Introduction

On-premises vector database for semantic search, RAG pipelines, and AI agent memory.

Installation

Install VectorAI DB with Docker. Takes about two minutes.

Quickstart

Create a collection, insert vectors, and run your first similarity search.

Core concepts

Understand the data model, architecture, and how search works.

Academy

Hands-on tutorials for semantic search, RAG, hybrid search, and more.

New to VectorAI DB?

Follow this path to get up and running.

1. Install

   Set up VectorAI DB locally using Docker.

2. Quickstart

   Run the Quickstart to create a collection, insert vectors, and run a similarity search.

3. Learn the core concepts

   Read the Overview to understand the data model, architecture, and how a search works. It links into the full reference from there.

4. Build something

   Try the Academy tutorials for hands-on walkthroughs, or connect to LangChain or LlamaIndex.

Use cases

Use case	Description
Semantic search	Find documents, products, or records by meaning rather than exact keywords. Store embeddings from any model and query them with a vector.
Hybrid search	Combine dense vector similarity with sparse or keyword scoring using fusion strategies such as RRF or DBSF.
RAG pipelines	Use VectorAI DB as the retrieval layer in LangChain, LlamaIndex, or a custom pipeline. Retrieve the most relevant context chunks before sending to a language model.
Agent memory	Give AI agents persistent, queryable memory. Store past interactions as vectors and retrieve semantically relevant history at runtime.
Air-gapped / edge deployment	Run fully on-premises with no outbound network requirements. Suitable for secure or regulated environments.