Overview
VectorAI DB organizes data in a hierarchical structure. Collections contain points, and each point consists of a vector with optional payload metadata. This structure enables semantic search: you query with a vector, and VectorAI DB finds points with similar vectors in your collection.| Category | Component | Description |
|---|---|---|
| Data structure | Collections | Named containers that store points, similar to tables in relational databases. Each collection has a fixed vector dimension and distance metric. |
| Data structure | Points | Individual data units within a collection. Each point has a unique ID and contains a vector with optional metadata. |
| Data structure | Vectors | Numerical embeddings that represent your data semantically. Generated by embedding models from text, images, or other content. |
| Data structure | Payload | Optional JSON metadata attached to points. Use payloads for filtering and storing contextual information. |
| Operations | Search | Vector similarity search using distance metrics to find semantically similar content. |
| Operations | Filtering | Combine vector similarity with metadata conditions using must, should, and must-not filters. |
| Configuration | Indexing | HNSW algorithm for efficient approximate nearest neighbor search at scale. |
| Configuration | Distance metrics | Cosine similarity, Euclidean distance, and dot product for measuring vector similarity. |
Next steps
- Start with Collections to understand how data is organized.
- Learn about Points to manage your vector data.
- Explore Search to query your collections.