Announcing MCP Toolbox Java SDK v1.0: Agentic data access for the enterprise
cloud.google.com·6d ago
Cloud & Infrastructure
AWS Blog·Aug 5·5 min read
AWS Blog·Aug 5·5 min readAmazon DynamoDB now supports real-time vector search at any scale
TL;DR
Amazon DynamoDB now offers native support for real-time vector search, enabling users to perform similarity searches on vector embeddings without needing a separate database. This integration allows for seamless scaling and operational efficiency, even at trillions of vectors.
✦ Why It Matters
Start integrating vector search into your existing DynamoDB applications to enhance data retrieval capabilities today.
Key Takeaways
- Integrate vector search directly into existing DynamoDB tables.
- Use models like Amazon Bedrock for generating vector embeddings.
- Leverage the SearchVectors API for efficient similarity searches.
- Utilize inline filtering to refine search results based on attributes.
How It Works
Vector search in DynamoDB allows users to create a vector index on attributes storing embeddings, which are numerical representations of text. Users generate these embeddings using machine learning models and store them in a List data type.
The SearchVectors API then enables querying with a vector, returning results ranked by similarity based on chosen distance functions like Cosine or Euclidean.