meddle 0.1.2
Agentic Medical Deep Learning Engineer
Requires Python: >=3.11

# Med-DLE: Agentic Medical Deep Learning Engineer 

MedDLE is designed to be a codegen agentic system for medical deep learning tasks. 

# Quick Start
## Setup environment
1. we recommand use `conda` to create a virtual environment:
```
conda create --name meddle python=3.11 
conda activate meddle
```
2. Then install `meddle` via:
```
pip install uv
uv pip install -r requirements.txt
uv pip install -e .
```
3. remember to set up your private api key of OPENAI/ANTROPIC/OPENROUNTER for openai-compatible endpoint
```
export OPENAI_BASE_URL="<your base url>" # (e.g. https://api.openai.com/v1)
export OPENAI_API_KEY="<your key>"
```
### Setup knowledge base for RAG
You can create the knowledge base with `create_monai_knowledge_base.sh` or download from [link](http://ug.link/haoyushare/filemgr/share-download/?id=b8c77eb1346c4ecca4dfb510253d32fe).

You could use this command to check whether the creation succeeds.
```
python -m meddle.monai_rag.query_rag_db
``` 

### Setup dataset
1. Get data from [link](https://drive.google.com/file/d/1-EE5ib7xfq9q0luiixgtnyYCkBh21Gxe/view?usp=drive_link) and move the data into `med_dl_tasks`. This zip file contains both MedMNIST only.
2. The goals and evaluation targets for each dataset can be found in the corresponding shell scripts:
   - For MedMNIST datasets, see `run_medmnist_all.sh`.
   - For MCO (Molecular and Cellular Oncology) datasets, see `run_mco_all.sh`.

## Try MedDLE
```
meddle data_dir=<your dataset> \
     goal=<your goal> \
     eval=<your metrics> \
     agent.steps=30 agent.enable_monai_knowledge_base=true
```
here's a detailed example:
```
meddle data_dir="meddle/example_tasks/retinamnist_224" exp_name="torch-example_task_retina"\
     goal="Predict the category of each given Fundus Camera image by selecting the most appropriate one from the 5 available classes." \
     eval="Use the accuracy and the area under curve (AUC) between the predicted class and ground-truth class on the test set." \
     agent.step_plan.proposal=20 agent.step_plan.tuning=10 agent.step_plan.hpo=5
```

You can set general `steps` (MedDLE with auto-schedule steps for multiple agents) or detailed `step_plan` for 
each agent.

# Advanced Features
## RAG Mode
We support both basic RAG and advanced (query2doc) mode. They are controlled with the following configs.
```
agent.enable_monai_knowledge_base=true agent.enable_query2doc=true
```

## Time Limits
MedDLE supports configurable timeouts for code execution. The default timeout is 3600 seconds, which can be adjusted via:
- `exec.timeout`: Base timeout for all agents (default: 3600s)
- HPO agent automatically gets 2x timeout: 7200s when running hyperparameter optimization

To set a total time limit for all steps, use `agent.total_time_limit`:
```
meddle data_dir="meddle/example_tasks/retinamnist_224" exp_name="test_timelimit-example_task_retina"\
     goal="Predict the category of each given Fundus Camera image by selecting the most appropriate one from the 5 available classes." \
     eval="Use the accuracy and the area under curve (AUC) between the predicted class and ground-truth class on the test set." \
     agent.force_monai_with_prompt=false agent.enable_monai_knowledge_base=false agent.expose_prediction=true \
     agent.steps=100 agent.total_time_limit=60
```

# 🙏 Acknowledgement
- We thank all medical workers and dataset owners for making public datasets available to the community.
- Thanks to the open-source of the following projects, our code is developed based on their contributions:
     - [aideml](https://github.com/WecoAI/aideml)
     - [monai](https://github.com/Project-MONAI)
     - [AutoGluon](https://github.com/autogluon/autogluon)