EduVisAgent: A Multi-Agent Framework for Pedagogical Visualization

Overview

This project implements a multi-agent system designed to generate comprehensive educational content based on user queries. It leverages a series of specialized language model (LM) agents working collaboratively to create detailed teaching plans, improve them through iterative feedback, and build structured content for various presentation formats. The core of the system is the TeachAgentsIntermediateSystem, which coordinates the entire workflow.

Requirements

1. Python 3.12
2. OpenAI API Key
3. Conda (recommended for environment management)

Installation

1. Clone the repository:

   git clone https://github.com/aiming-lab/EduVisAgent
   cd EduVisAgent

2. Create and activate Conda environment:

   conda create -n eduvis python=3.12
   conda activate eduvis

3. Install dependencies:

   bash install.sh

4. Set up environment variables: Export your OpenAI API key as an environment variable in your terminal session.

   export OPENAI_API_KEY="your_openai_api_key_here"

Usage

Basic Operation

Execute the main script to run the educational content generation system:

python scripts/run_teach_intermediate.py --question "Your educational question here"

By default, if no --question argument is provided, the script uses a predefined example query.

Output will be saved in the outputs/teach_intermediate/ directory, with subdirectory names containing a timestamp and a brief description of the query (e.g., outputs/teach_intermediate/YYYYMMDD_HHMMSS_explain_concept_photosynthesis/).

Batch Processing of Educational Questions

For processing multiple educational questions from a dataset (e.g., train.jsonl), you can use the run_teach_intermediate_batch.py script. This script iterates through questions in a JSONL file and runs the full TeachAgentsIntermediateSystem for each.

python scripts/run_teach_intermediate_batch.py --data_file mydatasets/train.jsonl --limit 5

This script supports the following parameters:

• --data_file (required): Path to the JSONL data file containing the questions (e.g., mydatasets/train.jsonl). Each JSON object in the file should have a "question" field.
• --limit (optional): Number of questions to process from the beginning of the file. If not specified, all questions will be processed.

Output for each question will be saved in a separate subdirectory within outputs/teach_intermediate_batch/. Each subdirectory will be uniquely named using the line number from the input file, a timestamp, and a slug derived from the question.

Directory Structure

EduVisAgent/
├── agents/                         # Core agent logic
├── config/                         # Configuration files
├── models/                         # Language model interfaces
├── mydatasets/                     # Dataset files
├── outputs/                        # Generated outputs
├── scripts/                        # Executable scripts
├── image/                          # Image resources
├── utils/                          # Utility functions and classes
└── README.md                       # This file