Rockset as a vector database
Create vector embeddings using any machine learning model (Hugging Face, OpenAI, Cohere, etc.) and index them for fast similarity search. Build with LangChain or LlamaIndex. Use Rockset for retrieval augmented generation (RAG), personalization engines, semantic search, anomaly detection and more.
Hybrid search as easy as a SQL WHERE clause
Store and index vectors alongside text, JSON, geo and time-series data to create relevant AI experiences. Exploit the power of the search index with an integrated SQL engine so your searches are always executed quickly.
Build apps with real-time updates
Insert, update and delete vectors and metadata with indexes built on RocksDB. New data is reflected in your searches in milliseconds. No expensive reindexing.
Separation of indexing and search
With compute-compute separation, similarity indexing of vectors will not affect search performance. Indexing happens on a different virtual instance than search for predictable performance. Take your AI applications to production with confidence.
We saw the immense power of real-time analytics and AI to transform JetBlue’s operations and stitching together 3-4 database solutions would have slowed down application development. With Rockset, we found a database that could keep up with the fast pace of innovation at JetBlue.
Sai Ravuru, Senior Manager of Data Science and AnalyticsRead case study