Multi-Query Retrieval: Ask the Same Question Five Different Ways

Imagine you’re searching for the perfect recipe. You wouldn’t just type “chocolate cake,” would you? You might try “best chocolate cake recipe,” “easy chocolate cake,” “chocolate fudge cake,” or even “chocolate cake with ganache.” Each query targets slightly different aspects, increasing your chances of finding exactly what you want. Multi-query retrieval applies this same principle to retrieval-augmented generation (RAG) pipelines, dramatically boosting recall and improving overall performance. Let’s dive into how it works and how you can implement it.

What is Multi-Query Retrieval?

At its core, multi-query retrieval is a simple yet powerful technique: instead of relying on a single user query, you generate multiple variations of that query, retrieve relevant documents for each variation, and then merge the results. This increases the diversity of information retrieved, leading to more comprehensive and relevant context for your language model.

Think of it like casting a wider net when fishing. A single query is like a single cast in one spot. Multi-query retrieval is like casting multiple nets in different areas, increasing your chances of catching the “right fish” (relevant documents).

The beauty of this approach lies in its ability to overcome the limitations of a single query. Users often phrase their questions in ways that might not perfectly align with the way information is indexed in your vector database or knowledge base. By generating variations, you cover more ground and improve the chances of finding the most relevant pieces of information.

Implementing Multi-Query Retrieval

Let’s get practical. Here’s how you can implement multi-query retrieval using Python and LangChain. We’ll use a simple example to illustrate the core concepts.

First, we’ll need a language model (LLM) to generate query variations. Here, we’ll use the OpenAI API, but you can easily adapt this to other LLMs.

import os
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

# Set your OpenAI API key
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

llm = OpenAI(temperature=0) # Temperature 0 for more deterministic outputs

# Prompt to generate query variations
prompt_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}"""

prompt = PromptTemplate(input_variables=["question"], template=prompt_template)
chain = LLMChain(llm=llm, prompt=prompt)

# Example user question
question = "What are the key differences between Llama 2 and GPT-3?"

# Generate query variations
generated_queries = chain.run(question)

print(generated_queries)

This code snippet defines a prompt that instructs the LLM to generate five different versions of the user’s question. The temperature=0 setting ensures that the LLM generates more consistent and predictable outputs, which is crucial for generating reliable query variations.

Next, we need to retrieve documents for each of these generated queries. This involves querying your vector database (e.g., Chroma, Pinecone, FAISS) with each query variation and collecting the results. For the sake of simplicity, let’s assume you have a function called retrieve_documents that takes a query and returns a list of relevant documents.

# Placeholder for your document retrieval function
def retrieve_documents(query):
    # Replace with your actual document retrieval logic
    # This is just a mock function for demonstration purposes
    if "Llama 2" in query:
        return ["Llama 2 is an open-source LLM...", "Llama 2's architecture..."]
    elif "GPT-3" in query:
        return ["GPT-3 is a powerful LLM...", "GPT-3's capabilities..."]
    else:
        return ["No relevant documents found."]

# Retrieve documents for each query variation
retrieved_documents = []
for query in generated_queries.split('
'): # Split the variations into a list.
    retrieved_documents.extend(retrieve_documents(query))

# Deduplicate the retrieved documents (optional)
retrieved_documents = list(set(retrieved_documents))

print(retrieved_documents)

Finally, you would pass these retrieved documents to your language model for generating the final answer. The more comprehensive context provided by multi-query retrieval will enable the LLM to provide more accurate and informative responses.

In a Quartalis context, this multi-query retrieval could be incorporated into the RAG pipeline orchestrated by the Quartalis AI ecosystem. The retrieve_documents function could leverage the Quartalis’ built-in connectors to various data sources and vector databases, streamlining the integration process.

Advantages of Multi-Query Retrieval

The benefits of multi-query retrieval are numerous:

• Improved Recall: By exploring different angles of the same question, you significantly increase the chances of retrieving all relevant documents.
• Robustness to Query Phrasing: Users don’t need to phrase their questions perfectly. The query variations capture different nuances and improve retrieval accuracy even with imperfect queries.
• Better Context for LLMs: The richer context provided by the expanded set of retrieved documents leads to more informed and accurate responses from your language model.

Evaluating Multi-Query Retrieval

How do you know if multi-query retrieval is actually improving your RAG pipeline? Here are some key metrics to consider:

• Recall: This measures the proportion of relevant documents that are retrieved. Multi-query retrieval should lead to a significant increase in recall compared to single-query retrieval.
• Precision: This measures the proportion of retrieved documents that are actually relevant. While multi-query retrieval prioritises recall, it’s important to ensure that precision doesn’t suffer too much.
• Answer Accuracy: Ultimately, the goal is to improve the accuracy and quality of the answers generated by your language model. Evaluate the answers generated with and without multi-query retrieval to see if there’s a noticeable improvement.
• User Satisfaction: Collect user feedback to gauge whether the improved recall translates to a better user experience. Are users finding the answers they’re looking for more easily?

To evaluate these metrics, you’ll need a dataset of questions and corresponding ground truth answers. You can then use standard information retrieval evaluation techniques to compare the performance of your RAG pipeline with and without multi-query retrieval.

For example, you could use the following Python code to calculate recall:

def calculate_recall(retrieved_documents, relevant_documents):
  """Calculates the recall score.

  Args:
    retrieved_documents: A list of retrieved documents.
    relevant_documents: A list of relevant documents.

  Returns:
    The recall score.
  """
  relevant_retrieved = sum(1 for doc in retrieved_documents if doc in relevant_documents)
  if not relevant_documents:
    return 1.0 if not retrieved_documents else 0.0 # Special handling for no relevant documents
  return relevant_retrieved / len(relevant_documents)

# Example usage:
retrieved_docs = ["Llama 2 is an open-source LLM...", "GPT-3 is a powerful LLM...", "Another document"]
relevant_docs = ["Llama 2 is an open-source LLM...", "GPT-3 is a powerful LLM...", "Llama 2's architecture..."]

recall = calculate_recall(retrieved_docs, relevant_docs)
print(f"Recall: {recall}")

Remember to adapt the retrieve_documents function and the relevant_docs list to match your specific data and use case.

Addressing Potential Challenges

While multi-query retrieval offers significant benefits, it’s important to be aware of potential challenges:

• Increased Computational Cost: Generating multiple queries and retrieving documents for each one can increase the computational cost of your RAG pipeline. Optimise your vector database and retrieval functions to minimise latency.
• Redundancy: The retrieved documents may contain redundant information. Implement deduplication techniques to remove duplicates and reduce noise.
• Precision Degradation: While recall usually improves, precision can sometimes suffer if the generated queries retrieve irrelevant documents. Carefully tune the prompt used to generate query variations to minimise this issue.
• Prompt Engineering: The quality of the generated queries depends heavily on the prompt you use. Experiment with different prompts to find the one that works best for your specific use case.

What’s Next

Multi-query retrieval is a powerful technique for improving the performance of RAG pipelines. By generating multiple query variations, you can significantly increase recall, improve robustness to query phrasing, and provide richer context for your language model. However, it’s important to be aware of the potential challenges, such as increased computational cost and redundancy, and to implement appropriate mitigation strategies.

As you delve deeper, explore more advanced techniques like query rewriting and adaptive retrieval, which dynamically adjust the number and type of queries based on the initial retrieval results. Tools within the Quartalis ecosystem can help streamline the integration of these advanced techniques into your existing RAG pipelines, making it easier to build and deploy high-performance AI applications. Experiment with different LLMs, vector databases, and evaluation metrics to find the optimal configuration for your specific use case. The possibilities are endless, and the potential for improving the accuracy and effectiveness of your RAG pipelines is immense.