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.
Build a RAG pipeline
Create a Retrieval-Augmented Generation system that answers questions from your documents with context-aware AI responses.
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 multi-modal systems
Store, search, and fuse text, image, and metadata embeddings in a single collection using named vectors, multi-stage prefetch, and server-side fusion.
Re-rank search results
Improve search relevance with multi-stage prefetch pipelines, cross-encoder scoring, payload-based boosting, and fusion re-ranking.
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 | Build a RAG pipeline | Document chunking, retrieval, LLM integration |
| 5 | Use open-source embedding models | Model selection, dimensionality, quantization |
| 6 | Build multi-modal systems | Named vectors, multi-stage prefetch, fusion |
| 7 | Re-rank search results | Two-stage retrieval, cross-encoders, result optimization |
| 8 | Optimize retrieval quality | Evaluation metrics, HNSW tuning, benchmarking |
| 9 | 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 multi-modal systems | 35 min | Advanced |
| Re-rank search results | 30 min | Advanced |
| Optimize retrieval quality | 30 min | Advanced |
| Build adaptive RAG systems | 40 min | Advanced |