Building Enterprise RAG Systems with PostgreSQL - A Comprehensive Implementation Guide

Overview

Learn to build an enterprise-grade Retrieval Augmented Generation (RAG) system using PostgreSQL in this comprehensive technical tutorial. Explore the advantages of using Postgres over third-party services, mastering both vector and text search capabilities through practical demonstrations. Dive deep into database setup, document processing, and advanced features including BM25 optimization, asynchronous database calls, and performance tuning. Follow along with hands-on examples of document chunking strategies, batch processing implementation, and search functionality while understanding the technical considerations for building scalable RAG applications. Master essential concepts like vector embeddings, text search algorithms, and database optimization techniques through detailed explanations and real-world performance comparisons, culminating in a complete implementation walkthrough with command-line interface setup.

Syllabus

- Introduction to RAG and Postgres implementation
- Background on using Postgres vs third-party services
- Overview of PG vector and PG best match
- Video structure outline
- Demo of RAG application
- Document upload and chunking process
- Data collation and vector/text search explanation
- Speed optimization using Cerebras/Groq
- Advantages of using Postgres
- Vector vs text search comparison
- Combining search methods
- Performance comparison data
- Vector search explanation
- Text search and BM25 explanation
- Postgres tools overview
- Database setup and configuration
- Basic RAG implementation
- Document embedding process
- Search implementation
- Advanced features:
- BM25 optimization
- Text processing improvements
- Asynchronous database calls
- Performance evaluation
- Command line interface setup
- Document chunking strategies
- Batch processing implementation
- Search functionality demo
- Final implementation review
- Conclusion and resources