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LightRAG/lightrag/api/routers/document_routes.py
yangdx 61b57cbb5d Add PDF decryption support for password-protected files 
• Add PDF_DECRYPT_PASSWORD env variable
• Check encryption status before reading
• Handle decrypt errors gracefully
• Log detailed error messages
• Support both encrypted/plain PDFs
2025-11-01 15:01:17 +08:00

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"""
This module contains all document-related routes for the LightRAG API.
"""
import asyncio
from lightrag.utils import logger, get_pinyin_sort_key
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 DeletionResult, DocProcessingStatus, DocStatus
from lightrag.utils import generate_track_id
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__"
def sanitize_filename(filename: str, input_dir: Path) -> str:
"""
Sanitize uploaded filename to prevent Path Traversal attacks.
Args:
filename: The original filename from the upload
input_dir: The target input directory
Returns:
str: Sanitized filename that is safe to use
Raises:
HTTPException: If the filename is unsafe or invalid
"""
# Basic validation
if not filename or not filename.strip():
raise HTTPException(status_code=400, detail="Filename cannot be empty")
# Remove path separators and traversal sequences
clean_name = filename.replace("/", "").replace("\\", "")
clean_name = clean_name.replace("..", "")
# Remove control characters and null bytes
clean_name = "".join(c for c in clean_name if ord(c) >= 32 and c != "\x7f")
# Remove leading/trailing whitespace and dots
clean_name = clean_name.strip().strip(".")
# Check if anything is left after sanitization
if not clean_name:
raise HTTPException(
status_code=400, detail="Invalid filename after sanitization"
)
# Verify the final path stays within the input directory
try:
final_path = (input_dir / clean_name).resolve()
if not final_path.is_relative_to(input_dir.resolve()):
raise HTTPException(status_code=400, detail="Unsafe filename detected")
except (OSError, ValueError):
raise HTTPException(status_code=400, detail="Invalid filename")
return clean_name
class ScanResponse(BaseModel):
"""Response model for document scanning operation
Attributes:
status: Status of the scanning operation
message: Optional message with additional details
track_id: Tracking ID for monitoring scanning progress
"""
status: Literal["scanning_started"] = Field(
description="Status of the scanning operation"
)
message: Optional[str] = Field(
default=None, description="Additional details about the scanning operation"
)
track_id: str = Field(description="Tracking ID for monitoring scanning progress")
class Config:
json_schema_extra = {
"example": {
"status": "scanning_started",
"message": "Scanning process has been initiated in the background",
"track_id": "scan_20250729_170612_abc123",
}
}
class ReprocessResponse(BaseModel):
"""Response model for reprocessing failed documents operation
Attributes:
status: Status of the reprocessing operation
message: Message describing the operation result
track_id: Tracking ID for monitoring reprocessing progress
"""
status: Literal["reprocessing_started"] = Field(
description="Status of the reprocessing operation"
)
message: str = Field(description="Human-readable message describing the operation")
track_id: str = Field(
description="Tracking ID for monitoring reprocessing progress"
)
class Config:
json_schema_extra = {
"example": {
"status": "reprocessing_started",
"message": "Reprocessing of failed documents has been initiated in background",
"track_id": "retry_20250729_170612_def456",
}
}
class CancelPipelineResponse(BaseModel):
"""Response model for pipeline cancellation operation
Attributes:
status: Status of the cancellation request
message: Message describing the operation result
"""
status: Literal["cancellation_requested", "not_busy"] = Field(
description="Status of the cancellation request"
)
message: str = Field(description="Human-readable message describing the operation")
class Config:
json_schema_extra = {
"example": {
"status": "cancellation_requested",
"message": "Pipeline cancellation has been requested. Documents will be marked as FAILED.",
}
}
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
track_id: Tracking ID for monitoring processing status
"""
status: Literal["success", "duplicated", "partial_success", "failure"] = Field(
description="Status of the operation"
)
message: str = Field(description="Message describing the operation result")
track_id: str = Field(description="Tracking ID for monitoring processing status")
class Config:
json_schema_extra = {
"example": {
"status": "success",
"message": "File 'document.pdf' uploaded successfully. Processing will continue in background.",
"track_id": "upload_20250729_170612_abc123",
}
}
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
This model is kept for API compatibility but no longer accepts any parameters.
All cache will be cleared regardless of the request content.
"""
class Config:
json_schema_extra = {"example": {}}
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_ids: List[str] = Field(..., description="The IDs of the documents to delete.")
delete_file: bool = Field(
default=False,
description="Whether to delete the corresponding file in the upload directory.",
)
delete_llm_cache: bool = Field(
default=False,
description="Whether to delete cached LLM extraction results for the documents.",
)
@field_validator("doc_ids", mode="after")
@classmethod
def validate_doc_ids(cls, doc_ids: List[str]) -> List[str]:
if not doc_ids:
raise ValueError("Document IDs list cannot be empty")
validated_ids = []
for doc_id in doc_ids:
if not doc_id or not doc_id.strip():
raise ValueError("Document ID cannot be empty")
validated_ids.append(doc_id.strip())
# Check for duplicates
if len(validated_ids) != len(set(validated_ids)):
raise ValueError("Document IDs must be unique")
return validated_ids
class DeleteEntityRequest(BaseModel):
entity_name: str = Field(..., description="The name of the entity to delete.")
@field_validator("entity_name", mode="after")
@classmethod
def validate_entity_name(cls, entity_name: str) -> str:
if not entity_name or not entity_name.strip():
raise ValueError("Entity name cannot be empty")
return entity_name.strip()
class DeleteRelationRequest(BaseModel):
source_entity: str = Field(..., description="The name of the source entity.")
target_entity: str = Field(..., description="The name of the target entity.")
@field_validator("source_entity", "target_entity", mode="after")
@classmethod
def validate_entity_names(cls, entity_name: str) -> str:
if not entity_name or not entity_name.strip():
raise ValueError("Entity name cannot be empty")
return entity_name.strip()
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)")
track_id: Optional[str] = Field(
default=None, description="Tracking ID for monitoring progress"
)
chunks_count: Optional[int] = Field(
default=None, description="Number of chunks the document was split into"
)
error_msg: 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",
"track_id": "upload_20250729_170612_abc123",
"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",
"track_id": "upload_20250331_100000_abc123",
"chunks_count": None,
"error": None,
"metadata": None,
"file_path": "pending_doc.pdf",
}
],
"PREPROCESSED": [
{
"id": "doc_789",
"content_summary": "Document pending final indexing",
"content_length": 7200,
"status": "preprocessed",
"created_at": "2025-03-31T09:30:00",
"updated_at": "2025-03-31T09:35:00",
"track_id": "upload_20250331_093000_xyz789",
"chunks_count": 10,
"error": None,
"metadata": None,
"file_path": "preprocessed_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",
"track_id": "insert_20250331_090000_def456",
"chunks_count": 8,
"error": None,
"metadata": {"author": "John Doe"},
"file_path": "processed_doc.pdf",
}
],
}
}
}
class TrackStatusResponse(BaseModel):
"""Response model for tracking document processing status by track_id
Attributes:
track_id: The tracking ID
documents: List of documents associated with this track_id
total_count: Total number of documents for this track_id
status_summary: Count of documents by status
"""
track_id: str = Field(description="The tracking ID")
documents: List[DocStatusResponse] = Field(
description="List of documents associated with this track_id"
)
total_count: int = Field(description="Total number of documents for this track_id")
status_summary: Dict[str, int] = Field(description="Count of documents by status")
class Config:
json_schema_extra = {
"example": {
"track_id": "upload_20250729_170612_abc123",
"documents": [
{
"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",
"track_id": "upload_20250729_170612_abc123",
"chunks_count": 12,
"error": None,
"metadata": {"author": "John Doe", "year": 2025},
"file_path": "research_paper.pdf",
}
],
"total_count": 1,
"status_summary": {"PROCESSED": 1},
}
}
class DocumentsRequest(BaseModel):
"""Request model for paginated document queries
Attributes:
status_filter: Filter by document status, None for all statuses
page: Page number (1-based)
page_size: Number of documents per page (10-200)
sort_field: Field to sort by ('created_at', 'updated_at', 'id', 'file_path')
sort_direction: Sort direction ('asc' or 'desc')
"""
status_filter: Optional[DocStatus] = Field(
default=None, description="Filter by document status, None for all statuses"
)
page: int = Field(default=1, ge=1, description="Page number (1-based)")
page_size: int = Field(
default=50, ge=10, le=200, description="Number of documents per page (10-200)"
)
sort_field: Literal["created_at", "updated_at", "id", "file_path"] = Field(
default="updated_at", description="Field to sort by"
)
sort_direction: Literal["asc", "desc"] = Field(
default="desc", description="Sort direction"
)
class Config:
json_schema_extra = {
"example": {
"status_filter": "PROCESSED",
"page": 1,
"page_size": 50,
"sort_field": "updated_at",
"sort_direction": "desc",
}
}
class PaginationInfo(BaseModel):
"""Pagination information
Attributes:
page: Current page number
page_size: Number of items per page
total_count: Total number of items
total_pages: Total number of pages
has_next: Whether there is a next page
has_prev: Whether there is a previous page
"""
page: int = Field(description="Current page number")
page_size: int = Field(description="Number of items per page")
total_count: int = Field(description="Total number of items")
total_pages: int = Field(description="Total number of pages")
has_next: bool = Field(description="Whether there is a next page")
has_prev: bool = Field(description="Whether there is a previous page")
class Config:
json_schema_extra = {
"example": {
"page": 1,
"page_size": 50,
"total_count": 150,
"total_pages": 3,
"has_next": True,
"has_prev": False,
}
}
class PaginatedDocsResponse(BaseModel):
"""Response model for paginated document queries
Attributes:
documents: List of documents for the current page
pagination: Pagination information
status_counts: Count of documents by status for all documents
"""
documents: List[DocStatusResponse] = Field(
description="List of documents for the current page"
)
pagination: PaginationInfo = Field(description="Pagination information")
status_counts: Dict[str, int] = Field(
description="Count of documents by status for all documents"
)
class Config:
json_schema_extra = {
"example": {
"documents": [
{
"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",
"track_id": "upload_20250729_170612_abc123",
"chunks_count": 12,
"error_msg": None,
"metadata": {"author": "John Doe", "year": 2025},
"file_path": "research_paper.pdf",
}
],
"pagination": {
"page": 1,
"page_size": 50,
"total_count": 150,
"total_pages": 3,
"has_next": True,
"has_prev": False,
},
"status_counts": {
"PENDING": 10,
"PROCESSING": 5,
"PREPROCESSED": 5,
"PROCESSED": 130,
"FAILED": 5,
},
}
}
class StatusCountsResponse(BaseModel):
"""Response model for document status counts
Attributes:
status_counts: Count of documents by status
"""
status_counts: Dict[str, int] = Field(description="Count of documents by status")
class Config:
json_schema_extra = {
"example": {
"status_counts": {
"PENDING": 10,
"PROCESSING": 5,
"PREPROCESSED": 5,
"PROCESSED": 130,
"FAILED": 5,
}
}
}
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,
workspace: str = "", # New parameter for workspace isolation
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
),
):
# Store the base input directory and workspace
self.base_input_dir = Path(input_dir)
self.workspace = workspace
self.supported_extensions = supported_extensions
self.indexed_files = set()
# Create workspace-specific input directory
# If workspace is provided, create a subdirectory for data isolation
if workspace:
self.input_dir = self.base_input_dir / workspace
else:
self.input_dir = self.base_input_dir
# 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.glob(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)
def validate_file_path_security(file_path_str: str, base_dir: Path) -> Optional[Path]:
"""
Validate file path security to prevent Path Traversal attacks.
Args:
file_path_str: The file path string to validate
base_dir: The base directory that the file must be within
Returns:
Path: Safe file path if valid, None if unsafe or invalid
"""
if not file_path_str or not file_path_str.strip():
return None
try:
# Clean the file path string
clean_path_str = file_path_str.strip()
# Check for obvious path traversal patterns before processing
# This catches both Unix (..) and Windows (..\) style traversals
if ".." in clean_path_str:
# Additional check for Windows-style backslash traversal
if (
"\\..\\" in clean_path_str
or clean_path_str.startswith("..\\")
or clean_path_str.endswith("\\..")
):
# logger.warning(
# f"Security violation: Windows path traversal attempt detected - {file_path_str}"
# )
return None
# Normalize path separators (convert backslashes to forward slashes)
# This helps handle Windows-style paths on Unix systems
normalized_path = clean_path_str.replace("\\", "/")
# Create path object and resolve it (handles symlinks and relative paths)
candidate_path = (base_dir / normalized_path).resolve()
base_dir_resolved = base_dir.resolve()
# Check if the resolved path is within the base directory
if not candidate_path.is_relative_to(base_dir_resolved):
# logger.warning(
# f"Security violation: Path traversal attempt detected - {file_path_str}"
# )
return None
return candidate_path
except (OSError, ValueError, Exception) as e:
logger.warning(f"Invalid file path detected: {file_path_str} - {str(e)}")
return None
def get_unique_filename_in_enqueued(target_dir: Path, original_name: str) -> str:
"""Generate a unique filename in the target directory by adding numeric suffixes if needed
Args:
target_dir: Target directory path
original_name: Original filename
Returns:
str: Unique filename (may have numeric suffix added)
"""
from pathlib import Path
import time
original_path = Path(original_name)
base_name = original_path.stem
extension = original_path.suffix
# Try original name first
if not (target_dir / original_name).exists():
return original_name
# Try with numeric suffixes 001-999
for i in range(1, 1000):
suffix = f"{i:03d}"
new_name = f"{base_name}_{suffix}{extension}"
if not (target_dir / new_name).exists():
return new_name
# Fallback with timestamp if all 999 slots are taken
timestamp = int(time.time())
return f"{base_name}_{timestamp}{extension}"
async def pipeline_enqueue_file(
rag: LightRAG, file_path: Path, track_id: str = None
) -> tuple[bool, str]:
"""Add a file to the queue for processing
Args:
rag: LightRAG instance
file_path: Path to the saved file
track_id: Optional tracking ID, if not provided will be generated
Returns:
tuple: (success: bool, track_id: str)
"""
# Generate track_id if not provided
if track_id is None:
track_id = generate_track_id("unknown")
try:
content = ""
ext = file_path.suffix.lower()
file_size = 0
# Get file size for error reporting
try:
file_size = file_path.stat().st_size
except Exception:
file_size = 0
file = None
try:
async with aiofiles.open(file_path, "rb") as f:
file = await f.read()
except PermissionError as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "[File Extraction]Permission denied - cannot read file",
"original_error": str(e),
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(
f"[File Extraction]Permission denied reading file: {file_path.name}"
)
return False, track_id
except FileNotFoundError as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "[File Extraction]File not found",
"original_error": str(e),
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(f"[File Extraction]File not found: {file_path.name}")
return False, track_id
except Exception as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "[File Extraction]File reading error",
"original_error": str(e),
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(
f"[File Extraction]Error reading file {file_path.name}: {str(e)}"
)
return False, track_id
# Process based on file type
try:
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:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "[File Extraction]Empty file content",
"original_error": "File contains no content or only whitespace",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(
f"[File Extraction]Empty content in file: {file_path.name}"
)
return False, track_id
# Check if content looks like binary data string representation
if content.startswith("b'") or content.startswith('b"'):
error_files = [
{
"file_path": str(file_path.name),
"error_description": "[File Extraction]Binary data in text file",
"original_error": "File appears to contain binary data representation instead of text",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(
f"[File Extraction]File {file_path.name} appears to contain binary data representation instead of text"
)
return False, track_id
except UnicodeDecodeError as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "[File Extraction]UTF-8 encoding error, please convert it to UTF-8 before processing",
"original_error": f"File is not valid UTF-8 encoded text: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(
f"[File Extraction]File {file_path.name} is not valid UTF-8 encoded text. Please convert it to UTF-8 before processing."
)
return False, track_id
case ".pdf":
try:
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")
if not pm.is_installed("pycryptodome"): # type: ignore
pm.install("pycryptodome")
from PyPDF2 import PdfReader # type: ignore
from io import BytesIO
pdf_file = BytesIO(file)
reader = PdfReader(pdf_file)
# Check if PDF is encrypted
if reader.is_encrypted:
pdf_password = global_args.pdf_decrypt_password
if not pdf_password:
# PDF is encrypted but no password provided
error_files = [
{
"file_path": str(file_path.name),
"error_description": "[File Extraction]PDF is encrypted but no password provided",
"original_error": "Please set PDF_DECRYPT_PASSWORD environment variable to decrypt this PDF file",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(
f"[File Extraction]PDF is encrypted but no password provided: {file_path.name}"
)
return False, track_id
# Try to decrypt with password
try:
decrypt_result = reader.decrypt(pdf_password)
if decrypt_result == 0:
# Password is incorrect
error_files = [
{
"file_path": str(file_path.name),
"error_description": "[File Extraction]Failed to decrypt PDF - incorrect password",
"original_error": "The provided PDF_DECRYPT_PASSWORD is incorrect for this file",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(
f"[File Extraction]Incorrect PDF password: {file_path.name}"
)
return False, track_id
except Exception as decrypt_error:
# Decryption process error
error_files = [
{
"file_path": str(file_path.name),
"error_description": "[File Extraction]PDF decryption failed",
"original_error": f"Error during PDF decryption: {str(decrypt_error)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(
f"[File Extraction]PDF decryption error for {file_path.name}: {str(decrypt_error)}"
)
return False, track_id
# Extract text from PDF (encrypted PDFs are now decrypted, unencrypted PDFs proceed directly)
for page in reader.pages:
content += page.extract_text() + "\n"
except Exception as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "[File Extraction]PDF processing error",
"original_error": f"Failed to extract text from PDF: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(
f"[File Extraction]Error processing PDF {file_path.name}: {str(e)}"
)
return False, track_id
case ".docx":
try:
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]
)
except Exception as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "[File Extraction]DOCX processing error",
"original_error": f"Failed to extract text from DOCX: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(
f"[File Extraction]Error processing DOCX {file_path.name}: {str(e)}"
)
return False, track_id
case ".pptx":
try:
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"
except Exception as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "[File Extraction]PPTX processing error",
"original_error": f"Failed to extract text from PPTX: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(
f"[File Extraction]Error processing PPTX {file_path.name}: {str(e)}"
)
return False, track_id
case ".xlsx":
try:
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"
except Exception as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "[File Extraction]XLSX processing error",
"original_error": f"Failed to extract text from XLSX: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(
f"[File Extraction]Error processing XLSX {file_path.name}: {str(e)}"
)
return False, track_id
case _:
error_files = [
{
"file_path": str(file_path.name),
"error_description": f"[File Extraction]Unsupported file type: {ext}",
"original_error": f"File extension {ext} is not supported",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(
f"[File Extraction]Unsupported file type: {file_path.name} (extension {ext})"
)
return False, track_id
except Exception as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "[File Extraction]File format processing error",
"original_error": f"Unexpected error during file extracting: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(
f"[File Extraction]Unexpected error during {file_path.name} extracting: {str(e)}"
)
return False, track_id
# Insert into the RAG queue
if content:
# Check if content contains only whitespace characters
if not content.strip():
error_files = [
{
"file_path": str(file_path.name),
"error_description": "[File Extraction]File contains only whitespace",
"original_error": "File content contains only whitespace characters",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.warning(
f"[File Extraction]File contains only whitespace characters: {file_path.name}"
)
return False, track_id
try:
await rag.apipeline_enqueue_documents(
content, file_paths=file_path.name, track_id=track_id
)
logger.info(
f"Successfully extracted and enqueued file: {file_path.name}"
)
# Move file to __enqueued__ directory after enqueuing
try:
enqueued_dir = file_path.parent / "__enqueued__"
enqueued_dir.mkdir(exist_ok=True)
# Generate unique filename to avoid conflicts
unique_filename = get_unique_filename_in_enqueued(
enqueued_dir, file_path.name
)
target_path = enqueued_dir / unique_filename
# Move the file
file_path.rename(target_path)
logger.debug(
f"Moved file to enqueued directory: {file_path.name} -> {unique_filename}"
)
except Exception as move_error:
logger.error(
f"Failed to move file {file_path.name} to __enqueued__ directory: {move_error}"
)
# Don't affect the main function's success status
return True, track_id
except Exception as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "Document enqueue error",
"original_error": f"Failed to enqueue document: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(f"Error enqueueing document {file_path.name}: {str(e)}")
return False, track_id
else:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "No content extracted",
"original_error": "No content could be extracted from file",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(f"No content extracted from file: {file_path.name}")
return False, track_id
except Exception as e:
# Catch-all for any unexpected errors
try:
file_size = file_path.stat().st_size if file_path.exists() else 0
except Exception:
file_size = 0
error_files = [
{
"file_path": str(file_path.name),
"error_description": "Unexpected processing error",
"original_error": f"Unexpected error: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(f"Enqueuing file {file_path.name} error: {str(e)}")
logger.error(traceback.format_exc())
return False, track_id
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)}")
async def pipeline_index_file(rag: LightRAG, file_path: Path, track_id: str = None):
"""Index a file with track_id
Args:
rag: LightRAG instance
file_path: Path to the saved file
track_id: Optional tracking ID
"""
try:
success, returned_track_id = await pipeline_enqueue_file(
rag, file_path, track_id
)
if success:
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], track_id: str = None
):
"""Index multiple files sequentially to avoid high CPU load
Args:
rag: LightRAG instance
file_paths: Paths to the files to index
track_id: Optional tracking ID to pass to all files
"""
if not file_paths:
return
try:
enqueued = False
# Use get_pinyin_sort_key for Chinese pinyin sorting
sorted_file_paths = sorted(
file_paths, key=lambda p: get_pinyin_sort_key(str(p))
)
# Process files sequentially with track_id
for file_path in sorted_file_paths:
success, _ = await pipeline_enqueue_file(rag, file_path, track_id)
if success:
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,
track_id: str = None,
):
"""Index a list of texts with track_id
Args:
rag: LightRAG instance
texts: The texts to index
file_sources: Sources of the texts
track_id: Optional tracking ID
"""
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, track_id=track_id
)
await rag.apipeline_process_enqueue_documents()
async def run_scanning_process(
rag: LightRAG, doc_manager: DocumentManager, track_id: str = None
):
"""Background task to scan and index documents
Args:
rag: LightRAG instance
doc_manager: DocumentManager instance
track_id: Optional tracking ID to pass to all scanned files
"""
try:
new_files = doc_manager.scan_directory_for_new_files()
total_files = len(new_files)
logger.info(f"Found {total_files} files to index.")
if new_files:
# Check for files with PROCESSED status and filter them out
valid_files = []
processed_files = []
for file_path in new_files:
filename = file_path.name
existing_doc_data = await rag.doc_status.get_doc_by_file_path(filename)
if existing_doc_data and existing_doc_data.get("status") == "processed":
# File is already PROCESSED, skip it with warning
processed_files.append(filename)
logger.warning(f"Skipping already processed file: {filename}")
else:
# File is new or in non-PROCESSED status, add to processing list
valid_files.append(file_path)
# Process valid files (new files + non-PROCESSED status files)
if valid_files:
await pipeline_index_files(rag, valid_files, track_id)
if processed_files:
logger.info(
f"Scanning process completed: {len(valid_files)} files Processed {len(processed_files)} skipped."
)
else:
logger.info(
f"Scanning process completed: {len(valid_files)} files Processed."
)
else:
logger.info(
"No files to process after filtering already processed files."
)
else:
# No new files to index, check if there are any documents in the queue
logger.info(
"No upload file found, check if there are any documents in the queue..."
)
await rag.apipeline_process_enqueue_documents()
except Exception as e:
logger.error(f"Error during scanning process: {str(e)}")
logger.error(traceback.format_exc())
async def background_delete_documents(
rag: LightRAG,
doc_manager: DocumentManager,
doc_ids: List[str],
delete_file: bool = False,
delete_llm_cache: bool = False,
):
"""Background task to delete multiple documents"""
from lightrag.kg.shared_storage import (
get_namespace_data,
get_pipeline_status_lock,
)
pipeline_status = await get_namespace_data("pipeline_status")
pipeline_status_lock = get_pipeline_status_lock()
total_docs = len(doc_ids)
successful_deletions = []
failed_deletions = []
# Double-check pipeline status before proceeding
async with pipeline_status_lock:
if pipeline_status.get("busy", False):
logger.warning("Error: Unexpected pipeline busy state, aborting deletion.")
return # Abort deletion operation
# Set pipeline status to busy for deletion
pipeline_status.update(
{
"busy": True,
"job_name": f"Deleting {total_docs} Documents",
"job_start": datetime.now().isoformat(),
"docs": total_docs,
"batchs": total_docs,
"cur_batch": 0,
"latest_message": "Starting document deletion process",
}
)
# Use slice assignment to clear the list in place
pipeline_status["history_messages"][:] = ["Starting document deletion process"]
if delete_llm_cache:
pipeline_status["history_messages"].append(
"LLM cache cleanup requested for this deletion job"
)
try:
# Loop through each document ID and delete them one by one
for i, doc_id in enumerate(doc_ids, 1):
# Check for cancellation at the start of each document deletion
async with pipeline_status_lock:
if pipeline_status.get("cancellation_requested", False):
cancel_msg = f"Deletion cancelled by user at document {i}/{total_docs}. {len(successful_deletions)} deleted, {total_docs - i + 1} remaining."
logger.info(cancel_msg)
pipeline_status["latest_message"] = cancel_msg
pipeline_status["history_messages"].append(cancel_msg)
# Add remaining documents to failed list with cancellation reason
failed_deletions.extend(
doc_ids[i - 1 :]
) # i-1 because enumerate starts at 1
break # Exit the loop, remaining documents unchanged
start_msg = f"Deleting document {i}/{total_docs}: {doc_id}"
logger.info(start_msg)
pipeline_status["cur_batch"] = i
pipeline_status["latest_message"] = start_msg
pipeline_status["history_messages"].append(start_msg)
file_path = "#"
try:
result = await rag.adelete_by_doc_id(
doc_id, delete_llm_cache=delete_llm_cache
)
file_path = (
getattr(result, "file_path", "-") if "result" in locals() else "-"
)
if result.status == "success":
successful_deletions.append(doc_id)
success_msg = (
f"Document deleted {i}/{total_docs}: {doc_id}[{file_path}]"
)
logger.info(success_msg)
async with pipeline_status_lock:
pipeline_status["history_messages"].append(success_msg)
# Handle file deletion if requested and file_path is available
if (
delete_file
and result.file_path
and result.file_path != "unknown_source"
):
try:
deleted_files = []
# SECURITY FIX: Use secure path validation to prevent arbitrary file deletion
safe_file_path = validate_file_path_security(
result.file_path, doc_manager.input_dir
)
if safe_file_path is None:
# Security violation detected - log and skip file deletion
security_msg = f"Security violation: Unsafe file path detected for deletion - {result.file_path}"
logger.warning(security_msg)
async with pipeline_status_lock:
pipeline_status["latest_message"] = security_msg
pipeline_status["history_messages"].append(
security_msg
)
else:
# check and delete files from input_dir directory
if safe_file_path.exists():
try:
safe_file_path.unlink()
deleted_files.append(safe_file_path.name)
file_delete_msg = f"Successfully deleted input_dir file: {result.file_path}"
logger.info(file_delete_msg)
async with pipeline_status_lock:
pipeline_status["latest_message"] = (
file_delete_msg
)
pipeline_status["history_messages"].append(
file_delete_msg
)
except Exception as file_error:
file_error_msg = f"Failed to delete input_dir file {result.file_path}: {str(file_error)}"
logger.debug(file_error_msg)
async with pipeline_status_lock:
pipeline_status["latest_message"] = (
file_error_msg
)
pipeline_status["history_messages"].append(
file_error_msg
)
# Also check and delete files from __enqueued__ directory
enqueued_dir = doc_manager.input_dir / "__enqueued__"
if enqueued_dir.exists():
# SECURITY FIX: Validate that the file path is safe before processing
# Only proceed if the original path validation passed
base_name = Path(result.file_path).stem
extension = Path(result.file_path).suffix
# Search for exact match and files with numeric suffixes
for enqueued_file in enqueued_dir.glob(
f"{base_name}*{extension}"
):
# Additional security check: ensure enqueued file is within enqueued directory
safe_enqueued_path = (
validate_file_path_security(
enqueued_file.name, enqueued_dir
)
)
if safe_enqueued_path is not None:
try:
enqueued_file.unlink()
deleted_files.append(enqueued_file.name)
logger.info(
f"Successfully deleted enqueued file: {enqueued_file.name}"
)
except Exception as enqueued_error:
file_error_msg = f"Failed to delete enqueued file {enqueued_file.name}: {str(enqueued_error)}"
logger.debug(file_error_msg)
async with pipeline_status_lock:
pipeline_status[
"latest_message"
] = file_error_msg
pipeline_status[
"history_messages"
].append(file_error_msg)
else:
security_msg = f"Security violation: Unsafe enqueued file path detected - {enqueued_file.name}"
logger.warning(security_msg)
if deleted_files == []:
file_error_msg = f"File deletion skipped, missing or unsafe file: {result.file_path}"
logger.warning(file_error_msg)
async with pipeline_status_lock:
pipeline_status["latest_message"] = file_error_msg
pipeline_status["history_messages"].append(
file_error_msg
)
except Exception as file_error:
file_error_msg = f"Failed to delete file {result.file_path}: {str(file_error)}"
logger.error(file_error_msg)
async with pipeline_status_lock:
pipeline_status["latest_message"] = file_error_msg
pipeline_status["history_messages"].append(
file_error_msg
)
elif delete_file:
no_file_msg = (
f"File deletion skipped, missing file path: {doc_id}"
)
logger.warning(no_file_msg)
async with pipeline_status_lock:
pipeline_status["latest_message"] = no_file_msg
pipeline_status["history_messages"].append(no_file_msg)
else:
failed_deletions.append(doc_id)
error_msg = f"Failed to delete {i}/{total_docs}: {doc_id}[{file_path}] - {result.message}"
logger.error(error_msg)
async with pipeline_status_lock:
pipeline_status["latest_message"] = error_msg
pipeline_status["history_messages"].append(error_msg)
except Exception as e:
failed_deletions.append(doc_id)
error_msg = f"Error deleting document {i}/{total_docs}: {doc_id}[{file_path}] - {str(e)}"
logger.error(error_msg)
logger.error(traceback.format_exc())
async with pipeline_status_lock:
pipeline_status["latest_message"] = error_msg
pipeline_status["history_messages"].append(error_msg)
except Exception as e:
error_msg = f"Critical error during batch deletion: {str(e)}"
logger.error(error_msg)
logger.error(traceback.format_exc())
async with pipeline_status_lock:
pipeline_status["history_messages"].append(error_msg)
finally:
# Final summary and check for pending requests
async with pipeline_status_lock:
pipeline_status["busy"] = False
pipeline_status["pending_requests"] = False # Reset pending requests flag
pipeline_status["cancellation_requested"] = (
False # Always reset cancellation flag
)
completion_msg = f"Deletion completed: {len(successful_deletions)} successful, {len(failed_deletions)} failed"
pipeline_status["latest_message"] = completion_msg
pipeline_status["history_messages"].append(completion_msg)
# Check if there are pending document indexing requests
has_pending_request = pipeline_status.get("request_pending", False)
# If there are pending requests, start document processing pipeline
if has_pending_request:
try:
logger.info(
"Processing pending document indexing requests after deletion"
)
await rag.apipeline_process_enqueue_documents()
except Exception as e:
logger.error(f"Error processing pending documents after deletion: {e}")
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 and track_id
"""
# Generate track_id with "scan" prefix for scanning operation
track_id = generate_track_id("scan")
# Start the scanning process in the background with track_id
background_tasks.add_task(run_scanning_process, rag, doc_manager, track_id)
return ScanResponse(
status="scanning_started",
message="Scanning process has been initiated in the background",
track_id=track_id,
)
@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:
# Sanitize filename to prevent Path Traversal attacks
safe_filename = sanitize_filename(file.filename, doc_manager.input_dir)
if not doc_manager.is_supported_file(safe_filename):
raise HTTPException(
status_code=400,
detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}",
)
# Check if filename already exists in doc_status storage
existing_doc_data = await rag.doc_status.get_doc_by_file_path(safe_filename)
if existing_doc_data:
# Get document status information for error message
status = existing_doc_data.get("status", "unknown")
return InsertResponse(
status="duplicated",
message=f"File '{safe_filename}' already exists in document storage (Status: {status}).",
track_id="",
)
file_path = doc_manager.input_dir / safe_filename
# Check if file already exists in file system
if file_path.exists():
return InsertResponse(
status="duplicated",
message=f"File '{safe_filename}' already exists in the input directory.",
track_id="",
)
with open(file_path, "wb") as buffer:
shutil.copyfileobj(file.file, buffer)
track_id = generate_track_id("upload")
# Add to background tasks and get track_id
background_tasks.add_task(pipeline_index_file, rag, file_path, track_id)
return InsertResponse(
status="success",
message=f"File '{safe_filename}' uploaded successfully. Processing will continue in background.",
track_id=track_id,
)
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:
# Check if file_source already exists in doc_status storage
if (
request.file_source
and request.file_source.strip()
and request.file_source != "unknown_source"
):
existing_doc_data = await rag.doc_status.get_doc_by_file_path(
request.file_source
)
if existing_doc_data:
# Get document status information for error message
status = existing_doc_data.get("status", "unknown")
return InsertResponse(
status="duplicated",
message=f"File source '{request.file_source}' already exists in document storage (Status: {status}).",
track_id="",
)
# Generate track_id for text insertion
track_id = generate_track_id("insert")
background_tasks.add_task(
pipeline_index_texts,
rag,
[request.text],
file_sources=[request.file_source],
track_id=track_id,
)
return InsertResponse(
status="success",
message="Text successfully received. Processing will continue in background.",
track_id=track_id,
)
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:
# Check if any file_sources already exist in doc_status storage
if request.file_sources:
for file_source in request.file_sources:
if (
file_source
and file_source.strip()
and file_source != "unknown_source"
):
existing_doc_data = await rag.doc_status.get_doc_by_file_path(
file_source
)
if existing_doc_data:
# Get document status information for error message
status = existing_doc_data.get("status", "unknown")
return InsertResponse(
status="duplicated",
message=f"File source '{file_source}' already exists in document storage (Status: {status}).",
track_id="",
)
# Generate track_id for texts insertion
track_id = generate_track_id("insert")
background_tasks.add_task(
pipeline_index_texts,
rag,
request.texts,
file_sources=request.file_sources,
track_id=track_id,
)
return InsertResponse(
status="success",
message="Texts successfully received. Processing will continue in background.",
track_id=track_id,
)
except Exception as e:
logger.error(f"Error /documents/texts: {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.full_entities,
rag.full_relations,
rag.entity_chunks,
rag.relation_chunks,
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:
namespace = storages[i].namespace
workspace = storages[i].workspace
logger.info(
f"Successfully dropped {storage_name}: {workspace}/{namespace}"
)
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 only files in the current directory, preserve files in subdirectories
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 (limited to latest 1000 entries,
with truncation message if more than 1000 messages exist)
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
# and limit to latest 1000 entries with truncation message if needed
if "history_messages" in status_dict:
history_list = list(status_dict["history_messages"])
total_count = len(history_list)
if total_count > 1000:
# Calculate truncated message count
truncated_count = total_count - 1000
# Take only the latest 1000 messages
latest_messages = history_list[-1000:]
# Add truncation message at the beginning
truncation_message = (
f"[Truncated history messages: {truncated_count}/{total_count}]"
)
status_dict["history_messages"] = [
truncation_message
] + latest_messages
else:
# No truncation needed, return all messages
status_dict["history_messages"] = history_list
# 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))
# TODO: Deprecated, use /documents/paginated instead
@router.get(
"", response_model=DocsStatusesResponse, dependencies=[Depends(combined_auth)]
)
async def documents() -> DocsStatusesResponse:
"""
Get the status of all documents in the system. This endpoint is deprecated; use /documents/paginated instead.
To prevent excessive resource consumption, a maximum of 1,000 records is returned.
This endpoint retrieves the current status of all documents, grouped by their
processing status (PENDING, PROCESSING, PREPROCESSED, PROCESSED, FAILED). The results are
limited to 1000 total documents with fair distribution across all statuses.
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.
Maximum 1000 documents total will be returned.
Raises:
HTTPException: If an error occurs while retrieving document statuses (500).
"""
try:
statuses = (
DocStatus.PENDING,
DocStatus.PROCESSING,
DocStatus.PREPROCESSED,
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()
total_documents = 0
max_documents = 1000
# Convert results to lists for easier processing
status_documents = []
for idx, result in enumerate(results):
status = statuses[idx]
docs_list = []
for doc_id, doc_status in result.items():
docs_list.append((doc_id, doc_status))
status_documents.append((status, docs_list))
# Fair distribution: round-robin across statuses
status_indices = [0] * len(
status_documents
) # Track current index for each status
current_status_idx = 0
while total_documents < max_documents:
# Check if we have any documents left to process
has_remaining = False
for status_idx, (status, docs_list) in enumerate(status_documents):
if status_indices[status_idx] < len(docs_list):
has_remaining = True
break
if not has_remaining:
break
# Try to get a document from the current status
status, docs_list = status_documents[current_status_idx]
current_index = status_indices[current_status_idx]
if current_index < len(docs_list):
doc_id, doc_status = docs_list[current_index]
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),
track_id=doc_status.track_id,
chunks_count=doc_status.chunks_count,
error_msg=doc_status.error_msg,
metadata=doc_status.metadata,
file_path=doc_status.file_path,
)
)
status_indices[current_status_idx] += 1
total_documents += 1
# Move to next status (round-robin)
current_status_idx = (current_status_idx + 1) % len(status_documents)
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["deletion_started", "busy", "not_allowed"] = Field(
description="Status of the deletion operation"
)
message: str = Field(description="Message describing the operation result")
doc_id: str = Field(description="The ID of the document to delete")
@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,
background_tasks: BackgroundTasks,
) -> DeleteDocByIdResponse:
"""
Delete documents and all their associated data by their IDs using background processing.
Deletes specific documents and all their associated data, including their status,
text chunks, vector embeddings, and any related graph data. When requested,
cached LLM extraction responses are removed after graph deletion/rebuild completes.
The deletion process runs in the background to avoid blocking the client connection.
This operation is irreversible and will interact with the pipeline status.
Args:
delete_request (DeleteDocRequest): The request containing the document IDs and deletion options.
background_tasks: FastAPI BackgroundTasks for async processing
Returns:
DeleteDocByIdResponse: The result of the deletion operation.
- status="deletion_started": The document deletion has been initiated in the background.
- status="busy": The pipeline is busy with another operation.
Raises:
HTTPException:
- 500: If an unexpected internal error occurs during initialization.
"""
doc_ids = delete_request.doc_ids
try:
from lightrag.kg.shared_storage import get_namespace_data
pipeline_status = await get_namespace_data("pipeline_status")
# Check if pipeline is busy
if pipeline_status.get("busy", False):
return DeleteDocByIdResponse(
status="busy",
message="Cannot delete documents while pipeline is busy",
doc_id=", ".join(doc_ids),
)
# Add deletion task to background tasks
background_tasks.add_task(
background_delete_documents,
rag,
doc_manager,
doc_ids,
delete_request.delete_file,
delete_request.delete_llm_cache,
)
return DeleteDocByIdResponse(
status="deletion_started",
message=f"Document deletion for {len(doc_ids)} documents has been initiated. Processing will continue in background.",
doc_id=", ".join(doc_ids),
)
except Exception as e:
error_msg = f"Error initiating document deletion for {delete_request.doc_ids}: {str(e)}"
logger.error(error_msg)
logger.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=error_msg)
@router.post(
"/clear_cache",
response_model=ClearCacheResponse,
dependencies=[Depends(combined_auth)],
)
async def clear_cache(request: ClearCacheRequest):
"""
Clear all cache data from the LLM response cache storage.
This endpoint clears all cached LLM responses regardless of mode.
The request body is accepted for API compatibility but is ignored.
Args:
request (ClearCacheRequest): The request body (ignored for compatibility).
Returns:
ClearCacheResponse: A response object containing the status and message.
Raises:
HTTPException: If an error occurs during cache clearing (500).
"""
try:
# Call the aclear_cache method (no modes parameter)
await rag.aclear_cache()
# Prepare success message
message = "Successfully cleared all cache"
return ClearCacheResponse(status="success", message=message)
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))
@router.delete(
"/delete_entity",
response_model=DeletionResult,
dependencies=[Depends(combined_auth)],
)
async def delete_entity(request: DeleteEntityRequest):
"""
Delete an entity and all its relationships from the knowledge graph.
Args:
request (DeleteEntityRequest): The request body containing the entity name.
Returns:
DeletionResult: An object containing the outcome of the deletion process.
Raises:
HTTPException: If the entity is not found (404) or an error occurs (500).
"""
try:
result = await rag.adelete_by_entity(entity_name=request.entity_name)
if result.status == "not_found":
raise HTTPException(status_code=404, detail=result.message)
if result.status == "fail":
raise HTTPException(status_code=500, detail=result.message)
# Set doc_id to empty string since this is an entity operation, not document
result.doc_id = ""
return result
except HTTPException:
raise
except Exception as e:
error_msg = f"Error deleting entity '{request.entity_name}': {str(e)}"
logger.error(error_msg)
logger.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=error_msg)
@router.delete(
"/delete_relation",
response_model=DeletionResult,
dependencies=[Depends(combined_auth)],
)
async def delete_relation(request: DeleteRelationRequest):
"""
Delete a relationship between two entities from the knowledge graph.
Args:
request (DeleteRelationRequest): The request body containing the source and target entity names.
Returns:
DeletionResult: An object containing the outcome of the deletion process.
Raises:
HTTPException: If the relation is not found (404) or an error occurs (500).
"""
try:
result = await rag.adelete_by_relation(
source_entity=request.source_entity,
target_entity=request.target_entity,
)
if result.status == "not_found":
raise HTTPException(status_code=404, detail=result.message)
if result.status == "fail":
raise HTTPException(status_code=500, detail=result.message)
# Set doc_id to empty string since this is a relation operation, not document
result.doc_id = ""
return result
except HTTPException:
raise
except Exception as e:
error_msg = f"Error deleting relation from '{request.source_entity}' to '{request.target_entity}': {str(e)}"
logger.error(error_msg)
logger.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=error_msg)
@router.get(
"/track_status/{track_id}",
response_model=TrackStatusResponse,
dependencies=[Depends(combined_auth)],
)
async def get_track_status(track_id: str) -> TrackStatusResponse:
"""
Get the processing status of documents by tracking ID.
This endpoint retrieves all documents associated with a specific tracking ID,
allowing users to monitor the processing progress of their uploaded files or inserted texts.
Args:
track_id (str): The tracking ID returned from upload, text, or texts endpoints
Returns:
TrackStatusResponse: A response object containing:
- track_id: The tracking ID
- documents: List of documents associated with this track_id
- total_count: Total number of documents for this track_id
Raises:
HTTPException: If track_id is invalid (400) or an error occurs (500).
"""
try:
# Validate track_id
if not track_id or not track_id.strip():
raise HTTPException(status_code=400, detail="Track ID cannot be empty")
track_id = track_id.strip()
# Get documents by track_id
docs_by_track_id = await rag.aget_docs_by_track_id(track_id)
# Convert to response format
documents = []
status_summary = {}
for doc_id, doc_status in docs_by_track_id.items():
documents.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),
track_id=doc_status.track_id,
chunks_count=doc_status.chunks_count,
error_msg=doc_status.error_msg,
metadata=doc_status.metadata,
file_path=doc_status.file_path,
)
)
# Build status summary
# Handle both DocStatus enum and string cases for robust deserialization
status_key = str(doc_status.status)
status_summary[status_key] = status_summary.get(status_key, 0) + 1
return TrackStatusResponse(
track_id=track_id,
documents=documents,
total_count=len(documents),
status_summary=status_summary,
)
except HTTPException:
raise
except Exception as e:
logger.error(f"Error getting track status for {track_id}: {str(e)}")
logger.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e))
@router.post(
"/paginated",
response_model=PaginatedDocsResponse,
dependencies=[Depends(combined_auth)],
)
async def get_documents_paginated(
request: DocumentsRequest,
) -> PaginatedDocsResponse:
"""
Get documents with pagination support.
This endpoint retrieves documents with pagination, filtering, and sorting capabilities.
It provides better performance for large document collections by loading only the
requested page of data.
Args:
request (DocumentsRequest): The request body containing pagination parameters
Returns:
PaginatedDocsResponse: A response object containing:
- documents: List of documents for the current page
- pagination: Pagination information (page, total_count, etc.)
- status_counts: Count of documents by status for all documents
Raises:
HTTPException: If an error occurs while retrieving documents (500).
"""
try:
# Get paginated documents and status counts in parallel
docs_task = rag.doc_status.get_docs_paginated(
status_filter=request.status_filter,
page=request.page,
page_size=request.page_size,
sort_field=request.sort_field,
sort_direction=request.sort_direction,
)
status_counts_task = rag.doc_status.get_all_status_counts()
# Execute both queries in parallel
(documents_with_ids, total_count), status_counts = await asyncio.gather(
docs_task, status_counts_task
)
# Convert documents to response format
doc_responses = []
for doc_id, doc in documents_with_ids:
doc_responses.append(
DocStatusResponse(
id=doc_id,
content_summary=doc.content_summary,
content_length=doc.content_length,
status=doc.status,
created_at=format_datetime(doc.created_at),
updated_at=format_datetime(doc.updated_at),
track_id=doc.track_id,
chunks_count=doc.chunks_count,
error_msg=doc.error_msg,
metadata=doc.metadata,
file_path=doc.file_path,
)
)
# Calculate pagination info
total_pages = (total_count + request.page_size - 1) // request.page_size
has_next = request.page < total_pages
has_prev = request.page > 1
pagination = PaginationInfo(
page=request.page,
page_size=request.page_size,
total_count=total_count,
total_pages=total_pages,
has_next=has_next,
has_prev=has_prev,
)
return PaginatedDocsResponse(
documents=doc_responses,
pagination=pagination,
status_counts=status_counts,
)
except Exception as e:
logger.error(f"Error getting paginated documents: {str(e)}")
logger.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e))
@router.get(
"/status_counts",
response_model=StatusCountsResponse,
dependencies=[Depends(combined_auth)],
)
async def get_document_status_counts() -> StatusCountsResponse:
"""
Get counts of documents by status.
This endpoint retrieves the count of documents in each processing status
(PENDING, PROCESSING, PROCESSED, FAILED) for all documents in the system.
Returns:
StatusCountsResponse: A response object containing status counts
Raises:
HTTPException: If an error occurs while retrieving status counts (500).
"""
try:
status_counts = await rag.doc_status.get_all_status_counts()
return StatusCountsResponse(status_counts=status_counts)
except Exception as e:
logger.error(f"Error getting document status counts: {str(e)}")
logger.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e))
@router.post(
"/reprocess_failed",
response_model=ReprocessResponse,
dependencies=[Depends(combined_auth)],
)
async def reprocess_failed_documents(background_tasks: BackgroundTasks):
"""
Reprocess failed and pending documents.
This endpoint triggers the document processing pipeline which automatically
picks up and reprocesses documents in the following statuses:
- FAILED: Documents that failed during previous processing attempts
- PENDING: Documents waiting to be processed
- PROCESSING: Documents with abnormally terminated processing (e.g., server crashes)
This is useful for recovering from server crashes, network errors, LLM service
outages, or other temporary failures that caused document processing to fail.
The processing happens in the background and can be monitored using the
returned track_id or by checking the pipeline status.
Returns:
ReprocessResponse: Response with status, message, and track_id
Raises:
HTTPException: If an error occurs while initiating reprocessing (500).
"""
try:
# Generate track_id with "retry" prefix for retry operation
track_id = generate_track_id("retry")
# Start the reprocessing in the background
background_tasks.add_task(rag.apipeline_process_enqueue_documents)
logger.info(
f"Reprocessing of failed documents initiated with track_id: {track_id}"
)
return ReprocessResponse(
status="reprocessing_started",
message="Reprocessing of failed documents has been initiated in background",
track_id=track_id,
)
except Exception as e:
logger.error(f"Error initiating reprocessing of failed documents: {str(e)}")
logger.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e))
@router.post(
"/cancel_pipeline",
response_model=CancelPipelineResponse,
dependencies=[Depends(combined_auth)],
)
async def cancel_pipeline():
"""
Request cancellation of the currently running pipeline.
This endpoint sets a cancellation flag in the pipeline status. The pipeline will:
1. Check this flag at key processing points
2. Stop processing new documents
3. Cancel all running document processing tasks
4. Mark all PROCESSING documents as FAILED with reason "User cancelled"
The cancellation is graceful and ensures data consistency. Documents that have
completed processing will remain in PROCESSED status.
Returns:
CancelPipelineResponse: Response with status and message
- status="cancellation_requested": Cancellation flag has been set
- status="not_busy": Pipeline is not currently running
Raises:
HTTPException: If an error occurs while setting cancellation flag (500).
"""
try:
from lightrag.kg.shared_storage import (
get_namespace_data,
get_pipeline_status_lock,
)
pipeline_status = await get_namespace_data("pipeline_status")
pipeline_status_lock = get_pipeline_status_lock()
async with pipeline_status_lock:
if not pipeline_status.get("busy", False):
return CancelPipelineResponse(
status="not_busy",
message="Pipeline is not currently running. No cancellation needed.",
)
# Set cancellation flag
pipeline_status["cancellation_requested"] = True
cancel_msg = "Pipeline cancellation requested by user"
logger.info(cancel_msg)
pipeline_status["latest_message"] = cancel_msg
pipeline_status["history_messages"].append(cancel_msg)
return CancelPipelineResponse(
status="cancellation_requested",
message="Pipeline cancellation has been requested. Documents will be marked as FAILED.",
)
except Exception as e:
logger.error(f"Error requesting pipeline cancellation: {str(e)}")
logger.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e))
return router
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