> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vectoraidb.actian.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction to VectorAI DB

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

<CardGroup cols={2}>
  <Card title="Installation" icon="download" href="/home/installation/instructions">
    Install VectorAI DB with Docker. Takes about two minutes.
  </Card>

  <Card title="Quickstart" icon="rocket" href="/home/quickstart/quickstart">
    Create a collection, insert vectors, and run your first similarity search.
  </Card>

  <Card title="Core concepts" icon="book-open" href="/home/getting-started/overview">
    Understand the data model, architecture, and how search works.
  </Card>

  <Card title="Academy" icon="graduation-cap" href="/academy/tutorials">
    Hands-on tutorials for semantic search, RAG, hybrid search, and more.
  </Card>
</CardGroup>

## New to VectorAI DB?

Follow this path to get up and running.

<Steps>
  <Step title="Install">
    [Set up VectorAI DB](/home/installation/instructions) locally using Docker.
  </Step>

  <Step title="Quickstart">
    [Run the Quickstart](/home/quickstart/quickstart) to create a collection, insert vectors, and run a similarity search.
  </Step>

  <Step title="Learn the core concepts">
    Read the [Overview](/home/getting-started/overview) to understand the data model, architecture, and how a search works. It links into the full reference from there.
  </Step>

  <Step title="Build something">
    Try the [Academy tutorials](/academy/tutorials) for hands-on walkthroughs, or connect to [LangChain or LlamaIndex](/docs/integrations/index).
  </Step>
</Steps>

## Use cases

| Use case                         | Description                                                                                                                                                          | Real-world example                                                                                                                                        |
| -------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Semantic search**              | Find documents, products, or records by meaning rather than exact keywords. Store embeddings from any model and query them with a vector.                            | A major e-commerce platform processes 216M+ embeddings daily so shoppers can find products by intent, not exact keywords, across millions of storefronts. |
| **Hybrid search**                | Combine dense vector similarity with sparse or keyword scoring using fusion strategies such as RRF or DBSF.                                                          | A global travel platform activated over a billion reviews with hybrid retrieval to power its AI trip planner, driving 2–3x more revenue per engaged user. |
| **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. | A conversational AI platform retrieves from 100M+ vectors across 12,000+ isolated namespaces to power personalized AI agents in under a second.           |
| **Agent memory**                 | Give AI agents persistent, queryable memory. Store past interactions as vectors and retrieve semantically relevant history at runtime.                               | A food delivery platform's AI shopping agents use a persistent memory layer that lifted grocery-checkout conversion \~24% and cut intent misreads \~33%.  |
| **Air-gapped / edge deployment** | Run fully on-premises with no outbound network requirements. Suitable for secure or regulated environments.                                                          | A defense contractor's AI platform lets U.S. government agencies fine-tune models on classified infrastructure with zero external network connections.    |