File size: 4,890 Bytes
f7ab812 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
from fastapi import FastAPI, HTTPException, File, UploadFile
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}")
LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-4o-mini")
print(f"LLM_MODEL: {LLM_MODEL}")
EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large")
print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}")
EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192))
print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}")
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(
LLM_MODEL,
prompt,
system_prompt=system_prompt,
history_messages=history_messages,
**kwargs,
)
# Embedding function
async def embedding_func(texts: list[str]) -> np.ndarray:
return await openai_embedding(
texts,
model=EMBEDDING_MODEL,
)
async def get_embedding_dim():
test_text = ["This is a test sentence."]
embedding = await embedding_func(test_text)
embedding_dim = embedding.shape[1]
print(f"{embedding_dim=}")
return embedding_dim
# Initialize RAG instance
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=asyncio.run(get_embedding_dim()),
max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
func=embedding_func,
),
)
# Data models
class QueryRequest(BaseModel):
query: str
mode: str = "hybrid"
only_need_context: bool = False
class InsertRequest(BaseModel):
text: 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, only_need_context=request.only_need_context
),
),
)
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(file: UploadFile = File(...)):
try:
file_content = await file.read()
# Read file content
try:
content = file_content.decode("utf-8")
except UnicodeDecodeError:
# If UTF-8 decoding fails, try other encodings
content = file_content.decode("gbk")
# 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 {file.filename} 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"
|