vectoreStore：该组件通常用来做内存向量存储的，同时利用该存储区获取他的retrieval检索

内存向量存储使用的参数为：文档、embeddings、输出
由于他的输出分为vectoreStore向量库存储、retrieval向量检索器，因此需要拿到他的输出分别做处理

步骤：
第一步：获取值
第二步：定义类型并实例化[采用mmr做他的检索处理]
第三步：对他的输出结果分别做处理

学习链接：

https://www.langchain.com.cn/modules/prompts/example_selectors/examples/mmr

from typing import Any, Dict, Optional,List,Union
from langchain.schema import Document, BaseRetriever
from langchain.vectorstores import Chroma
from langchain.vectorstores.base import VectorStore
from langchain.embeddings.base import Embeddings
import chromadb,osclass MemoryVectorStore():def __init__(self,param_dict: Optional[Dict[str, Any]] = None):documents  = param_dict.get("document")embeddings :str  = param_dict.get("embeddings")if documents is None or len(documents) <= 0:raise Exception()if embeddings is None or not isinstance(embeddings, Embeddings):raise Exception()texts = []for doc in documents:if len(doc)>=1:for doc_copy in doc:doc_page = doc_copy.page_content.replace("\n","")texts.append(doc_page)self.__vectorstore = Chroma.from_documents(client=chromadb_client,documents=texts, embedding=embeddings)outputs: list = param_dict.get("outputs")self.__output = outputs['output'] if outputs is not None and 0 < len(outputs) else "retriever"def source(self) -> Optional[Union[BaseRetriever, VectorStore]]:if self.__output.lower() == "retriever".lower():retriver = self.__vectorstore.as_retriever()retriver.search_type = 'mmr'return retriverelif self.__output.lower() == "vectorStore".lower():return self.__vectorstoreelse:return None