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LLaMa.CPP

Llama.cpp Embeddings API

The Embeddings API allows you to convert input text into high-dimensional vector representations (embeddings). These embeddings are useful for various tasks such as semantic search, clustering, recommendation systems, or as input for other machine learning models.

Example: Generating Embeddings

use Partitech\PhpMistral\Clients\LlamaCpp\LlamaCppClient;
use Partitech\PhpMistral\MistralClientException;

$llamacppUrl = getenv('LLAMACPP_URL');
$llamacppApiKey = getenv('LLAMACPP_API_KEY');

$client = new LlamaCppClient(apiKey: $llamacppApiKey, url: $llamacppUrl);

// Define the inputs for which you want embeddings
$inputs = [
    "What is the best French cheese?",
];

try {
    // Request embeddings for the given inputs
    $embeddingsBatchResponse = $client->embeddings($inputs);

    print_r($embeddingsBatchResponse); // Full response, including metadata and embeddings

} catch (MistralClientException $e) {
    echo $e->getMessage();
    exit(1);
}

Example Output

Array
(
    [model] => llama-2-7b
    [object] => list
    [usage] => Array
        (
            [prompt_tokens] => 9
            [total_tokens] => 9
        )
    [data] => Array
        (
            [0] => Array
                (
                    [embedding] => Array
                        (
                            [0] => 0.026056783273816
                            [1] => -0.052360784262419
                            ...
                            [383] => 0.074204355478287
                        )
                    [index] => 0
                    [object] => embedding
                )
        )
)
  • model: The model used for generating embeddings.
  • usage: Token usage for the input (helpful for cost monitoring).
  • data: The generated embeddings for each input. Each embedding is a list of floating-point numbers representing the input in a high-dimensional vector space.

Batch Embeddings

The embeddings method supports multiple inputs at once. You can pass an array of strings to generate embeddings for each.

$inputs = [
    "What is the best French cheese?",
    "How to make Dijon mayonnaise?",
];

$embeddingsBatchResponse = $client->embeddings($inputs);

Use Cases

  • Semantic search: Compare embeddings using cosine similarity to find similar texts.
  • Clustering: Group similar texts together based on their embeddings.
  • Recommendation systems: Recommend content based on embedding proximity.
  • Dimensionality reduction: Visualize data with techniques like PCA or t-SNE.

Error Handling

The embeddings() method can throw a MistralClientException if:

  • The server is unreachable.
  • The model does not support embeddings.
  • The input is invalid.

Always wrap your calls in a try-catch block.

try {
    $embeddingsBatchResponse = $client->embeddings($inputs);
} catch (MistralClientException $e) {
    // Handle error gracefully
}