0e4efd0efc
This PR adds an example of `parallel-agents` that runs multiple instances of Magentic-One in parallel, with support for early termination and final answer aggregation. --------- Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
GAIA Benchmark
This scenario implements the GAIA agent benchmark. Before you begin, make sure you have followed instruction in ../README.md to prepare your environment.
Setup Environment Variables for AgBench
Navigate to GAIA
cd benchmarks/GAIA
Update config.yaml to point to your model host, as appropriate. The default configuration points to 'gpt-4o'.
Now initialize the tasks.
python Scripts/init_tasks.py
Note: This will attempt to download GAIA from Hugginface, but this requires authentication.
The resulting folder structure should look like this:
.
./Downloads
./Downloads/GAIA
./Downloads/GAIA/2023
./Downloads/GAIA/2023/test
./Downloads/GAIA/2023/validation
./Scripts
./Templates
./Templates/TeamOne
Then run Scripts/init_tasks.py again.
Once the script completes, you should now see a folder in your current directory called Tasks that contains one JSONL file per template in Templates.
Running GAIA
Now to run a specific subset of GAIA use:
agbench run Tasks/gaia_validation_level_1__MagenticOne.jsonl
You should see the command line print the raw logs that shows the agents in action To see a summary of the results (e.g., task completion rates), in a new terminal run the following:
agbench tabulate Results/gaia_validation_level_1__MagenticOne/
References
GAIA: a benchmark for General AI Assistants <br/>
Grégoire Mialon, Clémentine Fourrier, Craig Swift, Thomas Wolf, Yann LeCun, Thomas Scialom <br/>
https://arxiv.org/abs/2311.12983