Summary

  • 1 minute

In this module, you learn how to build a scalable RAG application on Azure Database for PostgreSQL using the azure_ai and pgvector extensions. You set up efficient embedding storage and similarity search, apply vector indexing with IVFFlat, HNSW, and DiskANN, and tune the retrieval pipeline with hybrid search. You also integrate a lightweight knowledge graph through GraphRAG to pull in relationship context, then apply Semantic Ranking to refine results so answers stay precise and relevant. The full RAG application runs with Azure Database for PostgreSQL, Python, and LangChain to keep the workflow straightforward.

These skills translate to faster query execution, improved retrieval accuracy, and an architecture that scales to millions of rows with low latency and high recall. The knowledge graph layer improves domain understanding and helps resolve unclear questions for complex, real world use cases. Semantic Ranking tightens ordering when similarity alone isn't enough. The outcome is reliable, context-aware responses that support decision making and automation.

After completing this module, you learn:

  • Set up a PostgreSQL database with azure_ai and pgvector extensions for embedding storage and similarity searches.
  • To optimize retrieval speed and accuracy, create and tune vector indexes like IVFFlat, HNSW, and DiskANN.
  • Build a RAG application using Azure Database for PostgreSQL, Python, LangChain, and Azure OpenAI for generating context-aware answers.
  • Implement advanced retrieval techniques, including hybrid search, semantic ranking, and graph narrowed vector searches.
  • To enhance precision for multi-concept queries, integrate a lightweight knowledge graph into a PostgreSQL database.

Additional Reading: