New to vector databases? Vector databases store numerical representations of your data called embeddings, and find results by similarity rather than exact keyword matching. Use one when meaning matters more than exact text: semantic search, RAG pipelines, agent memory.
Architecture
VectorAI DB runs as a single Docker container with no external dependencies. Data is persisted to a volume-mounted directory on the host.
The Python and JavaScript SDKs communicate over gRPC by default.
Data model
How a search works
1
Generate an embedding
Your application converts a query (text, image, or other data) into a vector using an embedding model.
2
Send a search request
The vector is sent to VectorAI DB over gRPC or REST.
3
HNSW nearest-neighbour search
VectorAI DB traverses the HNSW index to find the closest vectors, applying any payload filters inline.
4
Ranked results returned
Results come back with IDs, similarity scores, and optional payloads. No post-processing needed.
- Semantic search for meaning-based retrieval
- Hybrid search to combine dense and sparse scores
- Filtering for must, should, and must-not payload conditions
Next steps
Installation
Install VectorAI DB with Docker.
Quickstart
Create a collection, insert vectors, and run a search.
Fundamentals
Collections, points, vectors, search, and filtering.
Academy
Hands-on tutorials for semantic search, RAG, and more.