open_ai_simplified

Description:

open ai simplified

OpenAi Simplified
OpenAI Simplified is an application that aims to provide easy and intuitive access to OpenAI services. With it, developers can integrate some of OpenAI's artificial intelligence technologies into their own applications in a simple and efficient manner. #
Status: Developing 👷🏿
⚠️ Some of the tools provided by OpenAI are still under development and will be implemented over time
Installation #
To instal OpenAi Simplified, you can use either of the following methods:
Method 1: flutter pub add #
flutter pub add open_ai_simplified
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Method 2: pubspec.yaml #
Alternatively, you can add the following line to your pubspec.yaml file:
dependencies:
open_ai_simplified: ^VERSION
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Replace VERSION with the latest version of OpenAI Simplified
Import the library into your code by adding the following line at the top of your file:
import 'package:open_ai_simplified/open_ai_simplified.dart';
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Features #
Below are some of the features available.



Feature
Availability




Models
✅


Completions
✅


Edits
✅


Images
✅


Embeddings
✅


Files
✅


Fine-tunes
✅


Moderations
✅



Getting started #
OpenAI Simplified aims to make it easier to use its capabilities, so almost no configuration is required to use it. Just initialize its repository and provide it with your OpenAI API secret key, and all the features already use the suggested configuration in the API as the default. It is also possible to change the settings if necessary.
To configure the parameters of the desired functionality, methods are available, one where it is possible to use one of the objects from the package itself and another passing a Map with the data structure provided by the OpenAI API.
Usage #
void main() async {
// Load env
await dotenv.load(fileName: ".env");
// Create a new repository.
OpenIARepository openAi = OpenIARepository();
// Add the api key.
openAi.addApiKey(dotenv.env['API_KEY']!);
// Retrive the models avaibles.
final models = await openAi.getRawModelsList();
// Print the models
log(models.toString());
// Configure the completion params, this can also be done using the method configCompletionFromMap
openAi.configCompletionFromConfig(ConfigCompletion(temperature: 0.6));
// Create a completion
final completion = await openAi.getCompletion('what times it?');
// Print the completion
log(completion.choices[0].text);
// Configure the edits params, this can also be done using the method configEditsFromMap
openAi.configEditsFromConfig(config: ConfigEdits(temperature: 0.8));
// Print the edits
final edit = await openAi.getEdits(
input: 'helo piple', instruction: 'fix the spelling mistakes');
// Print the edits
log(edit.choices[0].text);
// Configure the images params, this can also be done using the method configImagesFromMap
// The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024
openAi.configImagesFromConfig(ConfigImages(n: 1, size: '256x256'));
// Create the images
final images = await openAi.getImages('horse with golden hair and dragons');
// Print the url with the images
images.data.forEach((element) {
log(element.url);
});
// Create a variation of an image, the image should be a png file with less then 4MB and a square
final imageVariation = await openAi.createAImageVariation(
imageFile: await downloadFile(images.data[0].url),
);
log(imageVariation.data[0].url);
// Edits an image, you also can pass another image as mask
final editImage = await openAi.editImage(
image: await downloadFile(images.data[0].url),
prompt: 'give a new hair style to the horse');
// Print the url with the image
log(editImage.data[0].url);

/// create an embedding
final embedding = await openAi.createEmbedding(prompt: 'sabado a noite');

/// print the embeddings
log(embedding.data[0].embedding.toString());

/// The file need to be an JsonL, another types of file will not work
/// Retrives the list of stored files as ListFileResponse object. you can have
/// the data as Map if use the method getRawFilesList
final list = await openAi.getFilesList();

/// print the list
log(list.data.toString());

/// Upload a file that contains document(s) to be used across various endpoints/features.
/// Currently, the size of all the files uploaded by one organization can be up to 1 GB.
final uploadedFile = await openAi.uploadFile(
file: File('director/xxx.jsonl'), purpose: 'fine-tune');

/// print the uploadedFile
log(uploadedFile.toString());

/// Deletes a file and return informations about de file deleted
final deletedFile = await openAi.deleteFile(fileId: 'fileId');

/// print the information about the file deleted
log(deletedFile.toString());

/// Get the info about a file as a FileData object
/// you also can get the info as Map with the method retriveRawFileInfo
final fileInfo = await openAi.retriveFileInfo(fileId: 'fileId');

/// print the file info
log(fileInfo.toString());

/// Get the content of a specific file.
final fileContent = await openAi.retriveFileContent(fileId: 'fileId');

/// print the content
log(fileContent.toString());

/// Classifies if text violates OpenAI's Content Policy, returns ModerationResponse object
final moderationInfo =
await openAi.moderationCheck(input: 'I want to kill them.');

/// print moderation info
log(moderationInfo.results[0].categories.toString());

/// Creates a job that fine-tunes a specified model from a given dataset. return a FineTunesResponse object
/// The ID of an uploaded file that contains training data. you also can get the Map with the method createRawFineTunes
final fineTunesCreated =
await openAi.createFineTunes(trainingFile: 'ID of an uploaded file');

/// print fineTune created info
log(fineTunesCreated.toString());

/// List your organization's fine-tuning jobs, return a ListFineTunesResponse object
/// you also can get the Map with the method getRawListFineTunes
final listFineTunes = await openAi.getListFineTunes();

/// print fineTune list info
log(listFineTunes.data[0].toString());

/// Gets info about the fine-tune job. return a FineTunesResponse object
/// you also can get the Map with the method retriveRawFineTune
final retrivedFileTune =
await openAi.retriveFineTune(fineTuneId: 'ID of an uploaded file');

/// print fineTune info
log(retrivedFileTune.toString());

/// Immediately cancel a fine-tune job. return a FineTunesResponse object
/// you also can get the Map with the method cancelRawFineTune
final canceledFineTune =
await openAi.cancelFineTune(fineTuneId: 'fineTuneId');

/// print fineTune canceled info
log(canceledFineTune.toString());

/// Delete a fine-tuned model. You must have the Owner role in your organization. Return a Map
final deletedFineTune = await openAi.deleteFineTunelModel(model: 'model');

/// print fineTune deleted info
log(deletedFineTune.toString());
}

Future<File> downloadFile(String url) async {
Dio simple = Dio();
String savePath = '${Directory.systemTemp.path}/${url.split('/').last}';
await simple.download(url, savePath,
options: Options(responseType: ResponseType.bytes));
File file = File(savePath);
return file;
}


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Contributing #
We welcome contributions to the OpenAi Simplified project! If you would like to contribute, please feel free to reach out to the current maintainers for more information.
Issues #
If you encounter any issues while using Focus on It, please open a new issue in the issue tracker. Please include as much information as possible, such as the version of Focus on It you are using and steps to reproduce the issue.
Lincense #
OpenAi Simplified is licensed under the MIT license. See LICENSE.