Summary

A new query engine, SQLAutoVectorQueryEngine, is introduced within LlamaIndex to handle complex natural language queries by leveraging both SQL databases and vector stores, enabling it to process structured and unstructured data effectively. This engine utilizes the expressive capabilities of SQL for structured data and combines it with the contextual insights from vector databases, allowing for queries that require a blend of both data types. The engine employs a two-step approach: it first uses a selector to determine whether to query the SQL database or the vector database, and then synthesizes the final response by integrating results from both sources. This approach offers enhanced query capabilities, such as answering multifaceted questions that draw on structured analytics and semantic understanding from unstructured data. Experiments demonstrate its efficacy in handling diverse queries, showcasing its potential to bridge the gap between traditional SQL queries and modern vector-based search methodologies.

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