Hey there, tech enthusiasts! I’m thrilled to share an exciting project I’ve been working on at Flipick – the Supervisor AI Agent, a core component of our Retrieval-Augmented Generation (RAG)-based chatbot system. This project has been a fascinating journey into the world of AI agents, intelligent automation, and dynamic query routing. If you’re curious about how AI can transform customer interactions or want to dive into the tech behind it, this post is for you!
At Flipick, we’re passionate about leveraging AI to create seamless, intelligent solutions. The Supervisor AI Agent is a perfect example of that vision – it dynamically routes user queries to specialized agents, ensuring precise, context-aware responses. In this blog, I’ll walk you through the architecture, the tech stack we used, the workflow, and the real challenges we tackled. Let’s dive into building this RAG-powered AI system!
What is the Supervisor AI Agent?
Imagine a user asking a complex question on a chatbot – maybe they’re seeking insights from a PDF document, a YouTube video, or a webpage. A generic chatbot might struggle, but our Supervisor AI Agent steps in to make things smarter. It acts as an orchestrator, analyzing the user’s query, identifying the best-suited agents, and ensuring the response is accurate and meaningful.
Built on Retrieval-Augmented Generation (RAG), this system combines document retrieval with generative AI to deliver high-quality answers. It’s like having a team of specialized AI experts working together – with the Supervisor as their manager!
The Architecture: A High-Level Overview
Here’s how the Supervisor AI Agent works:
- User Query Input: Users submit queries via our web-based chat interface.
- Supervisor Process: The Supervisor analyzes the query and selects the best agents – solo or collaborative.
- Document Retrieval: Agents search indexed content (PDFs, URLs, YouTube transcripts) for relevant data.
- Answer Generation: Responses are crafted from retrieved data and delivered to the user.
- History Tracking: Queries and responses are logged for context and improvement.
This workflow ensures our AI chatbot delivers accurate, context-aware answers every time.
The Tech Stack: What Powers the Supervisor AI Agent?
Here’s the robust tech stack behind this intelligent automation system:
- LangChain: AI Processing and Retrieval
Why: Simplifies RAG integration, supports multiple data sources (PDFs, URLs), and offers LLM-agnostic coding. - LangGraph: Workflow Orchestration
Why: Perfect for complex processes with branching logic and context retention. - OpenAI (ChatGPT): Analysis and Response Generation
Why: Exceptional language skills and pre-trained efficiency. - Milvus: Vector Search for Optimized Retrieval
Why: Lightning-fast vector search and scalability. - PostgreSQL: Robust Data Management
Why: Reliable, with strong support for structured data. - Django: Seamless Web UI
Why: Secure, rapid development with built-in features. - Python: The Glue That Ties It All Together
Why: Rich ecosystem for AI and web development.
How It All Comes Together: The Workflow
- User Query Submission: Users type into the Django chat UI.
- Supervisor Analysis: LangChain and LangGraph analyze queries with OpenAI.
- Agent Selection: The Supervisor picks agents based on query needs.
- Document Retrieval: Agents fetch content from Milvus.
- Answer Generation: OpenAI crafts responses from retrieved data.