Multi-query retrieval for better recall in LangChain

Introduction

Multi-query retrieval helps find better answers by asking several questions instead of just one. It improves how well you get the right information.

When you want to get more complete answers from a large set of documents.

When a single question might miss important details in the data.

When you want to combine results from different angles or keywords.

When you need to improve recall in search systems or chatbots.

When working with complex topics that need multiple queries to cover fully.

Syntax

LangChain

from langchain.vectorstores import FAISS
from langchain.chains import MultiQueryRetriever

# Create a vector store (example with FAISS)
vector_store = FAISS.load_local("path_to_index", embedding_function)

# Create a MultiQueryRetriever
multi_query_retriever = MultiQueryRetriever(
    retriever=vector_store.as_retriever(),
    queries=["query 1", "query 2", "query 3"]
)

# Retrieve documents
results = multi_query_retriever.get_relevant_documents("main question")

The MultiQueryRetriever takes multiple queries to improve recall.

You need a vector store retriever like FAISS or others to use it.

Examples

Using two queries to find documents about climate change and global warming for the main question.

LangChain

multi_query_retriever = MultiQueryRetriever(
    retriever=vector_store.as_retriever(),
    queries=["climate change", "global warming"]
)
results = multi_query_retriever.get_relevant_documents("effects on environment")

If no extra queries are given, it works like a normal single query retriever.

LangChain

multi_query_retriever = MultiQueryRetriever(
    retriever=vector_store.as_retriever(),
    queries=[]
)
results = multi_query_retriever.get_relevant_documents("renewable energy")

Multiple related queries help find more documents about ethical AI topics.

LangChain

multi_query_retriever = MultiQueryRetriever(
    retriever=vector_store.as_retriever(),
    queries=["AI ethics", "machine learning fairness", "bias in AI"]
)
results = multi_query_retriever.get_relevant_documents("ethical AI development")

Sample Program

This program loads a saved FAISS vector store, sets up a multi-query retriever with three related queries about AI history, and retrieves documents for a main question. It then prints the titles of the found documents.

LangChain

from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import MultiQueryRetriever

# Setup embeddings and vector store
embedding_function = OpenAIEmbeddings()

# Assume we have a FAISS index saved locally
vector_store = FAISS.load_local("./faiss_index", embedding_function)

# Create MultiQueryRetriever with multiple queries
multi_query_retriever = MultiQueryRetriever(
    retriever=vector_store.as_retriever(),
    queries=["history of AI", "AI milestones", "AI breakthroughs"]
)

# Retrieve documents for a main question
documents = multi_query_retriever.get_relevant_documents("important events in AI development")

# Print titles of retrieved documents
print("Retrieved document titles:")
for doc in documents:
    print(doc.metadata.get('title', 'No Title'))

Important Notes

Multi-query retrieval improves recall by combining results from several queries.

Time complexity depends on the number of queries and size of the vector store.

Common mistake: Using unrelated queries can reduce result quality.

Use multi-query retrieval when you want broader coverage than a single query.

Summary

Multi-query retrieval asks several related questions to get better search results.

It works by combining results from multiple queries in a vector store retriever.

This method helps find more complete and relevant documents for complex topics.