douyin/grpc_m/client.py

import asyncio
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
import asyncio

from grpclib.client import Channel

from grpc_m.vector import vector_grpc, vector_pb2  
from db.docs import DocumentsRepository  
from db.user_oauth import test_add  # 假设这里有必要的导入来获取open_id  
from config import logger,GRPC_VECTOR

async def load_user_docs_async(open_id: str, file_path: list[str]):  
    async with grpc.insecure_channel(GRPC_VECTOR) as channel:  
        stub = vector_service_pb2_grpc.VectorServiceStub(channel)  
  
        request = vector_service_pb2.LoadUserDocRequest(  
            open_id=open_id,  
            data=[vector_service_pb2.Document(path=file_path, id=str(open_id))]  
        )  
        response = await stub.LoadUserDoc(request)  
  
        # 输出响应结果  
        logger.info("Response: success = {}".format(response.success))  
        return response  

async def run():  
    # 创建gRPC通道和存根  
    async with Channel('localhost',18600) as channel:  
        stub = vector_grpc.VectorServiceStub(channel)  
          
        # 准备gRPC请求并发送  
        request =  vector_pb2.SearchRequest(collection_name="some-uuid-1", query="价格多少")  
        responses:vector_pb2.SearchResponses = await stub.SimilaritySearch(request)  
        data = responses.data
        # 输出响应结果  
        logger.info("Response: success = {}".format(data))  
        for item in data:
            chunk_data = item.chunk
            metadata_dict = dict(item.metadata)
            score_value = item.score
            uuid_value = item.uuid

            print(f"Chunk: {chunk_data}")
            print(f"Metadata: {metadata_dict}")
            print(f"Score: {score_value}")
            print(f"UUID: {uuid_value}")
  
if __name__ == '__main__':  
    asyncio.run(run())