基于大模型的单轮文档问答

import os
os.environ['OPENAI_API_KEY']='sk-xxxxx'
# Load docs
from langchain.document_loaders import WebBaseLoader
loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
data = loader.load()
 
# Split
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size = 500, chunk_overlap = 0)
all_splits = text_splitter.split_documents(data)
 
# Store splits
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
if not os.path.exists('VectorStore'):
    vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbeddings(),
                                        persist_directory="VectorStore")
    vectorstore.persist()
else:
    vectorstore = Chroma(persist_directory='VectorStore', embedding_function=OpenAIEmbeddings())
 
# RAG prompt
from langchain import hub
prompt = hub.pull("rlm/rag-prompt")
 
# LLM
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
 
# RetrievalQA
qa_chain = RetrievalQA.from_chain_type(
    llm,
    retriever=vectorstore.as_retriever(),
    chain_type_kwargs={"prompt": prompt}
)
question = "What are the approaches to Task Decomposition?"
result = qa_chain({"query": question})
print(result["result"])

最终是使用如下prompt让大模型去做回答：

question：用户的问题

context: 基于语义匹配的最相似段落作为上下文

You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\nQuestion: {question} \nContext: {context} \nAnswer:"