Vespa Vector DB

Vespa version 8 is a fully featured search engine and AI Vector Database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real time.

What is Vespa used for?

Query Capabilities in Vespa

Vespa supports querying by:

A document can have any number of fields of these types that can be queried efficiently in the same query, and each field – even vector fields – can have multiple values in the same document.

Operators querying fields in these ways can be combined freely by AND and OR in queries while still executing efficiently. This makes it possible to express a wide range of behavior, such as retrieving in vector spaces with filters or performing hybrid text and semantic retrieval.

In addition, Vespa provides a grouping language which lets queries specify how matches should be grouped and aggregated. This makes it possible to group matches by unique field values or buckets, and aggregate, count or compute values over the groups. Groupings can be arbitrarily deep and multiple groupings can be made in the same query. Even though grouping applies to all matches across all nodes, it executes efficiently as a distributed computation.

Hybrid Search - QuickStart

There are a few key criteria to evaluate what Vector DB is most suited for your project.

To get started quickly, we’ve provided a Quick Start with Vespa Cloud that walks you through the process step by step, also available as a Colab notebook.

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