Rockset introduces support for vector search to power efficient AI and machine learning

Rockset, the real-time analytics database built for the cloud, is offering native support for vector embeddings, empowering organizations to build high-performance vector search applications at scale, in the cloud.

“By extending our existing real-time search and analytics capabilities into vector search, we give AI/ML developers access to real-time data and fast queries with a fully managed cloud service,” said Rockset co-founder and CEO Venkat Venkataramani. “We now enable hybrid metadata filtering, keyword search, and vector search, simply using SQL. Combining this ease of use with our compute efficiency in the cloud makes AI/ML a lot more accessible for every organization.”

By extending its real-time SQL-based search and analytics capabilities, Rockset now allows developers to combine vector search with filtering and aggregations to enhance the search experience and optimize relevance by enabling hybrid search.

According to the company, Rockset delivers fast, efficient search, aggregations, and joins on real-time data at massive scale, by using a Converged Index stored on RocksDB.

Vector databases, such as Milvus, Pinecone, Weaviate, and other popular alternatives like Elasticsearch, store and index vectors to make vector search efficient. With this release, Rockset provides a more powerful alternative that combines vector operations with the ability to filter on metadata, do keyword search and join vector similarity scores with other data to create richer, more relevant ML and AI powered experiences in real-time, according to the vendor.

With the new release, Rockset supports vector operations along with the following benefits:

• Real-time data: High velocity real-time indexing with support for updates
• Fast search: Combine vector search, keyword search and metadata filtering for fast, more efficient results
• Hybrid search and analytics: Join vector similarity scores with other data to create richer, more relevant experiences, using SQL
• Fully managed cloud service: A horizontally scalable, highly available cloud-native database with compute-storage and compute-compute separation for cost-efficient scaling in the cloud

Rockset can also be used with OpenAI’s Embeddings API, to generate, index, and query language embeddings.

In addition, the company announced an integration with the Feast Feature Store to streamline the management of vector embeddings across multiple data sources and frequently updated models.

For more information about this news, visit www.rockset.com.