检索增强生成（RAG）

from langchain import hub
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import ChatOpenAI

# 加载文档并创建向量存储
loader = WebBaseLoader("https://example.com")
documents = loader.load()
vectorstore = Chroma.from_documents(documents)

# 创建检索器
retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 5})

# 创建生成模型
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)

# 定义RAG链
def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

rag_chain = (
    {"context": retriever | format_docs, "question": "What is the role of Microsoft?"}
    | llm
)
response = rag_chain.invoke("What does Microsoft do?")
print(response)

pip install langchain openai

knowledge_base = {
    "What is LangChain?": "LangChain is an open-source framework for developing applications powered by large language models.",
    "What is the capital of France?": "The capital of France is Paris.",
    "Who is the CEO of OpenAI?": "As of 2023, Sam Altman is the CEO of OpenAI."
}

from langchain import PromptTemplate

template = "Answer the following question based on the knowledge base: {question}"
prompt = PromptTemplate(template=template, input_variables=["question"])

import os
from langchain.llms import OpenAI

os.environ["OPENAI_API_KEY"] = "*******"  # 替换为自己的API密钥
llm = OpenAI(model_name="gpt-3.5-turbo")

from langchain.chains import LLMChain

# 创建问答链
qa_chain = LLMChain(llm=llm, prompt=prompt)

def answer_question(question):
    # 检查知识库中是否有答案
    if question in knowledge_base:
        answer = knowledge_base[question]
    else:
        answer = "I'm sorry, I don't know the answer to that question."
    
    # 使用LLM生成回答
    result = qa_chain.run(question)
    
    return result

if __name__ == "__main__":
    user_question = input("Please ask a question: ")
    response = answer_question(user_question)
    print(response)