Merge pull request #33 from aydinfatih/main

feat: Add batchEmbedContents method to embeddingModel

README.md

@@ -67,8 +67,28 @@ $yourApiKey = getenv('YOUR_API_KEY');
 $client = Gemini::client($yourApiKey);
 
 $result = $client->geminiPro()->generateContent('Hello');
+$result->text(); // Hello! How can I assist you today?
+
+// Custom Model
+$result = $client->geminiPro()->generativeModel(model: 'models/gemini-1.5-flash-001');
+$result->text(); // Hello! How can I assist you today?
+
+
+// Enum usage
+$result = $client->geminiPro()->generativeModel(model: ModelType::GEMINI_FLASH);
+$result->text(); // Hello! How can I assist you today?
+
 
+// Enum method usage
+$result = $client->geminiPro()->generativeModel(
+    model: ModelType::generateGeminiModel(
+      variation: ModelVariation::FLASH,
+      generation: 1.5,
+      version: "002"
+    ), // models/gemini-1.5-flash-002
+);
 $result->text(); // Hello! How can I assist you today?
+
 ```
 
 If necessary, it is possible to configure and create a separate client.
@@ -231,6 +251,46 @@ print_r($response->embedding->values);
 //]
 ```
 
+```php
+$response = $client
+ ->embeddingModel()
+ ->batchEmbedContents("Bu bir testtir", "Deneme123");
+
+print_r($response->embeddings);
+// [
+// [0] => Gemini\Data\ContentEmbedding Object
+// (
+//     [values] => Array
+//         (
+//         [0] => 0.035855837
+//         [1] => -0.049537655
+//         [2] => -0.06834927
+//         [3] => -0.010445258
+//         [4] => 0.044641383
+//         [5] => 0.031156342
+//         [6] => -0.007810312
+//         [7] => -0.0106866965
+//         ...
+//         ),
+// ),
+// [1] => Gemini\Data\ContentEmbedding Object
+// (
+//     [values] => Array
+//         (
+//         [0] => 0.035855837
+//         [1] => -0.049537655
+//         [2] => -0.06834927
+//         [3] => -0.010445258
+//         [4] => 0.044641383
+//         [5] => 0.031156342
+//         [6] => -0.007810312
+//         [7] => -0.0106866965
+//         ...
+//         ),
+// ),
+// ]
+```
+
 ### Models
 
 #### List Models

src/Contracts/Resources/EmbeddingModalContract.php

@@ -7,6 +7,8 @@
 use Gemini\Data\Blob;
 use Gemini\Data\Content;
 use Gemini\Enums\TaskType;
+use Gemini\Requests\GenerativeModel\EmbedContentRequest;
+use Gemini\Responses\GenerativeModel\BatchEmbedContentsResponse;
 use Gemini\Responses\GenerativeModel\EmbedContentResponse;
 
 interface EmbeddingModalContract
@@ -15,4 +17,13 @@ interface EmbeddingModalContract
      * @param  string|Blob|array<string|Blob>|Content  $content
      */
     public function embedContent(string|Blob|array|Content $content, ?TaskType $taskType = null, ?string $title = null): EmbedContentResponse;
+
+    /**
+     * Generates multiple embedding vectors from the input Content which consists of a batch of strings represented as EmbedContentRequest objects.
+     *
+     * @see https://ai.google.dev/api/embeddings#method:-models.batchembedcontents
+     *
+     * @param  string|Blob|array<string|Blob>|Content|EmbedContentRequest  ...$parts
+     */
+    public function batchEmbedContents(string|Blob|array|Content|EmbedContentRequest ...$parts): BatchEmbedContentsResponse;
 }

src/Requests/GenerativeModel/BatchEmbedContentRequest.php

@@ -0,0 +1,58 @@
+<?php
+
+declare(strict_types=1);
+
+namespace Gemini\Requests\GenerativeModel;
+
+use Gemini\Concerns\HasContents;
+use Gemini\Data\Blob;
+use Gemini\Data\Content;
+use Gemini\Enums\Method;
+use Gemini\Foundation\Request;
+use Gemini\Requests\Concerns\HasJsonBody;
+
+class BatchEmbedContentRequest extends Request
+{
+    use HasContents;
+    use HasJsonBody;
+
+    protected Method $method = Method::POST;
+
+    /**
+     * @param  string  $model  The model's resource name. This serves as an ID for the Model to use.
+     * @param  array<string|Blob|array<string|Blob>|Content|EmbedContentRequest>  $parts
+     */
+    public function __construct(
+        protected readonly string $model,
+        protected readonly array $parts,
+    ) {}
+
+    public function resolveEndpoint(): string
+    {
+        return "{$this->model}:batchEmbedContents";
+    }
+
+    /**
+     * Default body
+     *
+     * @return array<string, mixed>
+     */
+    protected function defaultBody(): array
+    {
+        return [
+            'requests' => array_map(
+                fn (EmbedContentRequest|string|Blob|array|Content $part) => [
+                    'model' => $this->model,
+                    ...match (true) {
+                        $part instanceof EmbedContentRequest => $part->body(),
+                        default => (new EmbedContentRequest(
+                            model: $this->model,
+                            part: $part
+                        ))->body(),
+                    },
+                ],
+                $this->parts
+            ),
+        ];
+    }
+}

src/Resources/EmbeddingModel.php

@@ -11,7 +11,9 @@
 use Gemini\Data\Content;
 use Gemini\Enums\ModelType;
 use Gemini\Enums\TaskType;
+use Gemini\Requests\GenerativeModel\BatchEmbedContentRequest;
 use Gemini\Requests\GenerativeModel\EmbedContentRequest;
+use Gemini\Responses\GenerativeModel\BatchEmbedContentsResponse;
 use Gemini\Responses\GenerativeModel\EmbedContentResponse;
 use Gemini\Transporters\DTOs\ResponseDTO;
 
@@ -49,4 +51,24 @@ public function embedContent(string|Blob|array|Content $content, ?TaskType $task
 
         return EmbedContentResponse::from($response->data());
     }
+
+    /**
+     * Generates multiple embedding vectors from the input Content which consists of a batch of strings represented as EmbedContentRequest objects.
+     *
+     * @see https://ai.google.dev/api/embeddings#method:-models.batchembedcontents
+     *
+     * @param  string|Blob|array<string|Blob>|Content|EmbedContentRequest  ...$parts
+     */
+    public function batchEmbedContents(string|Blob|array|Content|EmbedContentRequest ...$parts): BatchEmbedContentsResponse
+    {
+        /** @var ResponseDTO<array{ embeddings: array{ array{ values: array<float> } } }> $response */
+        $response = $this->transporter->request(
+            request: new BatchEmbedContentRequest(
+                model: $this->model,
+                parts: $parts
+            )
+        );
+
+        return BatchEmbedContentsResponse::from($response->data());
+    }
 }

src/Responses/GenerativeModel/BatchEmbedContentsResponse.php

@@ -0,0 +1,49 @@
+<?php
+
+declare(strict_types=1);
+
+namespace Gemini\Responses\GenerativeModel;
+
+use Gemini\Contracts\ResponseContract;
+use Gemini\Data\ContentEmbedding;
+use Gemini\Testing\Responses\Concerns\Fakeable;
+
+/**
+ * https://ai.google.dev/api/rest/v1/models/embedContent#response-body
+ */
+final class BatchEmbedContentsResponse implements ResponseContract
+{
+    use Fakeable;
+
+    /**
+     * @param  array<ContentEmbedding>  $embeddings
+     */
+    private function __construct(
+        public readonly array $embeddings,
+    ) {}
+
+    /**
+     * @param  array{ embeddings: array{ array{ values: array<float> } } }  $attributes
+     */
+    public static function from(array $attributes): self
+    {
+        $embeddings = array_map(
+            static fn (array $embedding): ContentEmbedding => ContentEmbedding::from($embedding),
+            $attributes['embeddings'],
+        );
+
+        return new self(
+            embeddings: $embeddings
+        );
+    }
+
+    public function toArray(): array
+    {
+        return [
+            'embeddings' => array_map(
+                static fn (ContentEmbedding $embedding): array => $embedding->toArray(),
+                $this->embeddings
+            ),
+        ];
+    }
+}

src/Testing/Resources/EmbeddingModelTestResource.php

@@ -8,7 +8,9 @@
 use Gemini\Data\Blob;
 use Gemini\Data\Content;
 use Gemini\Enums\TaskType;
+use Gemini\Requests\GenerativeModel\EmbedContentRequest;
 use Gemini\Resources\EmbeddingModel;
+use Gemini\Responses\GenerativeModel\BatchEmbedContentsResponse;
 use Gemini\Responses\GenerativeModel\EmbedContentResponse;
 use Gemini\Testing\Resources\Concerns\Testable;
 
@@ -25,4 +27,16 @@ public function embedContent(Blob|Content|array|string $content, ?TaskType $task
     {
         return $this->record(method: __FUNCTION__, args: func_get_args(), model: $this->model);
     }
+
+    /**
+     * Generates multiple embedding vectors from the input Content which consists of a batch of strings represented as EmbedContentRequest objects.
+     *
+     * @see https://ai.google.dev/api/embeddings#method:-models.batchembedcontents
+     *
+     * @param  string|Blob|array<string|Blob>|Content|EmbedContentRequest  ...$parts
+     */
+    public function batchEmbedContents(string|Blob|array|Content|EmbedContentRequest ...$parts): BatchEmbedContentsResponse
+    {
+        return $this->record(method: __FUNCTION__, args: func_get_args(), model: $this->model);
+    }
 }

src/Testing/Responses/Fixtures/GenerativeModel/BatchEmbedContentsResponseFixture.php

@@ -0,0 +1,35 @@
+<?php
+
+declare(strict_types=1);
+
+namespace Gemini\Testing\Responses\Fixtures\GenerativeModel;
+
+final class BatchEmbedContentsResponseFixture
+{
+    public const ATTRIBUTES = [
+        'embeddings' => [
+            [
+                'values' => [
+                    0.008624583,
+                    -0.030451821,
+                    -0.042496547,
+                    -0.029230341,
+                    0.05486475,
+                    0.006694871,
+                    0.004025645,
+                ],
+            ],
+            [
+                'values' => [
+                    0.008624583,
+                    -0.030451821,
+                    -0.042496547,
+                    -0.029230341,
+                    0.05486475,
+                    0.006694871,
+                    0.004025645,
+                ],
+            ],
+        ],
+    ];
+}

tests/Resources/EmbeddingModel.php

@@ -3,6 +3,7 @@
 use Gemini\Data\ContentEmbedding;
 use Gemini\Enums\Method;
 use Gemini\Enums\ModelType;
+use Gemini\Responses\GenerativeModel\BatchEmbedContentsResponse;
 use Gemini\Responses\GenerativeModel\EmbedContentResponse;
 
 test('embed content', function () {
@@ -16,6 +17,18 @@
         ->embedding->toBeInstanceOf(ContentEmbedding::class)
         ->embedding->values->toBeArray()->toHaveCount(7);
 });
+test('batch embed contents', function () {
+    $modelType = ModelType::EMBEDDING;
+    $client = mockClient(method: Method::POST, endpoint: "{$modelType->value}:batchEmbedContents", response: BatchEmbedContentsResponse::fake());
+
+    $result = $client->embeddingModel()->batchEmbedContents('Test', 'Test2');
+
+    expect($result)
+        ->toBeInstanceOf(BatchEmbedContentsResponse::class)
+        ->embeddings->toBeArray()->toHaveCount(2)
+        ->embeddings->each->toBeInstanceOf(ContentEmbedding::class)
+        ->embeddings->each(fn ($embeddingModel) => $embeddingModel->values->toBeArray()->toHaveCount(7));
+});
 
 test('embed content for custom model', function () {
     $modelType = 'models/gemini-1.0-pro-001';

tests/Responses/GenerativeModel/BatchEmbedContentsResponse.php

@@ -0,0 +1,62 @@
+<?php
+
+use Gemini\Data\ContentEmbedding;
+use Gemini\Responses\GenerativeModel\BatchEmbedContentsResponse;
+
+test('from', function () {
+    $response = BatchEmbedContentsResponse::from(BatchEmbedContentsResponse::fake()->toArray());
+
+    expect($response)
+        ->toBeInstanceOf(BatchEmbedContentsResponse::class)
+        ->embeddings->each->toBeInstanceOf(ContentEmbedding::class)
+        ->embeddings->each(fn ($embeddingModel) => $embeddingModel->values->toBeArray()->toMatchArray([
+            0.008624583,
+            -0.030451821,
+            -0.042496547,
+            -0.029230341,
+            0.05486475,
+            0.006694871,
+            0.004025645,
+        ]));
+});
+
+test('fake', function () {
+    $response = BatchEmbedContentsResponse::fake();
+
+    expect($response)
+        ->toBeInstanceOf(BatchEmbedContentsResponse::class)
+        ->embeddings->each->toBeInstanceOf(ContentEmbedding::class)
+        ->embeddings->each(fn ($embeddingModel) => $embeddingModel->values->toBeArray()->toMatchArray([
+            0.008624583,
+            -0.030451821,
+            -0.042496547,
+            -0.029230341,
+            0.05486475,
+            0.006694871,
+            0.004025645,
+        ]));
+});
+
+test('to array', function () {
+    $attributes = BatchEmbedContentsResponse::fake()->toArray();
+    $response = BatchEmbedContentsResponse::from($attributes);
+
+    expect($response->toArray())
+        ->toBeArray()->toBe($attributes);
+});
+
+test('fake with override', function () {
+    $response = BatchEmbedContentsResponse::fake([
+        'embeddings' => [
+            [
+                'values' => [
+                    0,
+                ],
+            ],
+        ],
+    ]);
+    expect($response)
+        ->embeddings->{0}->values->toBeArray()->toMatchArray([
+            0,
+        ]);
+});