llm-testbed

Python 3 interface used to extract data from PubMed publications using LLMs, part of the PubLLican project.

See the Experimental branch for latest updates, although this may not run without configureation changes.

Contents

Setup
Configuration
Running the workflow

Setup

1. Create create and activate a virtual environment if your IDE does not do so automatically

2. Install package dependencies by running pip install requirements.txt

3. Create .env file by running cp .env.example .env
   Be careful as this will overwrite your current .env file in case you already have one setup

4. Add any API keys or other environment variables to .env file

5. Create a config file by running cp config.json.example config.json
   Be carefulas this will overwrite your current config.json file in case you already have one setup

6. Run setup script by running python setup.py

Configuration

Most things are able to be configured in config.json if desired. The fields are pretty self-explanatory.

Changing LLM

In the config file, there is a field called "llm", which looks something like this:

{
  "llm": {
    "current": {
      "type": "anthropic",
      "model": "claude-3-haiku-20240307"
    }
  },
  "rest of config.json file..."
}

• The type parameter tells the llms package what model type it is and what code to run for it to work with that model. Here are the currently supported types:

  Type	Description	Requirements
  anthropic	Anthropic's language-based models e.g. Claude	$ANTHROPIC_API_KEY environment variable must be set
  openai	OpenAI's language-based models e.g. ChatGPT	$OPENAI_API_KEY environment variable must be set

• The model parameter tells the API what specific model to use (if applicable). See documentation for more details.

PRs adding support for more LLMs are welcome

Running the workflow

(Pipeline for the whole workflow is coming soon. For now, the steps can be run manually.)

To run the workflow manually:

1. Download the paper. There are two options:

   • To get the paper JSON (preferred), run python getPaperJSON.py <pmid>
   • To get the paper PDF, run: python getPaperPDF.py <pmid>

   Note that not every publication will have a downloadable PDF, in which case getPaperJSON can be used instead

2. Convert the paper into plaintext

   • If getPaperJSON was used, run python getTextFromJSON.py <pmid>

   • If getPaperPDF was used, run python getTextFromPDF.py <pmid>

3. Query the LLM for the paper's species by running python getPaperSpecies.py <pmid>

4. Query the LLM for the paper's genes by running python getPaperGeness.py <pmid>

5. Query the LLM for the paper's GO terms by running python getPaperGOTerms.py <pmid>

6. Validate the GO terms by running python validateGOTermDescriptions.py <pmid>