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projectsaturnstudios/llm-speak
==============================

Universal LLM interface for Laravel applications.

0.8.2(7mo ago)0221MITPHPPHP ^8.2

Since Jun 10Pushed 7mo agoCompare

[ Source](https://github.com/projectsaturnstudios/llm-speak)[ Packagist](https://packagist.org/packages/projectsaturnstudios/llm-speak)[ RSS](/packages/projectsaturnstudios-llm-speak/feed)WikiDiscussions main Synced 1mo ago

READMEChangelogDependencies (7)Versions (11)Used By (0)

LLM Speak
=========

[](#llm-speak)

### A Laravel package to interact with LLMs using Eloquent-like syntax.

[](#a-laravel-package-to-interact-with-llms-using-eloquent-like-syntax)

Installation
------------

[](#installation)

### 1) Install via Composer

[](#1-install-via-composer)

```
composer require llm-speak/core
```

### 2) Publish configuration (optional)

[](#2-publish-configuration-optional)

```
# Publish all LLM Speak configs and installed provider configs
php artisan vendor:publish --tag="llms"

# Publish Tokenization configs and installed provider Tokenization configs
php artisan vendor:publish --tag="llms.tt"

# Publish Embeddings configs and installed provider Embeddings configs
php artisan vendor:publish --tag="llms.ve"

# Publish OneShot Inferencing configs and installed provider Inferencing configs
php artisan vendor:publish --tag="llms.mi"
```

### 3) Install Drivers

[](#3-install-drivers)

Drivers include:

- OpenAI Compatible — Embeddings, Inferencing, Chat Completions
- Ollama — Embeddings, Inferencing, Chat Completions
- LMStudio — Embeddings, Inferencing, Chat Completions
- MistralAI — Embeddings, Inferencing, Chat Completions
- Gemini — Embeddings, Inferencing, Chat Completions
- xAI — Tokenization, Chat Completions
- OpenRouter — Inferencing, Chat Completions
- Anthropic — Chat Completions
- OpenAI Responses — Chat Completions
- HuggingFace — Chat Completions

Usage
-----

[](#usage)

### Create a Neural Model

[](#create-a-neural-model)

1. Create a Neural Model that extends the type of interaction you want (e.g., Tokenization, Embeddings, Inference, Completions).
2. Available Neural Models: `TokenizationModel`, `EmbeddingsModel`, `InferenceModel`, `CompletionsModel`.
3. When creating the model, the default provider and model are read from the model's config.
4. You can override by setting the model's `$connection` and `$model_id` properties.

```
use LLMSpeak\Core\NeuralModels\CompletionsModel;

class SomeModel extends CompletionsModel
{
    // Optional: override the default provider/model from config
    protected string $connection = 'oaic';
    protected string $model_id = 'gpt-3.5-turbo-instruct';
}
```

```
use LLMSpeak\Core\NeuralModels\InferenceModel;

class SomeModel extends InferenceModel
{
    // Optional: override the default provider/model from config
    protected string $connection = 'oaic';
    protected string $model_id = 'gpt-3.5-turbo-instruct';
}
```

```
use LLMSpeak\Core\NeuralModels\EmbeddingsModel;

class SomeModel extends EmbeddingsModel
{
    // Optional: override the default provider/model from config
    protected string $connection = 'oaic';
    protected string $model_id = 'text-embedding-ada-002';
}
```

```
use LLMSpeak\Core\NeuralModels\TokenizationModel;

class SomeTokenizerModel extends TokenizationModel
{
    // Optional: override the default provider/model from config
    protected string $connection = 'x-ai';
    protected string $model_id = 'grok-4-0709';
}
```

### Chat Completions

[](#chat-completions)

Configure your provider in `config/chat-completions.php`:

```
// Sample OpenAICompatible config in config/text-tokenization.php
'connections' => [
    'oaic' => [
        'driver' => 'open-ai-compatible',
        'url' => env('OAIC_URL', 'https://api.openai.com'),
        'model' => env('OAIC_EMBED_MODEL', 'gpt-5'),
        'headers' => [
            'Content-Type' => 'application/json',
            'Accept' => 'application/json',
            'Authorization' => 'Bearer ' . env('OAIC_API_KEY', ''),
        ],
    ],
    'local' => [
        'driver' => 'ollama',
        'url' => env('OLLAMA_URL', 'http://localhost:11434/api'),
        'model' => env('OLLAMA_EMBED_MODEL', 'llama3:latest'),
        'headers' => [
            'Content-Type' => 'application/json',
            'Accept' => 'application/json',
        ],
    ],
]
```

Use the Completions model to have a model reply to your input.

#### Instance usage

[](#instance-usage)

```
use App\NeuralModels\Embeddings\OllamaEmbeddingsModel;

// Start tokenization query
$model = new OneShotModel();

// Build prompts
$query = $model->where('prompt', 'Why is the sky blue?');
$query = $model->wherePrompt('Why is the sky blue?');
$query = $model->whereIn('prompt', ['Why is the sky blue?', 'What is Rayleigh scattering?']);
$query = $model->wherePrompt('Why is the sky blue?')->wherePrompt('What is Rayleigh scattering?');

// Execute
$modelCollection = $model->get();        // Collection of models (responses)
$tokensModel     = $model->first();      // Single model (latest response)
```

#### Fluent usage

[](#fluent-usage)

```
$modelCollection = (new Chatmodel())
    ->where('message', 'Why is the sky blue?')
    ->get();

$model = (new Chatmodel())
    ->where('message', 'Why is the sky blue?')
    ->first();
```

#### Manual builder usage

[](#manual-builder-usage)

```
$query = (new Chatmodel())->newQuery();
$query = $query->where('message', 'Why is the sky blue?');
$modelCollection = $query->get();
```

#### Advanced usage

[](#advanced-usage)

These additional parameters can be set on the Completions model like so.

```
$model = (new Chatmodel())
    ->where('message', 'Why is the sky blue?')
    ->whereTemperature(0.7)  // Set temperature that controls randomness
    ->first();

// Max tokens to use in response
$model->whereMaxTokens($max_tokens);
// Penalize new tokens based on their existing frequency in the text so far
$model->whereFrequencyPenalty($frequency_penalty);
// Penalize new tokens based on whether they appear in the text so far
$model->wherePresencePenalty($presence_penalty);
// Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
$model->whereSeed($seed);
// Number of completions to generate for each prompt
$model->whereN($n);
// Modify the behavior of the model with system instructions
$model->whereSystemInstructions($instructions);
// Add tool functions for the model to request.
$model->whereTools($user_id);
```

NOTE: Not all providers support all these parameters.

### OneShot Inferencing

[](#oneshot-inferencing)

Configure your provider in `config/inferencing.php`:

```
// Sample OpenAICompatible config in config/text-tokenization.php
'connections' => [
    'oaic' => [
        'driver' => 'open-ai-compatible',
        'url' => env('OAIC_URL', 'https://api.openai.com'),
        'model' => env('OAIC_EMBED_MODEL', 'gpt-3.5-turbo-instruct'),
        'headers' => [
            'Content-Type' => 'application/json',
            'Accept' => 'application/json',
            'Authorization' => 'Bearer ' . env('OAIC_API_KEY', ''),
        ],
    ],
    'local' => [
        'driver' => 'ollama',
        'url' => env('OLLAMA_URL', 'http://localhost:11434/api'),
        'model' => env('OLLAMA_EMBED_MODEL', 'llama3:latest'),
        'headers' => [
            'Content-Type' => 'application/json',
            'Accept' => 'application/json',
        ],
    ],
]
```

Use the Inference model to have a model response to your input.

#### Instance usage

[](#instance-usage-1)

```
use App\NeuralModels\Embeddings\OllamaEmbeddingsModel;

// Start tokenization query
$model = new OneShotModel();

// Build prompts
$query = $model->where('prompt', 'Why is the sky blue?');
$query = $model->wherePrompt('Why is the sky blue?');
$query = $model->whereIn('prompt', ['Why is the sky blue?', 'What is Rayleigh scattering?']);
$query = $model->wherePrompt('Why is the sky blue?')->wherePrompt('What is Rayleigh scattering?');

// Execute
$modelCollection = $model->get();        // Collection of models (responses)
$tokensModel     = $model->first();      // Single model (latest response)
```

#### Fluent usage

[](#fluent-usage-1)

```
$modelCollection = (new OneShotModel())
    ->where('prompt', 'Why is the sky blue?')
    ->get();

$model = (new OneShotModel())
    ->where('prompt', 'Why is the sky blue?')
    ->first();
```

#### Manual builder usage

[](#manual-builder-usage-1)

```
$query = (new OneShotModel())->newQuery();
$query = $query->where('prompt', 'Why is the sky blue?');
$modelCollection = $query->get();
```

#### Advanced usage

[](#advanced-usage-1)

These additional parameters can be set on the Embeddings model like so.

```
$model = (new OneShotModel())
    ->where('prompt', 'Why is the sky blue?')
    ->whereTemperature(0.7)  // Set temperature that controls randomness
    ->first();

// Max tokens to use in response
$model->whereMaxTokens($max_tokens);
// Penalize new tokens based on their existing frequency in the text so far
$model->whereFrequencyPenalty($frequency_penalty);
// Penalize new tokens based on whether they appear in the text so far
$model->wherePresencePenalty($presence_penalty);
// Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
$model->whereSeed($seed);
// Number of completions to generate for each prompt
$model->whereN($n);
```

NOTE: Not all providers support all these parameters.

### Vector Embeddings

[](#vector-embeddings)

Configure your provider in `config/vector-embeddings.php`:

```
// Sample OpenAICompatible config in config/text-tokenization.php
'connections' => [
    'oaic' => [
        'driver' => 'open-ai-compatible',
        'url' => env('OAIC_URL', 'https://api.openai.com'),
        'model' => env('OAIC_EMBED_MODEL', 'text-embedding-ada-002'),
        'headers' => [
            'Content-Type' => 'application/json',
            'Accept' => 'application/json',
            'Authorization' => 'Bearer ' . env('OAIC_API_KEY', ''),
        ],
    ],
    'local' => [
        'driver' => 'ollama',
        'url' => env('OLLAMA_URL', 'http://localhost:11434/api'),
        'model' => env('OLLAMA_EMBED_MODEL', 'llama3:latest'),
        'headers' => [
            'Content-Type' => 'application/json',
            'Accept' => 'application/json',
        ],
    ],
]
```

Use the Embeddings model to convert text to vectors.

#### Instance usage

[](#instance-usage-2)

```
use App\NeuralModels\Embeddings\OllamaEmbeddingsModel;

// Start tokenization query
$model = new OllamaEmbeddingsModel();

// Build prompts
$query = $model->where('text', 'Why is the sky blue?');
$query = $model->whereInput('Why is the sky blue?');
$query = $model->whereIn('text', ['Why is the sky blue?', 'What is Rayleigh scattering?']);
$query = $model->whereInput('Why is the sky blue?')->whereInput('What is Rayleigh scattering?');

// Execute
$modelCollection = $model->get();        // Collection of models (responses)
$tokensModel     = $model->first();      // Single model (latest response)
```

#### Fluent usage

[](#fluent-usage-2)

```
$modelCollection = (new OllamaEmbeddingsModel())
    ->where('input', 'Why is the sky blue?')
    ->get();

$model = (new OllamaEmbeddingsModel())
    ->where('input', 'Why is the sky blue?')
    ->first();
```

#### Manual builder usage

[](#manual-builder-usage-2)

```
$query = (new OllamaEmbeddingsModel())->newQuery();
$query = $query->where('input', 'Why is the sky blue?');
$modelCollection = $query->get();
```

#### Advanced usage

[](#advanced-usage-2)

These additional parameters can be set on the Embeddings model like so.

```
$model = (new OAICEmbeddingsModel())
    ->where('input', 'Why is the sky blue?')
    ->whereDimensions(1536)  // Set dimension if needed
    ->first();

$model->whereEncodingFormat($encoding_format);
$model->whereUser($user_id);
$model->whereTitle($title);
$model->whereTaskType($task_type);
```

NOTE: Not all providers support all these parameters.

### Tokenization

[](#tokenization)

Configure your provider in `config/text-tokenization.php`:

```
// Sample xAI config in config/text-tokenization.php
'connections' => [
    'x-ai' => [
        'driver' => 'x-ai',
        'url' => env('XAI_URL', 'https://api.x.ai'),
        'model' => env('XAI_TOKEN_MODEL', 'grok-4-0709'),
        'headers' => [
            'Content-Type' => 'application/json',
            'Accept' => 'application/json',
            'Authorization' => 'Bearer ' . env('XAI_API_KEY', ''),
        ],
    ],
]
```

Use the Tokenization model to convert text to tokens.

#### Instance usage

[](#instance-usage-3)

```
use App\NeuralModels\Tokenizations\GrokTokensModel;

// Start tokenization query
$model = new GrokTokensModel();

// Build prompts
$query = $model->where('text', 'Why is the sky blue?');
$query = $model->whereText('Why is the sky blue?');
$query = $model->whereIn('text', ['Why is the sky blue?', 'What is Rayleigh scattering?']);
$query = $model->whereText('Why is the sky blue?')->whereText('What is Rayleigh scattering?');

// Execute
$modelCollection = $model->get();        // Collection of models (responses)
$tokensModel     = $model->first();      // Single model (latest response)
```

#### Fluent usage

[](#fluent-usage-3)

```
$modelCollection = (new GrokTokensModel())
    ->where('text', 'Why is the sky blue?')
    ->get();

$model = (new GrokTokensModel())
    ->where('text', 'Why is the sky blue?')
    ->first();
```

#### Manual builder usage

[](#manual-builder-usage-3)

```
$query = (new GrokTokensModel())->newQuery();
$query = $query->where('text', 'Why is the sky blue?');
$modelCollection = $query->get();
```

###  Health Score

32

—

LowBetter than 72% of packages

Maintenance63

Regular maintenance activity

Popularity7

Limited adoption so far

Community7

Small or concentrated contributor base

Maturity45

Maturing project, gaining track record

 Bus Factor1

Top contributor holds 100% of commits — single point of failure

How is this calculated?**Maintenance (25%)** — Last commit recency, latest release date, and issue-to-star ratio. Uses a 2-year decay window.

**Popularity (30%)** — Total and monthly downloads, GitHub stars, and forks. Logarithmic scaling prevents top-heavy scores.

**Community (15%)** — Contributors, dependents, forks, watchers, and maintainers. Measures real ecosystem engagement.

**Maturity (30%)** — Project age, version count, PHP version support, and release stability.

###  Release Activity

Cadence

Every ~12 days

Recently: every ~20 days

Total

10

Last Release

226d ago

### Community

Maintainers

![](https://www.gravatar.com/avatar/2169021b88d520bd58d9a37f51fa55058af7adbf7362c8cade65b261d644874c?d=identicon)[projectsaturnstudios](/maintainers/projectsaturnstudios)

---

Top Contributors

[![projectsaturnstudios](https://avatars.githubusercontent.com/u/10563160?v=4)](https://github.com/projectsaturnstudios "projectsaturnstudios (10 commits)")

---

Tags

large-language-modelsllms

###  Code Quality

TestsPest

### Embed Badge

![Health badge](/badges/projectsaturnstudios-llm-speak/health.svg)

```
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```

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