> ## 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

> Hands-on tutorials to build vector search applications with Actian VectorAI DB.

Learn Actian VectorAI DB through practical, task-focused tutorials. Each tutorial teaches specific skills you can apply immediately to your projects.

## Choose your learning path

Use this flowchart to find the tutorial track that matches your goals:

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

    Q --> |Learn basics| GS[Getting started]
    Q --> |Build features| Core[Core features]
    Q --> |Go deeper| Adv[Advanced topics]

    GS --> App[Build your first application]

    Core --> Search[Similarity search]
    Core --> Filters[Predicate filters]
    Core --> RAG[RAG pipeline]

    Adv --> Embed[Open-source embedding models]
    Adv --> Multi[Multi-modal systems]
    Adv --> Rerank[Reranking]
    Adv --> Quality[Retrieval quality]
    Adv --> Adaptive[Adaptive RAG]
```

## Getting started

Build foundational skills by creating your first VectorAI DB application.

<CardGroup cols={2}>
  <Card title="Build your first application" href="/academy/tutorials/first-application">
    Create a complete semantic search application from scratch. Learn to connect, store vectors, and query data.
  </Card>
</CardGroup>

## Core features

Master the essential features for production vector search applications.

<CardGroup cols={2}>
  <Card title="Similarity search fundamentals" href="/academy/tutorials/similarity-search">
    Learn the core vector search workflow — from embedding and storing vectors to searching, scoring, batching, and paginating results.
  </Card>

  <Card title="Predicate filters" href="/academy/tutorials/predicate-filters">
    Combine vector search with structured payload filters using the type-safe Filter DSL and logical operators.
  </Card>
</CardGroup>

## Advanced topics

Take your skills further with advanced techniques and architectures.

<CardGroup cols={2}>
  <Card title="Use open-source embedding models" href="/academy/tutorials/leverage-open-source-embedding-models">
    Choose, configure, and integrate Sentence Transformers, BGE, and other open-source models. Covers dimensionality trade-offs, quantization, and re-embedding workflows.
  </Card>

  <Card title="Build multimodal systems" href="/academy/tutorials/multimodel-system">
    Store, search, and fuse text, image, and metadata embeddings in a single collection using named vectors, multistage prefetch, and server-side fusion.
  </Card>

  <Card title="Rerank search results" href="/academy/tutorials/re-ranking">
    Improve search relevance with multistage prefetch pipelines, cross-encoder scoring, payload-based boosting, and fusion reranking.
  </Card>

  <Card title="Optimize retrieval quality" href="/academy/tutorials/retrieval-quality">
    Measure and improve search accuracy by tuning HNSW parameters, distance metrics, quantization, score thresholds, and payload indexes.
  </Card>

  <Card title="Build adaptive RAG systems" href="/academy/tutorials/adaptive-rag">
    Create RAG pipelines that adapt retrieval strategy at runtime based on query type, confidence signals, and user feedback.
  </Card>
</CardGroup>

## Recommended learning order

Follow this sequence to build skills progressively. Start with the beginner tutorials to build a strong foundation — each tutorial builds on concepts from previous ones, so following the recommended order helps you learn efficiently.

| Stage | Tutorial                                                                                     | Skills learned                                           |
| ----- | -------------------------------------------------------------------------------------------- | -------------------------------------------------------- |
| 1     | [Build your first application](/academy/tutorials/first-application)                         | Connection, basic operations, search fundamentals        |
| 2     | [Similarity search fundamentals](/academy/tutorials/similarity-search)                       | Search patterns, score thresholds, batch queries         |
| 3     | [Predicate filters](/academy/tutorials/predicate-filters)                                    | Metadata filtering, logical operators, combined queries  |
| 4     | [Use open-source embedding models](/academy/tutorials/leverage-open-source-embedding-models) | Model selection, dimensionality, quantization            |
| 5     | [Build multimodal systems](/academy/tutorials/multimodel-system)                             | Named vectors, multistage prefetch, fusion               |
| 6     | [Rerank search results](/academy/tutorials/re-ranking)                                       | Two-stage retrieval, cross-encoders, result optimization |
| 7     | [Optimize retrieval quality](/academy/tutorials/retrieval-quality)                           | Evaluation metrics, HNSW tuning, benchmarking            |
| 8     | [Build adaptive RAG systems](/academy/tutorials/adaptive-rag)                                | Query classification, dynamic retrieval, self-correction |

## Time estimates

Use these estimates to plan your learning sessions and choose tutorials that fit your available time.

| Tutorial                         | Duration | Difficulty   |
| -------------------------------- | -------- | ------------ |
| Build your first application     | 15 min   | Beginner     |
| Similarity search fundamentals   | 20 min   | Beginner     |
| Predicate filters                | 25 min   | Intermediate |
| Build a RAG pipeline             | 30 min   | Intermediate |
| Use open-source embedding models | 25 min   | Intermediate |
| Build multimodal systems         | 35 min   | Advanced     |
| Rerank search results            | 30 min   | Advanced     |
| Optimize retrieval quality       | 30 min   | Advanced     |
| Build adaptive RAG systems       | 40 min   | Advanced     |