kdbai

What problem does it solve?

KDB.AI provides a scalable vector database and tooling to implement AI-ready similarity search, hybrid search, and time-series pattern matching across large datasets.

Core Features & Use Cases

• Vector and similarity search over large datasets using KDB.AI embeddings and indices.
• Hybrid search combining dense vectors with sparse signals for reranking and relevance.
• Time-series similarity (TSS) and dynamic time warping (DTW) support for time-aware queries.
• Reranking with built-in methods and integration points for external rerankers.
• Use Case: Build a product search experience that combines semantic similarity with keyword filters on a KDB.AI-backed catalog.

Quick Start

Run a basic KDB.AI vector search example using the Python client to retrieve top-k similar items from a sample dataset.