01 - pgvector Tutorial: Turn PostgreSQL into a Vector Database
Complete guide to pgvector Tutorial: Turn PostgreSQL into a Vector Database: architecture, practical implementation and best practices for developers and technical teams.
PostgreSQL and Vector Search for AI
PostgreSQL for AI applications: pgvector for similarity search, RAG with relational databases, hybrid search, embedding management, and vector query optimization.
This series collects 6 articles (approximately 94 minutes of total reading time), designed for a level Advanced. The main topics covered are PostgreSQL, pgvector, vector search, RAG.
6Articles94 minTotal reading timeAdvancedLevel
PostgreSQLpgvectorvector searchRAGembeddings
Series Articles
- 1
- 2
02 - Vector Embeddings Explained: Theory and Practice
Complete guide to Vector Embeddings Explained: Theory and Practice: architecture, practical implementation and best practices for developers and technical teams.
- 3
03 - Building RAG Systems with PostgreSQL
Complete guide to Building RAG Systems with PostgreSQL: architecture, practical implementation and best practices for developers and technical teams.
- 4
04 - Vector Similarity Search: Algorithms and Implementation
Complete guide to Vector Similarity Search: Algorithms and Implementation: architecture, practical implementation and best practices for developers and technical teams.
- 5
05 - pgvector Indexing Strategies: HNSW vs IVFFlat
Complete guide to pgvector Indexing Strategies: HNSW vs IVFFlat: architecture, practical implementation and best practices for developers and technical teams.
- 6
06 - Scaling RAG Systems: Performance and Production
Complete guide to Scaling RAG Systems: Performance and Production: architecture, practical implementation and best practices for developers and technical teams.
Test your knowledge!
Have you read all the articles? Check how much you've learned by taking this series' quizzes.