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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:
BioChatter is part of the BioCypher ecosystem, connecting natively to BioCypher knowledge graphs. The BioChatter paper is being written here and the current version can be read here.
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BioChatter natively extends BioCypher knowledge graphs. Check there for more information.
We have also recently published a perspective on connecting knowledge and machine learning to enable causal reasoning in biomedicine, with a particular focus on the currently emerging "foundation models." You can read it here.
Installation
To use the package, install it from PyPI, for instance using pip (pip install
biochatter
) or Poetry (poetry add biochatter
).
Extras
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 -
ollama
: support for querying open-source LLMs through Ollama -
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 BioChatter Light)
Documentation and Tutorials
For a description of the features of the framework, see the Features
option in
the main menu. For examples of usage and customisation, check out the
Vignettes
section. For a more detailed reference of functions and classes, see
the API Reference
.