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.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.
RAG pipeline
Build a retrieval-augmented generation pipeline to power document question-and-answer systems.
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.
Re-Ranking
Learn how to improve relevance with cross-encoder and reciprocal rank fusion re-ranking.
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.
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.Legal contract intelligence
Build an agent that analyzes legal contracts using cross-collection lookup, ranked retrieval, and quantization-aware search.
Multi-Agent systems
Build a reliable multi-agent system using distance metrics, scalar quantization, IVF indexing, and score fusion.
Insurance split liability
Build an insurance liability agent using named vectors, prefetch queries, geo-radius, and datetime filters.
Network threat hunting
Build a threat detection agent using full-text search, batched queries, nested filters, and condition operators.
Scalable agent memory
Build persistent agent memory with cross-collection lookup, WAL tuning, optimizer configuration, and strict deletion.
Recipe recommendation
Build a personalized recipe recommendation agent using semantic search, payload filters, and preference learning.
Visual RAG
Build a visual document intelligence system using CLIP embeddings, multimodal retrieval, and GPT-4o vision.
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.
Financial document analysis
Build a financial document analysis system using semantic search and metadata filtering.
Facial recognition
Build a facial recognition system using face embeddings, identity verification, and similarity search.
Customer support avatar
Build an avatar-based customer support assistant using knowledge retrieval and personalized response generation.
View all articles
See the full article overview organized by category with a feature summary table.
Examples
Runnable code and integration guides to accelerate your development.Jupyter notebooks
Explore interactive notebooks you can run locally for hands-on experimentation with VectorAI DB.
Sample applications
Browse complete reference applications you can clone and run as starting points for your own projects.
Embedding models guide
Learn how to choose the right embedding model for your use case and data type.
OpenAI embeddings
Learn how to use OpenAI embedding models with VectorAI DB.
Cohere embeddings
Learn how to use Cohere embedding models with VectorAI DB.
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 |
| Building a RAG system | Build a RAG pipeline |
| Designing an AI agent | Legal contract intelligence |
| Working with images and text | Multimodal systems |
| Optimizing search quality | Retrieval quality |
| Need runnable code fast | Jupyter notebooks |