OntoGPT is a Python package for extracting structured information from text with large language models (LLMs), instruction prompts, and ontology-based grounding.
Two different strategies for knowledge extraction are currently implemented in OntoGPT:
For more details, please see the full documentation.
OntoGPT runs on the command line, though there's also a minimal web app interface (see Web Application section below).
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Ensure you have Python 3.9 or greater installed.
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Install with
pip:pip install ontogpt
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Set your OpenAI API key:
runoak set-apikey -e openai <your openai api key>
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See the list of all OntoGPT commands:
ontogpt --help
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Try a simple example of information extraction:
echo "One treatment for high blood pressure is carvedilol." > example.txt ontogpt extract -i example.txt -t drug
OntoGPT will retrieve the necessary ontologies and output results to the command line. Your output will provide all extracted objects under the heading
extracted_object.
There is a bare bones web application for running OntoGPT and viewing results.
First, install the required dependencies with pip by running the following command:
pip install ontogpt[web]Then run this command to start the web application:
web-ontogptNOTE: We do not recommend hosting this webapp publicly without authentication.
OpenAI's functions have been evaluated on test data. Please see the full documentation for details on these evaluations and how to reproduce them.
The information extraction approach used in OntoGPT, SPIRES, is described further in: Caufield JH, Hegde H, Emonet V, Harris NL, Joachimiak MP, Matentzoglu N, et al. Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning. arXiv publication: http://arxiv.org/abs/2304.02711
The gene summarization approach used in OntoGPT, SPINDOCTOR, is described further in: Joachimiak MP, Caufield JH, Harris NL, Kim H, Mungall CJ. Gene Set Summarization using Large Language Models. arXiv publication: http://arxiv.org/abs/2305.13338
This project is part of the Monarch Initiative. We also gratefully acknowledge Bosch Research for their support of this research project.