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Building a Crew AI Team with Lightning

Sep 26, 2024

Lecture: Setting Up a Crew AI Team using Lightning AI

Introduction

  • Presenter: Crew AI Founder
  • Focus: Building a Crew AI team using Lightning AI
  • Tools Discussed: Lightning AI, mixol, mistol
  • Sponsor: Lightning AI

Benefits of Lightning AI

  • Cloud-based code editor
  • Collaborate on code in the cloud
  • Power open-source models
  • Environment management is simplified (fresh Python environment each time)

Steps to Build Crew AI Code Framework

1. Create a Lightning Account

  • Sign up to receive free credits
  • Create a new code studio

2. Building the Code Structure

  • Modular Structure:
    • Tools Area
    • Agents and Tasks Definition: Using YAML files
    • Main.py File: Short and concise

3. Setting Up the Project

  • Spin up a new Lightning studio
  • Create a source folder
  • Create a financial analyst crew folder within source

4. Configuration

  • Create a config folder for tasks and agents
  • Define tasks and agents in .yaml files

Task Definitions

  • Research Company Task:
    • Description: Use search tool for stock information
    • Expected Output: Financial information
  • Analyze Company Task:
    • Description: Analyze financial information
    • Expected Output: Financial analysis including various ratios

Agent Definitions

  • Company Researcher:
    • Role: Financial researcher
    • Allow delegation: False
    • Verbose: True
  • Company Analyst:
    • Role: Financial analyst
    • Similar structure to the researcher

Main File Creation

  • Import Libraries:
    • crew AI, Lang chain, grok
  • Define Crew Base:
    • Load agents and tasks
    • Set grok information
  • Agent and Task Setup:
    • Use decorators for definitions

Running the Project

  • Use poetry for dependency management
  • Ensure all necessary libraries are installed
  • Run project using poetry run

Switching to Open-Source Model

Using Lightning AI

  • Access studio templates
  • Use pre-configured templates (e.g., mixture of experts)
  • Set API Builder for serving
  • Expose API endpoint

Integrating with Code

  • Import oama from Lang chain
  • Define new LLM with olama and set base URL

Conclusion

  • Successfully structured a Crew AI
  • Integrated open-source models using Lightning AI
  • Exposed API and validated functionality

Additional Notes:

  • Automatic file saving with Lightning AI
  • Use of grok for processing and Lang chain for integration
  • Consideration for authentication in production environments

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These notes summarize the key points and steps outlined in the lecture for setting up a Crew AI team using Lightning AI's capabilities.

Full transcript