Multi-Query

Multi-query is a technique used in advanced RAG models that improve the retrieval of relevant documents for complex user questions.

Imagine a user asks the following question: “What is LangSmith, and why do we need it?” A simple retrieval system might only find documents containing the exact phrase “LangSmith.” But what if the document discusses LangSmith using synonyms or related concepts? Here, multi-query helps by generating multiple variations of the original question, capturing different aspects of the user’s intent. This broadens the search and retrieves documents that might not contain the exact keywords but still hold valuable information.

What is multi-query?

Multi-query utilizes an LLM to automatically generate reformulations of the user’s original question. It aims to create multiple versions that capture different perspectives on the user’s intent, increasing the chances of finding relevant documents even when the wording differs slightly. Here’s how it works:

• Single user input: It all starts with a single question the user poses.

• Query diversification: The core concept of multi-query is to expand the search beyond the original query. This is achieved by using LLMs or other techniques to rephrase the question into various forms. Imagine asking the question differently to capture the full scope of what you’re looking for.

• Multiple queries generation: The LLM generates several reformulated versions of the original query, each capturing a different aspect or perspective of the user’s intent.

• Parallel search execution: These multiple reformulated queries are then used to perform parallel searches across the document collection.

• Document retrieval: Each reformulated query retrieves a set of documents that are relevant to that specific phrasing of the question.

• Results aggregation: The retrieved documents from all the different queries are aggregated. This aggregation ensures a broader and more comprehensive set of documents that might contain the relevant information.

• Enhanced relevance assessment: The aggregated documents are then evaluated for relevance, ensuring that the most pertinent information is identified from the diverse set of retrieved documents.

• Foundation for further steps: The retrieved documents based on each query variation become the building blocks for subsequent steps in the RAG process.