如何使用 MultiQueryRetriever

基于距离的向量数据库检索在高维空间中嵌入（表示）查询，并根据距离度量查找类似的嵌入文档。但是，检索可能会产生不同的结果，因为查询措辞的细微变化，或者嵌入不能很好地捕获数据的语义。有时进行提示工程 / 调整是为了手动解决这些问题，但可能很乏味。

MultiQueryRetriever 通过使用 LLM 从不同角度为给定的用户输入查询生成多个查询，从而自动执行提示调整过程。对于每个查询，它检索一组相关文档，并在所有查询中采用唯一的联合，以获取更大的潜在相关文档集。通过对同一问题生成多个视角，MultiQueryRetriever可以减轻基于距离的检索的一些限制，并获得更丰富的结果集。

让我们使用 RAG 教程中 Lilian Weng 的 LLM Powered Autonomous Agents 博客文章构建一个矢量存储：

# Build a sample vectorDB
from langchain_chroma import Chroma
from langchain_community.document_loaders import WebBaseLoader
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter

# Load blog post
loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
data = loader.load()

# Split
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
splits = text_splitter.split_documents(data)

# VectorDB
embedding = OpenAIEmbeddings()
vectordb = Chroma.from_documents(documents=splits, embedding=embedding)

API 参考：WebBaseLoader | OpenAI 嵌入 | 递归角色文本拆分器

USER_AGENT environment variable not set, consider setting it to identify your requests.

使用简单

指定要用于查询生成的 LLM，检索器将完成其余工作。

from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain_openai import ChatOpenAI

question = "What are the approaches to Task Decomposition?"
llm = ChatOpenAI(temperature=0)
retriever_from_llm = MultiQueryRetriever.from_llm(
    retriever=vectordb.as_retriever(), llm=llm
)

API 参考：MultiQueryRetriever | 聊天OpenAI

# Set logging for the queries
import logging

logging.basicConfig()
logging.getLogger("langchain.retrievers.multi_query").setLevel(logging.INFO)

unique_docs = retriever_from_llm.invoke(question)
len(unique_docs)

INFO:langchain.retrievers.multi_query:Generated queries: ['1. How can Task Decomposition be achieved through different methods?', '2. What strategies are commonly used for Task Decomposition?', '3. What are the various ways to break down tasks in Task Decomposition?']

5

请注意，检索器生成的底层查询记录在INFO水平。

提供您自己的提示

在引擎盖下，MultiQueryRetriever使用特定提示生成查询。要自定义此提示：

1. 使用问题的输入变量创建 PromptTemplate;
2. 实现如下所示的输出解析器，以将结果拆分为查询列表。

提示和输出解析器必须一起支持生成查询列表。

from typing import List

from langchain_core.output_parsers import BaseOutputParser
from langchain_core.prompts import PromptTemplate
from pydantic import BaseModel, Field


# Output parser will split the LLM result into a list of queries
class LineListOutputParser(BaseOutputParser[List[str]]):
    """Output parser for a list of lines."""

    def parse(self, text: str) -> List[str]:
        lines = text.strip().split("\n")
        return list(filter(None, lines))  # Remove empty lines


output_parser = LineListOutputParser()

QUERY_PROMPT = PromptTemplate(
    input_variables=["question"],
    template="""You are an AI language model assistant. Your task is to generate five 
    different versions of the given user question to retrieve relevant documents from a vector 
    database. By generating multiple perspectives on the user question, your goal is to help
    the user overcome some of the limitations of the distance-based similarity search. 
    Provide these alternative questions separated by newlines.
    Original question: {question}""",
)
llm = ChatOpenAI(temperature=0)

# Chain
llm_chain = QUERY_PROMPT | llm | output_parser

# Other inputs
question = "What are the approaches to Task Decomposition?"

API 参考：BaseOutputParser | 提示模板

# Run
retriever = MultiQueryRetriever(
    retriever=vectordb.as_retriever(), llm_chain=llm_chain, parser_key="lines"
)  # "lines" is the key (attribute name) of the parsed output

# Results
unique_docs = retriever.invoke("What does the course say about regression?")
len(unique_docs)

INFO:langchain.retrievers.multi_query:Generated queries: ['1. Can you provide insights on regression from the course material?', '2. How is regression discussed in the course content?', '3. What information does the course offer regarding regression?', '4. In what way is regression covered in the course?', "5. What are the course's teachings on regression?"]

9