How to Chat with a Database Using AI - No Coding Required
TLDRIn this tutorial, Liam Otley teaches how to create an AI agent system that chats with a database. The system is built using low-code software, making it accessible without extensive coding knowledge. It's designed to query event data from a database and provide answers to user queries, a highly demanded function in the AI space. The tutorial includes a step-by-step guide available for free, along with a template for an AI agent that interfaces with a database to fetch event details. Liam also discusses monetization strategies for such a system.
Takeaways
- 😀 Liam Otley presents a tutorial on building an AI agent system that can chat with a database.
- 🔍 The system is designed to query a database and retrieve event data based on user questions.
- 💼 This AI-chatting system is highly sought after in the business world for its potential to handle large amounts of data.
- 🛠️ The tutorial uses low-code and no-code software to simplify the process of setting up the AI agent.
- 📝 Joshua Lock, a community member, successfully built a similar system for Bondi Lons, a Sydney startup.
- 🔗 The system can handle common queries and reduce the workload of human operators by automating responses.
- 💻 Liam provides a step-by-step guide and all necessary resources for free on the School Community platform.
- 🗓️ The database is set up to include event details such as name, date, time, venue, genre, description, and link.
- 🔗 The AI agent uses the Relevance AI tool to query the database based on user inputs like genre and date.
- 📊 The tutorial covers how to connect the AI agent to the database and includes testing the system to ensure it works correctly.
- 🌐 The system can be deployed on various platforms like WhatsApp, Messenger, or a web chat widget.
Q & A
What is the main topic of the tutorial?
-The tutorial is about setting up an AI agent system that allows users to chat with a database using AI, without requiring any coding.
What is the significance of being able to chat with a database using AI?
-Chatting with a database using AI is an in-demand use case because businesses have a lot of data and they want to be able to ask questions to it. This functionality can be difficult to set up, but the tutorial offers a low-tech way to achieve it with low-code and no-code software.
Who is the presenter of the tutorial?
-The presenter of the tutorial is Liam O'Tooley, who has been building and selling AI solutions for nearly two years through his AI automation agency called Morningside AI.
What is the example use case provided by Joshua Lock?
-Joshua Lock built a chat database system for Bondi Lons, a Sydney startup, to make a chat that returns nightlife events from the database to users. This system was designed to handle a high volume of direct messages and provide event recommendations.
What is the architecture of the chat-to-database AI agent system discussed in the tutorial?
-The architecture involves a user asking questions, an AI agent receiving these questions and querying a database, and then returning the information to the user. The AI agent uses a special tool to connect and communicate with the database.
What is Agentive and how is it used in the tutorial?
-Agentive is a software used in the tutorial that allows users to create AI agents without coding. It is built on top of the OpenAI Assistant API and is used to handle the queries sent by users and to interact with the database.
What is Superbase and how does it fit into the system?
-Superbase is a tool used to set up a database. In the tutorial, it is used to create a database with event data that the AI agent can query to provide answers to user questions.
How is the database populated with data in the tutorial?
-The database is populated with dummy data using an SQL command that inserts a series of events into the 'events' table, which includes fields like name, date, time, venue, genre, description, and event link.
What is the role of the Relevance AI tool in this system?
-The Relevance AI tool acts as an intermediary between the AI agent and the database. It takes the user's query, extracts the necessary information, and constructs a query to the database to retrieve the relevant data.
How can users test the functionality of the chat-to-database AI agent system?
-Users can test the system by asking questions to the AI agent through various deployment options like WhatsApp, Messenger, or a web chat widget. The system should respond with information retrieved from the database.
What additional features can be added to enhance the AI agent system?
-Additional features can include the ability to fetch the current date and time, which allows the system to handle relative timing queries without explicitly asking the user for the current date.
Outlines
🚀 Introduction to Building an AI Chat-to-Database Agent System
Liam Mle introduces a tutorial on setting up an AI agent system that enables users to chat with a database. The system is designed to handle event data and allows users to ask questions, with the AI agent querying the database and returning information. Liam emphasizes the high demand for such systems in the business world, where companies seek to query their data efficiently. He mentions his own experience in running an AI automation agency and offers a step-by-step guide available for free on the School Community platform. The tutorial is inspired by Joshua Lock, who successfully built and sold a similar system for Bondi Lons, a Sydney-based startup, to manage their large volume of customer inquiries.
🛠️ Setting Up the AI Agent and Database
The tutorial continues with instructions on setting up the AI agent using Agentive software, which requires an OpenAI API key. Liam provides a link to clone his pre-configured 'Event Genie Sydney' agent template. He then guides viewers to set up a Superbase database, create a new project, and add a table with specific columns for event data. The table is populated with dummy data using an SQL command provided in the Figma board. Liam discusses potential monetization strategies, such as using the system for affiliate marketing or charging clients for the AI assistant's services.
🔗 Connecting the AI Agent to the Database
Liam demonstrates how to create a Relevance AI account and clone a pre-built tool designed to query the Superbase database based on user inputs. The tool is configured to accept optional fields such as genre, venue, start date, and end date. He explains the importance of accurate descriptions for the AI agent's understanding and use of the tool. The tutorial includes instructions for updating the tool's settings with the user's Superbase URL and API key. Liam also shows how to connect the Relevance AI tool to the Agentive agent and test its functionality by querying the database for specific events.
🌐 Deployment and Sharing the AI Agent System
The final part of the tutorial covers deploying the AI agent system on various platforms like WhatsApp, Messenger, and a web chat widget. Liam provides guidance on how to share the agent as a demo with clients, allowing them to interact with the chatbot and test its functionality. He wraps up the tutorial by directing viewers to the School Community for detailed steps and resources, and encourages them to explore other videos for further insights into building AI agent systems.
Mindmap
Keywords
💡AI agent system
💡Database
💡Event data
💡Query
💡Low code
💡No code
💡API key
💡Agentive
💡Superbase
💡Relevance AI
💡Deployment
Highlights
Tutorial on setting up an AI agent system to chat with a database without coding.
Building a basic database using event data.
Creating an AI agent that queries a database and retrieves information.
AI's ability to chat with databases is a high-demand use case in the business sector.
The tutorial provides a low-tech method to connect AI agents with databases using low-code software.
The skill of setting up AI to chat with databases is essential for AI automation agencies.
The tutorial includes a step-by-step guide available for free on the School Community.
The system is based on a successful example by Joshua Lock for Bondi Lons.
The chat system built by Joshua substitutes human effort in responding to DMs about events.
The AI system provides recommendations for the best events in Sydney or Melbourne.
The architecture involves a user asking questions, an AI agent, and a database.
The AI agent extracts information from user queries and sends it to an AI tool.
The AI tool queries a database and returns information to the agent.
The agent formats the returned information into a readable response.
The tutorial includes a 15-step guide to set up the system.
The end result allows clients to chat with the AI agent and ask questions about events.
The database is set up using dummy Sydney event data.
The tutorial shows how to connect the AI agent to the database.
The system can be monetized by taking a percentage of revenue from link clicks.
The tutorial includes instructions for deploying the AI agent on platforms like WhatsApp.
The final step is sharing the AI agent as a demo with clients.
