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LLM-based ontological extraction tools, including SPIRES

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# OntoGPT

![OntoGPT Logo](https://raw.githubusercontent.com/monarch-initiative/ontogpt/main/images/ontogpt_logo_3.jpg)

[![DOI](https://zenodo.org/badge/13996/monarch-initiative/ontogpt.svg)](https://zenodo.org/badge/latestdoi/13996/monarch-initiative/ontogpt)
![PyPI](https://img.shields.io/pypi/v/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 3.10 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.

## Model APIs

OntoGPT uses [LiteLLM](https://docs.litellm.ai/docs/) to interface with LLMs.

This means OntoGPT can work with a much broader range of providers than just OpenAI. If a provider and model are supported by the installed LiteLLM version, they will generally work in OntoGPT as well. This includes OpenAI, Azure OpenAI, Anthropic, Mistral, Groq, Cohere, Vertex AI, Replicate, and many others.

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, the most reliable form is a provider-qualified LiteLLM model name such as `openai/gpt-4o`, `anthropic/claude-3-5-sonnet`, `groq/llama-3.1-8b-instant`, or `mistral/mistral-large-latest`.

Credential handling now follows LiteLLM first. Standard LiteLLM environment variables such as `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `GROQ_API_KEY`, `MISTRAL_API_KEY`, `AZURE_API_KEY`, `AZURE_API_BASE`, and `AZURE_API_VERSION` are supported directly. For backward compatibility, OntoGPT also checks Oaklib credentials created with `runoak set-apikey` and passes them through to LiteLLM when the corresponding provider settings are missing.

Examples:

```bash
runoak set-apikey -e openai <your openai api key>
runoak set-apikey -e anthropic-key <your anthropic api key>
runoak set-apikey -e mistral-key <your mistral api key>
runoak set-apikey -e groq-key <your groq api key>
```

Some endpoints, such as Azure OpenAI, require additional details. These may be set similarly:

```bash
runoak set-apikey -e azure-key <your azure api key>
runoak set-apikey -e azure-base <your azure endpoint url>
runoak set-apikey -e azure-version <your azure api version, e.g. "2023-05-15">
```

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"
```

If the provider is not encoded in the model name, use `--model-provider` to specify it explicitly. This is most common for OpenAI-compatible proxy endpoints.

For the current list of supported providers, model naming rules, and credential environment variables, see the LiteLLM docs:

* <https://docs.litellm.ai/docs/providers>
* <https://docs.litellm.ai/docs/set_keys>

## Open Models

Open LLMs may be retrieved and run through the `ollama` package (<https://ollama.com/>).

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 <modelname>`, 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.

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