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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 paper; for a more hands-on experience, check out our two web app implementations:

•   BioChatter Light

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  Agile framework in pure Python built with Streamlit, for fast prototyping and iteration.

  Go To BioChatter Light

•   BioChatter Next

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  Advanced client-server architecture based on FastAPI and Next.js.

  Go To BioChatter Next

BioChatter is part of the BioCypher ecosystem, connecting natively to BioCypher knowledge graphs.

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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.