LightRAG/examples/lightrag_api_openai_compatible_demo.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import os
from lightrag import LightRAG, QueryParam
from lightrag.llm import openai_complete_if_cache, openai_embedding
from lightrag.utils import EmbeddingFunc
import numpy as np
from typing import Optional
import asyncio
import nest_asyncio

# Apply nest_asyncio to solve event loop issues
nest_asyncio.apply()

DEFAULT_RAG_DIR = "index_default"
app = FastAPI(title="LightRAG API", description="API for RAG operations")

# Configure working directory
WORKING_DIR = os.environ.get('RAG_DIR', f'{DEFAULT_RAG_DIR}')
print(f"WORKING_DIR: {WORKING_DIR}")
if not os.path.exists(WORKING_DIR):
    os.mkdir(WORKING_DIR)

# LLM model function


async def llm_model_func(
    prompt, system_prompt=None, history_messages=[], **kwargs
) -> str:
    return await openai_complete_if_cache(
        "gpt-4o-mini",
        prompt,
        system_prompt=system_prompt,
        history_messages=history_messages,
        api_key='YOUR_API_KEY',
        base_url="YourURL/v1",
        **kwargs,
    )

# Embedding function


async def embedding_func(texts: list[str]) -> np.ndarray:
    return await openai_embedding(
        texts,
        model="text-embedding-3-large",
        api_key='YOUR_API_KEY',
        base_url="YourURL/v1",
    )

# Initialize RAG instance
rag = LightRAG(
    working_dir=WORKING_DIR,
    llm_model_func=llm_model_func,
    embedding_func=EmbeddingFunc(
        embedding_dim=3072, max_token_size=8192, func=embedding_func
    ),
)

# Data models


class QueryRequest(BaseModel):
    query: str
    mode: str = "hybrid"


class InsertRequest(BaseModel):
    text: str


class InsertFileRequest(BaseModel):
    file_path: str


class Response(BaseModel):
    status: str
    data: Optional[str] = None
    message: Optional[str] = None

# API routes


@app.post("/query", response_model=Response)
async def query_endpoint(request: QueryRequest):
    try:
        loop = asyncio.get_event_loop()
        result = await loop.run_in_executor(
            None,
            lambda: rag.query(
                request.query, param=QueryParam(mode=request.mode))
        )
        return Response(
            status="success",
            data=result
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/insert", response_model=Response)
async def insert_endpoint(request: InsertRequest):
    try:
        loop = asyncio.get_event_loop()
        await loop.run_in_executor(None, lambda: rag.insert(request.text))
        return Response(
            status="success",
            message="Text inserted successfully"
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/insert_file", response_model=Response)
async def insert_file(request: InsertFileRequest):
    try:
        # Check if file exists
        if not os.path.exists(request.file_path):
            raise HTTPException(
                status_code=404,
                detail=f"File not found: {request.file_path}"
            )

        # Read file content
        try:
            with open(request.file_path, 'r', encoding='utf-8') as f:
                content = f.read()
        except UnicodeDecodeError:
            # If UTF-8 decoding fails, try other encodings
            with open(request.file_path, 'r', encoding='gbk') as f:
                content = f.read()

        # Insert file content
        loop = asyncio.get_event_loop()
        await loop.run_in_executor(None, lambda: rag.insert(content))

        return Response(
            status="success",
            message=f"File content from {request.file_path} inserted successfully"
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.get("/health")
async def health_check():
    return {"status": "healthy"}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8020)

# Usage example
# To run the server, use the following command in your terminal:
# python lightrag_api_openai_compatible_demo.py

# Example requests:
# 1. Query:
# curl -X POST "http://127.0.0.1:8020/query" -H "Content-Type: application/json" -d '{"query": "your query here", "mode": "hybrid"}'

# 2. Insert text:
# curl -X POST "http://127.0.0.1:8020/insert" -H "Content-Type: application/json" -d '{"text": "your text here"}'

# 3. Insert file:
# curl -X POST "http://127.0.0.1:8020/insert_file" -H "Content-Type: application/json" -d '{"file_path": "path/to/your/file.txt"}'

# 4. Health check:
# curl -X GET "http://127.0.0.1:8020/health"