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How to Build an AI Agent to Handle Multi-Color Product Queries

A free step-by-step mini-course using Python, Pinecone, and LlamaIndex to build smarter AI systems for real-world search challenges.

Preview the course before you enroll​

You can access part of the course right now - no signup needed. The first lesson, including setup instructions, is open in the left-side menu ⬅️

The rest of the course is locked 🔒 and requires free enrollment via Gumroad.

Once you sign up, you'll get login details to access everything.

About the mini-course​

Learn how to build a practical AI agent capable of handling complex customer queries, all within Jupyter Notebook - no deployment required.

In this free mini-course, you'll create an AI agent that can process multi-color product searches, such as, "I need women's black shoes with red details":

The AI agent looks through the retrieved vector database results and provide a response with recommended shoes

This hands-on course guides you through:

  • Preparing your product embeddings and performing vectorization with AWS Titan multimodal model

  • Building an AI-powered query engine using LlamaIndex and GPT-4o

  • Applying metadata filtering to refine results and deliver accurate, RAG-based recommendations

By the end, you'll have a working AI agent in your Jupyter Notebook, that goes beyond naive RAG systems, leveraging tools like Amazon Bedrock, Pinecone, and the ChatGPT API for smarter product search handling.

What you'll learn​

  • Data Preparation: Vectorize product data using AWS Titan multimodal model

  • Metadata Filtering: Build and apply filters for multi-attribute queries with LlamaIndex, including color combinations, gender, and product usage

  • Vector Database Integration: Learn to store and query data efficiently with Pinecone

  • AI Agent Development: Build a smarter, metadata-aware AI agent with LlamaIndex and GPT-4o

  • Visualization: Test and visualize results to ensure your system delivers accurate recommendations

  • Comparison: Understand the difference between naive RAG systems and advanced AI agent workflows

How to enroll​

  1. Enroll for free via Gumroad

  2. After enrolling, you'll receive an email with your login details:

  • Username (your Gumroad email)

  • A temporary password to access the course platform

  1. Check your spam folder if you don't see the email in your inbox

  2. Log in to the course platform to start learning immediately!

Why this course is valuable​

Simple RAG retrieval systems often fail to handle nuanced customer queries, like filtering products by multiple colors or attributes.

This free course equips you with the tools and skills to solve these limitations.

Whether you're a developer, data scientist, or someone exploring AI-powered e-commerce solutions, you'll learn actionable skills for building real-world chatbots, query engines, and product search tools.

Who should take this course?​

  • Developers interested in building smarter search systems and query engines

  • Data scientists exploring practical AI applications in e-commerce

  • Beginners looking for hands-on experience with Python, Pinecone, and LlamaIndex

  • E-commerce enthusiasts curious about chatbots and AI-powered product recommendations

What's Included​

  • Step-by-step lessons with clear, actionable instructions

  • Jupyter Notebook code for all exercises - no deployment required

  • Hands-on experience with real-world tools like Pinecone, Amazon Bedrock, and LlamaIndex

What makes this course unique?​

  • Real-World Scenarios: Build an agent to solve practical challenges, like multi-color filtering in e-commerce

  • Free and Accessible: 100% free with no hidden costs

  • Jupyter Notebook-Based: Work in an easy-to-use environment without worrying about deployment

  • Industry-Relevant Tools: Gain experience with product embeddings, vector databases, and GPT-powered AI workflows

How to get started​

  1. Enroll for free and access the course materials instantly

  2. Follow along in your Jupyter Notebook as you build an AI agent from scratch

  3. Use the provided dataset and code to test and refine your system

Frequently asked questions​

Q: Do I need prior experience with AI?
A: Basic knowledge of Python is recommended, but the course is designed to be beginner-friendly, with clear explanations and code examples.

Q: Will I learn to deploy the AI agent?
A: No, this course is focused entirely on building and testing the AI agent within Jupyter Notebook. Deployment is not covered, making it ideal for beginners.

Q: Is this course really free?
A: Yes! This mini-course is 100% free and includes all the resources you need to complete it.

Q: Can I use the skills from this course in other projects?
A: Absolutely. The techniques and tools you'll learn can be applied to a wide range of AI and e-commerce projects.