| License |  |
Python |  |
| Package |    |
Build status |   |
| Tests | Coverage coming soon. | Docker |   |
| Development |   |
Contributions |   |
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. This repository contains the
biochatter Python package, a generic backend library for the connection of
biomedical applications to conversational AI.
The library is described in this preprint and used in various demo applications for showcasing its use:
-
a simple Python-based frontend called BioChatter Light, which we develop at https://github.com/biocypher/biochatter-light;
-
an advanced Next.js-based frontend called BioChatter Next, which we develop at https://github.com/biocypher/biochatter-next;
-
a RESTful API server for use by the Next frontend (and any other REST-based application) at https://github.com/biocypher/biochatter-server.
BioChatter is part of the BioCypher ecosystem, connecting natively to BioCypher knowledge graphs. The BioChatter paper is being written here.
To use the package, install it from PyPI, for instance using pip (pip install 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)
Check out the documentation for
examples, use cases, and more information. Many common functionalities covered
by BioChatter can be seen in use in the
ChatGSE code base.
Check out this repository for more info on computational biology usage of large language models.
If you're on Apple Silicon, you may encounter issues with the grpcio
dependency (grpc library, which is used in pymilvus). If so, try to install
the binary from source after removing the installed package from the virtual
environment from
here:
pip uninstall grpcio
export GRPC_PYTHON_LDFLAGS=" -framework CoreFoundation"
pip install grpcio==1.53.0 --no-binary :all: