Tigris Data Unveils Beta Launch of New Vector Search Tool

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Tigris Data has beta launched a new vector search tool for building personalized recommendations and search applications. Available now as a free beta, Vector Search is meant for use cases like retail and e-commerce, as well as financial applications and event stores.

Vector search leverages deep learning to provide search results based on similar semantic meanings and is an alternative to keyword-based searches that rely on direct matching of keywords. Instead, a vector search engine matches an input term to a vector, which is an array of features generated from an object catalog. Each vector contains tens to hundreds of dimensions that each describe aspects of an item in a catalog, resulting in context-based search results. Companies like Home Depot are using vector search algorithms on their websites to make it easier to search for products and receive recommendations on related products.

Another feature in beta is a Database to Search automatic synchronization that the company says allows users to automatically create search indexes and synchronize data from Tigris Database to Tigris Search. Additionally, Tigris has also released a tutorial on how to use the OpenAI Embeddings API to generate embeddings for documents and use Tigris to index the embeddings to build a vector search engine.

Vector Search is part of the Tigris Data platform, which is an open source, NoSQL, multi-cloud database and search platform that the company claims is 4x less expensive than DynamoDB, the NoSQL database offered by AWS.

Tigris says its distributed, cloud-native architecture allows developers to leverage cloud infrastructure services such as auto-scaling and automatic backups without the need for infrastructure management. The platform has a single API that spans search, event streaming, and transactional document store while supporting multiple programming languages and frameworks. Tigris is based on FoundationDB, a distributed database open sourced by Apple in 2018 under the Apache 2.0 license.

Tigris Data launched with $6.9 million in seed funding in 2022. The company’s investors include General Catalyst and Basis Set Ventures, along with Guillermo Rauch, CEO at Vercel, and Rob Skillington, CTO and Co-Founder of Chronosphere.

The vector search workflow with OpenAI and Tigris. (Source: Tigris)

The company was founded by Ovais Tariq, Himank Chaudhary and Yevgeniy Firsov, who led the development of data storage and management at Uber. The team’s experiences with data growth and infrastructure sprawl led to its creation of a developer data platform that could simplify data applications without sacrificing speed or scalability, according to a prior release. CEO Tariq previously commented that the goal of building Tigris was to develop a single approach to data management in a developer-friendly environment that lets developers focus on building instead of managing infrastructure. He also noted that building Tigris as an open source platform was important to the team to ensure developers can avoid lock-in.

“With Vector Search, Tigris Data gives developers the ability to deliver fast, accurate, and personalized recommendations to their users,” said Tariq in a release. “This powerful tool is designed to help companies unlock the full potential of their data by making search and recommendation applications more effective and customer-centric.”

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