Crafting a Jeopardy Simulation with AutoGen
Feb 02, 2024Introducing AI Jeopardy
Welcome to AI Jeopardy: This concept introduces a version of Jeopardy where AI models are the players. The goal is to explore if we can build a system where AIs compete in Jeopardy against each other.
AutoGen Agents in Action
The core of our project was AutoGen, a tool developed to simplify setting up interactions between AI agents. Using AutoGen, we created a competitive environment for AI models like GPT and Llama 2 to demonstrate their abilities. We designed AI agents for each model, with GPT-4 acting as the game's host.
The integration of these language models was just one part of the project. To replicate the Jeopardy game structure, we built a basic SQL database. This database stored all game elements, including questions, answers, categories, and points. It served as the backbone of the game, enabling the host to select questions and manage scores efficiently.
Behind the Scenes: LM Studio and Flask
The project also served as a tutorial for integrating local language models using LM Studio, a user-friendly interface that simplifies the operation of LLMs across different systems. This was crucial for incorporating Llama 2 into our setup, ensuring it could compete on equal footing with GPT.
To bring the simulation to life beyond the command line, we utilized Flask, a Python web framework, along with web sockets. This combination allowed us to create a dynamic, browser-based interface where viewers could follow the game in real time, transforming the simulation into an interactive experience.
Setting Up the AI Agents with AutoGen
In this simulation, I utilized distinct AI models: GPT-3.5 and LLaMA 2 as the contestants, and GPT-4 as the game moderator.
GPT-3.5 and LLaMA 2 were chosen due to their robust abilities in understanding and generating language, qualities essential for participating in a game like Jeopardy. GPT-4, with its more sophisticated capabilities, was perfectly suited for the moderator's role, overseeing the game's progression.
AutoGen was vital for this setup, enabling the creation of interactive agents, both players and the moderator, for the game. Its importance lies in managing the communication between these agents, which is essential for simulating the game.
Within AutoGen, it's possible to assign specific configurations to each agent. This feature allowed for matchups like GPT-3.5 against LLaMA 2, with each agent leveraging its distinct language model for the competition.
Setting Up the Moderator
The initial step involved configuring the moderator, which operates on GPT-4. The moderator is responsible for guiding the game's progression, including choosing questions and verifying answers. Below is an example of how the moderator was configured:
Setting Up the Players
For the contestants, we designated Peter and Bob. Peter was programmed to utilize the local LLaMA2 model, while Bob was equipped with GPT-3.5. This diversity in AI models introduced an engaging dynamic to the gameplay.
Integrating the Agents
Once the agents were set up, the following task was to integrate them into a group chat setting for interaction. This integration was achieved with AutoGen's GroupChat and GroupChatManager functionalities, facilitating seamless communication and smooth flow of the game.
Database Configuration and Function Integration
To accurately simulate the Jeopardy game, it was necessary to establish a database for questions and implement a method for tracking the players' scores. SQLite, known for its simplicity and efficiency, was chosen for the database, which was then integrated with the AutoGen framework.
Database Configuration
The database's structure was crafted to hold an array of Jeopardy questions, complete with their answers, categories, and point values. This diversity was essential in offering a wide range of questions for the AI agents to address.
Managing the scores was a pivotal element of the game's framework. To address this, I developed functions within the database that would allow for adding to and retrieving player scores. This functionality was key to maintaining an accurate and up-to-date leaderboard.
Function Mapping
Function mapping plays a crucial role in our Jeopardy simulation, as it connects the AI agents with specific actions within the SQLite database. This connection is essential for enabling the agents to execute tasks such as pulling questions from the database. A prime example of this is the get_random_question method.
The get_random_question method is crafted to select a random question from the database's questions table. This functionality is fundamental to the Jeopardy game, serving as the mechanism through which the moderator presents questions to the contestants.
A declaration serves as a functional blueprint, outlining the structure and prerequisites of the function. This blueprint clarifies for the AI agents the manner in which they can engage with the function.
Regarding the get_random_question method, its declaration encompasses specifics such as the function's name, a narrative of its objective, and the parameters it necessitates. This ensures the AI agents are well-informed on how to correctly utilize the function within the game's context.
In our Jeopardy simulation, the moderator uses this setup to know how to invoke the get_random_question method and which parameter (specifically, the category) should be passed to it. This ensures the moderator can efficiently fetch questions from the designated category, maintaining the game's flow.
Bringing the AI Jeopardy Game to Life
With the AI agents and the database prepared, the subsequent step involved activating the Jeopardy game through an intuitive interface coupled with ingenious programming methods, such as monkey patching.
Gameplay Dynamics
Throughout the game, the AI agents engage within a group chat setup. The moderator poses questions sourced from the database, while the contestant agents, Bob and Peter, provide answers drawing on their respective AI model's strengths. These answers are assessed, leading to the appropriate adjustments in their scores.
Flask SocketIO played a crucial role in establishing real-time communication between the server and the web interface. It enabled the dynamic push of updates to the web page in sync with the game's progress, allowing players to witness the unfolding of the game live.
Let's take a look at a captivating exchange between our AI moderator and Bob during the gameplay.
Final Reflections
The AI Jeopardy game showcased not just the seamless integration of AI models but also addressed substantial technical hurdles, culminating in a dynamic web-based gaming experience. More than its technical achievements, the game acted as a compelling and informative demonstration of AI's capabilities within the realms of gaming and entertainment.
