Build a FREE AI Chatbot with LLAMA 3.2 & FlowiseAI (NO CODE)

Summary

In this tutorial, viewers will learn how to create a free local RAG chatbot using Meta's new Llama 3.2 model. The video explores the model's features, including vision capabilities and lightweight text-only options that can run on mobile devices. The tutorial covers downloading and running the model with Olama, setting up the open-source platform FlowWise, and creating a custom knowledge base with documents. By the end, users will have a fully functional AI assistant that can answer queries based on their personalized data, all while being guided through an engaging and straightforward process.

Takeaways

• 😀 Llama 3.2 introduces both vision-capable and lightweight text-only models for diverse applications.
• 🚀 The smaller models can run on edge and mobile devices, enabling on-device AI.
• 📄 The Llama 3.2 model has a massive context length of 128,000 tokens, ideal for creating powerful chatbots.
• 🛠️ To start, users need to download Olama and install it for running the Llama model locally.
• 🔍 The tutorial includes steps for searching and downloading the Llama 3.2 model via the Olama platform.
• 📂 FlowWise is a free, open-source platform that allows users to create AI applications using a drag-and-drop interface.
• 📚 Users can set up a custom knowledge base by uploading documents like Word files and CSVs to FlowWise.
• 🔄 Document stores in FlowWise help manage knowledge bases by allowing easy addition and removal of data sources.
• 📈 The tutorial explains how to configure a vector database for efficient retrieval of relevant documents.
• 🤖 Finally, a RAG chatbot is created that can respond to user queries based on the knowledge base, and it runs locally.

Q & A

What is the primary purpose of this tutorial?

The tutorial aims to guide viewers in creating a free local RAG (Retrieval-Augmented Generation) chatbot using the Llama 3.2 model.

What are the key features of the Llama 3.2 model?

Llama 3.2 includes vision-capable models (11 billion and 90 billion parameters) and lightweight text-only models (1 billion and 3 billion parameters), with a massive context length of 128,000 tokens.

How do smaller models in Llama 3.2 enhance on-device AI capabilities?

The smaller models can run on edge and mobile devices, opening up new possibilities for efficient on-device AI applications.

What platform is used to build the chatbot in this tutorial?

The chatbot is built using FlowWise, an open-source platform that simplifies the creation of AI applications with a drag-and-drop interface.

How do you verify if Olama is installed correctly?

You can verify Olama's installation by opening the command prompt or terminal and entering the command 'olama.' If installed correctly, you will receive a response.

What steps are involved in uploading documents to the custom knowledge base?

You need to create a document store, add document loaders for the files (like Word or CSV), preview and chunk the documents, and then process them to add to the knowledge base.

Why is it important to split large documents into smaller chunks?

Splitting large documents into smaller chunks reduces token usage and improves the efficiency of information retrieval for the chatbot.

What is the role of the vector store in the chatbot?

The vector store allows the chatbot to retrieve the most relevant documents related to user queries, enhancing the accuracy of responses.

How do you test the chatbot after building it?

You can test the chatbot by entering questions in the chat interface to see if it retrieves and responds accurately based on the knowledge base.

What should you do if you want to update the knowledge base later?

To update the knowledge base, you can open the document store, add new document loaders, remove existing items, and click on 'upsert config' to reload the data into the vector database.