How does LlamaIndex handle query expansion?

LlamaIndex handles query expansion by using techniques that refine or broaden a user’s original query to improve retrieval accuracy in retrieval-augmented generation (RAG) systems. Query expansion works by generating variations of the input query, adding context, or breaking complex questions into smaller parts. This helps the system retrieve more relevant documents or data chunks, especially when the original query is ambiguous, overly specific, or lacks necessary keywords. LlamaIndex implements this through built-in modules and integrations with language models (LLMs) to automate query transformations.

For example, one approach is hypothetical answer generation, where the system uses an LLM to create a hypothetical response to the original query. This generated text is then used as an expanded query to search for relevant data. Another method is sub-question decomposition, which splits a complex query into simpler, focused sub-questions. Each sub-question is processed independently, and the combined results provide a comprehensive answer. Additionally, step-back prompting can be used to ask broader conceptual questions related to the original query, adding contextual layers to the search. These methods are configurable, allowing developers to choose the best strategy for their use case.

The benefits of query expansion in LlamaIndex include better handling of semantic variations and improved recall in document retrieval. For instance, a user asking, “How do I fix a Python ValueError?” might receive an expanded query that includes terms like “exception handling,” “try-except blocks,” or common error scenarios. This ensures the system retrieves documentation, tutorials, or code examples that match the intent, even if the exact term “ValueError” isn’t present. Developers can customize these steps by adjusting prompt templates, selecting LLMs, or combining multiple expansion techniques. This flexibility makes LlamaIndex adaptable to scenarios like technical support, research, or enterprise knowledge bases where precise retrieval is critical.