gardel/lightrag
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Remove api demo (reference to LightRAG Server instead)

Browse Source
This commit is contained in:
yangdx
2025-04-01 18:17:17 +08:00
parent 1e31b26cbe
commit ce74879258
2 changed files with 0 additions and 392 deletions
Download Patch File Download Diff File Expand all files Collapse all files

188
examples/lightrag_api_ollama_demo.py
View File

@@ -1,188 +0,0 @@
from fastapi import FastAPI, HTTPException, File, UploadFile
from contextlib import asynccontextmanager
from pydantic import BaseModel
import os
from lightrag import LightRAG, QueryParam
from lightrag.llm.ollama import ollama_embed, ollama_model_complete
from lightrag.utils import EmbeddingFunc
from typing import Optional
import asyncio
import nest_asyncio
import aiofiles
from lightrag.kg.shared_storage import initialize_pipeline_status
# Apply nest_asyncio to solve event loop issues
nest_asyncio.apply()
DEFAULT_RAG_DIR = "index_default"
DEFAULT_INPUT_FILE = "book.txt"
INPUT_FILE = os.environ.get("INPUT_FILE", f"{DEFAULT_INPUT_FILE}")
print(f"INPUT_FILE: {INPUT_FILE}")
# 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)
async def init():
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=ollama_model_complete,
llm_model_name="gemma2:9b",
llm_model_max_async=4,
llm_model_max_token_size=8192,
llm_model_kwargs={
"host": "http://localhost:11434",
"options": {"num_ctx": 8192},
},
embedding_func=EmbeddingFunc(
embedding_dim=768,
max_token_size=8192,
func=lambda texts: ollama_embed(
texts, embed_model="nomic-embed-text", host="http://localhost:11434"
),
),
)
# Add initialization code
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
@asynccontextmanager
async def lifespan(app: FastAPI):
global rag
rag = await init()
print("done!")
yield
app = FastAPI(
title="LightRAG API", description="API for RAG operations", lifespan=lifespan
)
# 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))
# insert by text
@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))
# insert by file in payload
@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))
# insert by local default file
@app.post("/insert_default_file", response_model=Response)
@app.get("/insert_default_file", response_model=Response)
async def insert_default_file():
try:
# Read file content from book.txt
async with aiofiles.open(INPUT_FILE, "r", encoding="utf-8") as file:
content = await file.read()
print(f"read input file {INPUT_FILE} successfully")
# 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 {INPUT_FILE} 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: multipart/form-data" -F "file=@path/to/your/file.txt"
# 4. Health check:
# curl -X GET "http://127.0.0.1:8020/health"

204
examples/lightrag_api_openai_compatible_demo.py
View File

@@ -1,204 +0,0 @@
from fastapi import FastAPI, HTTPException, File, UploadFile
from contextlib import asynccontextmanager
from pydantic import BaseModel
import os
from lightrag import LightRAG, QueryParam
from lightrag.llm.openai import openai_complete_if_cache, openai_embed
from lightrag.utils import EmbeddingFunc
import numpy as np
from typing import Optional
import asyncio
import nest_asyncio
from lightrag.kg.shared_storage import initialize_pipeline_status
# 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}")
BASE_URL = os.environ.get("BASE_URL", "https://api.openai.com/v1")
print(f"BASE_URL: {BASE_URL}")
API_KEY = os.environ.get("API_KEY", "xxxxxxxx")
print(f"API_KEY: {API_KEY}")
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=[], keyword_extraction=False, **kwargs
) -> str:
return await openai_complete_if_cache(
model=LLM_MODEL,
prompt=prompt,
system_prompt=system_prompt,
history_messages=history_messages,
base_url=BASE_URL,
api_key=API_KEY,
**kwargs,
)
# Embedding function
async def embedding_func(texts: list[str]) -> np.ndarray:
return await openai_embed(
texts=texts,
model=EMBEDDING_MODEL,
base_url=BASE_URL,
api_key=API_KEY,
)
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
async def init():
embedding_dimension = await get_embedding_dim()
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=embedding_dimension,
max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
func=embedding_func,
),
)
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
@asynccontextmanager
async def lifespan(app: FastAPI):
global rag
rag = await init()
print("done!")
yield
app = FastAPI(
title="LightRAG API", description="API for RAG operations", lifespan=lifespan
)
# 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: multipart/form-data" -F "file=@path/to/your/file.txt"
# 4. Health check:
# curl -X GET "http://127.0.0.1:8020/health"