Vector is a semantic search database built on SlateDB and object storage. Each database stores documents conforming to a user-defined schema. The schema defines fields with various types, along with a “vector” field that stores dense vectors with a fixed number of dimensions. Vector serves search queries that find the nearst N documents to a given query vector. Because all data is durably stored on object storage, a single replica provides full durability with no operational overhead for data replication or backups. Writes are limited to a single database instance, but queries can be scaled horizontally across multiple stateless reader instances.Documentation Index
Fetch the complete documentation index at: https://opendata.dev/docs/llms.txt
Use this file to discover all available pages before exploring further.
Quickstart
Install Vector and write your first documents in under five minutes
API Reference
Browse the full REST API for writing and searching vectors
Data Model
Understanding Vector’s Data Model
Configuration
Vector configuration reference
Storage Design
How Vector indexes documents to support efficient similarity search
Getting to Production
Deploy, monitor, and secure Vector for production workloads
GitHub
View the source code, open issues, and contribute