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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.

AI legal contract intelligence agent

Build an AI-powered legal contract analysis system with cross-collection lookup, retrieval sorted with OrderBy, connection pooling, and quantization-aware search.

AI clinical trial patient matching agent

Build a clinical trial patient matching system using Euclidean and Manhattan distance metrics, scalar quantization, IVF indexing, server-side fusion, and UUID payload indexes.

AI insurance split liability agent

Build an insurance split liability workflow with named vectors, multi-stage prefetch queries, batch search, datetime filters, geo-radius filtering, and payload mutation.

AI network threat hunting agent

Build a network anomaly detection system with SmartBatcher streaming ingestion, full-text search, query batching, nested filters, condition operators, and collection lifecycle management.

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.

Financial document analysis

Build AI-powered systems for analyzing financial documents using semantic search and structured metadata filtering.

Facial recognition with vector embeddings

Implement facial recognition using face embeddings and VectorAI DB for identity verification and search.

Avatar-Based assistant for customer support

Build an intelligent avatar assistant that retrieves knowledge and provides personalized customer support.

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.
ArticleDomainKey VectorAI DB features
Legal contract intelligenceLegalCross-collection lookup, retrieval sorted with OrderBy, connection pooling, quantization
Multi-agent systemsHealthcareEuclidean/Manhattan distance, scalar quantization, IVF, fusion
Insurance split liabilityInsuranceNamed vectors, prefetch, batch search, geo-radius, datetime filters
Network threat huntingCybersecuritySmartBatcher, full-text search, query batching, nested filters, condition operators
Scalable agent memoryInfrastructureCross-collection, WAL tuning, optimizer config, strict deletion
Recipe recommendationConsumerSemantic search, payload filters, preference learning
Visual RAGDocument AICLIP embeddings, multimodal retrieval, GPT-4o vision
Multimodal product discoveryE-commerceCLIP + BM25 hybrid search, sparse/dense fusion
Supply chain riskLogisticsSemantic retrieval, payload filters, risk reasoning
Financial document analysisFinanceSemantic search, structured metadata filtering
Facial recognitionSecurityFace embeddings, identity verification, similarity search
Customer support avatarCustomer serviceKnowledge retrieval, personalized responses
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.