Generating embeddings

Create embeddings to enable AI-powered similarity search.

Embeddings are numeric vectors that you can create for data (like a text or an image) to capture meanings, contexts, or relationships related to the data. You can then search the data by running intelligent queries over its embeddings using vector search to find content by similarity rather than exact match.

RavenDB allows you to create embeddings using native ongoing embeddings-generation tasks that systematically process document collections and convert document fields (like texts or arrays) into embeddings. To create the embeddings, the tasks can use either an external AI model (such as OpenAI) or RavenDB's default embedding model.
You can also create embeddings using external embeddings providers and store them in your database (e.g., to handle other content types such as images).
You can avoid pre-generating embeddings, and let vector search operations generate embeddings on-the-fly, while searching.
Embeddings can be used by other RavenDB AI features. E.g., AI agents can use vector search to retrieve relevant data requested by the LLM.

Use cases

Embeddings generation tasks can be used to prepare your data for AI-powered search, analysis, and usage, e.g., for -

Enterprise knowledge bases
Generate embeddings for thousands of documents, policies, and procedures to enable instant semantic search
Legal document libraries
Process case law, contracts, and regulations to build searchable legal repositories
Product catalogs
Convert product descriptions, specifications, and reviews into embeddings for enhanced e-commerce search
Content management systems
Transform blog posts, articles, and marketing materials into searchable vector representations