mac999/LLM-RAG-Agent-Tutorial

Covers transformer architecture, vector RAG with FAISS/Chroma DB, and agentic workflows using LangChain and MCP (Model Context Protocol) for tool integration. Includes hands-on Jupyter notebooks for fine-tuning models like Llama and Gemma, building retrieval systems, and deploying chatbots via Ollama with Streamlit/Gradio interfaces. Provides foundational math modules (linear algebra, calculus, numerical analysis) and multimodal capabilities (CLIP, LLaVA, Stable Diffusion) alongside graph RAG implementations using Neo4j and FalkorDB.