Welcome to Firebase Data Connect's vector similarity search — Firebase's implementation of semantic search that integrates with Google Vertex AI.
At the core of this feature are vector embeddings, which are arrays of floating
point numbers representing the semantic meaning of text or media. By running a
nearest neighbor search using an input vector embedding, you can find all
semantically similar content. Data Connect uses PostgreSQL's
pgvector extension for this capability.
This powerful semantic search can drive use cases like recommendation engines and search engines. It's also a key component in retrieval-augmented generation in generative AI flows. The Vertex AI documentation is a great place to learn more.
You can rely on Data Connect's built-in support for generating vector embeddings automatically using Vertex AI's Embeddings API, or use that API to generate them manually.
This is an excerpt from the Data Connect documentation. When you sign up for the Data Connect preview, you'll have full access to this guide, which covers:
- Setting up to perform vector search
- Designing your Data Connect schema for vector search
- Generating and retrieving vector embeddings
- Performing vector searches
- Using custom embeddings
- Deploying vector search to production
- Syntax reference for vector search directives.