OpenAI, an AI research organization focused on creating friendly AI for humanity, offers the OpenAI API to access its powerful AI models for tasks like natural language processing and image generation.
The ballarinax/openai.finetunes
package offers APIs to connect and interact with the fine-tuning related endpoints of OpenAI REST API v1 allowing users to customize OpenAI's AI models to meet specific needs.
To use the OpenAI Connector, you must have access to the OpenAI API through a OpenAI Platform account and a project under it. If you do not have a OpenAI Platform account, you can sign up for one here.
-
Open the OpenAI Platform Dashboard.
-
Navigate to Dashboard -> API keys
- Click on the "Create new secret key" button
- Fill the details and click on Create secret key
- Store the API key securely to use in your application
To use the OpenAI Finetunes
connector in your Ballerina application, update the .bal
file as follows:
Import the openai.finetunes
module.
import ballerinax/openai.finetunes;
import ballerina/io;
Create a finetunes:ConnectionConfig
with the obtained API Key and initialize the connector.
configurable string token = ?;
final finetunes:Client openAIFinetunes = check new({
auth: {
token
}
});
Now, utilize the available connector operations.
Note: First, create a sample.jsonl file in the same directory. This file should contain the training data formatted according to the guidelines provided here.
public function main() returns error? {
finetunes:CreateFileRequest req = {
file: {fileContent: check io:fileReadBytes("sample.jsonl"), fileName: "sample.jsonl"},
purpose: "fine-tune"
};
finetunes:OpenAIFile fileRes = check openAIFinetunes->/files.post(req);
string fileId = fileRes.id;
finetunes:CreateFineTuningJobRequest fineTuneRequest = {
model: "gpt-3.5-turbo",
training_file: fileId
};
finetunes:FineTuningJob fineTuneResponse =
check openAIFinetunes->/fine_tuning/jobs.post(fineTuneRequest);
}
bal run
The OpenAI Finetunes
connector provides practical examples illustrating usage in various scenarios. Explore these examples, covering the following use cases:
-
Sarcastic bot - Fine-tune the GPT-3.5-turbo model to generate sarcastic responses
-
Sports headline analyzer - Fine-tune the GPT-4o-mini model to extract structured information (player, team, sport, and gender) from sports headlines.
-
Download and install Java SE Development Kit (JDK) version 17. You can download it from either of the following sources:
Note: After installation, remember to set the
JAVA_HOME
environment variable to the directory where JDK was installed. -
Download and install Ballerina Swan Lake.
-
Download and install Docker.
Note: Ensure that the Docker daemon is running before executing any tests.
-
Export Github Personal access token with read package permissions as follows,
export packageUser=<Username> export packagePAT=<Personal access token>
Execute the commands below to build from the source.
-
To build the package:
./gradlew clean build
-
To run the tests:
./gradlew clean test
-
To build the without the tests:
./gradlew clean build -x test
-
To run tests against different environments:
./gradlew clean test -Pgroups=<Comma separated groups/test cases>
-
To debug the package with a remote debugger:
./gradlew clean build -Pdebug=<port>
-
To debug with the Ballerina language:
./gradlew clean build -PbalJavaDebug=<port>
-
Publish the generated artifacts to the local Ballerina Central repository:
./gradlew clean build -PpublishToLocalCentral=true
-
Publish the generated artifacts to the Ballerina Central repository:
./gradlew clean build -PpublishToCentral=true
As an open-source project, Ballerina welcomes contributions from the community.
For more information, go to the contribution guidelines.
All the contributors are encouraged to read the Ballerina Code of Conduct.
- For more information go to the
openai.finetunes
package. - For example demonstrations of the usage, go to Ballerina By Examples.
- Chat live with us via our Discord server.
- Post all technical questions on Stack Overflow with the #ballerina tag.