End-to-End RAG Implementation using PostgreSQL, PGVector & OpenAI Embeddings

Hello everyone

Welcome back to my YouTube channel SummarizedAI.

In this video, we build a complete end-to-end RAG (Retrieval-Augmented Generation) system from scratch and understand how modern AI applications retrieve fresh, real-time, and accurate data from a vector database instead of relying on static model knowledge.

The video may feel a bit slow at the beginning because we focus on strong fundamentals, but stay with me for the next 10 minutes—by the end, you’ll have clear, in-depth insights that will put you ahead of most AI practitioners.

🔍 What you’ll learn in this video:

1. What is RAG (Retrieval-Augmented Generation)
2. Why LLM knowledge is static and how RAG solves it
3. PostgreSQL setup for RAG
4. PGVector and Vector Databases explained
5. Vector Embeddings (1536 vs 3072 dimensions)
6. Static vs Contextual Embeddings
7. Semantic Search vs Lexical Search
8. How embeddings are generated using OpenAI
9. Storing embeddings in PostgreSQL
10. Query-time embedding comparison
11. Best practices for RAG using PostgreSQL + pgvector

Best Practices Covered:
1. High-quality embedding models (text-embedding-3-small u0026 large)
2. Proper text cleaning and chunking strategy
3. PGVector indexing (IVFFLAT, HNSW)
4. Cosine distance for similarity search
5. Hybrid search (Keyword + Vector)
6. Re-ranking for better accuracy
7. Metadata filtering
8. Embedding refresh strategies
9. Performance optimization tips

Hands-On Implementation Includes:
1. Creating PostgreSQL vector tables
2. Storing blog data with embeddings
3. Converting user queries into embeddings
4. Performing semantic similarity search
5. Building the full RAG pipeline step-by-step

If you’re serious about AI, LangChain, RAG pipelines, and vector databases, this video is a must-watch.

GitHub Code Reference: https://github.com/toimrank/summarizedai/tree/main/postgre-rag

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