Build a Free Local RAG Chatbot with Llama 3.2 and Flowise
Learn how to create a local RAG chatbot using the Llama 3.2 model and the Flowise platform. This chatbot can access and understand your personal knowledge base.
Course Timeline
🎥 Introduction: Llama 3.2 and RAG Chatbots
Overview of Llama 3.2's capabilities, focusing on its suitability for building local RAG chatbots. Introduction to the concept of Retrieval Augmented Generation (RAG).
🚀 Setting up Llama 3.2 with Olama
Step-by-step guide on downloading and running Llama 3.2 locally using Olama, including downloading necessary models.
⚙️ Installing and Configuring Flowise
Instructions for installing Node.js and setting up the Flowise open-source platform for building the AI application.
📚 Creating a Custom Knowledge Base in Flowise
Guide on setting up a document store in Flowise, adding various document types (Word, CSV), splitting documents into chunks, and managing data sources.
🚀 Loading Data into a Vector Database
Explanation of using embeddings and the process of loading documents from the document store into a vector database (Fias) for efficient retrieval.
🤖 Building the RAG Chatbot in Flowise
Step-by-step creation of the RAG chatbot using Flowise's drag-and-drop interface, including adding nodes for the chat model, memory, and vector store retriever.
💬 Testing and Using the Chatbot
Testing the chatbot with sample queries, demonstrating its ability to answer questions based on the uploaded knowledge base and showcasing the local functionality.