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.

AI agent architectures

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

Scalable agent memory

Build a scalable agent memory system with cross-collection lookup, retrieval sorted with OrderBy, WAL and optimizer tuning, and strict deletion.

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.

Multimodal and retrieval

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

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.

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.

Industry applications

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

AI supply chain inventory risk intelligence agent

Build a supply chain risk intelligence workflow with semantic retrieval, payload filters, and a lightweight reasoning layer for stockout prediction.

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	Infrastructure	Cross-collection, WAL tuning, optimizer config, strict deletion
Recipe recommendation	Consumer	Semantic search, payload filters, preference learning
Visual RAG	Document AI	CLIP embeddings, multimodal retrieval, GPT-4o vision
Multimodal product discovery	E-commerce	CLIP + BM25 hybrid search, sparse/dense fusion
Supply chain risk	Logistics	Semantic retrieval, payload filters, risk reasoning

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 first to build foundational skills.