Graphs with Conditional Logic

In this lecture we are adding flexibility to our workflows by implementing conditional edges that route the agent to different nodes based on specific input criteria.

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