How VectorAI works
VectorAI stores and organizes embeddings for fast similarity searches. Unlike traditional SQL databases that use exact matches, VectorAI finds the nearest vectors to identify related content and power intelligent recommendations.Embed data into vectors
Transform raw data (text, images, audio) into numerical embeddings using ML models like BERT or CLIP. Each embedding is a fixed-length vector that preserves semantic meaning. Similar items cluster together in the vector space.
Store vectors in the database
Store generated vectors with their IDs and metadata in indexed collections. VectorAI supports multiple vector types and dimensions. This structure enables fast retrieval and filtering.
Embed queries into vectors
Convert user queries into vectors using the same ML model as your data. This ensures queries and data exist in the same vector space. Embeddings are generated in real time.
Run similarity searches
Find nearest neighbor vectors using distance metrics (cosine similarity, Euclidean distance, dot product). VectorAI uses Approximate Nearest Neighbor (ANN) algorithms for speed. Results are ranked by similarity score.
Differences from traditional databases
The table below compares VectorAI DB with traditional databases across key aspects including data models, query patterns, and use cases.| Aspect | Traditional DBs | VectorAI |
|---|---|---|
| Data model | Rows of scalar fields (text, numbers, dates) | High-dimensional vectors with metadata |
| Query pattern | Equality, range, or pattern matching | Similarity search (nearest neighbors) |
| Search type | Exact matches or structured queries | Semantic and contextual similarity |
| Performance | Optimized for OLTP/OLAP workloads | Optimized for ANN search algorithms |
| Use case | Transactional data, reporting | AI/ML applications, recommendations |
| Indexing | B-trees, hash indexes | HNSW, IVF, PQ for vector spaces |
| Role in AI | Data storage for ML training | External knowledge store for embeddings |
Next steps
Quickstart
Get started with VectorAI DB in under five minutes
Local installation
Install VectorAI DB using Docker
Key features
Explore the key features of VectorAI DB
Architecture
Understand the VectorAI DB architecture
Use cases
Explore common VectorAI DB use cases