一，代码：
from flask import Blueprint,jsonify,render_template,requestfrom app import milvus_client
from pymilvus import FieldSchema, DataType, CollectionSchema, Collection, connections
from helpers.utils import success_responsefrom sentence_transformers import SentenceTransformer
import astvector = Blueprint('vector', __name__)transformer = SentenceTransformer('/data/python/flask/study/all-MiniLM-L6-v2')# 连接 Milvus 集合
search_params = {'metric_type': 'COSINE','params': {'nprobe': 10 # 搜索时探测的聚类中心数量}
}def embed_search(search_string):search_embeddings = transformer.encode(search_string)return search_embeddingsdef search_for_articles(search_string):user_vector = embed_search(search_string)print("要搜索的内容:")print(user_vector)hits = milvus_client.search(collection_name="article",data=[user_vector],anns_field='content_vector',limit=1,search_params=search_params,filter='',output_fields=['id', 'content', 'product_ids'])return hits@vector.route("/search/", methods=['GET'])
def search():# collection.load()milvus_client.load_collection("article")search_string = "律师对这个事怎么看？"results = search_for_articles(search_string)print(results)#列出所有数据库resOne = {"id": results[0][0]['id'],"content": results[0][0]['entity']['content'],}return success_response(resOne)
二，测试结果:
![image]()
