"I want Llama3.1 to perform 10x with my private knowledge" - Self learning Local Llama3.1 405B

Introduction

This tutorial provides a step-by-step guide on how to set up and utilize the Llama 3.1 model for building AI agents. With Llama 3.1's advanced capabilities, you can create personalized agents that leverage your private knowledge to automate tasks and enhance productivity in real-world applications.

Chapter 1: Understanding Tool Calling and Agent Use Cases

• Tool Calling Overview

  • Tool calling refers to a model's ability to predict which function to call and the necessary inputs for that function based on a user's request.
  • This is crucial for building autonomous agents that can handle complex tasks, such as planning trips or managing schedules, requiring multiple tool calls and reasoning.

• Performance Analysis

  • Llama 3.1 performs well in tool calling compared to other models, especially in multi-turn dialogues, making it suitable for real-world applications.
  • The model can break down tasks into smaller steps, managing multiple calls effectively.

• Use Cases

  • One promising use case is digitizing domain experts within organizations, allowing employees to access knowledge 24/7.
  • Additionally, creators can build AI versions of themselves to engage with their communities, improving interaction and content distribution.

Chapter 2: Setting Up Llama 3.1 Locally

• Download Model
  • Install the O Llama package that allows you to run the Llama 3.1 model on your local machine.
  • Open your terminal and type:

    olama P Lama 3.1
    

  • This will download the model. If you want to run the model, execute:

    olama run llama 3.1
    

Chapter 3: Setting Up a RAG Pipeline

• Choose Between Fine-Tuning and RAG

  • Fine-Tuning:
    • Faster but more complex, as it requires extensive data preparation.
    • Knowledge is static and requires retraining for updates.
  • RAG (Retrieval-Augmented Generation):
    • Easier to implement and supports dynamic data sources.
    • Ideal for continuously updating knowledge bases.

• Using Llama Cloud

  • For a managed RAG pipeline, sign up for Llama Cloud.
  • Create an index for your knowledge base (e.g., a Notion database).
    • Obtain an integration token from Notion and ensure proper access.

Chapter 4: Integrating with Slack

• Create a Slack App
  • Navigate to Slack settings and manage apps.
  • Click on "Build" and create a new app from scratch.
  • Add necessary permissions like reading messages, reacting to messages, etc.
  • Once installed, note the OAuth token for later use.

Chapter 5: Connecting Llama 3.1 to Slack

• Set Up Environment Variables

  • Use Visual Studio Code to create an .env file for storing credentials (Slack token, Notion token, etc.).

• Code Setup

  • Import required libraries and create functions to handle Slack interactions.
  • Example function for posting messages:

    def post_message(channel, message):
        # Function to post messages to Slack
        pass
    

• Event Listeners

  • Set up event listeners for messages and mentions.
  • Use a webhook to receive messages and respond accordingly.

Chapter 6: Building the Knowledge Retrieval Agent

• Creating Functions

  • Implement functions for knowledge retrieval and message drafting.
  • Example for retrieving knowledge:

    def knowledge_retrieval(query):
        # Logic to fetch relevant information from Llama Cloud
        return response
    

• Agent Workflow

  • Establish an agent workflow that categorizes queries and decides whether to use direct answers or knowledge retrieval.

• Implementing Self-learning Capabilities

  • Add functionality for the agent to learn from interactions.
  • Implement functions to save new knowledge back to the Notion API.

Conclusion

This guide outlines how to set up and leverage Llama 3.1 for creating intelligent agents that can automate tasks and facilitate knowledge sharing within organizations. By following these steps, you can implement a functioning AI agent in your workspace. As you continue to explore and experiment with Llama 3.1, consider joining communities or resources that delve deeper into AI development for further learning and support.