System Info Version: 0.0.201 llm = ChatVertexAI(temperature=0) qa_chain_mr = RetrievalQA.from_chain_type( llm, retriever=vectordb.as_retriever(), chain_type="refine" ) result = qa_chain_mr({"query": question}) result["result"] Error: Att...
LangChain里的combine_documents_chain有4种:StuffDocumentsChain、RefineDocumentsChain、MapReduceDocumentsChain、MapRerankDocumentsChain。最常见的是StuffDocumentsChain:将检索到的内容组合成上下文,与原始输入一起喂入大模型。 document_prompt是用于组合上下文的PromptTemplate,llm_chain本身还有一个prompt。 document_prompt...
fromlangchain.promptsimportPromptTemplate# Build prompttemplate="""Use the following pieces of context to answer the question at the end.\If you don't know the answer, just say that you don't know, don't try to make up an answer.\Use three sentences maximum. Keep the answer as concise...
langchain。当我使用查询输入运行 QA 链时,此错误不断出现: ---> result = qa_chain({'query': question}) ValueError: Missing some input keys: {'query'} 这是我的代码: from langchain.chains import RetrievalQA from langchain.prompts import PromptTemplate # Build prompt template = """Given th...
langchain的retrievalqa方法首先通过检索阶段来获取与问题相关的答案候选集。该方法利用先进的语义检索技术,将问题转化为语义向量表示,并与语料库中的候选答案进行匹配。通过匹配度的评估,langchain能够高效地检索出问题的相关答案。 在第二阶段,langchain的retrievalqa方法使用提取法来获取最终的答案。该方法通过对候选答案...
Helpful Answer:"""QA_CHAIN_PROMPT = PromptTemplate(input_variables=["context","question"], template=template) llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0) qa = RetrievalQA.from_chain_type(llm, chain_type='stuff',
import*asfsfrom"fs"; import{loadQAMapReduceChain}from"langchain/chains"; // Initialize the LLM to use to answer the question. constmodel=newOpenAI({}); consttext=fs.readFileSync("state_of_the_union.txt","utf8"); consttextSplitter=newRecursiveCharacterTextSplitter({chunkSize:1000}); ...
result = chain({"question": args.question}) Which got me: UserWarning: VectorDBQAWithSourcesChain is deprecated - please use from langchain.chains import RetrievalQAWithSourcesChain warnings.warn( And I modified to this: qa= RetrievalQAWithSourcesChain.from_chain_type( llm=OpenAI(), chain_type...
I am confused between these functions in Langchain. what are the similarities and differences between these functions in Langchain: RetrievalQA, RetrievalQAWithSourcesChain Load_qa_chain ConversationalRetrievalChain and which one is better for implementing a retrieval chatbot with Memory and PromptTemplat...
in the code below you see how I built my RAG model with the ParentDocumentRetriever from Langchain with Memory. At the moment I am using the RetrievalQA-Chain with the default chain_type="stuff". However I want to try different chain types like "map_reduce". But when replacing chain_ty...