PHP code example of php-llm / llm-chain

    
        
<?php
require_once('vendor/autoload.php');

/* Start to develop here. Best regards https://php-download.com/ */

    

php-llm / llm-chain example snippets

use PhpLlm\LlmChain\OpenAI\Model\Embeddings;
use PhpLlm\LlmChain\OpenAI\Model\Gpt;
use PhpLlm\LlmChain\OpenAI\Model\Gpt\Version;
use PhpLlm\LlmChain\OpenAI\Platform\OpenAI;
use Symfony\Component\HttpClient\HttpClient;

// Platform: OpenAI
$platform = new OpenAI(HttpClient::create(), $_ENV['OPENAI_API_KEY']);

// Language Model: GPT (OpenAI)
$llm = new Gpt($platform, Version::gpt4oMini()); 

// Embeddings Model: Embeddings (OpenAI)
$embeddings = new Embeddings($platform);

use PhpLlm\LlmChain\Chain;
use PhpLlm\LlmChain\Message\MessageBag;
use PhpLlm\LlmChain\Message\SystemMessage;
use PhpLlm\LlmChain\Message\UserMessage;

// LLM instantiation

$chain = new Chain($llm);
$messages = new MessageBag(
    new SystemMessage('You are a helpful chatbot answering questions about LLM Chain.'),
    new UserMessage('Hello, how are you?'),
);
$response = $chain->call($messages);

echo $response->getContent(); // "I'm fine, thank you. How can I help you today?"

use PhpLlm\LlmChain\ToolBox\ChainProcessor;
use PhpLlm\LlmChain\ToolBox\ToolAnalyzer;
use PhpLlm\LlmChain\ToolBox\ToolBox;
use Symfony\Component\Serializer\Encoder\JsonEncoder;
use Symfony\Component\Serializer\Normalizer\ObjectNormalizer;
use Symfony\Component\Serializer\Serializer;

$yourTool = new YourTool();

$toolBox = new ToolBox(new ToolAnalyzer(), [$yourTool]);
$toolProcessor = new ChainProcessor($toolBox);

$chain = new Chain($llm, inputProcessor: [$toolProcessor], outputProcessor: [$toolProcessor]);

use PhpLlm\LlmChain\ToolBox\Attribute\AsTool;

#[AsTool('company_name', 'Provides the name of your company')]
final class CompanyName
{
    public function __invoke(): string
    {
        return 'ACME Corp.'
    }
}

use PhpLlm\LlmChain\DocumentEmbedder;
use PhpLlm\LlmChain\OpenAI\Model\Embeddings;
use PhpLlm\LlmChain\OpenAI\Platform\OpenAI;
use PhpLlm\LlmChain\Store\Pinecone\Store;
use Probots\Pinecone\Pinecone;
use Symfony\Component\HttpClient\HttpClient;

$embedder = new DocumentEmbedder(
    new Embeddings(new OpenAI(HttpClient::create(), $_ENV['OPENAI_API_KEY']);),
    new Store(Pinecone::client($_ENV['PINECONE_API_KEY'], $_ENV['PINECONE_HOST']),
);
$embedder->embed($documents);

use PhpLlm\LlmChain\Document\Metadata;
use PhpLlm\LlmChain\Document\TextDocument;

foreach ($entities as $entity) {
    $documents[] = new TextDocument(
        id: $entity->getId(),                       // UUID instance
        content: $entity->toString(),               // Text representation of relevant data for embedding
        metadata: new Metadata($entity->toArray()), // Array representation of entity to be stored additionally
    );
}

use PhpLlm\LlmChain\Chain;
use PhpLlm\LlmChain\DocumentEmbedder;
use PhpLlm\LlmChain\Message\Message;
use PhpLlm\LlmChain\Message\MessageBag;
use PhpLlm\LlmChain\ToolBox\ChainProcessor;
use PhpLlm\LlmChain\ToolBox\Tool\SimilaritySearch;
use PhpLlm\LlmChain\ToolBox\ToolAnalyzer;
use PhpLlm\LlmChain\ToolBox\ToolBox;

// Initialize Platform and LLM

$similaritySearch = new SimilaritySearch($embeddings, $store);
$toolBox = new ToolBox(new ToolAnalyzer(), [$similaritySearch]);
$processor = new ChainProcessor($toolBox);
$chain = new Chain(new Gpt($platform), [$processor], [$processor]);

$messages = new MessageBag(
    Message::forSystem(<<<PROMPT
        Please answer all user questions only using the similary_search tool. Do not add information and if you cannot
        find an answer, say so.
        PROMPT>>>),
    Message::ofUser('...') // The user's question.
);
$response = $chain->call($messages);

use PhpLlm\LlmChain\Chain;
use PhpLlm\LlmChain\Message\Message;
use PhpLlm\LlmChain\Message\MessageBag;
use PhpLlm\LlmChain\StructuredOutput\ChainProcessor;
use PhpLlm\LlmChain\StructuredOutput\ResponseFormatFactory;
use PhpLlm\LlmChain\Tests\StructuredOutput\Data\MathReasoning;
use Symfony\Component\Serializer\Encoder\JsonEncoder;
use Symfony\Component\Serializer\Normalizer\ObjectNormalizer;
use Symfony\Component\Serializer\Serializer;

// Initialize Platform and LLM

$serializer = new Serializer([new ObjectNormalizer()], [new JsonEncoder()]);
$processor = new ChainProcessor(new ResponseFormatFactory(), $serializer);
$chain = new Chain($llm, [$processor], [$processor]);

$messages = new MessageBag(
    Message::forSystem('You are a helpful math tutor. Guide the user through the solution step by step.'),
    Message::ofUser('how can I solve 8x + 7 = -23'),
);
$response = $chain->call($messages, ['output_structure' => MathReasoning::class]);

dump($response->getContent()); // returns an instance of `MathReasoning` class

use PhpLlm\LlmChain\Message\Message;
use PhpLlm\LlmChain\Message\MessageBag;

// Initialize Platform, LLM and Chain with processors and Clock tool

$messages = new MessageBag(Message::ofUser('What date and time is it?'));
$response = $chain->call($messages, ['response_format' => [
    'type' => 'json_schema',
    'json_schema' => [
        'name' => 'clock',
        'strict' => true,
        'schema' => [
            'type' => 'object',
            'properties' => [
                'date' => ['type' => 'string', 'description' => 'The current date in the format YYYY-MM-DD.'],
                'time' => ['type' => 'string', 'description' => 'The current time in the format HH:MM:SS.'],
            ],
            '

use PhpLlm\LlmChain\ToolBox\Attribute\AsTool;
use PhpLlm\LlmChain\ToolBox\Attribute\ToolParameter;

#[AsTool('my_tool', 'Example tool with parameters ic function __invoke(
        #[ToolParameter(pattern: '/([a-z0-1]){5}/')]
        string $name,
        #[ToolParameter(minimum: 0, maximum: 10)]   
        int $number,
    ): string {
        // ...
    }
}

use PhpLlm\LlmChain\Chain;
use PhpLlm\LlmChain\Message\Message;
use PhpLlm\LlmChain\Message\MessageBag;

// Initialize Platform and LLM

$chain = new Chain($llm);
$messages = new MessageBag(
    Message::forSystem('You are a thoughtful philosopher.'),
    Message::ofUser('What is the purpose of an ant?'),
);
$response = $chain->call($messages, [
    'stream' => true, // enable streaming of response text
]);

foreach ($response->getContent() as $word) {
    echo $word;
}

use PhpLlm\LlmChain\Message\Content\Image;
use PhpLlm\LlmChain\Message\Message;
use PhpLlm\LlmChain\Message\MessageBag;

// Initialize Platoform, LLM & Chain

$messages = new MessageBag(
    Message::forSystem('You are an image analyzer bot that helps identify the content of images.'),
    Message::ofUser(
        'Describe the image as a comedian would do it.',
        new Image(dirname(__DIR__).'/tests/Fixture/image.png'), // Path to an image file
        new Image('https://foo.com/bar.png'), // URL to an image
        new Image('data:image/png;base64,...'), // Data URL of an image
    ),
);
$response = $chain->call($messages);

use PhpLlm\LlmChain\OpenAI\Model\Embeddings;
use PhpLlm\LlmChain\OpenAI\Model\Embeddings\Version;

// Initialize Platform

$embeddings = new Embeddings($platform, Version::textEmbedding3Small());

$vector = $embeddings->create($textInput);

dump($vector->getData()); // Array of float values

use PhpLlm\LlmChain\Chain;

// Initialize LLM and processors

$chain = new Chain($llm, $inputProcessors, $outputProcessors);

use PhpLlm\LlmChain\Chain\Input;
use PhpLlm\LlmChain\Chain\InputProcessor;
use PhpLlm\LlmChain\Message\AssistantMessage

final class MyProcessor implements InputProcessor
{
    public function processInput(Input $input): void
    {
        // mutate options
        $options = $input->getOptions();
        $options['foo'] = 'bar';
        $input->setOptions($options);
        
        // mutate MessageBag
        $input->messages->append(new AssistantMessage(sprintf('Please answer using the locale %s', $this->locale)));
    }
}

use PhpLlm\LlmChain\Chain\Output;
use PhpLlm\LlmChain\Chain\OutputProcessor;
use PhpLlm\LlmChain\Message\AssistantMessage

final class MyProcessor implements OutputProcessor
{
    public function processOutput(Output $out): void
    {
        // mutate response
        if (str_contains($output->response->getContent, self::STOP_WORD)) {
            $output->reponse = new TextReponse('Sorry, we were unable to find relevant information.')
        }
    }
}

use PhpLlm\LlmChain\Chain\ChainAwareProcessor;
use PhpLlm\LlmChain\Chain\ChainAwareTrait;
use PhpLlm\LlmChain\Chain\Output;
use PhpLlm\LlmChain\Chain\OutputProcessor;
use PhpLlm\LlmChain\Message\AssistantMessage

final class MyProcessor implements OutputProcessor, ChainAwareProcessor
{
    use ChainAwareTrait;

    public function processOutput(Output $out): void
    {
        // additional chain interaction
        $response = $this->chain->call(...);
    }
}

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