qa_chain_mr = RetrievalQA.from_chain_type( llm, retriever=vectordb.as_retriever(), chain_type="map_reduce" ) result = qa_chain_mr({"query": question}) result["result"] ❝ 'There is no clear answer to this question based on the given portion of the document. The document mentions ...
fromlangchain.chainsimportRetrievalQAqa_stuff=RetrievalQA.from_chain_type(llm=llm,chain_type="stuff",retriever=retriever,verbose=True)# *** 调用 ***qa_stuff.query("List all your shirts with sun protection in a table") 后话: 文章中的图及代码已经放到git上,可自行下载。相关文件和图在anal/re...
from langchain.chains.retrieval_qa.base import RetrievalQAfrom langchain_openai import ChatOpenAIllm = ChatOpenAI(model_name="gpt-3.5-turbo",temperature=0,max_tokens=200,api_key="your key",base_url="https://api.openai-hk.com/v1",)retriever = vectordb.as_retriever(search_type="mmr",searc...
from langchain.chains import RetrievalQA chain = RetrievalQA.from_chain_type(llm=llm2, chain_type="stuff", retriever=index.vectorstore.as_retriever(), input_key="question") #get reply to our questions chain.run('What is the difference between a PLC and a PC?') 1. 2. 3. 4. 5. 6...
创建RetrievalQA 检索 在文本分割这个任务中,LangChain 支持了多种分割方式,例如按字符数的 CharacterTextSplitter,针对 Markdown 文档的 MarkdownTextSplitter,以及利用递归方法的 RecursiveCharacterTextSplitter,当然你也可以通过继成 TextSplitter 父类来实现自定义的 split_text 方法,例如在中文文档中,我们可以采用按每...
qa_stuff = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=retriever, verbose=True ) query = "Please list all your shirts with sun protection in a table in markdown and summarize each one." response = qa_stuff.run(query) ...
" {context} 问题:{question} 有用的回答:""" # 类型:langchain.prompts.prompt.PromptTemplate QA_CHAIN_PROMPT = PromptTemplate.from_template(template) # Run chain【加入Prompt】 qa_chain = RetrievalQA.from_chain_type( llm, retriever=vectordb.as_retriever(), return_source_documents=True, chain_...
qa = RetrievalQA.from_chain_type(llm = OpenAI(), chain_type="stuff", retriever=retriever) query ="How can should I eat in an acceptable manner?"qa.run(query)# If you eat while being just, cheerful, equable, temperate, and orderly, then you can eat in an acceptable manner to the ...
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...
RetrievalQA.from_chain_type创建一个RetrievalQA实例,用于回答用户的问题。 qa0({"query": question})用户的问题会先通过retriever检索到相关的文档,然后再交给LLM,通过llm来回答用户的问题。 让LLM 生成图片 这个比较简单,使用OpenAI的dall-e-2模型即可: ...