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from fastapi import FastAPI, HTTPException, File, UploadFile, Form
from pydantic import BaseModel
import logging
import argparse
from lightrag import LightRAG, QueryParam
from lightrag.llm import lollms_model_complete, lollms_embed
from lightrag.utils import EmbeddingFunc
from typing import Optional, List
from enum import Enum
from pathlib import Path
import shutil
import aiofiles
from ascii_colors import trace_exception
def parse_args():
parser = argparse.ArgumentParser(
description="LightRAG FastAPI Server with separate working and input directories"
)
# Server configuration
parser.add_argument(
"--host", default="0.0.0.0", help="Server host (default: 0.0.0.0)"
)
parser.add_argument(
"--port", type=int, default=9621, help="Server port (default: 9621)"
)
# Directory configuration
parser.add_argument(
"--working-dir",
default="./rag_storage",
help="Working directory for RAG storage (default: ./rag_storage)",
)
parser.add_argument(
"--input-dir",
default="./inputs",
help="Directory containing input documents (default: ./inputs)",
)
# Model configuration
parser.add_argument(
"--model",
default="mistral-nemo:latest",
help="LLM model name (default: mistral-nemo:latest)",
)
parser.add_argument(
"--embedding-model",
default="bge-m3:latest",
help="Embedding model name (default: bge-m3:latest)",
)
parser.add_argument(
"--lollms-host",
default="http://localhost:11434",
help="lollms host URL (default: http://localhost:11434)",
)
# RAG configuration
parser.add_argument(
"--max-async", type=int, default=4, help="Maximum async operations (default: 4)"
)
parser.add_argument(
"--max-tokens",
type=int,
default=32768,
help="Maximum token size (default: 32768)",
)
parser.add_argument(
"--embedding-dim",
type=int,
default=1024,
help="Embedding dimensions (default: 1024)",
)
parser.add_argument(
"--max-embed-tokens",
type=int,
default=8192,
help="Maximum embedding token size (default: 8192)",
)
# Logging configuration
parser.add_argument(
"--log-level",
default="INFO",
choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
help="Logging level (default: INFO)",
)
return parser.parse_args()
class DocumentManager:
"""Handles document operations and tracking"""
def __init__(self, input_dir: str, supported_extensions: tuple = (".txt", ".md")):
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(self) -> List[Path]:
"""Scan input directory for new files"""
new_files = []
for ext in self.supported_extensions:
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):
"""Mark a file as indexed"""
self.indexed_files.add(file_path)
def is_supported_file(self, filename: str) -> bool:
"""Check if file type is supported"""
return any(filename.lower().endswith(ext) for ext in self.supported_extensions)
# Pydantic models
class SearchMode(str, Enum):
naive = "naive"
local = "local"
global_ = "global"
hybrid = "hybrid"
class QueryRequest(BaseModel):
query: str
mode: SearchMode = SearchMode.hybrid
stream: bool = False
class QueryResponse(BaseModel):
response: str
class InsertTextRequest(BaseModel):
text: str
description: Optional[str] = None
class InsertResponse(BaseModel):
status: str
message: str
document_count: int
def create_app(args):
# Setup logging
logging.basicConfig(
format="%(levelname)s:%(message)s", level=getattr(logging, args.log_level)
)
# Initialize FastAPI app
app = FastAPI(
title="LightRAG API",
description="API for querying text using LightRAG with separate storage and input directories",
)
# Create working directory if it doesn't exist
Path(args.working_dir).mkdir(parents=True, exist_ok=True)
# Initialize document manager
doc_manager = DocumentManager(args.input_dir)
# Initialize RAG
rag = LightRAG(
working_dir=args.working_dir,
llm_model_func=lollms_model_complete,
llm_model_name=args.model,
llm_model_max_async=args.max_async,
llm_model_max_token_size=args.max_tokens,
llm_model_kwargs={
"host": args.lollms_host,
"options": {"num_ctx": args.max_tokens},
},
embedding_func=EmbeddingFunc(
embedding_dim=args.embedding_dim,
max_token_size=args.max_embed_tokens,
func=lambda texts: lollms_embed(
texts, embed_model=args.embedding_model, host=args.lollms_host
),
),
)
@app.on_event("startup")
async def startup_event():
"""Index all files in input directory during startup"""
try:
new_files = doc_manager.scan_directory()
for file_path in new_files:
try:
# Use async file reading
async with aiofiles.open(file_path, "r", encoding="utf-8") as f:
content = await f.read()
# Use the async version of insert directly
await rag.ainsert(content)
doc_manager.mark_as_indexed(file_path)
logging.info(f"Indexed file: {file_path}")
except Exception as e:
trace_exception(e)
logging.error(f"Error indexing file {file_path}: {str(e)}")
logging.info(f"Indexed {len(new_files)} documents from {args.input_dir}")
except Exception as e:
logging.error(f"Error during startup indexing: {str(e)}")
@app.post("/documents/scan")
async def scan_for_new_documents():
"""Manually trigger scanning for new documents"""
try:
new_files = doc_manager.scan_directory()
indexed_count = 0
for file_path in new_files:
try:
with open(file_path, "r", encoding="utf-8") as f:
content = f.read()
rag.insert(content)
doc_manager.mark_as_indexed(file_path)
indexed_count += 1
except Exception as e:
logging.error(f"Error indexing file {file_path}: {str(e)}")
return {
"status": "success",
"indexed_count": indexed_count,
"total_documents": len(doc_manager.indexed_files),
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/documents/upload")
async def upload_to_input_dir(file: UploadFile = File(...)):
"""Upload a file to the input directory"""
try:
if not doc_manager.is_supported_file(file.filename):
raise HTTPException(
status_code=400,
detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}",
)
file_path = doc_manager.input_dir / file.filename
with open(file_path, "wb") as buffer:
shutil.copyfileobj(file.file, buffer)
# Immediately index the uploaded file
with open(file_path, "r", encoding="utf-8") as f:
content = f.read()
rag.insert(content)
doc_manager.mark_as_indexed(file_path)
return {
"status": "success",
"message": f"File uploaded and indexed: {file.filename}",
"total_documents": len(doc_manager.indexed_files),
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/query", response_model=QueryResponse)
async def query_text(request: QueryRequest):
try:
response = await rag.aquery(
request.query,
param=QueryParam(mode=request.mode, stream=request.stream),
)
if request.stream:
result = ""
async for chunk in response:
result += chunk
return QueryResponse(response=result)
else:
return QueryResponse(response=response)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/query/stream")
async def query_text_stream(request: QueryRequest):
try:
response = rag.query(
request.query, param=QueryParam(mode=request.mode, stream=True)
)
async def stream_generator():
async for chunk in response:
yield chunk
return stream_generator()
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/documents/text", response_model=InsertResponse)
async def insert_text(request: InsertTextRequest):
try:
rag.insert(request.text)
return InsertResponse(
status="success",
message="Text successfully inserted",
document_count=len(rag),
)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/documents/file", response_model=InsertResponse)
async def insert_file(file: UploadFile = File(...), description: str = Form(None)):
try:
content = await file.read()
if file.filename.endswith((".txt", ".md")):
text = content.decode("utf-8")
rag.insert(text)
else:
raise HTTPException(
status_code=400,
detail="Unsupported file type. Only .txt and .md files are supported",
)
return InsertResponse(
status="success",
message=f"File '{file.filename}' successfully inserted",
document_count=len(rag),
)
except UnicodeDecodeError:
raise HTTPException(status_code=400, detail="File encoding not supported")
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/documents/batch", response_model=InsertResponse)
async def insert_batch(files: List[UploadFile] = File(...)):
try:
inserted_count = 0
failed_files = []
for file in files:
try:
content = await file.read()
if file.filename.endswith((".txt", ".md")):
text = content.decode("utf-8")
rag.insert(text)
inserted_count += 1
else:
failed_files.append(f"{file.filename} (unsupported type)")
except Exception as e:
failed_files.append(f"{file.filename} ({str(e)})")
status_message = f"Successfully inserted {inserted_count} documents"
if failed_files:
status_message += f". Failed files: {', '.join(failed_files)}"
return InsertResponse(
status="success" if inserted_count > 0 else "partial_success",
message=status_message,
document_count=len(rag),
)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.delete("/documents", response_model=InsertResponse)
async def clear_documents():
try:
rag.text_chunks = []
rag.entities_vdb = None
rag.relationships_vdb = None
return InsertResponse(
status="success",
message="All documents cleared successfully",
document_count=0,
)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/health")
async def get_status():
"""Get current system status"""
return {
"status": "healthy",
"working_directory": str(args.working_dir),
"input_directory": str(args.input_dir),
"indexed_files": len(doc_manager.indexed_files),
"configuration": {
"model": args.model,
"embedding_model": args.embedding_model,
"max_tokens": args.max_tokens,
"lollms_host": args.lollms_host,
},
}
return app
if __name__ == "__main__":
args = parse_args()
import uvicorn
app = create_app(args)
uvicorn.run(app, host=args.host, port=args.port)
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