This library implements OpenAI APIs with a low-level abstraction approach, supporting their REST and realtime APIs, request and response streaming, function calling and TikToken encoding.
Using the REST API, see https://platform.openai.com/docs/api-reference/making-requests
use com\openai\rest\OpenAIEndpoint;
use util\cmd\Console;
$ai= new OpenAIEndpoint('https://'.getenv('OPENAI_API_KEY').'@api.openai.com/v1');
$payload= [
'model' => 'gpt-4o-mini',
'messages' => [['role' => 'user', 'content' => $prompt]],
];
Console::writeLine($ai->api('/chat/completions')->invoke($payload));The REST API can use server-sent events to stream responses, see https://platform.openai.com/docs/api-reference/streaming
use com\openai\rest\OpenAIEndpoint;
use util\cmd\Console;
$ai= new OpenAIEndpoint('https://'.getenv('OPENAI_API_KEY').'@api.openai.com/v1');
$payload= [
'model' => 'gpt-4o-mini',
'messages' => [['role' => 'user', 'content' => $prompt]],
];
$stream= $ai->api('/chat/completions')->stream($payload);
foreach ($stream->deltas('content') as $delta) {
Console::write($delta);
}
Console::writeLine();To access the result object after streaming, use $stream->result(). It contains the choices list as well as model, filter results and usage information.
Encodes text to tokens. Download the vocabularies cl100k_base (used for GPT-3.5 and GPT-4.0) and o200k_base (used for Omni and O1) first!
use com\openai\{Encoding, TikTokenFilesIn};
$source= new TikTokenFilesIn('.');
// By name => [9906, 4435, 0]
$tokens= Encoding::named('cl100k_base')->load($source)->encode('Hello World!');
// By model => [13225, 5922, 0]
$tokens= Encoding::for('omni')->load($source)->encode('Hello World!');Instead of encode(), you can use count() to count the number of tokens.
To create an embedding for a given text, use https://platform.openai.com/docs/guides/embeddings/what-are-embeddings
use com\openai\rest\OpenAIEndpoint;
use util\cmd\Console;
$ai= new OpenAIEndpoint('https://'.getenv('OPENAI_API_KEY').'@api.openai.com/v1');
Console::writeLine($ai->api('/embeddings')->invoke([
'input' => $text,
'model' => 'text-embedding-3-small'],
));To stream generate audio, use the API's transmit() method, which sends the given payload and returns the response. See https://platform.openai.com/docs/guides/text-to-speech/overview
use com\openai\rest\OpenAIEndpoint;
use util\cmd\Console;
$ai= new OpenAIEndpoint('https://'.getenv('OPENAI_API_KEY').'@api.openai.com/v1');
$payload= [
'input' => $input,
'voice' => 'alloy', // or: echo, fable, onyx, nova, shimmer
'model' => 'tts-1',
];
$stream= $ai->api('/audio/speech')->transmit($payload)->stream();
while ($stream->available()) {
Console::write($stream->read());
}To convert audio into text, upload files via the API's open() method, which returns an Upload instance. See https://platform.openai.com/docs/guides/speech-to-text/overview
use com\openai\rest\OpenAIEndpoint;
use io\File;
use util\cmd\Console;
$ai= new OpenAIEndpoint('https://'.getenv('OPENAI_API_KEY').'@api.openai.com/v1');
$file= new File($argv[1]);
$response= $ai->api('/audio/transcriptions')
->open(['model', 'whisper-1'])
->transfer('file', $file->in(), $file->filename)
->finish()
;
Console::writeLine($response->value());You can also stream uploads from InputStreams as follows:
// ...setup code from above...
$upload= $ai->api('/audio/transcriptions')->open(['model', 'whisper-1']);
$stream= $upload->stream('file', 'audio.mp3');
while ($in->available()) {
$stream->write($in->read());
}
$response= $upload->finish();
Console::writeLine($response->value());REST API calls can be traced with the logging library:
use com\openai\rest\OpenAIEndpoint;
use util\log\Logging;
$ai= new OpenAIEndpoint('https://'.getenv('OPENAI_API_KEY').'@api.openai.com/v1');
$ai->setTrace(Logging::all()->toConsole());
// ...perform API calls...There are two types of tools: Built-ins like file_search and code_interpreter (available in the assistants API) as well as custom functions, see https://platform.openai.com/docs/guides/function-calling
Custom functions map to instance methods in a class:
use com\openai\tools\Param;
use webservices\rest\Endpoint;
class Weather {
private $endpoint;
public function __construct(string $base= 'https://wttr.in/') {
$this->endpoint= new Endpoint($base);
}
public function in(#[Param] string $city): string {
return $this->endpoint->resource('/{0}?0mT', [$city])->get()->content();
}
}The Param annnotation may define a description and a JSON schema type:
#[Param('The name of the city')] $name#[Param(type: ['type' => 'string', 'enum' => ['C', 'F']])] $unit
Custom functions are registered in a Functions instance and passed via tools inside the payload.
use com\openai\rest\OpenAIEndpoint;
use com\openai\tools\{Tools, Functions};
$functions= (new Functions())->register('weather', new Weather());
$ai= new OpenAIEndpoint('https://'.getenv('OPENAI_API_KEY').'@api.openai.com/v1');
$payload= [
'model' => 'gpt-4o-mini',
'tools' => new Tools($functions),
'messages' => [['role' => 'user', 'content' => $prompt]],
];If tool calls are requested by the LLM, invoke them and return to next completion cycle. See https://platform.openai.com/docs/guides/function-calling/configuring-parallel-function-calling
use util\cmd\Console;
// ...setup code from above...
$calls= $functions->calls()->catching(fn($t) => $t->printStackTrace());
complete: $result= $ai->api('/chat/completions')->invoke($payload));
// If tool calls are requested, invoke them and return to next completion cycle
if ('tool_calls' === ($result['choices'][0]['finish_reason'] ?? null)) {
$payload['messages'][]= $result['choices'][0]['message'];
foreach ($result['choices'][0]['message']['tool_calls'] as $call) {
$return= $calls->call($call['function']['name'], $call['function']['arguments']);
$payload['messages'][]= [
'role' => 'tool',
'tool_call_id' => $call['id'],
'content' => $return,
];
}
goto complete;
}
// Print out final result
Console::writeLine($result);Functions can be passed a context as follows by annotating parameters with the Context annotation:
use com\mongodb\{Collection, Document, ObjectId};
use com\openai\tools\{Context, Param};
// Declaration
class Memory {
public function __construct(private Collection $facts) { }
public function store(#[Context] Document $user, #[Param] string $fact): ObjectId {
return $this->facts->insert(new Document(['owner' => $user->id(), 'fact' => $fact]))->id();
}
}
// ...shortened for brevity...
$context= ['user' => $user];
$return= $calls->call($call['function']['name'], $call['function']['arguments'], $context);These endpoints differ slightly in how they are invoked, which is handled by the AzureAI implementation. See https://learn.microsoft.com/en-us/azure/ai-services/openai/overview
use com\openai\rest\AzureAIEndpoint;
use util\cmd\Console;
$ai= new AzureAIEndpoint(
'https://'.getenv('AZUREAI_API_KEY').'@example.openai.azure.com/openai/deployments/mini',
'2024-02-01'
);
$payload= [
'model' => 'gpt-4o-mini',
'messages' => [['role' => 'user', 'content' => $prompt]],
];
Console::writeLine($ai->api('/chat/completions')->invoke($payload));The Distributed endpoint allows to distribute requests over multiple endpoints. The ByRemainingRequests class uses the x-ratelimit-remaining-requests header to determine the target. See https://platform.openai.com/docs/guides/rate-limits
use com\openai\rest\{AzureAIEndpoint, Distributed, ByRemainingRequests};
use util\cmd\Console;
$endpoints= [
new AzureAIEndpoint('https://[email protected]/openai/deployments/mini', '2024-02-01'),
new AzureAIEndpoint('https://[email protected]/openai/deployments/mini', '2024-02-01'),
];
$ai= new Distributed($endpoints, new ByRemainingRequests());
$payload= [
'model' => 'gpt-4o-mini',
'messages' => [['role' => 'user', 'content' => $prompt]],
];
Console::writeLine($ai->api('/chat/completions')->invoke($payload));
foreach ($endpoints as $i => $endpoint) {
Console::writeLine('Endpoint #', $i, ': ', $endpoint->rateLimit());
}For more complex load balancing, have a look at this blog article using Azure API management
The realtime API allows streaming audio and/or text to and from language models, see https://platform.openai.com/docs/guides/realtime
use com\openai\realtime\RealtimeApi;
use util\cmd\Console;
$api= new RealtimeApi('wss://api.openai.com/v1/realtime?model=gpt-4o-realtime-preview');
$session= $api->connect([
'Authorization' => 'Bearer '.getenv('OPENAI_API_KEY'),
'OpenAI-Beta' => 'realtime=v1',
];
Console::writeLine($session);
// Send prompt
$api->transmit([
'type' => 'conversation.item.create',
'item' => [
'type' => 'message',
'role' => 'user',
'content' => [['type' => 'input_text', 'text' => $message]],
]
]);
// Receive response(s)
$api->send(['type' => 'response.create', 'response' => ['modalities' => ['text']]]);
do {
$event= $api->receive();
Console::writeLine($event);
} while ('response.done' !== $event['type'] && 'error' !== $event['type']);
$api->close();For Azure AI, the setup code is slightly different:
use com\openai\realtime\RealtimeApi;
use util\cmd\Console;
$api= new RealtimeApi('wss://example.openai.azure.com/openai/realtime?'.
'?api-version=2024-10-01-preview'.
'&deployment=gpt-4o-realtime-preview'
);
$session= $api->connect(['api-key' => getenv('AZUREAI_API_KEY')]);