OpenAI PHP is a community-maintained PHP API client that allows you to interact with the Open AI API. If you or your business relies on this package, it's important to support the developers who have contributed their time and effort to create and maintain this valuable tool:
- Nuno Maduro: github.com/sponsors/nunomaduro
- Sandro Gehri: github.com/sponsors/gehrisandro
Requires PHP 8.1+
First, install OpenAI via the Composer package manager:
composer require openai-php/clientEnsure that the php-http/discovery composer plugin is allowed to run or install a client manually if your project does not already have a PSR-18 client integrated.
composer require guzzlehttp/guzzleThen, interact with OpenAI's API:
$yourApiKey = getenv('YOUR_API_KEY');
$client = OpenAI::client($yourApiKey);
$result = $client->completions()->create([
'model' => 'text-davinci-003',
'prompt' => 'PHP is',
]);
echo $result['choices'][0]['text']; // an open-source, widely-used, server-side scripting language.If necessary, it is possible to configure and create a separate client.
$yourApiKey = getenv('YOUR_API_KEY');
$client = OpenAI::factory()
->withApiKey($yourApiKey)
->withOrganization('your-organization') // default: null
->withBaseUri('openai.example.com/v1') // default: api.openai.com/v1
->withHttpClient($client = new \GuzzleHttp\Client([])) // default: HTTP client found using PSR-18 HTTP Client Discovery
->withHttpHeader('X-My-Header', 'foo')
->withQueryParam('my-param', 'bar')
->withStreamHandler(fn (RequestInterface $request): ResponseInterface => $client->send($request, [
'stream' => true // Allows to provide a custom stream handler for the http client.
]))
->make();Lists the currently available models, and provides basic information about each one such as the owner and availability.
$response = $client->models()->list();
$response->object; // 'list'
foreach ($response->data as $result) {
$result->id; // 'text-davinci-003'
$result->object; // 'model'
// ...
}
$response->toArray(); // ['object' => 'list', 'data' => [...]]Retrieves a model instance, providing basic information about the model such as the owner and permissioning.
$response = $client->models()->retrieve('text-davinci-003');
$response->id; // 'text-davinci-003'
$response->object; // 'model'
$response->created; // 1642018370
$response->ownedBy; // 'openai'
$response->root; // 'text-davinci-003'
$response->parent; // null
foreach ($response->permission as $result) {
$result->id; // 'modelperm-7E53j9OtnMZggjqlwMxW4QG7'
$result->object; // 'model_permission'
$result->created; // 1664307523
$result->allowCreateEngine; // false
$result->allowSampling; // true
$result->allowLogprobs; // true
$result->allowSearchIndices; // false
$result->allowView; // true
$result->allowFineTuning; // false
$result->organization; // '*'
$result->group; // null
$result->isBlocking; // false
}
$response->toArray(); // ['id' => 'text-davinci-003', ...]Delete a fine-tuned model.
$response = $client->models()->delete('curie:ft-acmeco-2021-03-03-21-44-20');
$response->id; // 'curie:ft-acmeco-2021-03-03-21-44-20'
$response->object; // 'model'
$response->deleted; // true
$response->toArray(); // ['id' => 'curie:ft-acmeco-2021-03-03-21-44-20', ...]Creates a completion for the provided prompt and parameters.
$response = $client->completions()->create([
'model' => 'text-davinci-003',
'prompt' => 'Say this is a test',
'max_tokens' => 6,
'temperature' => 0
]);
$response->id; // 'cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7'
$response->object; // 'text_completion'
$response->created; // 1589478378
$response->model; // 'text-davinci-003'
foreach ($response->choices as $result) {
$result->text; // '\n\nThis is a test'
$result->index; // 0
$result->logprobs; // null
$result->finishReason; // 'length' or null
}
$response->usage->promptTokens; // 5,
$response->usage->completionTokens; // 6,
$response->usage->totalTokens; // 11
$response->toArray(); // ['id' => 'cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7', ...]Creates a streamed completion for the provided prompt and parameters.
$stream = $client->completions()->createStreamed([
'model' => 'text-davinci-003',
'prompt' => 'Hi',
'max_tokens' => 10,
]);
foreach($stream as $response){
$response->choices[0]->text;
}
// 1. iteration => 'I'
// 2. iteration => ' am'
// 3. iteration => ' very'
// 4. iteration => ' excited'
// ...Creates a completion for the chat message.
$response = $client->chat()->create([
'model' => 'gpt-3.5-turbo',
'messages' => [
['role' => 'user', 'content' => 'Hello!'],
],
]);
$response->id; // 'chatcmpl-6pMyfj1HF4QXnfvjtfzvufZSQq6Eq'
$response->object; // 'chat.completion'
$response->created; // 1677701073
$response->model; // 'gpt-3.5-turbo-0301'
foreach ($response->choices as $result) {
$result->index; // 0
$result->message->role; // 'assistant'
$result->message->content; // '\n\nHello there! How can I assist you today?'
$result->finishReason; // 'stop'
}
$response->usage->promptTokens; // 9,
$response->usage->completionTokens; // 12,
$response->usage->totalTokens; // 21
$response->toArray(); // ['id' => 'chatcmpl-6pMyfj1HF4QXnfvjtfzvufZSQq6Eq', ...]Creates a completion for the chat message with a function call.
$response = $client->chat()->create([
'model' => 'gpt-3.5-turbo-0613',
'messages' => [
['role' => 'user', 'content' => 'What\'s the weather like in Boston?'],
],
'functions' => [
[
'name' => 'get_current_weather',
'description' => 'Get the current weather in a given location',
'parameters' => [
'type' => 'object',
'properties' => [
'location' => [
'type' => 'string',
'description' => 'The city and state, e.g. San Francisco, CA',
],
'unit' => [
'type' => 'string',
'enum' => ['celsius', 'fahrenheit']
],
],
'required' => ['location'],
],
]
]
]);
$response->id; // 'chatcmpl-6pMyfj1HF4QXnfvjtfzvufZSQq6Eq'
$response->object; // 'chat.completion'
$response->created; // 1677701073
$response->model; // 'gpt-3.5-turbo-0613'
foreach ($response->choices as $result) {
$result->index; // 0
$result->message->role; // 'assistant'
$result->message->content; // null
$result->message->functionCall->name; // 'get_current_weather'
$result->message->functionCall->arguments; // "{\n \"location\": \"Boston, MA\"\n}"
$result->finishReason; // 'function_call'
}
$response->usage->promptTokens; // 82,
$response->usage->completionTokens; // 18,
$response->usage->totalTokens; // 100Creates a streamed completion for the chat message.
$stream = $client->chat()->createStreamed([
'model' => 'gpt-4',
'messages' => [
['role' => 'user', 'content' => 'Hello!'],
],
]);
foreach($stream as $response){
$response->choices[0]->toArray();
}
// 1. iteration => ['index' => 0, 'delta' => ['role' => 'assistant'], 'finish_reason' => null]
// 2. iteration => ['index' => 0, 'delta' => ['content' => 'Hello'], 'finish_reason' => null]
// 3. iteration => ['index' => 0, 'delta' => ['content' => '!'], 'finish_reason' => null]
// ...Transcribes audio into the input language.
$response = $client->audio()->transcribe([
'model' => 'whisper-1',
'file' => fopen('audio.mp3', 'r'),
'response_format' => 'verbose_json',
]);
$response->task; // 'transcribe'
$response->language; // 'english'
$response->duration; // 2.95
$response->text; // 'Hello, how are you?'
foreach ($response->segments as $segment) {
$segment->index; // 0
$segment->seek; // 0
$segment->start; // 0.0
$segment->end; // 4.0
$segment->text; // 'Hello, how are you?'
$segment->tokens; // [50364, 2425, 11, 577, 366, 291, 30, 50564]
$segment->temperature; // 0.0
$segment->avgLogprob; // -0.45045216878255206
$segment->compressionRatio; // 0.7037037037037037
$segment->noSpeechProb; // 0.1076972484588623
$segment->transient; // false
}
$response->toArray(); // ['task' => 'transcribe', ...]Translates audio into English.
$response = $client->audio()->translate([
'model' => 'whisper-1',
'file' => fopen('german.mp3', 'r'),
'response_format' => 'verbose_json',
]);
$response->task; // 'translate'
$response->language; // 'english'
$response->duration; // 2.95
$response->text; // 'Hello, how are you?'
foreach ($response->segments as $segment) {
$segment->index; // 0
$segment->seek; // 0
$segment->start; // 0.0
$segment->end; // 4.0
$segment->text; // 'Hello, how are you?'
$segment->tokens; // [50364, 2425, 11, 577, 366, 291, 30, 50564]
$segment->temperature; // 0.0
$segment->avgLogprob; // -0.45045216878255206
$segment->compressionRatio; // 0.7037037037037037
$segment->noSpeechProb; // 0.1076972484588623
$segment->transient; // false
}
$response->toArray(); // ['task' => 'translate', ...]Creates an embedding vector representing the input text.
$response = $client->embeddings()->create([
'model' => 'text-similarity-babbage-001',
'input' => 'The food was delicious and the waiter...',
]);
$response->object; // 'list'
foreach ($response->embeddings as $embedding) {
$embedding->object; // 'embedding'
$embedding->embedding; // [0.018990106880664825, -0.0073809814639389515, ...]
$embedding->index; // 0
}
$response->usage->promptTokens; // 8,
$response->usage->totalTokens; // 8
$response->toArray(); // ['data' => [...], ...]Returns a list of files that belong to the user's organization.
$response = $client->files()->list();
$response->object; // 'list'
foreach ($response->data as $result) {
$result->id; // 'file-XjGxS3KTG0uNmNOK362iJua3'
$result->object; // 'file'
// ...
}
$response->toArray(); // ['object' => 'list', 'data' => [...]]Delete a file.
$response = $client->files()->delete($file);
$response->id; // 'file-XjGxS3KTG0uNmNOK362iJua3'
$response->object; // 'file'
$response->deleted; // true
$response->toArray(); // ['id' => 'file-XjGxS3KTG0uNmNOK362iJua3', ...]Returns information about a specific file.
$response = $client->files()->retrieve('file-XjGxS3KTG0uNmNOK362iJua3');
$response->id; // 'file-XjGxS3KTG0uNmNOK362iJua3'
$response->object; // 'file'
$response->bytes; // 140
$response->createdAt; // 1613779657
$response->filename; // 'mydata.jsonl'
$response->purpose; // 'fine-tune'
$response->status; // 'succeeded'
$response->status_details; // null
$response->toArray(); // ['id' => 'file-XjGxS3KTG0uNmNOK362iJua3', ...]Upload a file that contains document(s) to be used across various endpoints/features.
$response = $client->files()->upload([
'purpose' => 'fine-tune',
'file' => fopen('my-file.jsonl', 'r'),
]);
$response->id; // 'file-XjGxS3KTG0uNmNOK362iJua3'
$response->object; // 'file'
$response->bytes; // 140
$response->createdAt; // 1613779657
$response->filename; // 'mydata.jsonl'
$response->purpose; // 'fine-tune'
$response->status; // 'succeeded'
$response->status_details; // null
$response->toArray(); // ['id' => 'file-XjGxS3KTG0uNmNOK362iJua3', ...]Returns the contents of the specified file.
$client->files()->download($file); // '{"prompt": "<prompt text>", ...'Creates a job that fine-tunes a specified model from a given dataset.
$response = $client->fineTuning()->createJob([
'training_file' => 'file-abc123',
'validation_file' => null,
'model' => 'gpt-3.5-turbo',
'hyperparameters' => [
'n_epochs' => 4,
],
'suffix' => null,
]);
$response->id; // 'ft-AF1WoRqd3aJAHsqc9NY7iL8F'
$response->object; // 'fine_tuning.job'
$response->model; // 'gpt-3.5-turbo-0613'
$response->fineTunedModel; // null
// ...
$response->toArray(); // ['id' => 'ft-AF1WoRqd3aJAHsqc9NY7iL8F', ...]List your organization's fine-tuning jobs.
$response = $client->fineTuning()->listJobs();
$response->object; // 'list'
foreach ($response->data as $result) {
$result->id; // 'ft-AF1WoRqd3aJAHsqc9NY7iL8F'
$result->object; // 'fine_tuning.job'
// ...
}
$response->toArray(); // ['object' => 'list', 'data' => [...]]You can pass additional parameters to the listJobs method to narrow down the results.
$response = $client->fineTuning()->listJobs([
'limit' => 3, // Number of jobs to retrieve (Default: 20)
'after' => 'ft-AF1WoRqd3aJAHsqc9NY7iL8F', // Identifier for the last job from the previous pagination request.
]);Get info about a fine-tuning job.
$response = $client->fineTuning()->retrieveJob('ft-AF1WoRqd3aJAHsqc9NY7iL8F');
$response->id; // 'ft-AF1WoRqd3aJAHsqc9NY7iL8F'
$response->object; // 'fine_tuning.job'
$response->model; // 'gpt-3.5-turbo-0613'
$response->createdAt; // 1614807352
$response->finishedAt; // 1692819450
$response->fineTunedModel; // 'ft:gpt-3.5-turbo-0613:jwe-dev::7qnxQ0sQ'
$response->organizationId; // 'org-jwe45798ASN82s'
$response->resultFiles[0]; // 'file-1bl05WrhsKDDEdg8XSP617QF'
$response->status; // 'succeeded'
$response->validationFile; // null
$response->trainingFile; // 'file-abc123'
$response->trainedTokens; // 5049
$response->hyperparameters->nEpochs; // 9
$response->toArray(); // ['id' => 'ft-AF1WoRqd3aJAHsqc9NY7iL8F', ...]Immediately cancel a fine-tune job.
$response = $client->fineTuning()->cancelJob('ft-AF1WoRqd3aJAHsqc9NY7iL8F');
$response->id; // 'ft-AF1WoRqd3aJAHsqc9NY7iL8F'
$response->object; // 'fine_tuning.job'
// ...
$response->status; // 'cancelled'
// ...
$response->toArray(); // ['id' => 'ft-AF1WoRqd3aJAHsqc9NY7iL8F', ...]Get status updates for a fine-tuning job.
$response = $client->fineTuning()->listJobEvents('ft-AF1WoRqd3aJAHsqc9NY7iL8F');
$response->object; // 'list'
foreach ($response->data as $result) {
$result->object; // 'fine_tuning.job.event'
$result->createdAt; // 1614807352
// ...
}
$response->toArray(); // ['object' => 'list', 'data' => [...]]You can pass additional parameters to the listJobEvents method to narrow down the results.
$response = $client->fineTuning()->listJobEvents('ft-AF1WoRqd3aJAHsqc9NY7iL8F', [
'limit' => 3, // Number of events to retrieve (Default: 20)
'after' => 'ftevent-kLPSMIcsqshEUEJVOVBVcHlP', // Identifier for the last event from the previous pagination request.
]);Creates a job that fine-tunes a specified model from a given dataset.
$response = $client->fineTunes()->create([
'training_file' => 'file-ajSREls59WBbvgSzJSVWxMCB',
'validation_file' => 'file-XjSREls59WBbvgSzJSVWxMCa',
'model' => 'curie',
'n_epochs' => 4,
'batch_size' => null,
'learning_rate_multiplier' => null,
'prompt_loss_weight' => 0.01,
'compute_classification_metrics' => false,
'classification_n_classes' => null,
'classification_positive_class' => null,
'classification_betas' => [],
'suffix' => null,
]);
$response->id; // 'ft-AF1WoRqd3aJAHsqc9NY7iL8F'
$response->object; // 'fine-tune'
// ...
$response->toArray(); // ['id' => 'ft-AF1WoRqd3aJAHsqc9NY7iL8F', ...]List your organization's fine-tuning jobs.
$response = $client->fineTunes()->list();
$response->object; // 'list'
foreach ($response->data as $result) {
$result->id; // 'ft-AF1WoRqd3aJAHsqc9NY7iL8F'
$result->object; // 'fine-tune'
// ...
}
$response->toArray(); // ['object' => 'list', 'data' => [...]]Gets info about the fine-tune job.
$response = $client->fineTunes()->retrieve('ft-AF1WoRqd3aJAHsqc9NY7iL8F');
$response->id; // 'ft-AF1WoRqd3aJAHsqc9NY7iL8F'
$response->object; // 'fine-tune'
$response->model; // 'curie'
$response->createdAt; // 1614807352
$response->fineTunedModel; // 'curie => ft-acmeco-2021-03-03-21-44-20'
$response->organizationId; // 'org-jwe45798ASN82s'
$response->resultFiles; // [
$response->status; // 'succeeded'
$response->validationFiles; // [
$response->trainingFiles; // [
$response->updatedAt; // 1614807865
foreach ($response->events as $result) {
$result->object; // 'fine-tune-event'
$result->createdAt; // 1614807352
$result->level; // 'info'
$result->message; // 'Job enqueued. Waiting for jobs ahead to complete. Queue number => 0.'
}
$response->hyperparams->batchSize; // 4
$response->hyperparams->learningRateMultiplier; // 0.1
$response->hyperparams->nEpochs; // 4
$response->hyperparams->promptLossWeight; // 0.1
foreach ($response->resultFiles as $result) {
$result->id; // 'file-XjGxS3KTG0uNmNOK362iJua3'
$result->object; // 'file'
$result->bytes; // 140
$result->createdAt; // 1613779657
$result->filename; // 'mydata.jsonl'
$result->purpose; // 'fine-tune'
$result->status; // 'succeeded'
$result->status_details; // null
}
foreach ($response->validationFiles as $result) {
$result->id; // 'file-XjGxS3KTG0uNmNOK362iJua3'
// ...
}
foreach ($response->trainingFiles as $result) {
$result->id; // 'file-XjGxS3KTG0uNmNOK362iJua3'
// ...
}
$response->toArray(); // ['id' => 'ft-AF1WoRqd3aJAHsqc9NY7iL8F', ...]Immediately cancel a fine-tune job.
$response = $client->fineTunes()->cancel('ft-AF1WoRqd3aJAHsqc9NY7iL8F');
$response->id; // 'ft-AF1WoRqd3aJAHsqc9NY7iL8F'
$response->object; // 'fine-tune'
// ...
$response->status; // 'cancelled'
// ...
$response->toArray(); // ['id' => 'ft-AF1WoRqd3aJAHsqc9NY7iL8F', ...]Get fine-grained status updates for a fine-tune job.
$response = $client->fineTunes()->listEvents('ft-AF1WoRqd3aJAHsqc9NY7iL8F');
$response->object; // 'list'
foreach ($response->data as $result) {
$result->object; // 'fine-tune-event'
$result->createdAt; // 1614807352
// ...
}
$response->toArray(); // ['object' => 'list', 'data' => [...]]Get streamed fine-grained status updates for a fine-tune job.
$stream = $client->fineTunes()->listEventsStreamed('ft-y3OpNlc8B5qBVGCCVsLZsDST');
foreach($stream as $response){
$response->message;
}
// 1. iteration => 'Created fine-tune: ft-y3OpNlc8B5qBVGCCVsLZsDST'
// 2. iteration => 'Fine-tune costs $0.00'
// ...
// xx. iteration => 'Uploaded result file: file-ajLKUCMsFPrT633zqwr0eI4l'
// xx. iteration => 'Fine-tune succeeded'Classifies if text violates OpenAI's Content Policy.
$response = $client->moderations()->create([
'model' => 'text-moderation-latest',
'input' => 'I want to k*** them.',
]);
$response->id; // modr-5xOyuS
$response->model; // text-moderation-003
foreach ($response->results as $result) {
$result->flagged; // true
foreach ($result->categories as $category) {
$category->category->value; // 'violence'
$category->violated; // true
$category->score; // 0.97431367635727
}
}
$response->toArray(); // ['id' => 'modr-5xOyuS', ...]Creates an image given a prompt.
$response = $client->images()->create([
'prompt' => 'A cute baby sea otter',
'n' => 1,
'size' => '256x256',
'response_format' => 'url',
]);
$response->created; // 1589478378
foreach ($response->data as $data) {
$data->url; // 'https://oaidalleapiprodscus.blob.core.windows.net/private/...'
$data->b64_json; // null
}
$response->toArray(); // ['created' => 1589478378, data => ['url' => 'https://oaidalleapiprodscus...', ...]]Creates an edited or extended image given an original image and a prompt.
$response = $client->images()->edit([
'image' => fopen('image_edit_original.png', 'r'),
'mask' => fopen('image_edit_mask.png', 'r'),
'prompt' => 'A sunlit indoor lounge area with a pool containing a flamingo',
'n' => 1,
'size' => '256x256',
'response_format' => 'url',
]);
$response->created; // 1589478378
foreach ($response->data as $data) {
$data->url; // 'https://oaidalleapiprodscus.blob.core.windows.net/private/...'
$data->b64_json; // null
}
$response->toArray(); // ['created' => 1589478378, data => ['url' => 'https://oaidalleapiprodscus...', ...]]Creates a variation of a given image.
$response = $client->images()->variation([
'image' => fopen('image_edit_original.png', 'r'),
'n' => 1,
'size' => '256x256',
'response_format' => 'url',
]);
$response->created; // 1589478378
foreach ($response->data as $data) {
$data->url; // 'https://oaidalleapiprodscus.blob.core.windows.net/private/...'
$data->b64_json; // null
}
$response->toArray(); // ['created' => 1589478378, data => ['url' => 'https://oaidalleapiprodscus...', ...]]OpenAI has deprecated the Edits API and will stop working by January 4, 2024. https://openai.com/blog/gpt-4-api-general-availability#deprecation-of-the-edits-api
Creates a new edit for the provided input, instruction, and parameters.
$response = $client->edits()->create([
'model' => 'text-davinci-edit-001',
'input' => 'What day of the wek is it?',
'instruction' => 'Fix the spelling mistakes',
]);
$response->object; // 'edit'
$response->created; // 1589478378
foreach ($response->choices as $result) {
$result->text; // 'What day of the week is it?'
$result->index; // 0
}
$response->usage->promptTokens; // 25,
$response->usage->completionTokens; // 32,
$response->usage->totalTokens; // 57
$response->toArray(); // ['object' => 'edit', ...]On all response objects you can access the meta information returned by the API via the meta() method.
$response = $client->completions()->create([
'model' => 'text-davinci-003',
'prompt' => 'Say this is a test',
]);
$meta = $response->meta();
$meta->requestId; // '574a03e2faaf4e9fd703958e4ddc66f5'
$meta->openai->model; // 'text-davinci-003'
$meta->openai->organization; // 'org-jwe45798ASN82s'
$meta->openai->version; // '2020-10-01'
$meta->openai->processingMs; // 425
$meta->requestLimit->limit; // 3000
$meta->requestLimit->remaining; // 2999
$meta->requestLimit->reset; // '20ms'
$meta->tokenLimit->limit; // 250000
$meta->tokenLimit->remaining; // 249984
$meta->tokenLimit->reset; // '3ms'The toArray() method returns the meta information in the form originally returned by the API.
$meta->toArray();
// [
// 'x-request-id' => '574a03e2faaf4e9fd703958e4ddc66f5',
// 'openai-model' => 'text-davinci-003',
// 'openai-organization' => 'org-jwe45798ASN82s',
// 'openai-processing-ms' => 402,
// 'openai-version' => '2020-10-01',
// 'x-ratelimit-limit-requests' => 3000,
// 'x-ratelimit-remaining-requests' => 2999,
// 'x-ratelimit-reset-requests' => '20ms',
// 'x-ratelimit-limit-tokens' => 250000,
// 'x-ratelimit-remaining-tokens' => 249983,
// 'x-ratelimit-reset-tokens' => '3ms',
// ]On streaming responses you can access the meta information on the reponse stream object.
$stream = $client->completions()->createStreamed([
'model' => 'text-davinci-003',
'prompt' => 'Say this is a test',
]);
$stream->meta(); For further details about the rates limits and what to do if you hit them visit the OpenAI documentation.
The package provides a fake implementation of the OpenAI\Client class that allows you to fake the API responses.
To test your code ensure you swap the OpenAI\Client class with the OpenAI\Testing\ClientFake class in your test case.
The fake responses are returned in the order they are provided while creating the fake client.
All responses are having a fake() method that allows you to easily create a response object by only providing the parameters relevant for your test case.
use OpenAI\Testing\ClientFake;
use OpenAI\Responses\Completions\CreateResponse;
$client = new ClientFake([
CreateResponse::fake([
'choices' => [
[
'text' => 'awesome!',
],
],
]),
]);
$completion = $client->completions()->create([
'model' => 'text-davinci-003',
'prompt' => 'PHP is ',
]);
expect($completion['choices'][0]['text'])->toBe('awesome!');In case of a streamed response you can optionally provide a resource holding the fake response data.
use OpenAI\Testing\ClientFake;
use OpenAI\Responses\Chat\CreateStreamedResponse;
$client = new ClientFake([
CreateStreamedResponse::fake(fopen('file.txt', 'r'););
]);
$completion = $client->chat()->createStreamed([
'model' => 'gpt-3.5-turbo',
'messages' => [
['role' => 'user', 'content' => 'Hello!'],
],
]);
expect($response->getIterator()->current())
->id->toBe('chatcmpl-6yo21W6LVo8Tw2yBf7aGf2g17IeIl');After the requests have been sent there are various methods to ensure that the expected requests were sent:
// assert completion create request was sent
$client->assertSent(Completions::class, function (string $method, array $parameters): bool {
return $method === 'create' &&
$parameters['model'] === 'text-davinci-003' &&
$parameters['prompt'] === 'PHP is ';
});
// or
$client->completions()->assertSent(function (string $method, array $parameters): bool {
// ...
});
// assert 2 completion create requests were sent
$client->assertSent(Completions::class, 2);
// assert no completion create requests were sent
$client->assertNotSent(Completions::class);
// or
$client->completions()->assertNotSent();
// assert no requests were sent
$client->assertNothingSent();To write tests expecting the API request to fail you can provide a Throwable object as the response.
$client = new ClientFake([
new \OpenAI\Exceptions\ErrorException([
'message' => 'The model `gpt-1` does not exist',
'type' => 'invalid_request_error',
'code' => null,
])
]);
// the `ErrorException` will be thrown
$completion = $client->completions()->create([
'model' => 'text-davinci-003',
'prompt' => 'PHP is ',
]);In order to use the Azure OpenAI Service, it is necessary to construct the client manually using the factory.
$client = OpenAI::factory()
->withBaseUri('{your-resource-name}.openai.azure.com/openai/deployments/{deployment-id}')
->withHttpHeader('api-key', '{your-api-key}')
->withQueryParam('api-version', '{version}')
->make();To use Azure, you must deploy a model, identified by the {deployment-id}, which is already incorporated into the API calls. As a result, you do not have to provide the model during the calls since it is included in the BaseUri.
Therefore, a basic sample completion call would be:
$result = $client->completions()->create([
'prompt' => 'PHP is'
]);OpenAI PHP is an open-sourced software licensed under the MIT license.