如何使用MultiQueryRetriever
基于距离的向量数据库检索嵌入（表示）查询在高维空间中，并根据距离度量找到相似的嵌入文档。但是，检索可能会因查询措辞的细微变化或嵌入未能很好地捕捉数据的语义而产生不同的结果。有时会进行提示工程/调整以手动解决这些问题，但这可能很繁琐。
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，自动完成提示调优的过程。对于每个查询，它检索一组相关文档，并取所有查询的唯一并集，以获得更大的一组可能相关的文档。通过生成同一问题的多个视角，MultiQueryRetriever 可以缓解基于距离检索的一些限制，并获得更丰富的结果集。
让我们使用来自RAG教程的Lilian Weng的LLM驱动的自主代理博客文章来构建一个向量存储：
# 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)
USER_AGENT environment variable not set, consider setting it to identify your requests.
简单用法
指定用于查询生成的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 Reference:MultiQueryRetriever | ChatOpenAI
# 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?']
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请注意，由retriever生成的基础查询在INFO级别记录。
提供你自己的提示
在底层，MultiQueryRetriever 使用特定的 prompt 生成查询。要自定义此提示：
- 创建一个PromptTemplate，其中包含一个用于问题的输入变量；
- 实现一个输出解析器，如下所示，将结果拆分为查询列表。
提示和输出解析器必须共同支持生成查询列表。
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 Reference:BaseOutputParser | PromptTemplate
# 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?"]
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