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Guide to Building Python and LLM-Based Multimodal Chatbots

Explore this AI chatbots course to build Python-based multimodal chatbots with Gradio, Rasa, Gemini, and Whisper v3. Learn LLM-powered techniques, RAG integration, and deploy on Hugging Face.

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Adaptive Learning

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This course includes

15 Lessons

38 Playgrounds

Course Overview

What You'll Learn

Course Content

Apply Your Skills

Course Overview

This hands-on course will transform how you build AI chatbots. Generative AI and large language models (LLMs) have revolutionized chatbot development, enabling smarter and more interactive systems. In this AI chatbots course, you’ll explore the evolution of chatbots and create your own, starting with a simple Python chatbot enhanced with Gradio for a seamless interface. Next, dive into the Rasa Open Source framework to understand pre-generative AI chatbot development. Progress to LLM-powered chatbots with...Show More

This hands-on course will transform how you build AI chatbots. Generative AI and large language models (LLMs) have revolutionize...Show More

TAKEAWAY SKILLS

Generative AI

Chatbot

Python

Gradio

Llama

What You'll Learn

Deep understanding of core concepts of chatbot development, including how chatbots work, their different types, and the essential elements of conversational design

Familiarity with the Rasa Open Source framework for building ML-powered chatbots

The ability to incorporate small language models (SLMs) into chatbots with Ollama

Hands-on experience with Groq for accessing Llama 3 for text, Gemini for image processing, and Whisper v3 for accurate speech recognition

The ability to use retrieval-augmented generation (RAG) with LlamaIndex to enhance chatbot knowledge and responses

The ability to deploy chatbots to Hugging Face for accessibility and sharing

Deep understanding of core concepts of chatbot development, including how chatbots work, their different types, and the essential elements of conversational design

Course Content

Getting Started

1 Lessons

Get an overview of how AI chatbots have evolved and the tools used for building them, including Python and LLMs.

Why AI Chatbots Are Transforming Industries?

Foundations of AI Chatbots

4 Lessons

Learn about the evolution, anatomy, and frameworks for developing AI chatbots, including using Rasa with Python.

What Are Chatbots—The Basics of Conversational AI

Types of Chatbots: Rule-Based, Pattern-Matching, and GenAI

Anatomy of a Chatbot: How They Work

Building Chatbots with Rasa and Python

Building a Generative AI-Powered Chatbot

5 Lessons

Build AI chatbots powered by advanced generative AI, integrating multimodal capabilities for real-world applications.

Building a Chatbot Using Small Language Model (SLM)

Accessing Large Language Models (LLMs) with Groq

Creating a Text-Based Chatbot with Meta’s Llama

Adding Image-to-Text Capabilities with Gemini

Integrating Speech-to-Text with Whisper v3

Enhancing Chatbots with Advanced Capabilities

4 Lessons

Explore retrieval-augmented generation (RAG) to improve chatbot responses and deploy your chatbots for wider accessibility.

Introduction to RAG with LlamaIndex

Integrating RAG with LlamaIndex for Smarter Chatbots

Deploying Our Chatbot to Hugging Face

Challenge: Creating a Quiz Bot

Conclusion

1 Lessons

Summarize key learnings and explore the future of building AI chatbots with multimodal and generative AI techniques.

Future of AI Chatbots

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Frequently Asked Questions

Can I build a multimodal RAG application with Google Gemini?

Yes, using Google Gemini, you can create multimodal RAG applications that handle text-to-text and image-to-text prompts, enabling advanced solutions like a customer service assistant with a Streamlit interface.

How does LangChain enhance RAG applications with Google Gemini?

LangChain simplifies the development of RAG applications by managing external knowledge retrieval, connecting with Gemini’s multimodal capabilities, and enabling seamless integration of text and image-based prompts.