README

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.

Completions

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));

Streaming

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.

TikToken

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.

Embeddings

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'],
));

Text to speech

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());
}

Speech to text

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());

Tracing the calls

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...

Tool 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

Defining functions

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

Passing custom functions

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]],
];

Invoking custom functions

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);

Passing context

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);

Azure OpenAI

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));

Distributing requests

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://...@r1.openai.azure.com/openai/deployments/mini', '2024-02-01'),
  new AzureAIEndpoint('https://...@r2.openai.azure.com/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

Realtime API

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')]);

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