nf-gpt plugin

nf-gpt is an experimental plugin to integrate GPT prompts into Nextflow scripts. It allows submitting prompts via OpenAI API and collect the response in form of structured data for downstream analysis.

Get started

1. Configure the OpenAI API key in your environment by using the following variable:

export OPENAI_API_KEY=<your api key>

2. Add the following snippet at the beginning of your script:

include { gptPromptForText } from 'plugin/nf-gpt'

3. Use the gptPromptForText operator to perform a ChatGPT prompt and get the response.

include { gptPromptForText } from 'plugin/nf-gpt'

println gptPromptForText('Tell me a joke')

4. run using Nextflow as usual

nextflow run <my script>

5. See the folder [examples] for more examples.

Reference

Function gptPromptForText

The gptPromptForText function carries out a Gpt chat prompt and return the corresponding message as response as a string. Example:

println gptPromptForText('Tell me a joke')

When the option numOfChoices is specified the response is a list of strings.

def response =  gptPromptForText('Tell me a joke', numOfChoices: 3)
for( String it : response )
  println it

Available options:

name	description
logitBias	Accepts an object mapping each token (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100
model	The AI model to be used (default: gpt-3.5-turbo)
maxTokens	The maximum number of tokens that can be generated in the chat completion
numOfChoices	How many chat completion choices to generate for each input message (default: 1)
temperature	What sampling temperature to use, between 0 and 2 (default: 0.7)

Function gptPromptForData

The gptPromptForData function carries out a GPT chat prompt and returns the response as a list of objects having the schema specified. For example:

def query = '''
        Extract information about a person from In 1968, amidst the fading echoes of Independence Day, 
        a child named John arrived under the calm evening sky. This newborn, bearing the surname Doe, 
        marked the start of a new journey.
        '''

def response = gptPromptForData(query, schema: [firstName: 'string', lastName: 'string', birthDate: 'date (YYYY-MM-DD)'])

println "First name: ${response[0].firstName}"
println "Last name: ${response[0].lastName}"
println "Birth date: ${response[0].birthDate}"

The following options are available:

name	description
model	The AI model to be used (default: gpt-3.5-turbo)
maxTokens	The maximum number of tokens that can be generated in the chat completion
schema	The expected strcuture for the result object represented as map object in which represent the attribute name and the value the attribute type
temperature	What sampling temperature to use, between 0 and 2 (default: 0.7)

Configuration file

The following config options can be specified in the nextflow.config file:

name	description
gpt.apiKey	Your OpenAI API key. If missing it uses the OPENAI_API_KEY env variable
gpt.endpoint	The OpenAI endpoint (defualt: https://api.openai.com)
gpt.model	The AI model to be used (default: gpt-3.5-turbo)
gpt.maxTokens	The maximum number of tokens that can be generated in the chat completion
gpt.temperature	What sampling temperature to use, between 0 and 2 (default: 0.7)

Development

To build and test the plugin during development, configure a local Nextflow build with the following steps:

1. Clone the Nextflow repository in your computer into a sibling directory:

   git clone --depth 1 https://github.com/nextflow-io/nextflow ../nextflow

2. Configure the plugin build to use the local Nextflow code:

   echo "includeBuild('../nextflow')" >> settings.gradle

   (Make sure to not add it more than once!)

3. Compile the plugin alongside the Nextflow code:

   make assemble

4. Run Nextflow with the plugin, using ./launch.sh as a drop-in replacement for the nextflow command, and adding the option -plugins nf-gpt to load the plugin:

   ./launch.sh run nextflow-io/hello -plugins nf-gpt

Testing without Nextflow build

The plugin can be tested without using a local Nextflow build using the following steps:

1. Build the plugin: make buildPlugins
2. Copy build/plugins/<your-plugin> to $HOME/.nextflow/plugins
3. Create a pipeline that uses your plugin and run it: nextflow run ./my-pipeline-script.nf

Package, upload, and publish

The project should be hosted in a GitHub repository whose name matches the name of the plugin, that is the name of the directory in the plugins folder (e.g. nf-gpt).

Follow these steps to package, upload and publish the plugin:

1. Create a file named gradle.properties in the project root containing the following attributes (this file should not be committed to Git):

   • github_organization: the GitHub organisation where the plugin repository is hosted.
   • github_username: The GitHub username granting access to the plugin repository.
   • github_access_token: The GitHub access token required to upload and commit changes to the plugin repository.
   • github_commit_email: The email address associated with your GitHub account.

2. Use the following command to package and create a release for your plugin on GitHub:

   ./gradlew :plugins:nf-gpt:upload

3. Create a pull request against nextflow-io/plugins to make the plugin accessible to Nextflow.