> For the complete documentation index, see [llms.txt](https://docs.nebulablock.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.nebulablock.com/core-services/overview/embedding_generation.md).

# Embedding Generation

Embedding models are machine learning models that convert data (such as text, images, or code) into dense numerical\
vectors in a continuous space. These vectors, called embeddings, capture the semantic relationships between different\
pieces of data, enabling efficient comparison and retrieval.

## Models available

* UAE-Large-V1: Good for general-purpose text embeddings with high accuracy.
* BGE Large EN v1.5: Optimized for English text embeddings with enhanced performance.

## Using the Models

### Through API Endpoint

This option is to use our API endpoint directly in your projects. Below are some code snippets to get you started!

> **NOTE:** Don't forget to use **your** API key. See the [API Reference](/overview/platform_api/authentication.md) and the [Overview](https://github.com/Nebula-Block-Data/docs/blob/main/API_Key/Overview.md) for more details on authentication.

#### Using cURL

```bash
curl -X POST "https://inference.nebulablock.com/v1/embeddings" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $NEBULA_API_KEY" \
    --data-raw '{
      "model":"WhereIsAI/UAE-Large-V1",
      "input":[ 
            "Bananas are berries, but strawberries are not, according to botanical classifications.",  
            "The Eiffel Tower in Paris was originally intended to be a temporary structure." 
        ] 
    }'
```

#### Using Python

```python
import requests 
import os

url = "https://inference.nebulablock.com/v1/embeddings" 

headers = {  
    "Content-Type": "application/json",  
    "Authorization": f"Bearer {os.environ.get('NEBULA_API_KEY')}" 
} 

data = {
    "model":"WhereIsAI/UAE-Large-V1",
    "input":[ 
        "Bananas are berries, but strawberries are not, according to botanical classifications.", 
        "The Eiffel Tower in Paris was originally intended to be a temporary structure." 
    ] 
}

response = requests.post(url, headers=headers, json=data) 
print(response.json())
```

#### Using JavaScript

```javascript
const url = 'https://inference.nebulablock.com/v1/embeddings';

const headers = {
    "Content-Type": "application/json",
    "Authorization": `Bearer ${process.env.NEBULA_API_KEY}`
};

const data = {
    "model": "WhereIsAI/UAE-Large-V1",
    "input": [
        "Bananas are berries, but strawberries are not, according to botanical classifications.",
        "The Eiffel Tower in Paris was originally intended to be a temporary structure."
    ]
};

fetch(url, {
    method: 'POST',
    headers: headers,
    body: JSON.stringify(data)
})
    .then(response => response.json())
    .then(data => {
        console.log(JSON.stringify(data, null, 2));
    })
    .catch(error => console.error('Error:', error));
```

#### Selecting a Model

To specify the desired model, use this mapping for the `model`:

* UAE-Large-V1: `WhereIsAI/UAE-Large-V1`
* BGE Large EN v1.5: `BAAI/bge-large-en-v1.5`

#### Response Example

A successful response body will return the embeddings in this format:

```json
{
    "model": "WhereIsAI/UAE-Large-V1",
    "data": [
        
      {
            "embedding": [
                -0.373046875,
                ..., 
                0.248046875
            ],
            "index": 0,
            "object": "embedding"
        },
        {
            "embedding": [
                -0.50390625,
                ...,
                0.01409912109375
            ],
            "index": 1,
            "object": "embedding"
        }
    ],
    "object": "list",
    "usage": {
        "completion_tokens": 0,
        "prompt_tokens": 33,
        "total_tokens": 33,
        "completion_tokens_details": null,
        "prompt_tokens_details": null
    }
}
```

> **NOTE:** Notice that there are 2 embeddings, each with its own index number. These embeddings correspond to the given input sentences,\
> of which there are 2. You can choose how many sentences to create embeddings for, this is just an example.

Feel free to explore refer to the [API Reference](/overview/inference-api-openai-compatible/generate_embeddings.md) for more details.