| Name | ontogpt JSON |
| Version |
0.3.11
JSON |
| download |
| home_page | None |
| Summary | OntoGPT |
| upload_time | 2024-04-14 19:40:56 |
| maintainer | None |
| docs_url | None |
| author | Chris Mungall |
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| license | BSD-3 |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# OntoGPT
[](https://zenodo.org/badge/latestdoi/13996/monarch-initiative/ontogpt)
## Introduction
_OntoGPT_ is a Python package for extracting structured information from text with large language models (LLMs), _instruction prompts_, and ontology-based grounding.
[For more details, please see the full documentation.](https://monarch-initiative.github.io/ontogpt/)
## Quick Start
OntoGPT runs on the command line, though there's also a minimal web app interface (see `Web Application` section below).
1. Ensure you have Python 3.9 or greater installed.
2. Install with `pip`:
```bash
pip install ontogpt
```
3. Set your OpenAI API key:
```bash
runoak set-apikey -e openai <your openai api key>
```
4. See the list of all OntoGPT commands:
```bash
ontogpt --help
```
5. Try a simple example of information extraction:
```bash
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`.
## Web Application
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:
```bash
pip install ontogpt[web]
```
Then run this command to start the web application:
```bash
web-ontogpt
```
NOTE: We do not recommend hosting this webapp publicly without authentication.
## Evaluations
OntoGPT's functions have been evaluated on test data. Please see the full documentation for details on these evaluations and how to reproduce them.
## Related Projects
* [TALISMAN](https://github.com/monarch-initiative/talisman/), a tool for generating summaries of functions enriched within a gene set. TALISMAN uses OntoGPT to work with LLMs.
## Tutorials and Presentations
- Presentation: "Staying grounded: assembling structured biological knowledge with help from large language models" - presented by Harry Caufield as part of the AgBioData Consortium webinar series (September 2023)
- [Slides](https://docs.google.com/presentation/d/1rMQVWaMju-ucYFif5nx4Xv3bNX2SVI_w89iBIT1bkV4/edit?usp=sharing)
- [Video](https://www.youtube.com/watch?v=z38lI6WyBsY)
- Presentation: "Transforming unstructured biomedical texts with large language models" - presented by Harry Caufield as part of the BOSC track at ISMB/ECCB 2023 (July 2023)
- [Slides](https://docs.google.com/presentation/d/1LsOTKi-rXYczL9vUTHB1NDkaEqdA9u3ZFC5ANa0x1VU/edit?usp=sharing)
- [Video](https://www.youtube.com/watch?v=a34Yjz5xPp4)
- Presentation: "OntoGPT: A framework for working with ontologies and large language models" - talk by Chris Mungall at Joint Food Ontology Workgroup (May 2023)
- [Slides](https://docs.google.com/presentation/d/1CosJJe8SqwyALyx85GWkw9eOT43B4HwDlAY2CmkmJgU/edit)
- [Video](https://www.youtube.com/watch?v=rt3wobA9hEs&t=1955s)
## Citation
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. _Bioinformatics_, Volume 40, Issue 3, March 2024, btae104, [https://doi.org/10.1093/bioinformatics/btae104](https://doi.org/10.1093/bioinformatics/btae104).
## Acknowledgements
This project is part of the [Monarch Initiative](https://monarchinitiative.org/). We also gratefully acknowledge [Bosch Research](https://www.bosch.com/research) for their support of this research project.
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"description": "# OntoGPT\n\n[](https://zenodo.org/badge/latestdoi/13996/monarch-initiative/ontogpt)\n\n\n## Introduction\n\n_OntoGPT_ is a Python package for extracting structured information from text with large language models (LLMs), _instruction prompts_, and ontology-based grounding.\n\n[For more details, please see the full documentation.](https://monarch-initiative.github.io/ontogpt/)\n\n## Quick Start\n\nOntoGPT runs on the command line, though there's also a minimal web app interface (see `Web Application` section below).\n\n1. Ensure you have Python 3.9 or greater installed.\n2. Install with `pip`:\n\n ```bash\n pip install ontogpt\n ```\n\n3. Set your OpenAI API key:\n\n ```bash\n runoak set-apikey -e openai <your openai api key>\n ```\n\n4. See the list of all OntoGPT commands:\n\n ```bash\n ontogpt --help\n ```\n\n5. Try a simple example of information extraction:\n\n ```bash\n echo \"One treatment for high blood pressure is carvedilol.\" > example.txt\n ontogpt extract -i example.txt -t drug\n ```\n\n 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`.\n\n## Web Application\n\nThere is a bare bones web application for running OntoGPT and viewing results.\n\nFirst, install the required dependencies with `pip` by running the following command:\n\n```bash\npip install ontogpt[web]\n```\n\nThen run this command to start the web application:\n\n```bash\nweb-ontogpt\n```\n\nNOTE: We do not recommend hosting this webapp publicly without authentication.\n\n## Evaluations\n\nOntoGPT's functions have been evaluated on test data. Please see the full documentation for details on these evaluations and how to reproduce them.\n\n## Related Projects\n\n* [TALISMAN](https://github.com/monarch-initiative/talisman/), a tool for generating summaries of functions enriched within a gene set. TALISMAN uses OntoGPT to work with LLMs.\n\n## Tutorials and Presentations\n\n- Presentation: \"Staying grounded: assembling structured biological knowledge with help from large language models\" - presented by Harry Caufield as part of the AgBioData Consortium webinar series (September 2023)\n - [Slides](https://docs.google.com/presentation/d/1rMQVWaMju-ucYFif5nx4Xv3bNX2SVI_w89iBIT1bkV4/edit?usp=sharing)\n - [Video](https://www.youtube.com/watch?v=z38lI6WyBsY)\n- Presentation: \"Transforming unstructured biomedical texts with large language models\" - presented by Harry Caufield as part of the BOSC track at ISMB/ECCB 2023 (July 2023)\n - [Slides](https://docs.google.com/presentation/d/1LsOTKi-rXYczL9vUTHB1NDkaEqdA9u3ZFC5ANa0x1VU/edit?usp=sharing)\n - [Video](https://www.youtube.com/watch?v=a34Yjz5xPp4)\n- Presentation: \"OntoGPT: A framework for working with ontologies and large language models\" - talk by Chris Mungall at Joint Food Ontology Workgroup (May 2023)\n - [Slides](https://docs.google.com/presentation/d/1CosJJe8SqwyALyx85GWkw9eOT43B4HwDlAY2CmkmJgU/edit)\n - [Video](https://www.youtube.com/watch?v=rt3wobA9hEs&t=1955s)\n\n## Citation\n\nThe 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. _Bioinformatics_, Volume 40, Issue 3, March 2024, btae104, [https://doi.org/10.1093/bioinformatics/btae104](https://doi.org/10.1093/bioinformatics/btae104).\n\n## Acknowledgements\n\nThis project is part of the [Monarch Initiative](https://monarchinitiative.org/). We also gratefully acknowledge [Bosch Research](https://www.bosch.com/research) for their support of this research project.\n\n",
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