Скачать или смотреть Build a Production RAG App with PostgreSQL + Streamlit | Complete Tutorial

Watch me build a full-featured document chat application from scratch using PostgreSQL as a vector database!
In this comprehensive tutorial, I'll show you how to create a production-ready RAG (Retrieval-Augmented Generation) application that lets you chat with your PDF documents using the power of PostgreSQL and OpenAI.
🎯 What You'll Learn:
✅ PostgreSQL Vector Setup - Install and configure pgvector extension
✅ Auto-Configuration - Smart environment variable management with .env files
✅ Real-time Validation - Test API keys and database connections before processing
✅ PDF Processing Pipeline - Extract, chunk, and embed documents efficiently
✅ Interactive Chat Interface - Build a clean Streamlit UI for document conversations
✅ Database Management - View, explore, and manage stored embeddings
✅ Production Deployment - Best practices for real-world applications
🛠️ Tech Stack Covered:
🔹 PostgreSQL + pgvector - Vector storage and similarity search
🔹 OpenAI API - Embeddings and chat completions
🔹 Streamlit - Rapid web application development
🔹 LangChain - RAG orchestration and document processing
🔹 Python - Backend logic and data processing

🔥 Key Features Demonstrated:
⚡ Smart Auto-Configuration - Automatically loads credentials from .env files
⚡ Connection Health Checks - Validates OpenAI API and PostgreSQL before processing
⚡ Intelligent Document Chunking - Optimized text splitting for better retrieval
⚡ Real-time Chat Interface - Natural conversation with instant responses
⚡ Vector Database Explorer - Browse and manage stored embeddings visually
⚡ Source Attribution - See exactly which document sections answer your questions
🎬 Live Demo Highlights:
📋 Upload a technical manual and ask complex questions
📊 Explore the database to see stored embeddings
🔍 Watch real-time similarity search in action
💬 Experience natural conversation with documents
🗑️ Manage collections and cleanup data
💡 Why PostgreSQL for RAG?
Instead of specialized vector databases, PostgreSQL offers:

Zero Additional Infrastructure - Use your existing database
ACID Compliance - Reliable transactions for business data
Cost Efficiency - No separate vector DB licensing fees
SQL Familiarity - Your team already knows how to operate it
Rich Ecosystem - Decades of tooling and community support

🚀 Perfect For:
👨‍💻 Developers learning modern AI application architecture
🏢 Companies wanting to implement internal knowledge bases
🎓 Students studying RAG systems and vector databases
📈 CTOs evaluating cost-effective AI infrastructure
📁 Resources & Code:
🔗 GitHub Repository: https://github.com/ancur4u/postgres_rag
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