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feat: Add Gemini Embeddings
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‎examples/store/mariadb-similarity-search-gemini.php

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<?php
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use Doctrine\DBAL\DriverManager;
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use Doctrine\DBAL\Tools\DsnParser;
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use PhpLlm\LlmChain\Chain\Chain;
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use PhpLlm\LlmChain\Chain\Toolbox\ChainProcessor;
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use PhpLlm\LlmChain\Chain\Toolbox\Tool\SimilaritySearch;
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use PhpLlm\LlmChain\Chain\Toolbox\Toolbox;
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use PhpLlm\LlmChain\Platform\Bridge\Google\Embeddings;
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use PhpLlm\LlmChain\Platform\Bridge\Google\Embeddings\TaskType;
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use PhpLlm\LlmChain\Platform\Bridge\Google\Gemini;
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use PhpLlm\LlmChain\Platform\Bridge\Google\PlatformFactory;
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use PhpLlm\LlmChain\Platform\Message\Message;
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use PhpLlm\LlmChain\Platform\Message\MessageBag;
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use PhpLlm\LlmChain\Store\Bridge\MariaDB\Store;
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use PhpLlm\LlmChain\Store\Document\Metadata;
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use PhpLlm\LlmChain\Store\Document\TextDocument;
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use PhpLlm\LlmChain\Store\Indexer;
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use Symfony\Component\Dotenv\Dotenv;
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use Symfony\Component\Uid\Uuid;
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require_once dirname(__DIR__, 2).'/vendor/autoload.php';
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(new Dotenv())->loadEnv(dirname(__DIR__, 2).'/.env');
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if (empty($_ENV['GOOGLE_API_KEY']) || empty($_ENV['MARIADB_URI'])) {
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echo 'Please set GOOGLE_API_KEY and MARIADB_URI environment variables.'.\PHP_EOL;
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exit(1);
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}
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// initialize the store
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$store = Store::fromDbal(
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connection: DriverManager::getConnection((new DsnParser())->parse($_ENV['MARIADB_URI'])),
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tableName: 'my_table',
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indexName: 'my_index',
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vectorFieldName: 'embedding',
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);
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// our data
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$movies = [
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['title' => 'Inception', 'description' => 'A skilled thief is given a chance at redemption if he can successfully perform inception, the act of planting an idea in someone\'s subconscious.', 'director' => 'Christopher Nolan'],
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['title' => 'The Matrix', 'description' => 'A hacker discovers the world he lives in is a simulated reality and joins a rebellion to overthrow its controllers.', 'director' => 'The Wachowskis'],
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['title' => 'The Godfather', 'description' => 'The aging patriarch of an organized crime dynasty transfers control of his empire to his reluctant son.', 'director' => 'Francis Ford Coppola'],
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];
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// create embeddings and documents
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foreach ($movies as $i => $movie) {
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$documents[] = new TextDocument(
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id: Uuid::v4(),
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content: 'Title: '.$movie['title'].\PHP_EOL.'Director: '.$movie['director'].\PHP_EOL.'Description: '.$movie['description'],
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metadata: new Metadata($movie),
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);
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}
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// initialize the table
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$store->initialize(['dimensions' => 768]);
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// create embeddings for documents
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$platform = PlatformFactory::create($_ENV['GOOGLE_API_KEY']);
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$embeddings = new Embeddings(options: ['dimensions' => 768, 'task_type' => TaskType::SemanticSimilarity]);
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$indexer = new Indexer($platform, $embeddings, $store);
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$indexer->index($documents);
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$model = new Gemini(Gemini::GEMINI_2_FLASH_LITE);
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$similaritySearch = new SimilaritySearch($platform, $embeddings, $store);
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$toolbox = Toolbox::create($similaritySearch);
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$processor = new ChainProcessor($toolbox);
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$chain = new Chain($platform, $model, [$processor], [$processor]);
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$messages = new MessageBag(
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Message::forSystem('Please answer all user questions only using SimilaritySearch function.'),
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Message::ofUser('Which movie fits the theme of the mafia?')
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);
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$response = $chain->call($messages);
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echo $response->getContent().\PHP_EOL;

‎src/Platform/Bridge/Google/Embeddings.php

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<?php
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declare(strict_types=1);
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namespace PhpLlm\LlmChain\Platform\Bridge\Google;
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use PhpLlm\LlmChain\Platform\Bridge\Google\Embeddings\TaskType;
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use PhpLlm\LlmChain\Platform\Capability;
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use PhpLlm\LlmChain\Platform\Model;
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/**
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* @author Valtteri R <[email protected]>
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*/
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class Embeddings extends Model
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{
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/** Supported dimensions: 3072, 1536, or 768 */
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public const GEMINI_EMBEDDING_EXP_03_07 = 'gemini-embedding-exp-03-07';
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/** Fixed 768 dimensions */
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public const TEXT_EMBEDDING_004 = 'text-embedding-004';
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/** Fixed 768 dimensions */
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public const EMBEDDING_001 = 'embedding-001';
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/**
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* @param array{dimensions?: int, task_type?: TaskType|string} $options
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*/
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public function __construct(string $name = self::GEMINI_EMBEDDING_EXP_03_07, array $options = [])
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{
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parent::__construct($name, [Capability::INPUT_MULTIPLE], $options);
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}
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}

‎src/Platform/Bridge/Google/Embeddings/ModelClient.php

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<?php
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namespace PhpLlm\LlmChain\Platform\Bridge\Google\Embeddings;
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use PhpLlm\LlmChain\Platform\Bridge\Google\Embeddings;
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use PhpLlm\LlmChain\Platform\Exception\RuntimeException;
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use PhpLlm\LlmChain\Platform\Model;
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use PhpLlm\LlmChain\Platform\ModelClientInterface;
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use PhpLlm\LlmChain\Platform\Response\VectorResponse;
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use PhpLlm\LlmChain\Platform\ResponseConverterInterface;
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use PhpLlm\LlmChain\Platform\Vector\Vector;
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use Symfony\Contracts\HttpClient\HttpClientInterface;
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use Symfony\Contracts\HttpClient\ResponseInterface;
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/**
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* @author Valtteri R <[email protected]>
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*/
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final readonly class ModelClient implements ModelClientInterface, ResponseConverterInterface
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{
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public function __construct(
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private HttpClientInterface $httpClient,
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#[\SensitiveParameter]
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private string $apiKey,
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) {
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}
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public function supports(Model $model): bool
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{
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return $model instanceof Embeddings;
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}
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public function request(Model $model, array|string $payload, array $options = []): ResponseInterface
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{
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$url = \sprintf('https://generativelanguage.googleapis.com/v1beta/models/%s:%s', $model->getName(), 'batchEmbedContents');
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$modelOptions = $model->getOptions();
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return $this->httpClient->request('POST', $url, [
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'headers' => [
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'x-goog-api-key' => $this->apiKey,
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],
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'json' => [
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'requests' => array_map(
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static fn (string $text) => array_filter([
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'model' => 'models/'.$model->getName(),
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'content' => ['parts' => [['text' => $text]]],
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'outputDimensionality' => $modelOptions['dimensions'] ?? null,
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'taskType' => $modelOptions['task_type'] ?? null,
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'title' => $options['title'] ?? null,
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]),
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\is_array($payload) ? $payload : [$payload],
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),
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],
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]);
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}
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public function convert(ResponseInterface $response, array $options = []): VectorResponse
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{
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$data = $response->toArray();
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if (!isset($data['embeddings'])) {
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throw new RuntimeException('Response does not contain data');
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}
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return new VectorResponse(
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...array_map(
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static fn (array $item): Vector => new Vector($item['values']),
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$data['embeddings'],
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),
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);
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}
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}

‎src/Platform/Bridge/Google/Embeddings/TaskType.php

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<?php
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namespace PhpLlm\LlmChain\Platform\Bridge\Google\Embeddings;
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enum TaskType: string
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{
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/** Unset value, which will default to one of the other enum values. */
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public const TaskTypeUnspecified = 'TASK_TYPE_UNSPECIFIED';
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/** Specifies the given text is a query in a search/retrieval setting. */
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public const RetrievalQuery = 'RETRIEVAL_QUERY';
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/** Specifies the given text is a document from the corpus being searched. */
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public const RetrievalDocument = 'RETRIEVAL_DOCUMENT';
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/** Specifies the given text will be used for STS. */
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public const SemanticSimilarity = 'SEMANTIC_SIMILARITY';
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/** Specifies that the given text will be classified. */
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public const Classification = 'CLASSIFICATION';
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/** Specifies that the embeddings will be used for clustering. */
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public const Clustering = 'CLUSTERING';
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/** Specifies that the given text will be used for question answering. */
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public const QuestionAnswering = 'QUESTION_ANSWERING';
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/** Specifies that the given text will be used for fact verification. */
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public const FactVerification = 'FACT_VERIFICATION';
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/** Specifies that the given text will be used for code retrieval. */
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public const CodeRetrievalQuery = 'CODE_RETRIEVAL_QUERY';
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}

‎src/Platform/Bridge/Google/PlatformFactory.php

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@@ -9,6 +9,7 @@
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use PhpLlm\LlmChain\Platform\Bridge\Google\Contract\ToolCallMessageNormalizer;
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use PhpLlm\LlmChain\Platform\Bridge\Google\Contract\ToolNormalizer;
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use PhpLlm\LlmChain\Platform\Bridge\Google\Contract\UserMessageNormalizer;
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use PhpLlm\LlmChain\Platform\Bridge\Google\Embeddings\ModelClient;
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use PhpLlm\LlmChain\Platform\Contract;
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use PhpLlm\LlmChain\Platform\Platform;
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use Symfony\Component\HttpClient\EventSourceHttpClient;
@@ -26,8 +27,9 @@ public static function create(
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): Platform {
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$httpClient = $httpClient instanceof EventSourceHttpClient ? $httpClient : new EventSourceHttpClient($httpClient);
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$responseHandler = new ModelHandler($httpClient, $apiKey);
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$embeddings = new ModelClient($httpClient, $apiKey);
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return new Platform([$responseHandler], [$responseHandler], Contract::create(
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return new Platform([$responseHandler, $embeddings], [$responseHandler, $embeddings], Contract::create(
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new AssistantMessageNormalizer(),
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new MessageBagNormalizer(),
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new ToolNormalizer(),

‎src/Platform/ModelClientInterface.php

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public function supports(Model $model): bool;
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/**
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* @param array<string, mixed> $payload
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* @param array<string|int, mixed> $payload
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* @param array<string, mixed> $options
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*/
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public function request(Model $model, array|string $payload, array $options = []): ResponseInterface;

‎tests/Platform/Bridge/Google/Embeddings/EmbeddingsModelClientTest.php

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<?php
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declare(strict_types=1);
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namespace PhpLlm\LlmChain\Tests\Platform\Bridge\Google\Embeddings;
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use PhpLlm\LlmChain\Platform\Bridge\Google\Embeddings;
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use PhpLlm\LlmChain\Platform\Bridge\Google\Embeddings\ModelClient;
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use PhpLlm\LlmChain\Platform\Response\VectorResponse;
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use PhpLlm\LlmChain\Platform\Vector\Vector;
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use PHPUnit\Framework\Attributes\CoversClass;
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use PHPUnit\Framework\Attributes\Small;
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use PHPUnit\Framework\Attributes\Test;
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use PHPUnit\Framework\Attributes\UsesClass;
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use PHPUnit\Framework\TestCase;
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use Symfony\Contracts\HttpClient\HttpClientInterface;
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use Symfony\Contracts\HttpClient\ResponseInterface;
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#[CoversClass(ModelClient::class)]
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#[Small]
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#[UsesClass(Vector::class)]
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#[UsesClass(VectorResponse::class)]
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#[UsesClass(Embeddings::class)]
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final class EmbeddingsModelClientTest extends TestCase
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{
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#[Test]
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public function itMakesARequestWithCorrectPayload(): void
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{
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$response = $this->createStub(ResponseInterface::class);
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$response
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->method('toArray')
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->willReturn(json_decode($this->getEmbeddingStub(), true));
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$httpClient = self::createMock(HttpClientInterface::class);
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$httpClient->expects(self::once())
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->method('request')
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->with(
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'POST',
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'https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-exp-03-07:batchEmbedContents',
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[
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'headers' => ['x-goog-api-key' => 'test'],
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'json' => [
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'requests' => [
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[
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'model' => 'models/gemini-embedding-exp-03-07',
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'content' => ['parts' => [['text' => 'payload1']]],
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'outputDimensionality' => 1536,
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'taskType' => 'CLASSIFICATION',
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],
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[
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'model' => 'models/gemini-embedding-exp-03-07',
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'content' => ['parts' => [['text' => 'payload2']]],
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'outputDimensionality' => 1536,
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'taskType' => 'CLASSIFICATION',
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],
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],
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],
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],
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)
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->willReturn($response);
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$model = new Embeddings(Embeddings::GEMINI_EMBEDDING_EXP_03_07, ['dimensions' => 1536, 'task_type' => 'CLASSIFICATION']);
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$httpResponse = (new ModelClient($httpClient, 'test'))->request($model, ['payload1', 'payload2']);
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self::assertSame(json_decode($this->getEmbeddingStub(), true), $httpResponse->toArray());
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}
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#[Test]
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public function itConvertsAResponseToAVectorResponse(): void
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{
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$response = $this->createStub(ResponseInterface::class);
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$response
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->method('toArray')
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->willReturn(json_decode($this->getEmbeddingStub(), true));
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$httpClient = self::createMock(HttpClientInterface::class);
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$vectorResponse = (new ModelClient($httpClient, 'test'))->convert($response);
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$convertedContent = $vectorResponse->getContent();
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self::assertCount(2, $convertedContent);
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self::assertSame([0.3, 0.4, 0.4], $convertedContent[0]->getData());
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self::assertSame([0.0, 0.0, 0.2], $convertedContent[1]->getData());
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}
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private function getEmbeddingStub(): string
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{
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return <<<'JSON'
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{
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"embeddings": [
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{
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"values": [0.3, 0.4, 0.4]
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},
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{
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"values": [0.0, 0.0, 0.2]
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}
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]
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}
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JSON;
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}
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}

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