内容简介
LangChain 中的 “RAG” 指的是 “Retrieval-Augmented Generation”，即检索增强生成。它是一种结合了检索（从大量数据中检索相关信息）和生成（基于检索到的信息生成文本）的技术，旨在改善和增强自然语言生成模型的性能。通过检索相关的信息作为输入，模型能够生成更准确、更丰富和更具相关性的输出。
我们可以将：
文本
图片
网页
等等数据进行向量化存储，使用RAG进行生成，会得到很好的效果。
安装依赖
pip install --upgrade --quiet langchain-core langchain-community langchain-openai pip install langchain docarray tiktoken • 1 • 2
编写代码
from langchain_community.vectorstores import DocArrayInMemorySearch from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnableParallel, RunnablePassthrough from langchain_openai.chat_models import ChatOpenAI from langchain_openai.embeddings import OpenAIEmbeddings # Requires: # pip install langchain docarray tiktoken vectorstore = DocArrayInMemorySearch.from_texts( ["harrison worked at kensho", "bears like to eat honey"], embedding=OpenAIEmbeddings(), ) retriever = vectorstore.as_retriever() template = """Answer the question based only on the following context: {context} Question: {question} """ prompt = ChatPromptTemplate.from_template(template) model = ChatOpenAI( model="gpt-3.5-turbo", ) output_parser = StrOutputParser() setup_and_retrieval = RunnableParallel( {"context": retriever, "question": RunnablePassthrough()} ) chain = setup_and_retrieval | prompt | model | output_parser message = chain.invoke("where did harrison work?") print(message)
如果你想要流式输出的话（类似打字机的效果），你需要：
# message = chain.invoke("where did harrison work?") stream = chain.stream("where did harrison work?") for chunk in stream: print(chunk, end="", flush=True)
运行结果
Harrison worked at Kensho. • 1
