Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/monarch-initiative/ontogpt
LLM-based ontological extraction tools, including SPIRES
https://github.com/monarch-initiative/ontogpt
ai chat-gpt data-modeling gpt-3 information-extraction language-models large-language-models linkml llm monarchinitiative named-entity-recognition ner nlp oaklib obofoundry relation-extraction
Last synced: about 1 month ago
JSON representation
LLM-based ontological extraction tools, including SPIRES
- Host: GitHub
- URL: https://github.com/monarch-initiative/ontogpt
- Owner: monarch-initiative
- License: bsd-3-clause
- Created: 2023-01-03T16:53:25.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-07T17:38:19.000Z (6 months ago)
- Last Synced: 2024-06-11T16:36:02.628Z (6 months ago)
- Topics: ai, chat-gpt, data-modeling, gpt-3, information-extraction, language-models, large-language-models, linkml, llm, monarchinitiative, named-entity-recognition, ner, nlp, oaklib, obofoundry, relation-extraction
- Language: Jupyter Notebook
- Homepage: https://monarch-initiative.github.io/ontogpt/
- Size: 64.9 MB
- Stars: 539
- Watchers: 18
- Forks: 67
- Open Issues: 84
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-ccamel - monarch-initiative/ontogpt - LLM-based ontological extraction tools, including SPIRES (Jupyter Notebook)
- awesome-ChatGPT-repositories - ontogpt - GPT-based ontological extraction tools, including SPIRES (NLP)
README
# 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
```
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.
## Model APIs
OntoGPT uses the `litellm` package () to interface with LLMs.
This means most APIs are supported, including OpenAI, Azure, Anthropic, Mistral, Replicate, and beyond.
The model name to use may be found from the command `ontogpt list-models` - use the name in the first column with the `--model` option.
In most cases, this will require setting the API key for a particular service as above:
```bash
runoak set-apikey -e anthropic-key
```
Some endpoints, such as OpenAI models through Azure, require setting additional details. These may be set similarly:
```bash
runoak set-apikey -e azure-key
runoak set-apikey -e azure-base
runoak set-apikey -e azure-version
```
These details may also be set as environment variables as follows:
```bash
export AZURE_API_KEY="my-azure-api-key"
export AZURE_API_BASE="https://example-endpoint.openai.azure.com"
export AZURE_API_VERSION="2023-05-15"
```
## Open Models
Open LLMs may be retrieved and run through the `ollama` package ().
You will need to install `ollama` (see the [GitHub repo](https://github.com/ollama/ollama)), and you may need to start it as a service with a command like `ollama serve` or `sudo systemctl start ollama`.
Then retrieve a model with `ollama pull `, e.g., `ollama pull llama3`.
The model may then be used in OntoGPT by prefixing its name with `ollama/`, e.g., `ollama/llama3`, along with the `--model` option.
Some ollama models may not be listed in `ontogpt list-models` but the full list of downloaded LLMs can be seen with `ollama list` command.
## 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.