Generative AI models have shown tremendous usefulness in increasing
accessibility and automation of a wide range of tasks. Yet, their application to
the biomedical domain is still limited, in part due to the lack of a common
framework for deploying, testing, and evaluating the diverse models and
auxiliary technologies that are needed.
biochatter is a Python package
implementing a generic backend library for the connection of biomedical
applications to conversational AI. We describe the framework in this
preprint; for a more hands-on experience,
check out our two web app implementations:
To use the package, install it from PyPI, for instance using pip (
biochatter) or Poetry (
poetry add biochatter).
The package has some optional dependencies that can be installed using the
following extras (e.g.
pip install biochatter[xinference]):
xinference: support for querying open-source LLMs through Xorbits Inference
podcast: support for podcast text-to-speech (for the free Google TTS; the paid OpenAI TTS can be used without this extra)
streamlit: support for streamlit UI functions (used in ChatGSE)