Build a RAG Application Using Spring AI | OpenAI + PGVector (Step-by-Step)
In this complete step-by-step tutorial, you'll learn how to implement a production-style RAG pipeline from scratch using Spring Boot, Spring AI, OpenAI Embeddings, and PGVector.
What is RAG - • RAG Explained Simply: Ingestion, Chunking,...
Spring AI Playlist - • Spring AI Basic to Advanced Tutorial | Ope...
Github code link - https://github.com/rajkumarsingh0907/...
00:00 - Introduction to RAG & Project Overview
01:26 - Project Setup (Spring Initializr)
02:06 - Adding Dependencies (Spring AI, PGVector, PDF Reader)
03:41 - Infrastructure Setup with Docker Compose
06:17 - Configuring Spring Boot & OpenAI Models
08:10 - Implementing the Ingestion Pipeline
11:51 - Document Metadata & Chunking Logic
14:34 - Testing the Ingestion API with Postman
16:00 - Verifying Data in PGAdmin
17:56 - Implementing the RAG Question-Answering Pipeline
19:10 - Creating the Chat Controller & Service
20:46 - Document Retrieval & Semantic Search Logic
23:02 - Prompt Engineering & Generating the Response
24:43 - Final End-to-End Testing & Log Analysis
26:37 - Conclusion & Next Steps
In this video you'll learn:
✅ Spring AI project setup
✅ Docker Compose with PGVector & pgAdmin
✅ OpenAI Chat Model & Embedding Model
✅ PDF ingestion pipeline
✅ Document chunking
✅ Embedding generation
✅ Storing embeddings in PGVector
✅ Semantic similarity search
✅ Context augmentation (RAG)
✅ Prompt engineering with ChatClient
✅ Reducing hallucinations using system prompts
✅ Returning source documents with metadata
✅ Complete end-to-end RAG implementation
#SpringAI #RAG #OpenAI #PGVector #SpringBoot #Java #LLM #GenerativeAI #VectorDatabase #backenddevelopment
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