile): The file to be processed Returns: InsertResponse: A response object containing the status of the operation. Raises: HTTPException: If the file type is not supported (400) or other errors occur (500). """ 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}", ) temp_path = await save_temp_file(doc_manager.input_dir, file) # Add to background tasks background_tasks.add_task(pipeline_index_file, rag, temp_path) return InsertResponse( status="success", message=f"File '{file.filename}' saved successfully. Processing will continue in background.", ) except Exception as e: logger.error(f"Error /documents/file: {str(e)}") logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) @router.post( "/file_batch", response_model=InsertResponse, dependencies=[Depends(combined_auth)], ) async def insert_batch( background_tasks: BackgroundTasks, files: List[UploadFile] = File(...) ): """ Process multiple files in batch mode. This endpoint allows uploading and processing multiple files simultaneously. It handles partial successes and provides detailed feedback about failed files. Args: background_tasks: FastAPI BackgroundTasks for async processing files (List[UploadFile]): List of files to process Returns: InsertResponse: A response object containing: - status: "success", "partial_success", or "failure" - message: Detailed information about the operation results Raises: HTTPException: If an error occurs during processing (500). """ 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(await save_temp_file(doc_manager.input_dir, file)) inserted_count += 1 else: failed_files.append(f"{file.filename} (unsupported type)") if temp_files: background_tasks.add_task(pipeline_index_files, rag, 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: logger.error(f"Error /documents/batch: {str(e)}") logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) @router.delete( "", response_model=InsertResponse, dependencies=[Depends(combined_auth)] ) async def clear_documents(): """ Clear all documents from the RAG system. This endpoint deletes all text chunks, entities vector database, and relationships vector database, effectively clearing all documents from the RAG system. Returns: InsertResponse: A response object containing the status and message. Raises: HTTPException: If an error occurs during the clearing process (500). """ 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: logger.error(f"Error DELETE /documents: {str(e)}") logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) @router.get( "/pipeline_status", dependencies=[Depends(combined_auth)], response_model=PipelineStatusResponse, ) async def get_pipeline_status() -> PipelineStatusResponse: """ Get the current status of the document indexing pipeline. This endpoint returns information about the current state of the document processing pipeline, including the processing status, progress information, and history messages. Returns: PipelineStatusResponse: A response object containing: - autoscanned (bool): Whether auto-scan has started - busy (bool): Whether the pipeline is currently busy - job_name (str): Current job name (e.g., indexing files/indexing texts) - job_start (str, optional): Job start time as ISO format string - docs (int): Total number of documents to be indexed - batchs (int): Number of batches for processing documents - cur_batch (int): Current processing batch - request_pending (bool): Flag for pending request for processing - latest_message (str): Latest message from pipeline processing - history_messages (List[str], optional): List of history messages Raises: HTTPException: If an error occurs while retrieving pipeline status (500) """ try: from lightrag.kg.shared_storage import ( get_namespace_data, get_all_update_flags_status, ) pipeline_status = await get_namespace_data("pipeline_status") # Get update flags status for all namespaces update_status = await get_all_update_flags_status() # Convert MutableBoolean objects to regular boolean values processed_update_status = {} for namespace, flags in update_status.items(): processed_flags = [] for flag in flags: # Handle both multiprocess and single process cases if hasattr(flag, "value"): processed_flags.append(bool(flag.value)) else: processed_flags.append(bool(flag)) processed_update_status[namespace] = processed_flags # Convert to regular dict if it's a Manager.dict status_dict = dict(pipeline_status) # Add processed update_status to the status dictionary status_dict["update_status"] = processed_update_status # Convert history_messages to a regular list if it's a Manager.list if "history_messages" in status_dict: status_dict["history_messages"] = list(status_dict["history_messages"]) # Format the job_start time if it exists if status_dict.get("job_start"): status_dict["job_start"] = str(status_dict["job_start"]) return PipelineStatusResponse(**status_dict) except Exception as e: logger.error(f"Error getting pipeline status: {str(e)}") logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) @router.get("", dependencies=[Depends(combined_auth)]) async def documents() -> DocsStatusesResponse: """ Get the status of all documents in the system. This endpoint retrieves the current status of all documents, grouped by their processing status (PENDING, PROCESSING, PROCESSED, FAILED). Returns: DocsStatusesResponse: A response object containing a dictionary where keys are DocStatus values and values are lists of DocStatusResponse objects representing documents in each status category. Raises: HTTPException: If an error occurs while retrieving document statuses (500). """ try: statuses = ( DocStatus.PENDING, DocStatus.PROCESSING, DocStatus.PROCESSED, DocStatus.FAILED, ) tasks = [rag.get_docs_by_status(status) for status in statuses] results: List[Dict[str, DocProcessingStatus]] = await asyncio.gather(*tasks) response = DocsStatusesResponse() for idx, result in enumerate(results): status = statuses[idx] for doc_id, doc_status in result.items(): if status not in response.statuses: response.statuses[status] = [] response.statuses[status].append( DocStatusResponse( id=doc_id, content_summary=doc_status.content_summary, content_length=doc_status.content_length, status=doc_status.status, created_at=DocStatusResponse.format_datetime( doc_status.created_at ), updated_at=DocStatusResponse.format_datetime( doc_status.updated_at ), chunks_count=doc_status.chunks_count, error=doc_status.error, metadata=doc_status.metadata, ) ) return response except Exception as e: logger.error(f"Error GET /documents: {str(e)}") logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) return router