> ## Documentation Index
> Fetch the complete documentation index at: https://actianvectorai-docs-feedback-implementation.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Browse and choose from deep-dive articles on building AI agents and intelligent applications with Actian VectorAI DB.

These articles cover real-world AI agent architectures, multimodal systems, and industry-specific applications built with Actian VectorAI DB. Each article walks through a complete implementation — from data modeling and vector ingestion to semantic retrieval, filtering, and reasoning.

## Choose your focus area

Use the flowchart below to navigate to the article category that matches your interest. Each branch leads to a group of articles organized by theme.

```mermaid theme={null}
flowchart TD
    Start[Start here] --> Q{What interests you?}

    Q --> |AI agents| Agents[AI agent architectures]
    Q --> |Multimodal & RAG| Multi[Multimodal & retrieval]
    Q --> |Industry solutions| Industry[Industry applications]

    Agents --> Memory[Scalable agent memory]
    Agents --> Recipe[Recipe recommendation]

    Multi --> Visual[Visual RAG]
    Multi --> Product[Multimodal product discovery]

    Industry --> Supply[Supply chain risk]
```

## AI agent architectures

These articles show how to build intelligent agents that combine semantic retrieval with domain-specific reasoning.

<CardGroup cols={2}>
  <Card title="Scalable agent memory" href="/academy/articles/building-a-scalable-agent-memory-with-Actian-vector-AI-database">
    Build a scalable agent memory system with cross-collection lookup, retrieval sorted with OrderBy, WAL and optimizer tuning, and strict deletion.
  </Card>

  <Card title="AI recipe recommendation agent" href="/academy/articles/AI-recipe-recommendation-agent">
    Build a recipe recommendation agent that matches cravings through semantic search, filters by dietary restrictions and ingredients, and learns preferences over time.
  </Card>
</CardGroup>

## Multimodal and retrieval

These articles cover how to combine text, image, and document embeddings for rich retrieval experiences.

<CardGroup cols={2}>
  <Card title="Multivector document intelligence with Visual RAG" href="/academy/articles/Multivector-Document-Intelligence-with-Visual-RAG">
    Build a multimodal document intelligence system that embeds PDF pages as images with CLIP and generates answers using GPT-4o vision.
  </Card>

  <Card title="Next-Gen product discovery with multimodal AI" href="/academy/articles/Next-Gen-Product-Discovery-with-Multimodal-AI">
    Build a multimodal hybrid search system combining CLIP dense embeddings and BM25 sparse scoring for semantic and keyword product retrieval.
  </Card>
</CardGroup>

## Industry applications

These articles apply vector search to solve real-world problems across specific industries.

<CardGroup cols={2}>
  <Card title="AI supply chain inventory risk intelligence agent" href="/academy/articles/supply-chain-inventory-management-agent">
    Build a supply chain risk intelligence workflow with semantic retrieval, payload filters, and a lightweight reasoning layer for stockout prediction.
  </Card>
</CardGroup>

## Article summary

The table below lists every article alongside its domain and the specific VectorAI DB features it covers, so you can find an article based on the capability you want to learn.

| Article                                                                                                    | Domain         | Key VectorAI DB features                                        |
| ---------------------------------------------------------------------------------------------------------- | -------------- | --------------------------------------------------------------- |
| [Scalable agent memory](/academy/articles/building-a-scalable-agent-memory-with-Actian-vector-AI-database) | Infrastructure | Cross-collection, WAL tuning, optimizer config, strict deletion |
| [Recipe recommendation](/academy/articles/AI-recipe-recommendation-agent)                                  | Consumer       | Semantic search, payload filters, preference learning           |
| [Visual RAG](/academy/articles/Multivector-Document-Intelligence-with-Visual-RAG)                          | Document AI    | CLIP embeddings, multimodal retrieval, GPT-4o vision            |
| [Multimodal product discovery](/academy/articles/Next-Gen-Product-Discovery-with-Multimodal-AI)            | E-commerce     | CLIP + BM25 hybrid search, sparse/dense fusion                  |
| [Supply chain risk](/academy/articles/supply-chain-inventory-management-agent)                             | Logistics      | Semantic retrieval, payload filters, risk reasoning             |

<Tip>
  Each article is self-contained — pick the one that matches your use case and follow along. If you are new to VectorAI DB, then start with the [tutorials](/academy/tutorials/index) first to build foundational skills.
</Tip>