> ## Documentation Index
> Fetch the complete documentation index at: https://actianvectorai-docs-low-effort-fixes.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Overview
> Learn Actian VectorAI DB through hands-on tutorials, deep-dive articles, and ready-to-run examples.
The VectorAI DB Academy covers building vector search applications and AI agents. Whether you are getting started with your first collection or building an AI agent, the Academy has a path for you.
## Choose your path
The diagram below shows three learning paths branching from a single entry point: tutorials for step-by-step guidance, articles for real-world architectures, and examples for runnable code. Follow the branch that matches your current goal.
```mermaid theme={null}
%% Learning path: each branch leads to a section of the Academy
flowchart TD
Start[Start here] --> Q{What do you need?}
Q --> |Step-by-step guidance| T["Tutorials\n8 hands-on walkthroughs"]
Q --> |Real-world architectures| A["Articles\n5 deep-dive implementations"]
T --> T1[Build your first app]
T --> T2[Search, filters, RAG]
T --> T3[Reranking, multimodal, adaptive RAG]
A --> A1[AI agent architectures]
A --> A2[Multimodal & retrieval]
A --> A3[Industry applications]
```
***
## Tutorials
Structured, step-by-step walkthroughs that teach VectorAI DB skills progressively. Each tutorial builds on the last, taking you from basic operations to advanced retrieval architectures.
Learn how to connect to VectorAI DB, store your first vectors, and run a semantic search query.
Learn how to search, score, batch, and paginate vector query results effectively.
Learn how to combine vector search with structured payload filters to narrow results.
Learn how to integrate open-source models like Sentence Transformers and BGE into your pipeline.
Learn how to fuse text, image, and metadata embeddings using named vectors.
Learn how to improve relevance with cross-encoder and reciprocal rank fusion reranking.
Learn how to measure and optimize search accuracy using precision, recall, and MRR.
Build RAG pipelines that automatically adapt their retrieval strategy based on query complexity.
See the full tutorial overview with a recommended learning order and time estimates.
***
## Articles
Deep-dive implementations of AI agents and real-world applications. Each article walks through a complete architecture, covering topics such as data modeling, retrieval strategies, and agent reasoning.
Build persistent agent memory with cross-collection lookup, WAL tuning, optimizer configuration, and strict deletion.
Build a visual document intelligence system using CLIP embeddings, multimodal retrieval, and GPT-4o vision.
Build a personalized recipe recommendation agent using semantic search, payload filters, and preference learning.
Build a product discovery system using CLIP and BM25 hybrid search with sparse and dense score fusion.
Build a supply chain risk agent using semantic retrieval, payload filters, and a reasoning layer.
See the full article overview organized by category with a feature summary table.
***
## Where to start
The table below maps common goals to the most relevant starting point in the Academy. Each link takes you directly to the tutorial, article, or example that best fits that goal.
| Your goal | Start here |
| ---------------------------- | ---------------------------------------------------------------------------------------------------------- |
| New to VectorAI DB | [Build your first application](/academy/tutorials/first-application) |
| Need to add search to an app | [Similarity search](/academy/tutorials/similarity-search) |
| Designing an AI agent | [Scalable agent memory](/academy/articles/building-a-scalable-agent-memory-with-Actian-vector-AI-database) |
| Working with images and text | [Multimodal systems](/academy/tutorials/multimodel-system) |
| Optimizing search quality | [Retrieval quality](/academy/tutorials/retrieval-quality) |
If you are new to vector databases, start with the tutorials — they build skills progressively from beginner to advanced. Articles are best when you have a specific use case in mind and want to see a complete implementation. Use examples when you need runnable code you can clone and adapt right away.