Vector Search

Tigris makes it easy to build high-performance vector search applications. It is a fully managed cloud-native database that allows you store and index documents and vector embeddings for fast and scalable vector search.

Tigris provides the following features:

High performance: Tigris is built from the ground up to provide fast vector search. It is optimized for low-latency vector search and high throughput.
Real-time: Tigris provides real-time updates to the index. You can add, update, and delete documents and embeddings in real-time.
Hybrid search: Tigris supports hybrid search, which allows you to combine vector search with attribute filtering for more relevant search results.
Fully managed: Tigris is a fully managed cloud-native database. You can get started with Tigris in minutes and scale as needed with ease.

Vector Search Workflow

A typical vector search workflow with Tigris involves the following steps:

Generate embeddings for your documents.
Index your documents and embeddings in Tigris.
Generate embeddings for your query.
Search for similar documents using the query embeddings.

Use Cases

Tigris is a general-purpose vector search engine. It can be used for a variety of use cases, including:

Similarity search: You can generate vector embeddings from your documents and index and search through the vectors using Tigris to power your similarity search application.
Product recommendations: You can store product information and embeddings in Tigris and use it to power your product recommendations for e-commerce.
Hybrind search: You can combine vector search with attribute filtering in Tigris for more relevant search results.
Image search: You can generate vector embeddings for image data and index them in Tigris to power your image search application.
Generative question answering: You can use Tigris to index relevant context in the form of vector embeddings and use it to suppliment a generative model to generate more accurate answers.