""" This module contains all document-related routes for the LightRAG API. """ import asyncio from pyuca import Collator from lightrag.utils import logger import aiofiles import shutil import traceback import pipmaster as pm from datetime import datetime, timezone from pathlib import Path from typing import Dict, List, Optional, Any, Literal 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 from ..config import global_args # Function to format datetime to ISO format string with timezone information def format_datetime(dt: Any) -> Optional[str]: """Format datetime to ISO format string with timezone information Args: dt: Datetime object, string, or None Returns: ISO format string with timezone information, or None if input is None """ if dt is None: return None if isinstance(dt, str): return dt # Check if datetime object has timezone information if isinstance(dt, datetime): # If datetime object has no timezone info (naive datetime), add UTC timezone if dt.tzinfo is None: dt = dt.replace(tzinfo=timezone.utc) # Return ISO format string with timezone information return dt.isoformat() router = APIRouter( prefix="/documents", tags=["documents"], ) # Temporary file prefix temp_prefix = "__tmp__" class ScanResponse(BaseModel): """Response model for document scanning operation Attributes: status: Status of the scanning operation message: Optional message with additional details """ status: Literal["scanning_started"] = Field( description="Status of the scanning operation" ) message: Optional[str] = Field( default=None, description="Additional details about the scanning operation" ) class Config: json_schema_extra = { "example": { "status": "scanning_started", "message": "Scanning process has been initiated in the background", } } class InsertTextRequest(BaseModel): """Request model for inserting a single text document Attributes: text: The text content to be inserted into the RAG system file_source: Source of the text (optional) """ text: str = Field( min_length=1, description="The text to insert", ) file_source: str = Field(default=None, min_length=0, description="File Source") @field_validator("text", mode="after") @classmethod def strip_text_after(cls, text: str) -> str: return text.strip() @field_validator("file_source", mode="after") @classmethod def strip_source_after(cls, file_source: str) -> str: return file_source.strip() class Config: json_schema_extra = { "example": { "text": "This is a sample text to be inserted into the RAG system.", "file_source": "Source of the text (optional)", } } class InsertTextsRequest(BaseModel): """Request model for inserting multiple text documents Attributes: texts: List of text contents to be inserted into the RAG system file_sources: Sources of the texts (optional) """ texts: list[str] = Field( min_length=1, description="The texts to insert", ) file_sources: list[str] = Field( default=None, min_length=0, description="Sources of the texts" ) @field_validator("texts", mode="after") @classmethod def strip_texts_after(cls, texts: list[str]) -> list[str]: return [text.strip() for text in texts] @field_validator("file_sources", mode="after") @classmethod def strip_sources_after(cls, file_sources: list[str]) -> list[str]: return [file_source.strip() for file_source in file_sources] class Config: json_schema_extra = { "example": { "texts": [ "This is the first text to be inserted.", "This is the second text to be inserted.", ], "file_sources": [ "First file source (optional)", ], } } class InsertResponse(BaseModel): """Response model for document insertion operations Attributes: status: Status of the operation (success, duplicated, partial_success, failure) message: Detailed message describing the operation result """ status: Literal["success", "duplicated", "partial_success", "failure"] = Field( description="Status of the operation" ) message: str = Field(description="Message describing the operation result") class Config: json_schema_extra = { "example": { "status": "success", "message": "File 'document.pdf' uploaded successfully. Processing will continue in background.", } } class ClearDocumentsResponse(BaseModel): """Response model for document clearing operation Attributes: status: Status of the clear operation message: Detailed message describing the operation result """ status: Literal["success", "partial_success", "busy", "fail"] = Field( description="Status of the clear operation" ) message: str = Field(description="Message describing the operation result") class Config: json_schema_extra = { "example": { "status": "success", "message": "All documents cleared successfully. Deleted 15 files.", } } class ClearCacheRequest(BaseModel): """Request model for clearing cache Attributes: modes: Optional list of cache modes to clear """ modes: Optional[ List[Literal["default", "naive", "local", "global", "hybrid", "mix"]] ] = Field( default=None, description="Modes of cache to clear. If None, clears all cache.", ) class Config: json_schema_extra = {"example": {"modes": ["default", "naive"]}} class ClearCacheResponse(BaseModel): """Response model for cache clearing operation Attributes: status: Status of the clear operation message: Detailed message describing the operation result """ status: Literal["success", "fail"] = Field( description="Status of the clear operation" ) message: str = Field(description="Message describing the operation result") class Config: json_schema_extra = { "example": { "status": "success", "message": "Successfully cleared cache for modes: ['default', 'naive']", } } """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) file_path: Path to the document file """ class DeleteDocRequest(BaseModel): doc_id: str = Field(..., description="The ID of the document to delete.") class DocStatusResponse(BaseModel): id: str = Field(description="Document identifier") content_summary: str = Field(description="Summary of document content") content_length: int = Field(description="Length of document content in characters") status: DocStatus = Field(description="Current processing status") created_at: str = Field(description="Creation timestamp (ISO format string)") updated_at: str = Field(description="Last update timestamp (ISO format string)") chunks_count: Optional[int] = Field( default=None, description="Number of chunks the document was split into" ) error: Optional[str] = Field( default=None, description="Error message if processing failed" ) metadata: Optional[dict[str, Any]] = Field( default=None, description="Additional metadata about the document" ) file_path: str = Field(description="Path to the document file") class Config: json_schema_extra = { "example": { "id": "doc_123456", "content_summary": "Research paper on machine learning", "content_length": 15240, "status": "PROCESSED", "created_at": "2025-03-31T12:34:56", "updated_at": "2025-03-31T12:35:30", "chunks_count": 12, "error": None, "metadata": {"author": "John Doe", "year": 2025}, "file_path": "research_paper.pdf", } } class DocsStatusesResponse(BaseModel): """Response model for document statuses Attributes: statuses: Dictionary mapping document status to lists of document status responses """ statuses: Dict[DocStatus, List[DocStatusResponse]] = Field( default_factory=dict, description="Dictionary mapping document status to lists of document status responses", ) class Config: json_schema_extra = { "example": { "statuses": { "PENDING": [ { "id": "doc_123", "content_summary": "Pending document", "content_length": 5000, "status": "PENDING", "created_at": "2025-03-31T10:00:00", "updated_at": "2025-03-31T10:00:00", "file_path": "pending_doc.pdf", } ], "PROCESSED": [ { "id": "doc_456", "content_summary": "Processed document", "content_length": 8000, "status": "PROCESSED", "created_at": "2025-03-31T09:00:00", "updated_at": "2025-03-31T09:05:00", "chunks_count": 8, "file_path": "processed_doc.pdf", } ], } } } 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 with timezone (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 @field_validator("job_start", mode="before") @classmethod def parse_job_start(cls, value): """Process datetime and return as ISO format string with timezone""" return format_datetime(value) 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.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.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 try: pm.install("python-docx") except Exception: 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.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.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 # Create Collator for Unicode sorting collator = Collator() sorted_file_paths = sorted(file_paths, key=lambda p: collator.sort_key(str(p))) # Process files sequentially for file_path in sorted_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], file_sources: List[str] = None ): """Index a list of texts Args: rag: LightRAG instance texts: The texts to index file_sources: Sources of the texts """ if not texts: return if file_sources is not None: if len(file_sources) != 0 and len(file_sources) != len(texts): [ file_sources.append("unknown_source") for _ in range(len(file_sources), len(texts)) ] await rag.apipeline_enqueue_documents(input=texts, file_paths=file_sources) await rag.apipeline_process_enqueue_documents() # TODO: deprecate after /insert_file is removed 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 # Process all files at once await pipeline_index_files(rag, new_files) logger.info(f"Scanning process completed: {total_files} files Processed.") 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", response_model=ScanResponse, 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: ScanResponse: A response object containing the scanning status """ # Start the scanning process in the background background_tasks.add_task(run_scanning_process, rag, doc_manager) return ScanResponse( status="scanning_started", message="Scanning process has been initiated in the background", ) @router.post( "/upload", response_model=InsertResponse, 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. status can be "success", "duplicated", or error is thrown. 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 # Check if file already exists if file_path.exists(): return InsertResponse( status="duplicated", message=f"File '{file.filename}' already exists in the input directory.", ) 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], file_sources=[request.file_source], ) 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, file_sources=request.file_sources, ) 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)) # TODO: deprecated, use /upload instead @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)) # TODO: deprecated, use /upload instead @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=ClearDocumentsResponse, dependencies=[Depends(combined_auth)] ) async def clear_documents(): """ Clear all documents from the RAG system. This endpoint deletes all documents, entities, relationships, and files from the system. It uses the storage drop methods to properly clean up all data and removes all files from the input directory. Returns: ClearDocumentsResponse: A response object containing the status and message. - status="success": All documents and files were successfully cleared. - status="partial_success": Document clear job exit with some errors. - status="busy": Operation could not be completed because the pipeline is busy. - status="fail": All storage drop operations failed, with message - message: Detailed information about the operation results, including counts of deleted files and any errors encountered. Raises: HTTPException: Raised when a serious error occurs during the clearing process, with status code 500 and error details in the detail field. """ from lightrag.kg.shared_storage import ( get_namespace_data, get_pipeline_status_lock, ) # Get pipeline status and lock pipeline_status = await get_namespace_data("pipeline_status") pipeline_status_lock = get_pipeline_status_lock() # Check and set status with lock async with pipeline_status_lock: if pipeline_status.get("busy", False): return ClearDocumentsResponse( status="busy", message="Cannot clear documents while pipeline is busy", ) # Set busy to true pipeline_status.update( { "busy": True, "job_name": "Clearing Documents", "job_start": datetime.now().isoformat(), "docs": 0, "batchs": 0, "cur_batch": 0, "request_pending": False, # Clear any previous request "latest_message": "Starting document clearing process", } ) # Cleaning history_messages without breaking it as a shared list object del pipeline_status["history_messages"][:] pipeline_status["history_messages"].append( "Starting document clearing process" ) try: # Use drop method to clear all data drop_tasks = [] storages = [ rag.text_chunks, rag.full_docs, rag.entities_vdb, rag.relationships_vdb, rag.chunks_vdb, rag.chunk_entity_relation_graph, rag.doc_status, ] # Log storage drop start if "history_messages" in pipeline_status: pipeline_status["history_messages"].append( "Starting to drop storage components" ) for storage in storages: if storage is not None: drop_tasks.append(storage.drop()) # Wait for all drop tasks to complete drop_results = await asyncio.gather(*drop_tasks, return_exceptions=True) # Check for errors and log results errors = [] storage_success_count = 0 storage_error_count = 0 for i, result in enumerate(drop_results): storage_name = storages[i].__class__.__name__ if isinstance(result, Exception): error_msg = f"Error dropping {storage_name}: {str(result)}" errors.append(error_msg) logger.error(error_msg) storage_error_count += 1 else: logger.info(f"Successfully dropped {storage_name}") storage_success_count += 1 # Log storage drop results if "history_messages" in pipeline_status: if storage_error_count > 0: pipeline_status["history_messages"].append( f"Dropped {storage_success_count} storage components with {storage_error_count} errors" ) else: pipeline_status["history_messages"].append( f"Successfully dropped all {storage_success_count} storage components" ) # If all storage operations failed, return error status and don't proceed with file deletion if storage_success_count == 0 and storage_error_count > 0: error_message = "All storage drop operations failed. Aborting document clearing process." logger.error(error_message) if "history_messages" in pipeline_status: pipeline_status["history_messages"].append(error_message) return ClearDocumentsResponse(status="fail", message=error_message) # Log file deletion start if "history_messages" in pipeline_status: pipeline_status["history_messages"].append( "Starting to delete files in input directory" ) # Delete all files in input_dir deleted_files_count = 0 file_errors_count = 0 for file_path in doc_manager.input_dir.glob("**/*"): if file_path.is_file(): try: file_path.unlink() deleted_files_count += 1 except Exception as e: logger.error(f"Error deleting file {file_path}: {str(e)}") file_errors_count += 1 # Log file deletion results if "history_messages" in pipeline_status: if file_errors_count > 0: pipeline_status["history_messages"].append( f"Deleted {deleted_files_count} files with {file_errors_count} errors" ) errors.append(f"Failed to delete {file_errors_count} files") else: pipeline_status["history_messages"].append( f"Successfully deleted {deleted_files_count} files" ) # Prepare final result message final_message = "" if errors: final_message = f"Cleared documents with some errors. Deleted {deleted_files_count} files." status = "partial_success" else: final_message = f"All documents cleared successfully. Deleted {deleted_files_count} files." status = "success" # Log final result if "history_messages" in pipeline_status: pipeline_status["history_messages"].append(final_message) # Return response based on results return ClearDocumentsResponse(status=status, message=final_message) except Exception as e: error_msg = f"Error clearing documents: {str(e)}" logger.error(error_msg) logger.error(traceback.format_exc()) if "history_messages" in pipeline_status: pipeline_status["history_messages"].append(error_msg) raise HTTPException(status_code=500, detail=str(e)) finally: # Reset busy status after completion async with pipeline_status_lock: pipeline_status["busy"] = False completion_msg = "Document clearing process completed" pipeline_status["latest_message"] = completion_msg if "history_messages" in pipeline_status: pipeline_status["history_messages"].append(completion_msg) @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"]) # Ensure job_start is properly formatted as a string with timezone information if "job_start" in status_dict and status_dict["job_start"]: # Use format_datetime to ensure consistent formatting status_dict["job_start"] = format_datetime(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( "", response_model=DocsStatusesResponse, 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=format_datetime(doc_status.created_at), updated_at=format_datetime(doc_status.updated_at), chunks_count=doc_status.chunks_count, error=doc_status.error, metadata=doc_status.metadata, file_path=doc_status.file_path, ) ) 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)) class DeleteDocByIdResponse(BaseModel): """Response model for single document deletion operation.""" status: Literal["success", "fail", "not_found", "busy"] = Field( description="Status of the deletion operation" ) message: str = Field(description="Message describing the operation result") doc_id: Optional[str] = Field( default=None, description="The ID of the document." ) @router.delete( "/delete_document", response_model=DeleteDocByIdResponse, dependencies=[Depends(combined_auth)], summary="Delete a document and all its associated data by its ID.", ) async def delete_document( delete_request: DeleteDocRequest, ) -> DeleteDocByIdResponse: """ Deletes a specific document and all its associated data, including its status, text chunks, vector embeddings, and any related graph data. This operation is irreversible and will interact with the pipeline status. Args: delete_request (DeleteDocRequest): The request containing the document ID. Returns: DeleteDocByIdResponse: The result of the deletion operation. - status="success": The document was successfully deleted. - status="not_found": The document with the specified ID was not found. - status="fail": The deletion operation failed. - status="busy": The pipeline is busy with another operation. Raises: HTTPException: - 500: If an unexpected internal error occurs. """ from lightrag.kg.shared_storage import ( get_namespace_data, get_pipeline_status_lock, ) doc_id = delete_request.doc_id pipeline_status = await get_namespace_data("pipeline_status") pipeline_status_lock = get_pipeline_status_lock() async with pipeline_status_lock: if pipeline_status.get("busy", False): return DeleteDocByIdResponse( status="busy", message="Cannot delete document while pipeline is busy", doc_id=doc_id, ) pipeline_status.update( { "busy": True, "job_name": f"Deleting Document: {doc_id}", "job_start": datetime.now().isoformat(), "latest_message": "Starting document deletion process", } ) # Use slice assignment to clear the list in place pipeline_status["history_messages"][:] = [ f"Starting deletion for doc_id: {doc_id}" ] try: result = await rag.adelete_by_doc_id(doc_id) response_data = { "doc_id": result.doc_id, "message": result.message, "status": result.status, } if "history_messages" in pipeline_status: pipeline_status["history_messages"].append(result.message) return DeleteDocByIdResponse(**response_data) except Exception as e: error_msg = f"Error deleting document {doc_id}: {str(e)}" logger.error(error_msg) logger.error(traceback.format_exc()) if "history_messages" in pipeline_status: pipeline_status["history_messages"].append(error_msg) # Re-raise as HTTPException for consistent error handling by FastAPI raise HTTPException(status_code=500, detail=error_msg) finally: async with pipeline_status_lock: pipeline_status["busy"] = False completion_msg = f"Document deletion process for {doc_id} completed." pipeline_status["latest_message"] = completion_msg if "history_messages" in pipeline_status: pipeline_status["history_messages"].append(completion_msg) @router.post( "/clear_cache", response_model=ClearCacheResponse, dependencies=[Depends(combined_auth)], ) async def clear_cache(request: ClearCacheRequest): """ Clear cache data from the LLM response cache storage. This endpoint allows clearing specific modes of cache or all cache if no modes are specified. Valid modes include: "default", "naive", "local", "global", "hybrid", "mix". - "default" represents extraction cache. - Other modes correspond to different query modes. Args: request (ClearCacheRequest): The request body containing optional modes to clear. Returns: ClearCacheResponse: A response object containing the status and message. Raises: HTTPException: If an error occurs during cache clearing (400 for invalid modes, 500 for other errors). """ try: # Validate modes if provided valid_modes = ["default", "naive", "local", "global", "hybrid", "mix"] if request.modes and not all(mode in valid_modes for mode in request.modes): invalid_modes = [ mode for mode in request.modes if mode not in valid_modes ] raise HTTPException( status_code=400, detail=f"Invalid mode(s): {invalid_modes}. Valid modes are: {valid_modes}", ) # Call the aclear_cache method await rag.aclear_cache(request.modes) # Prepare success message if request.modes: message = f"Successfully cleared cache for modes: {request.modes}" else: message = "Successfully cleared all cache" return ClearCacheResponse(status="success", message=message) except HTTPException: # Re-raise HTTP exceptions raise except Exception as e: logger.error(f"Error clearing cache: {str(e)}") logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) return router