Build a Local Chatbot with Llama OCR, Multimodal RAG, and a Local LLM
Learn to create a powerful, local chatbot using Llama OCR for visual data, multimodal RAG for efficient retrieval, and a local LLM for intelligent responses. This tutorial demonstrates building a chatbot for business or personal use.
Course Timeline
🎥 Introduction: Building a Powerful Local Chatbot
Overview of building a local chatbot with Llama OCR, multimodal RAG, and a local LLM, addressing the challenges of handling various document formats.
💡 Llama OCR and Multimodal RAG: Handling Complex Documents
Explaining Llama OCR, an open-source optical character recognition tool powered by Llama 3.2 Vision model, and how multimodal RAG enhances interaction with visual data for in-context learning.
🤖 Demo: Chatbot in Action
A live demo showcasing the chatbot's ability to answer complex questions by interacting with PDFs and combining text, visuals, tables, and charts for a comprehensive response using multimodal RAG and Kali.
⚙️ System Architecture and Setup
Details on adding images or PDFs, automatic generation of embeddings, duplicate checks, and organization within SQLite for seamless access, along with querying using natural language.
🔎 Why OCR Struggles and Introducing Kali
Discussion on why standard LLMs struggle with complex documents, introduction to Kali, its novel architecture and training strategy, and its efficient indexing based on visual features.
💻 Implementation: Code and Libraries
Practical coding demonstration using Baldi, cquin 2, pdf2image, and popular utils for embedding and retrieving images from PDF documents, and using Llama 3.2 Vision for text extraction or image-based questions.
🔍 Querying and Results: Visual Data Retrieval
Step-by-step guide on querying the index, retrieving top similar results using do_search, displaying results with document IDs, page numbers, and similarity scores, and visually displaying the matched page using base64 encoded images.
🚀 Llama 3.2 Vision: Local Execution
Information about Llama 3.2 Vision, its parameters, VRAM requirements, and its use for text extraction or question answering about images on a MacBook using Al.
🎉 Conclusion: Multimodal Document Retrieval
Summary of Llama OCR, its benefits for developers and content creators, and the overall efficiency and convenience of the system for processing complex documents.