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https://github.com/google-gemini-php/symfony
⚡️ Gemini PHP for Symfony is a community-maintained PHP API client that allows you to interact with the Gemini AI API.
https://github.com/google-gemini-php/symfony
gemini gemini-api google google-ai google-ai-platform symfony symfony-bundle
Last synced: about 1 month ago
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⚡️ Gemini PHP for Symfony is a community-maintained PHP API client that allows you to interact with the Gemini AI API.
- Host: GitHub
- URL: https://github.com/google-gemini-php/symfony
- Owner: google-gemini-php
- License: mit
- Created: 2024-02-13T05:44:41.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-02-17T09:31:51.000Z (9 months ago)
- Last Synced: 2024-09-30T16:04:48.550Z (about 2 months ago)
- Topics: gemini, gemini-api, google, google-ai, google-ai-platform, symfony, symfony-bundle
- Language: PHP
- Homepage:
- Size: 41 KB
- Stars: 11
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
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README
------
**Gemini PHP** for Symfony is a community-maintained PHP API client that allows you to interact with the Gemini AI API.
- Fatih AYDIN [github.com/aydinfatih](https://github.com/aydinfatih)
For more information, take a look at the [google-gemini-php/client](https://github.com/google-gemini-php/client) repository.
## Table of Contents
- [Prerequisites](#prerequisites)
- [Setup](#setup)
- [Installation](#installation)
- [Setup your API key](#setup-your-api-key)
- [Usage](#usage)
- [Chat Resource](#chat-resource)
- [Text-only Input](#text-only-input)
- [Text-and-image Input](#text-and-image-input)
- [Multi-turn Conversations (Chat)](#multi-turn-conversations-chat)
- [Stream Generate Content](#stream-generate-content)
- [Count tokens](#count-tokens)
- [Configuration](#configuration)
- [Embedding Resource](#embedding-resource)
- [Models](#models)
- [List Models](#list-models)
- [Get Model](#get-model)
- [Testing](#testing)
## Prerequisites
To complete this quickstart, make sure that your development environment meets the following requirements:
- Requires [PHP 8.1+](https://php.net/releases/)
- Requires [Symfony 5,6,7](https://symfony.com/)
## Setup
### Installation
First, install Gemini via the [Composer](https://getcomposer.org/) package manager:
```bash
composer require google-gemini-php/symfony
```
Next, register the bundle in your config/bundles.php:
```bash
return [
// ...
Gemini\Symfony\GeminiBundle::class => ['all' => true],
]
```
This will create a .env configuration file in your project, which you can modify to your needs using environment variables:
```
GEMINI_API_KEY=
```
You can also define the following environment variables.
```
GEMINI_BASE_URL=
```
### Setup your API key
To use the Gemini API, you'll need an API key. If you don't already have one, create a key in Google AI Studio.
[Get an API key](https://makersuite.google.com/app/apikey)
## Usage
Interact with Gemini's API:
```php
$result = $container->get('gemini')->geminiPro()->generateContent('Hello');
$result->text(); // Hello! How can I assist you today?
```
### Chat Resource
#### Text-only Input
Generate a response from the model given an input message. If the input contains only text, use the `gemini-pro` model.
```php
$result = $container->get('gemini')->geminiPro()->generateContent('Hello');
$result->text(); // Hello! How can I assist you today?
```
#### Text-and-image Input
If the input contains both text and image, use the `gemini-pro-vision` model.
```php
$result = $container->get('gemini')->geminiProVision()
->generateContent([
'What is this picture?',
new Blob(
mimeType: MimeType::IMAGE_JPEG,
data: base64_encode(
file_get_contents('https://storage.googleapis.com/generativeai-downloads/images/scones.jpg')
)
)
]);
$result->text(); // The picture shows a table with a white tablecloth. On the table are two cups of coffee, a bowl of blueberries, a silver spoon, and some flowers. There are also some blueberry scones on the table.
```
#### Multi-turn Conversations (Chat)
Using Gemini, you can build freeform conversations across multiple turns.
```php
$chat = $container->get('gemini')->chat()
->startChat(history: [
Content::parse(part: 'The stories you write about what I have to say should be one line. Is that clear?'),
Content::parse(part: 'Yes, I understand. The stories I write about your input should be one line long.', role: Role::MODEL)
]);
$response = $chat->sendMessage('Create a story set in a quiet village in 1600s France');
echo $response->text(); // Amidst rolling hills and winding cobblestone streets, the tranquil village of Beausoleil whispered tales of love, intrigue, and the magic of everyday life in 17th century France.
$response = $chat->sendMessage('Rewrite the same story in 1600s England');
echo $response->text(); // In the heart of England's lush countryside, amidst emerald fields and thatched-roof cottages, the village of Willowbrook unfolded a tapestry of love, mystery, and the enchantment of ordinary days in the 17th century.
```
>The `gemini-pro-vision` model (for text-and-image input) is not yet optimized for multi-turn conversations. Make sure to use gemini-pro and text-only input for chat use cases.
#### Stream Generate Content
By default, the model returns a response after completing the entire generation process. You can achieve faster interactions by not waiting for the entire result, and instead use streaming to handle partial results.
```php
$stream = $container->get('gemini')->geminiPro()
->streamGenerateContent('Write long a story about a magic backpack.');
foreach ($stream as $response) {
echo $response->text();
}
```
#### Count tokens
When using long prompts, it might be useful to count tokens before sending any content to the model.
```php
$response = $container->get('gemini')->geminiPro()
->countTokens('Write a story about a magic backpack.');
echo $response->totalTokens; // 9
```
#### Configuration
Every prompt you send to the model includes parameter values that control how the model generates a response. The model can generate different results for different parameter values. Learn more about [model parameters](https://ai.google.dev/docs/concepts#model_parameters).
Also, you can use safety settings to adjust the likelihood of getting responses that may be considered harmful. By default, safety settings block content with medium and/or high probability of being unsafe content across all dimensions. Learn more about [safety settings](https://ai.google.dev/docs/concepts#safety_setting).
```php
use Gemini\Data\GenerationConfig;
use Gemini\Enums\HarmBlockThreshold;
use Gemini\Data\SafetySetting;
use Gemini\Enums\HarmCategory;
$safetySettingDangerousContent = new SafetySetting(
category: HarmCategory::HARM_CATEGORY_DANGEROUS_CONTENT,
threshold: HarmBlockThreshold::BLOCK_ONLY_HIGH
);
$safetySettingHateSpeech = new SafetySetting(
category: HarmCategory::HARM_CATEGORY_HATE_SPEECH,
threshold: HarmBlockThreshold::BLOCK_ONLY_HIGH
);
$generationConfig = new GenerationConfig(
stopSequences: [
'Title',
],
maxOutputTokens: 800,
temperature: 1,
topP: 0.8,
topK: 10
);
$generativeModel = $container->get('gemini')->geminiPro()
->withSafetySetting($safetySettingDangerousContent)
->withSafetySetting($safetySettingHateSpeech)
->withGenerationConfig($generationConfig)
->generateContent("Write a story about a magic backpack.");
```
### Embedding Resource
Embedding is a technique used to represent information as a list of floating point numbers in an array. With Gemini, you can represent text (words, sentences, and blocks of text) in a vectorized form, making it easier to compare and contrast embeddings. For example, two texts that share a similar subject matter or sentiment should have similar embeddings, which can be identified through mathematical comparison techniques such as cosine similarity.
Use the `embedding-001` model with either `embedContents` or `batchEmbedContents`:
```php
$response = $container->get('gemini')->embeddingModel()
->embedContent("Write a story about a magic backpack.");
print_r($response->embedding->values);
//[
// [0] => 0.008624583
// [1] => -0.030451821
// [2] => -0.042496547
// [3] => -0.029230341
// [4] => 0.05486475
// [5] => 0.006694871
// [6] => 0.004025645
// [7] => -0.007294857
// [8] => 0.0057651913
// ...
//]
```
### Models
#### List Models
Use list models to see the available Gemini models:
```php
$response = $container->get('gemini')->models()->list();
$response->models;
//[
// [0] => Gemini\Data\Model Object
// (
// [name] => models/gemini-pro
// [version] => 001
// [displayName] => Gemini Pro
// [description] => The best model for scaling across a wide range of tasks
// ...
// )
// [1] => Gemini\Data\Model Object
// (
// [name] => models/gemini-pro-vision
// [version] => 001
// [displayName] => Gemini Pro Vision
// [description] => The best image understanding model to handle a broad range of applications
// ...
// )
// [2] => Gemini\Data\Model Object
// (
// [name] => models/embedding-001
// [version] => 001
// [displayName] => Embedding 001
// [description] => Obtain a distributed representation of a text.
// ...
// )
//]
```
#### Get Model
Get information about a model, such as version, display name, input token limit, etc.
```php
$response = $container->get('gemini')->models()->retrieve(ModelType::GEMINI_PRO);
$response->model;
//Gemini\Data\Model Object
//(
// [name] => models/gemini-pro
// [version] => 001
// [displayName] => Gemini Pro
// [description] => The best model for scaling across a wide range of tasks
// ...
//)
```
## Testing
The package provides a fake implementation of the `Gemini\Client` class that allows you to fake the API responses.
To test your code ensure you swap the `Gemini\Client` class with the `Gemini\Testing\ClientFake` class in your test case.
The fake responses are returned in the order they are provided while creating the fake client.
All responses are having a `fake()` method that allows you to easily create a response object by only providing the parameters relevant for your test case.
```php
use Gemini\Testing\ClientFake;
use Gemini\Responses\GenerativeModel\GenerateContentResponse;
$container->get('gemini')->fake([
GenerateContentResponse::fake([
'candidates' => [
[
'content' => [
'parts' => [
[
'text' => 'success',
],
],
],
],
],
]),
]);
$result = $container->get('gemini')->geminiPro()->generateContent('test');
expect($result->text())->toBe('success');
```
In case of a streamed response you can optionally provide a resource holding the fake response data.
```php
use Gemini\Testing\ClientFake;
use Gemini\Responses\GenerativeModel\GenerateContentResponse;
$container->get('gemini')->fake([
GenerateContentResponse::fakeStream(),
]);
$result = $container->get('gemini')->geminiPro()->streamGenerateContent('Hello');
expect($response->getIterator()->current())
->text()->toBe('In the bustling city of Aethelwood, where the cobblestone streets whispered');
```
After the requests have been sent there are various methods to ensure that the expected requests were sent:
```php
// assert list models request was sent
$container->get('gemini')->models()->assertSent(callback: function ($method) {
return $method === 'list';
});
// or
$container->get('gemini')->assertSent(resource: Models::class, callback: function ($method) {
return $method === 'list';
});
$container->get('gemini')->geminiPro()->assertSent(function (string $method, array $parameters) {
return $method === 'generateContent' &&
$parameters[0] === 'Hello';
});
// or
$container->get('gemini')->assertSent(resource: GenerativeModel::class, model: ModelType::GEMINI_PRO, callback: function (string $method, array $parameters) {
return $method === 'generateContent' &&
$parameters[0] === 'Hello';
});
// assert 2 generative model requests were sent
$container->get('gemini')->assertSent(resource: GenerativeModel::class, model: ModelType::GEMINI_PRO, callback: 2);
// or
$container->get('gemini')->geminiPro()->assertSent(2);
// assert no generative model requests were sent
$container->get('gemini')->assertNotSent(resource: GenerativeModel::class, model: ModelType::GEMINI_PRO);
// or
$container->get('gemini')->geminiPro()->assertNotSent();
// assert no requests were sent
$container->get('gemini')->assertNothingSent();
```
To write tests expecting the API request to fail you can provide a `Throwable` object as the response.
```php
$container->get('gemini')->fake([
new ErrorException([
'message' => 'The model `gemini-basic` does not exist',
'status' => 'INVALID_ARGUMENT',
'code' => 400,
]),
]);
// the `ErrorException` will be thrown
$container->get('gemini')->geminiPro()->generateContent('test');
```