Building Semantic Search with Spring Boot, PostgreSQL, and pgvector (RAG Retrieval)

1. 1Build a Spring Boot application to handle document indexing
2. 2Configure PostgreSQL database to store document embeddings
3. 3Install and configure pgvector for efficient vector similarity search
4. 4Implement RAG retrieval using the built indexing pipeline
5. 5Test the semantic search functionality with sample queries

Developers and data engineers can benefit from this tutorial to improve their search functionality in applications, especially those working with knowledge bases or document retrieval systems.

💡 Using pgvector with PostgreSQL enables efficient vector similarity search, crucial for semantic search and RAG retrieval

🚀 Build semantic search with Spring Boot, PostgreSQL, and pgvector for efficient RAG retrieval! 🚀