微信公众号小程序助手关键词优化建议

该document是用来检索文档的。

第一步：定义组件对象，该组件返回有两种类型：document和text。
第二步：获取需要的信息，向量存储库，这里我使用的是内存向量存储（用该组件拿到文档，并检索）
第三步：在做返回结果处理时，分开处理组件返回类型

from langchain.vectorstores.base import VectorStoreclass VectorStoreToDocument:def __init__(self,param_dict:Optional[dict[str,Any]] = None) -> None:vectorStore:VectorStore = param_dict.get("vectorStore")if param_dict.get("minScore") is None or len(str(param_dict.get("minScore")))<=0:minimumScore = 75else: minimumScore : float = param_dict.get("minScore")query : str = param_dict.get("question","")outputs:dict = param_dict.get("outputs")self.__output = outputs['output'] if outputs is not None and len(outputs)>0 else "text"self.__vectorStore = vectorStoreself.__miniumScore = minimumScoreself.__query = querydef source(self):docs = self.__vectorStore.similarity_search_with_score(self.__query)if self.__output.lower() == "document":finalDocs = []for doc in docs:if self.__miniumScore is not None and float(self.__miniumScore)/100 < doc[1]:finalDocs.append(doc[0])return finalDocselse:finalText = ""for doc in docs:if self.__miniumScore is not None and self.__miniumScore/100 < doc[1]:finalText += doc[0].page_content+'\n'return finalText

调用：

from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
import chromadb
from chromadb import Settings
# Load the document, split it into chunks, embed each chunk and load it into the vector store.
raw_documents = TextLoader('D:/Workspace/pythonProjectSpacework/state_of_the_union.txt').load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(raw_documents)
vectorstore = Chroma.from_documents(client=chromadb_client,documents=documents, embedding=embeddings)
retriever = vectorstore.as_retriever()
minimumScore=30
param_dict = {"vectorStore":vectorstore,"minimumScore":minimumScore,"output":"text","query":"president said"
}
from mth.main.flow_modules.document.MthVectorStoreToDocument import MthVectorStoreToDocumenttext = VectorStoreToDocument(param_dict=param_dict).source()
print(text)

由于这里的组件会在后续使用promptTemplate时，将查询到的文件信息传递给prompt，再由prompt将文档值和问题一起丢给大语言模型处理。因此，需要在进入promptTemplate之前对传入进来的值做处理。
处理逻辑的代码：
promptTemplate的值：
“promptValues”:“{
“context”:“vectoreStoreToDocument_0.data.instance”
}”

valueJson = json.loads(value) //输入进来的prompt组件信息
for valKey in valueJson:val = valueJson[valKey]if val.startswith("{{") and val.endswith("}}"):valReplace = val.replace("{{","").replace("}}","").split(".") // 去除插入表达式的符号，然后通过.分割分数组形式node = [x for x in allNodes if x["id"]==valReplace[0]] // 在该流中查找vectoreStoreToDocument的节点信息if len(node) == 0:continuekk = node[0]for i in range(1, len(valReplace)): // 获取节点信息，并取得该节点的值。即获取vectoreStoreToDocument实例化以后的值，通过输出传过来的值kk = kk[valReplace[i]]param_dict[valKey] = kk