Notes on AI Agent System for Chatting with a Database

Overview

• Presenter: Liam Motley
• Topic: Setting up an AI agent system to interact with a database.
• Purpose: Create a system for querying databases with AI to answer questions.
• Relevance: High demand for businesses to interact with their data effectively.

System Architecture

• Components:
  • A basic database using event data.
  • An AI agent to interpret user queries.
  • A connection tool to query the database and return results.
• Use Case: Example provided by Joshua Locke for Bondi Lines, Sydney startup.
  • Automated chat system for nightlife event queries.
  • Handled 500 DMs during busy nights.
• Outcome: Automates event recommendations and queries.

Building the System

• Software and Tools:
  • Figma Board: Contains all template and step-by-step instructions.
  • Agentive: Free software for agent management.
  • Relevance AI Tool: For querying the database.
• Database Setup:
  • Create a database with event details such as name, date, venue, etc.
  • Use Superbase for database creation.

Steps to Set Up

Step 1: Join Community

• Access resources on Figma board and school community.

Step 2: Sign up for Agentive

• Register and provide OpenAI API key.
• Clone Event Genie Sydney template.

Step 3: Set Up the Database

• Use Superbase
  • Create a table named 'events'.
  • Disable row level security.
  • Add columns: name, date, time, venue, genre, description, event link.
  • Populate with dummy data.

Step 4: Create a Relevance AI Account

• Clone the tool for querying the database.
• Configure URL and API key with Superbase details.

Step 5: Connect Tools

• Link Relevance AI tool with Agentive agent.
• Test the connection and functionality.

Testing and Deployment

• Perform queries to test system (e.g., "What techno events are on next weekend?").
• Check tool usage and query handling.
• Add additional features like current date queries for dynamic requests.

Deployment Options

• Deploy on platforms like WhatsApp, Messenger, or through a web chat widget.
• Share and demonstrate to clients.

Additional Resources

• Access tutorials and templates through the school community.
• Video resources available for further learning and examples.

Conclusion

• AI agents for database interaction offer valuable business solutions.
• Customizable and deployable across various platforms.
• Encouragement to explore and implement in business settings.

• Note: All resources mentioned are available in the community and through tutorial links provided by Liam Motley.