Embeddings

Convert text into high-dimensional numerical vectors for various AI tasks.

Embeddings are numerical representations of text that capture semantic meaning. They convert human-readable text into high-dimensional vectors that machine learning models can understand and process. These vectors enable tasks such as semantic search, recommendation systems, clustering, and anomaly detection.

How Embeddings Work

When you send text to the embeddings endpoint, the underlying AI model processes the text and outputs a list of floating-point numbers (a vector). Texts with similar meanings will have vectors that are numerically “closer” to each other in the high-dimensional space.

The Neosantara AI API provides an /v1/embeddings endpoint to generate these vectors.

Usage

To generate embeddings, send a POST request to the /v1/embeddings endpoint with your text input.

Endpoint

POST https://api.neosantara.xyz/v1/embeddings

Request Body

{
  "input": "The quick brown fox jumps over the lazy dog.",
  "model": "nusa-embedding-0001",
  "encoding_format": "float"
}

See full reference

Supported Embedding Models

The Neosantara AI API supports various embedding models, often utilizing intelligent fallbacks to ensure reliability. Some of the available models include:

• nusa-embedding-0001: The primary embedding model, offering good performance for general-purpose embeddings.

Example Response

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        0.00539745,
        -0.007137805,
        0.02102179,
        -0.003181825,
        // ... (truncated for brevity)
        -0.010260487
      ],
      "index": 0
    }
  ],
  "model": "nusa-embedding-0001",
  "usage": {
    "prompt_tokens": 9,
    "total_tokens": 9
  }
}

Examples with OpenAI SDK

You can generate embeddings using the OpenAI SDK by configuring its base_url to point to the Neosantara AI API.

Py

from openai import OpenAI

# Replace "YOUR_API_KEY" with your actual Neosantara AI API Key
client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.neosantara.xyz/v1"
)

def get_embeddings(texts: list[str], model: str = "nusa-embedding-0001"):
    """
    Generates embeddings for a list of texts using the Neosantara AI API.
    """
    print(f"Generating embeddings for model: {model}")

    try:
        response = client.embeddings.create(
            model=model,
            input=texts,
            encoding_format="float"
        )

        # The response.data contains a list of embedding objects
        # Each object has 'embedding' (the vector) and 'index'
        embeddings = [item.embedding for item in response.data]

        print(f"Generated {len(embeddings)} embeddings. Dimension: {len(embeddings[0]) if embeddings else 0}")
        print(f"Total tokens used: {response.usage.total_tokens}")

        return embeddings

    except Exception as e:
        print(f"An error occurred during embedding generation: {e}")
        return None

if __name__ == "__main__":
    example_texts = [
        "Artificial intelligence is transforming industries.",
        "Machine learning is a subset of AI.",
        "Deep learning enables neural networks."
    ]
    
    generated_vectors = get_embeddings(example_texts)

    if generated_vectors:
        print("\nFirst embedding vector (truncated):")
        print(generated_vectors[0][:10], "...") # Print first 10 elements