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.
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.
Continue to one of the following guides to learn how to get started with Vector Search using Tigris.