feat(platform): Add support for Google vertex AI

- Adds support for embeddings

Author: junaidbinfarooq

src/platform/src/Bridge/VertexAi/Embeddings/Model.php

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+<?php
+
+/*
+ * This file is part of the Symfony package.
+ *
+ * (c) Fabien Potencier <[email protected]>
+ *
+ * For the full copyright and license information, please view the LICENSE
+ * file that was distributed with this source code.
+ */
+
+namespace Symfony\AI\Platform\Bridge\VertexAi\Embeddings;
+
+use Symfony\AI\Platform\Capability;
+use Symfony\AI\Platform\Model as BaseModel;
+
+/**
+ * @author Junaid Farooq <[email protected]>
+ */
+class Model extends BaseModel
+{
+    /** Upto 3072 dimensions */
+    public const GEMINI_EMBEDDING_001 = 'gemini-embedding-001';
+    /** Upto 768 dimensions */
+    public const TEXT_EMBEDDING_005 = 'text-embedding-005';
+    /** Upto 768 dimensions */
+    public const TEXT_MULTILINGUAL_EMBEDDING_002 = 'text-multilingual-embedding-002';
+
+    /**
+     * @param array{task_type?: TaskType} $options
+     * @see https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api for various options
+     */
+    public function __construct(string $name = self::GEMINI_EMBEDDING_001, array $options = [])
+    {
+        parent::__construct($name, [Capability::INPUT_MULTIPLE], $options);
+    }
+}

src/platform/src/Bridge/VertexAi/Embeddings/ModelClient.php

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+<?php
+
+/*
+ * This file is part of the Symfony package.
+ *
+ * (c) Fabien Potencier <[email protected]>
+ *
+ * For the full copyright and license information, please view the LICENSE
+ * file that was distributed with this source code.
+ */
+
+namespace Symfony\AI\Platform\Bridge\VertexAi\Embeddings;
+
+use Symfony\AI\Platform\Model as BaseModel;
+use Symfony\AI\Platform\ModelClientInterface;
+use Symfony\AI\Platform\Result\RawHttpResult;
+use Symfony\Contracts\HttpClient\Exception\TransportExceptionInterface;
+use Symfony\Contracts\HttpClient\HttpClientInterface;
+
+/**
+ * @author Junaid Farooq <[email protected]>
+ */
+final readonly class ModelClient implements ModelClientInterface
+{
+    public function __construct(
+        private HttpClientInterface $httpClient,
+        private string $location,
+        private string $projectId,
+    ) {
+    }
+
+    public function supports(BaseModel $model): bool
+    {
+        return $model instanceof Model;
+    }
+
+    /**
+     * @throws TransportExceptionInterface
+     */
+    public function request(BaseModel $model, array|string $payload, array $options = []): RawHttpResult
+    {
+        $url = \sprintf(
+            'https://%s-aiplatform.googleapis.com/v1/projects/%s/locations/%s/publishers/google/models/%s:%s',
+            $this->location,
+            $this->projectId,
+            $this->location,
+            $model->getName(),
+            'predict',
+        );
+
+        $modelOptions = $model->getOptions();
+
+        if (isset($modelOptions['task_type']) && $modelOptions['task_type'] instanceof TaskType) {
+            $modelOptions['task_type'] = $modelOptions['task_type']->value;
+        }
+
+        $payload = [
+            'instances' => array_map(
+                static fn (string $text) => [
+                    'content' => ['parts' => [['text' => $text]]],
+                    'title' => $options['title'] ?? null,
+                    'task_type' => isset($modelOptions['task_type']) ? $modelOptions['task_type']->value : null,
+                ],
+                \is_array($payload) ? $payload : [$payload],
+            ),
+        ];
+
+        unset($modelOptions['task_type']);
+
+        return new RawHttpResult(
+            $this->httpClient->request(
+                'POST',
+                $url,
+                [
+                    'headers' => [
+                        'Content-Type' => 'application/json',
+                    ],
+                    'json' => array_merge($payload, $modelOptions),
+                ]
+            )
+        );
+    }
+}

src/platform/src/Bridge/VertexAi/Embeddings/ResultConverter.php

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+<?php
+
+/*
+ * This file is part of the Symfony package.
+ *
+ * (c) Fabien Potencier <[email protected]>
+ *
+ * For the full copyright and license information, please view the LICENSE
+ * file that was distributed with this source code.
+ */
+
+namespace Symfony\AI\Platform\Bridge\VertexAi\Embeddings;
+
+use Symfony\AI\Platform\Exception\RuntimeException;
+use Symfony\AI\Platform\Model as BaseModel;
+use Symfony\AI\Platform\Result\RawResultInterface;
+use Symfony\AI\Platform\Result\VectorResult;
+use Symfony\AI\Platform\ResultConverterInterface;
+use Symfony\AI\Platform\Vector\Vector;
+
+/**
+ * @author Junaid Farooq <[email protected]>
+ */
+final readonly class ResultConverter implements ResultConverterInterface
+{
+    public function supports(BaseModel $model): bool
+    {
+        return $model instanceof Model;
+    }
+
+    public function convert(RawResultInterface $result, array $options = []): VectorResult
+    {
+        $data = $result->getData();
+
+        if (!isset($data['predictions'])) {
+            throw new RuntimeException('Response does not contain data.');
+        }
+
+        return new VectorResult(
+            ...array_map(
+                static fn (array $item): Vector => new Vector($item['embeddings']['values']),
+                $data['predictions'],
+            ),
+        );
+    }
+}

src/platform/src/Bridge/VertexAi/Embeddings/TaskType.php

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+<?php
+
+/*
+ * This file is part of the Symfony package.
+ *
+ * (c) Fabien Potencier <[email protected]>
+ *
+ * For the full copyright and license information, please view the LICENSE
+ * file that was distributed with this source code.
+ */
+
+namespace Symfony\AI\Platform\Bridge\VertexAi\Embeddings;
+
+enum TaskType: string
+{
+    /** Used to generate embeddings that are optimized to classify texts according to preset labels. */
+    public const CLASSIFICATION = 'CLASSIFICATION';
+
+    /** Used to generate embeddings that are optimized to cluster texts based on their similarities */
+    public const CLUSTERING = 'CLUSTERING';
+
+    /** Specifies the given text is a document from the corpus being searched. */
+    public const RETRIEVAL_DOCUMENT = 'RETRIEVAL_DOCUMENT';
+
+    /** Specifies the given text is a query in a search/retrieval setting.
+     * This is the recommended default for all the embeddings use case
+     * that do not align with a documented use case.
+     */
+    public const RETRIEVAL_QUERY = 'RETRIEVAL_QUERY';
+
+    /** Specifies that the given text will be used for question answering. */
+    public const QUESTION_ANSWERING = 'QUESTION_ANSWERING';
+
+    /** Specifies that the given text will be used for fact verification. */
+    public const FACT_VERIFICATION = 'FACT_VERIFICATION';
+
+    /** Used to retrieve a code block based on a natural language query,
+     * such as sort an array or reverse a linked list.
+     * Embeddings of the code blocks are computed using RETRIEVAL_DOCUMENT.
+     */
+    public const CODE_RETRIEVAL_QUERY = 'CODE_RETRIEVAL_QUERY';
+
+    /** Used to generate embeddings that are optimized to assess text similarity.
+     * This is not intended for retrieval use cases.
+     */
+    public const SEMANTIC_SIMILARITY = 'SEMANTIC_SIMILARITY';
+}