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.