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:

Getting started

Build foundational skills by creating your first VectorAI DB application.

Build your first application

Create a complete semantic search application from scratch. Learn to connect, store vectors, and query data.

Core features

Master the essential features for production vector search applications.

Similarity search fundamentals

Learn the core vector search workflow — from embedding and storing vectors to searching, scoring, batching, and paginating results.

Predicate filters

Combine vector search with structured payload filters using the type-safe Filter DSL and logical operators.

Advanced topics

Take your skills further with advanced techniques and architectures.

Use 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.

Build multimodal systems

Store, search, and fuse text, image, and metadata embeddings in a single collection using named vectors, multistage prefetch, and server-side fusion.

Rerank search results

Improve search relevance with multistage prefetch pipelines, cross-encoder scoring, payload-based boosting, and fusion reranking.

Optimize retrieval quality

Measure and improve search accuracy by tuning HNSW parameters, distance metrics, quantization, score thresholds, and payload indexes.

Build adaptive RAG systems

Create RAG pipelines that adapt retrieval strategy at runtime based on query type, confidence signals, and user feedback.

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	Connection, basic operations, search fundamentals
2	Similarity search fundamentals	Search patterns, score thresholds, batch queries
3	Predicate filters	Metadata filtering, logical operators, combined queries
4	Use open-source embedding models	Model selection, dimensionality, quantization
5	Build multimodal systems	Named vectors, multistage prefetch, fusion
6	Rerank search results	Two-stage retrieval, cross-encoders, result optimization
7	Optimize retrieval quality	Evaluation metrics, HNSW tuning, benchmarking
8	Build adaptive RAG systems	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