LangChain-04 RAG Retrieval-Augmented Generation 检索增强生成-编程知识

LangChain-04 RAG Retrieval-Augmented Generation 检索增强生成

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
PDF
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。

在这里插入图片描述

安装依赖

pip install --upgrade --quiet  langchain-core langchain-community langchain-openai
pip install langchain docarray tiktoken

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 tiktokenvectorstore = 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_parsermessage = 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)