AI Agents With Tools

In this lecture we will transform a standard chatbot into an agent by granting it access to external Python functions, such as a stock price retrieve.

Contents

1: Welcome to the Gen AI Experience

1.1: Agentic AI and Your Career

1.2: Who Should Take this Course? Pre-requisites

1.3: Course Overview

2: Theoretical Fundamentals

2.1: AI Family Tree

2.2: Essential Concepts: Machine Learning

2.3: Essential Concepts: Deep Learning

2.4: Quiz

3: Introduction to Generative AI and Agentic AI

3.1: What is Generative AI?

3.2: Traditional AI vs Gen AI

3.3: What are AI Agents and Agentic AI?

3.4: Gen AI vs AI Agents vs Agentic AI

3.5: Real-world Applications for Gen AI & Agentic AI

3.6: Steps to Build Gen AI and Agentic Applications

3.7: Quiz

3.8: Exercise

3.9: Chapter Summary

4: Gen AI: Key Concepts and Foundations

4.1: What are Large Language Models?

4.2: How LLMs Work?

4.3: Context Window, Temperature, Top-p and Top-k

4.4: Challenges: Hallucinations, Security and Cost

4.5: What is a Vector Database?

4.6: What is RAG (Retrieval Augmented Generation)?

4.7: Quiz

4.8: Exercise

4.9: Chapter Summary

5: Gen AI: Langchain and Prompting Essentials

5.1: Elements of a Good Prompt

5.2: Zero-Shot, One-Shot, and Few-Shot Prompting

5.3: LangChain Installation

5.4: Groq and Ollama Setup

5.5: Calling LLM from Langchain

5.6: Prompt Templates & Chains

5.7: Output Parser

5.8: Build Financial Data Extraction App

5.9: Quiz

5.10: Exercise

5.11: Chapter Summary

6: Gen AI: Vector Database

6.1: Chromadb: Introduction and Installation

6.2: Basic Operations in Chromadb

6.3: Add, Update, Delete, Query

6.4: Metadata Filtering

6.5: Euclidean and Cosine Distance

6.6: Quiz

6.7: Exercise

6.8: Chapter Summary

7: Gen AI: Business Project 1- Real Estate Assistant Using RAG

7.1: Problem Statement

7.2: RAG Based Technical Architecture

7.3: Document Loaders

7.4: Text Splitters

7.5: Store Data in Vector Store (Chroma DB)

7.6: Retrieval and Answer Generation

7.7: Streamlit UI

7.8: Learning Lessons

7.9: Exercise

8: Gen AI: Business Project 2 - E-Commerce Chatbot

8.1: Problem Statement

8.2: SOW & Technical Architecture

8.3: Implement FAQ Handling

8.4: Routing using semantic-router

8.5: Streamlit UI: FAQ Handling

8.6: SQLite Database Setup

8.7: Implement Product Handling: SQL Query Generation

8.8: Implement Product Handling: Data Comprehension

8.9: Streamlit UI: Product Questions Handling

8.10: Bonus: Web Scraping

8.11: Exercise

9: Agentic AI: A Hands-on Approach

9.1: Agency in AI

9.2: Build Your First Agent using Llama and Agno

9.3: Agent with Custom Tool

9.4: What are Reasoning Models?

9.5: Building a Reasoning Agent with Agno

9.6: Multimodal Agent