Building Custom RAG AI Agents using PostgreSQL and AWS

As a developer, I’ve worked on various projects where our team needed to access vast amounts of data quickly. That’s when I realized the potential of Custom RAG AI Agents. Shahi Raj built an AI assistant using PostgreSQL and AWS, which revolutionized our workflow. I’ll share my experience in building this Custom RAG AI Agent.

Here’s a high-level overview of the architecture:

• PostgreSQL as the primary database for storing our company’s knowledge base.
• AWS for hosting the AI agent and ensuring scalability.
• n8n for workflow automation and integration.

The AI agent uses Vector DBs to enable fast vector similarity searches, making it possible to find the exact detail our team needed in seconds. With this setup, we reduced our team’s search time significantly, allowing them to focus on high-quality work. If you’re interested in building a Custom RAG AI Agent for your company, contact me or Shahi Raj to discuss further.