前言
试试这个数据库
Milvus安装
python接口：pip install pymilvus
docker安装：
通过docker-compose + xxx.yml文件实现
wget https://github.com/milvus-io/milvus/releases/download/v2.4.0/milvus-standalone-docker-compose.yml -O docker-compose.ymlsudo docker-compose up -d
如果没有安装过docker-compose，会报
执行下面这行代码，再重复上面的代码，当然如果你网络不好，可能就需要考虑添加镜像源啥的了
sudo apt install docker-compose
安装完之后，运行
docker ps会显示你在运行中的容器，这边安装好之后会出现
Milvus数据类型与python对应的数据类型
| Milvus | Python |
| DataType.INT64 | numpy.int64 |
| DataType.INT32 | numpy.int32 |
| DataType.INT16 | numpy.int16 |
| DataType.BOOL | bool |
| DataType.FLOAT | numpy.float32 |
| DataType.DOUBLE | numpy.double |
| DataType.ARRAY | list |
| DataType.VARCHAR | str |
| DataType.JSON | dict |
| FLOAT_VECTOR（浮点数向量） | numpy.ndarray or list （元素为numpy.float） |
Milvus操作
from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection,utility
from pymilvus import MilvusClient
from tqdm import tqdm
from datetime import datetimeclass milvus_db():def __init__(self,url:str='0.0.0.0',collection_name:str='data_store'):# milvus_client = MilvusClient(uri="./milvus_demo.db")# collection_name = "my_rag_collection"connections.connect(host=url, port="19530")#self.delete_collection(collection_name)if utility.has_collection(collection_name):self.collection = Collection(name=collection_name)else:schema = self.get_schema()self.collection = Collection(name=collection_name, schema=schema)print(self.collection.schema)def get_schema(self):id = FieldSchema(name="id", dtype=DataType.VARCHAR,max_length=128,is_primary=True) # 主键索引text = FieldSchema(name="text", dtype=DataType.VARCHAR,max_length=58192)file_name = FieldSchema(name="file_name", dtype=DataType.VARCHAR,max_length=512)text_embedding = FieldSchema(name="text_embedding", dtype=DataType.FLOAT_VECTOR,dim=1024) # 向量，dim=2代表向量只有两列，自己的数据的话一个向量有多少个元素就多少列schema = CollectionSchema(fields=[id, text,file_name,text_embedding], description="文本与文本嵌入存储") # 描述return schemadef change_collection(self,collection_name):schema = self.get_schema()self.collection = Collection(name=collection_name,schema=schema)def delete_collection(self,collection_name):utility.drop_collection(collection_name)def create_index(self,metric_type='L2',index_name='L2'):#utility.drop_collection(collection_name=collection_name)# self.collection = Collection(name=collection_name, schema=schema)index_params = {"index_type": "AUTOINDEX","metric_type":metric_type,"params": {}}self.collection.create_index(field_name="text_embedding",index_params=index_params,index_name=index_name)self.collection.load()def drop_index(self):self.collection.release()self.collection.drop_index()def insert_data(self,text_id_list,text_list,file_name_list,text_embedding_list):data_list = []start = datetime.now()for id,text,file_name,text_embedding in zip(text_id_list,text_list,file_name_list,text_embedding_list):#data_list.append([[id],[text],[file_name],[text_embedding]])self.collection.insert([[id],[text],[file_name],[text_embedding]])end = datetime.now()print(f'插入数据消化时间{end-start}')def search(self,query_embedding, top_k=10,metric_type='L2'):search_params = {"metric_type": metric_type,"params": {"level": 2}}results = self.collection.search([query_embedding],anns_field="text_embedding",param=search_params,limit=top_k,output_fields=["text", "file_name"])return resultsdef list_collections(self):collections_list = utility.list_collections()return collections_listdef reranker_init(self,model_name_or_path,device="cpu"):self.reranker = bge_rf = BGERerankFunction(model_name=model_name_or_path, # Specify the model name. Defaults to `BAAI/bge-reranker-v2-m3`.device="cpu" # Specify the device to use, e.g., 'cpu' or 'cuda:0')def rereank(self,query,serach_result,top_k,rerank_client=None):documents_list = [i.entity.get('text') for i in serach_result[0]]#如果外部传入非milvus集成的rerankif rerank_client:response = rerank_client.rerank(query=query,documents=documents_list,top_n=top_k,)rerank_results = response['results']results = []for i in rerank_results:index = i['index']results.append(serach_result[0][index])else:results = self.reranker(query=query,documents=documents_list,top_k=top_k,)return resultsMilvus可视化
安装好milvus docker之后，哪怕milvus在运行着都可以继续接下来的步骤哦。
因为输入下面的代码就行
# 执行命令，加个 -d 在后台运行
docker run -d -p 8000:3000 -e MILVUS_URL=127.0.0.1:19530 zilliz/attu:v2.2.8如果像我一样在服务器上跑着，想在本地电脑上看的话，就在MILVUS_URL输入服务器的ip就行啦，然后attu：v 版本尽量接近你milvus的版本就行
之后输入对应链接就行啦。例如我的http://192.0.0.181:8000
参考链接：Milvus向量数据库基础用法及注意细节
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