Overview

Learn Actian VectorAI DB through hands-on tutorials, deep-dive articles, and ready-to-run examples.

The VectorAI DB Academy is your learning hub for building vector search applications and AI agents. Whether you are getting started with your first collection or designing production-grade multiagent systems, 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.

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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.

Build your first application

Learn how to connect to VectorAI DB, store your first vectors, and run a semantic search query.

Similarity search

Learn how to search, score, batch, and paginate vector query results effectively.

Predicate filters

Learn how to combine vector search with structured payload filters to narrow results.

Open-Source embeddings

Learn how to integrate open-source models like Sentence Transformers and BGE into your pipeline.

Multimodal systems

Learn how to fuse text, image, and metadata embeddings using named vectors.

Reranking

Learn how to improve relevance with cross-encoder and reciprocal rank fusion reranking.

Retrieval quality

Learn how to measure and optimize search accuracy using precision, recall, and MRR.

Adaptive RAG

Build RAG pipelines that automatically adapt their retrieval strategy based on query complexity.

View all tutorials

See the full tutorial overview with a recommended learning order and time estimates.

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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.

Scalable agent memory

Build persistent agent memory with cross-collection lookup, WAL tuning, optimizer configuration, and strict deletion.

Visual RAG

Build a visual document intelligence system using CLIP embeddings, multimodal retrieval, and GPT-4o vision.

Recipe recommendation

Build a personalized recipe recommendation agent using semantic search, payload filters, and preference learning.

Multimodal product discovery

Build a product discovery system using CLIP and BM25 hybrid search with sparse and dense score fusion.

Supply chain risk

Build a supply chain risk agent using semantic retrieval, payload filters, and a reasoning layer.

View all articles

See the full article overview organized by category with a feature summary table.

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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
Need to add search to an app	Similarity search
Designing an AI agent	Scalable agent memory
Working with images and text	Multimodal systems
Optimizing search quality	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.