This project is a learning exercise on learning LLM agents using AutoGen.
Examples of items to explore:
- How to use agents to solve a task.
- What are the limitations? For example, do we need GPT-4, or could a simpler (and cheaper) model do the job?
- How many tokens do we consume for the agents and the behind-the-scenes processing (the selection and coordination of the agents)?
If you haven't done so yet, prepare the environment. If you have already prepared the environment, activate it with source venv/bin/activate
.
Each notebook is a self-contained experiment. You can run them in any order.
You may need to select a Python kernel the first time you open a notebook. Choose the kernel from the virtual environment you created.
- Sequential chat: Given a starting and ending number, select math operators (agents) to transform the starting number into the ending number.
Create a file named .env
in the root of the project with the following content:
OPENAI_API_KEY=your-openai-api-key
This file is in the .gitignore
. It will never be committed to the repository.
This is a one-time step. If you have already done this, just activate the virtual environment with source venv/bin/activate
.
Run the following commands to create a virtual environment and install the required packages.
python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt