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Tailor your search engine with AI-powered hybrid search in Spanner
- Google's Spanner database is capable of hybrid search with vector search, full-text search, and machine learning (ML) model reranking capabilities using a familiar SQL interface. Spanner's AI-powered hybrid search capabilities tackle user expectations of fast, accurate, and contextually relevant results. Applications using Spanner can build a tailored search engine using vector search for semantic relevance and full-text search for precise keyword matching. Result fusion and ML model reranking allows for advanced result refinement.
- Developers can build an intelligent search pipeline, enabling organizations to deliver effective search experiences that deliver much more than just a traditional single search method, even for industries such as e-commerce that need more functionality to satisfy users.
- Spanner's integration with Google's Vertex AI makes it possible to perform ML model-based reranking directly within Spanner without the need for multiple technical stacks, complex ETL pipelines, and intricate application logic. Results are refined after initial retrieval by using the advanced (and computationally expensive) model on the narrowed set of initial candidates.
- RRF (Reciprocal Rank Fusion) is a highly effective technique that is implemented with Spanner SQL interface, resulting in other developers exploring and implementing various RRF methods. The search experience can be further tailored to specific application requirements by normalizing scores across different searches to a common range and then combining them using a weighted sum. It is ideal for fine-grained control over search experiences.
- Spanner's versatile SQL interface empowers application developers to explore and implement diverse result fusion methods. It reduces architectural and operational overhead, eliminates the need for complex ETL pipelines and avoids performance inefficiencies.
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