What is multi-query retriever in RAG
multi-query retriever in Retrieval-Augmented Generation (RAG) is a retrieval method that issues multiple queries per input to fetch diverse relevant documents from a knowledge base, improving answer accuracy and coverage. It enhances the retrieval step by capturing different facets of the input question before the language model generates a response.Multi-query retriever is a retrieval technique in Retrieval-Augmented Generation (RAG) that sends multiple queries per input to retrieve a broader set of relevant documents for better answer generation.How it works
A multi-query retriever works by decomposing a single user query into multiple sub-queries or reformulations. Each sub-query targets a different aspect or phrasing of the original question. These multiple queries are sent to the retrieval system, which returns a diverse set of documents or passages. This approach increases the chance of retrieving all relevant information needed for the language model to generate a comprehensive and accurate answer.
Think of it like searching a library: instead of asking the librarian one way, you ask several related questions to get books covering all angles of your topic.
Concrete example
Here is a simplified Python example using the OpenAI SDK to illustrate a multi-query retriever approach in RAG. We send multiple reformulated queries to a vector store and combine the retrieved documents before passing them to the gpt-4o model for answer generation.
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
# Original user query
user_query = "What are the health benefits of green tea?"
# Reformulated queries for multi-query retrieval
queries = [
user_query,
"Benefits of drinking green tea",
"Green tea effects on health",
"Why is green tea good for you?"
]
# Simulated retrieval function (replace with actual vector search)
def retrieve_documents(query):
# Placeholder: returns dummy docs per query
return [f"Document about: {query} - fact {i}" for i in range(1, 3)]
# Retrieve documents for all queries
retrieved_docs = []
for q in queries:
retrieved_docs.extend(retrieve_documents(q))
# Combine retrieved docs into context
context = "\n".join(retrieved_docs)
# Generate answer using combined context
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": f"Answer the question based on these documents:\n{context}\nQuestion: {user_query}"}
]
response = client.chat.completions.create(
model="gpt-4o",
messages=messages
)
print(response.choices[0].message.content)Document about: What are the health benefits of green tea? - fact 1 Document about: What are the health benefits of green tea? - fact 2 Document about: Benefits of drinking green tea - fact 1 Document about: Benefits of drinking green tea - fact 2 Document about: Green tea effects on health - fact 1 Document about: Green tea effects on health - fact 2 Document about: Why is green tea good for you? - fact 1 Document about: Why is green tea good for you? - fact 2 [LLM generated answer based on combined documents]
When to use it
Use a multi-query retriever in RAG when your input queries are complex, ambiguous, or cover multiple topics that a single retrieval query might miss. It is ideal for:
- Improving recall by capturing diverse relevant documents.
- Handling queries with multiple facets or sub-questions.
- Enhancing robustness in knowledge-intensive tasks.
Avoid multi-query retrieval when latency or cost is critical, as issuing multiple queries increases retrieval time and resource usage.
Key terms
| Term | Definition |
|---|---|
| Multi-query retriever | A retrieval method that sends multiple reformulated queries per input to fetch diverse relevant documents. |
| Retrieval-Augmented Generation (RAG) | An AI architecture combining a retrieval system with a language model to generate grounded answers. |
| Language model | A model like gpt-4o that generates text based on input context. |
| Vector store | A database that indexes documents by vector embeddings for similarity search. |
Key Takeaways
- Multi-query retrievers improve retrieval recall by issuing multiple queries per input.
- They help capture different aspects of complex or ambiguous questions in RAG pipelines.
- Use multi-query retrieval when accuracy and coverage outweigh latency and cost concerns.