open-syndrome-python

Installation

You can install it from PyPI or Docker. By default, the conversion features use Ollama running locally. Cloud providers (OpenAI, Anthropic, Mistral, DeepSeek, Gemini) are also supported and require only an API key.

From PyPi, install the package with pip install opensyndrome. Then run it with opensyndrome --help.

From Docker, you can run the following command to build the image, tagged opensyndrome:

docker build -t opensyndrome .

Run the container interactively, removing it when it exits

docker run --rm -it opensyndrome

To read a .env file, mount it:

docker run --rm -it \
  -v "$(pwd)/.env:/app/.env:ro" \
  opensyndrome

To name the container and keep it around:

docker run --name opensyndrome-cli -it opensyndrome

Usage

First, download the schema and definitions in order to work with the CLI locally.

opensyndrome download schema
opensyndrome download definitions

The files will be placed in the folder .open_syndrome in $HOME.

Providers and configuration

The provider and model can be set via environment variables so you don't have to pass them on every command:

OPENSYNDROME_PROVIDER=ollama   # ollama (default), openai, anthropic, mistral, deepseek, gemini
OPENSYNDROME_MODEL=mistral     # overrides the provider's default model

Copy .env.example to .env and fill in the relevant values:

Provider	Required env var	Default model
ollama	— (runs locally)	mistral
openai	OPENAI_API_KEY	gpt-4o
anthropic	ANTHROPIC_API_KEY	claude-3-haiku-20240307
mistral	MISTRAL_API_KEY	mistral-large-latest
deepseek	DEEPSEEK_API_KEY	deepseek-chat
gemini	GEMINI_API_KEY	gemini-1.5-flash

For Ollama, the model must be pulled before use: ollama pull mistral. You can also override the Ollama base URL with OLLAMA_BASE_URL (default: http://localhost:11434).

Ollama models tested: llama3.2, mistral, deepseek-r1. Known to not work well with structured output: qwen2.5-coder.

Convert a human-readable syndrome definition to a machine-readable JSON

If you do not pass -hr or -hf, an editor will open for you to enter the definition.

# see some examples from ECDC: https://www.ecdc.europa.eu/en/all-topics/eu-case-definitions

# pass the definition as inline text
opensyndrome convert -hr "Any person with pneumonia"

# pass the definition from a TXT file
opensyndrome convert -hf definition.txt

# use a specific provider and model
opensyndrome convert -hr "Any person with pneumonia" --provider openai --model gpt-4o

# to have the JSON translated to a specific language and edit it just after conversion
opensyndrome convert --language "Português do Brasil" --edit

# include a validation step after conversion
opensyndrome convert --validate

Convert a machine-readable JSON syndrome definition to a human-readable format

opensyndrome humanize <path-to-json-file>
opensyndrome humanize <path-to-json-file> --provider anthropic
opensyndrome humanize <path-to-json-file> --model mistral-large-latest --language "Português do Brasil"

Validate a machine-readable JSON syndrome definition

opensyndrome validate <path-to-json-file>

Development

To get started with development, you need to have uv installed.

Install dependencies

uv sync

Generate Ollama-compatible JSON

You only need to do this if you are a maintainer adding a new OSI schema or updating an existing one.

Since Ollama requires a specific, more simple, JSON format, we need to generate an Ollama-compatible schema. To do this, we use datamodel-code-generator to generate a Pydantic schema. Run the following command to update it:

make ollama_schema

It will create a schema.py file in the root of the project. Be careful when editing this file manually.

Citing & Authors

If you find this repository helpful, feel free to cite our publication: The Open Syndrome Definition

@misc{ferreira2025opensyndromedefinition,
      title={The Open Syndrome Definition},
      author={Ana Paula Gomes Ferreira and Aleksandar Anžel and Izabel Oliva Marcilio de Souza and Helen Hughes and Alex J Elliot and Jude Dzevela Kong and Madlen Schranz and Alexander Ullrich and Georges Hattab},
      year={2025},
      eprint={2509.25434},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2509.25434},
}