使用LangChain和Llama-Index实现多重检索RAG

from langchain.llms import LlamaIndex
from langchain.chains import RetrievalQA
from langchain.document_loaders import MarkdownLoader
from langchain.vectorstores import VectorStore

# 创建LlamaIndex
llama_index = LlamaIndex(model_name="tiktoker/rag-llama-7b-int4")

# 创建MarkdownLoader来加载文档
loader = MarkdownLoader()

# 加载文档
documents = loader.load_from_file("documents.md")

# 将文档添加到LlamaIndex中
llama_index.add_documents(documents)

# 创建一个基于LlamaIndex的VectorStore
vectorstore = VectorStore.from_llm(llama_index)

# 创建多重检索问答链
qa_chain = RetrievalQA(
    vectorstore=vectorstore,
    retriever_options={"k": 10},
    reader_options={"top_p": 0.95, "max_length": 500},
)

# 设置用户查询
query = "请解释RAG模型的工作原理。"

# 执行查询并获取结果
result = qa_chain.run(query)
print(result)