What will you learn in Guide to Building Python and LLM-Based Multimodal Chatbots Course
- Chatbot fundamentals & evolution: Understand rules-based, Rasa, and GenAI-powered chatbot architectures, exploring core conversational design and framework differences.
- From simple to multimodal systems: Build Python chatbots with Gradio, integrate small LLMs (Ollama, Llama), and add speech (Whisper v3) and image (Gemini) capabilities.
- RAG-enhanced chatbot pipelines: Apply Retrieval-Augmented Generation with LlamaIndex to ground responses in external knowledge.
- Deployment & integration: Connect Gradio-based bots to Hugging Face, and explore frontend integration with React/OpenAI.
Program Overview
Module 1: Getting Started with AI Chatbots
⏳ ~30 minutes
Topics: Evolution of chatbots (rule-based → GenAI), intro to Gradio framework.
Hands‑on: Build a simple Python chatbot with Gradio and complete related quizzes.
Module 2: Foundations of AI Chatbots with Rasa
⏳ ~45 minutes
Topics: Rasa overview, conversational components, Python integration.
Hands‑on: Create a rule-based chatbot using Rasa and Python in-browser.
Module 3: Generative Chatbots with Small LLMs
⏳ ~1 hour
Topics: Using Ollama and Llama small LLMs within Gradio interface.
Hands‑on: Run and customize an SLM-powered chatbot and compare across frameworks.
Module 4: Multimodal Capabilities – Speech & Vision
⏳ ~1 hour
Topics: Add speech via Whisper v3 and image understanding with Gemini.
Hands‑on: Integrate audio input and image-to-text responses in your chatbot.
Module 5: RAG Integration with LlamaIndex
⏳ ~45 minutes
Topics: RAG basics, document indexing, query augmentation.
Hands‑on: Implement a retrieval-augmented chatbot using LlamaIndex and test knowledge accuracy.
Module 6: Deployment & Frontend with Hugging Face & React
⏳ ~45 minutes
Topics: Deploy models via Hugging Face; build React frontends with OpenAI integration.
Hands‑on: Launch your chatbot and integrate with React components and API keys.
Module 7: Capstone & Challenges
⏳ ~30 minutes
Topics: Combine multimodal RAG chatbot elements; finalize structure and design.
Hands‑on: Merge speech, image, RAG, and deploy via Gradio executing full-stack project.
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Job Outlook
- Cutting-edge skills: Multimodal Generative AI expertise is sought after for roles in LLM engineering, product/tool development, and chatbot design.
- ML & AI product career paths: Build full-stack AI assistants—ideal for AI developer, prompt engineer, and ML product roles.
- Impressive portfolio projects: Showcases Gradio, Rasa, LLMs, multimodal, RAG, deployment—all in one learning experience.
- Freelance & prototyping: Enables creating advanced chatbots for startups, websites, and internal tools.
Specification: Guide to Building Python and LLM-Based Multimodal Chatbots
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