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Generating embeddings

  • Embeddings are numeric vectors that represent the meaning, context, or relationships within the data (e.g., text or images). They allow you to search for similar content using Vector search, rather than exact matches.

  • RavenDB can generate embeddings automatically using Embeddings-generation tasks that you define. These tasks process text fields from documents and convert them into vectors using either an AI model from external providers (e.g., OpenAI) or RavenDB’s built-in model. The resulting vectors are stored in a dedicated collection in your database.

  • Alternatively, you can skip pre-generating embeddings via Embedding-generation tasks and let RavenDB generate them on-the-fly during dynamic vector search queries. Learn more in Vector search using a dynamic query.

  • RavenDB does not generate embeddings for non-text content such as images. However, you can import embeddings for such content from external providers and store them in your database for similarity search.

  • Embeddings also power other AI features in RavenDB, such as GenAI and AI agents,
    which may use vector search queries to retrieve relevant documents in response to LLM requests.

  • On this start page, you'll find:

Use cases​

Embeddings-generation tasks can prepare your data for AI-powered search, analysis, and retrieval.
Common use cases include:

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

Technical documentation links ✨​

Embeddings with RavenDB and External ModelsExternal

Embeddings with RavenDB and External Models

Step-by-step setup guide for AI-powered semantic search, by Paweł Lachowski

Read article
The integration of embeddings generation in RavenDBExternal

The integration of embeddings generation in RavenDB

The reasoning and architecture behind RavenDB's embeddings feature, by Oren Eini

Read article

Related lives & videos​

Learn more about enhancing your applications using vector search operations.

Taking over the world with AI and RavenDBExternal

Taking over the world with AI and RavenDB

Learn how to integrate AI into your apps using RavenDB for semantic search, vector embeddings, and LLM-powered features - with real examples from Oren Eini.

Watch
Vector search in RavenDBExternal

Vector search in RavenDB

The AI trend developers simply cannot ignore.

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