# 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](https://docs.nebulablock.com/overview/platform_api/authentication) 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](https://docs.nebulablock.com/overview/inference-api-openai-compatible/generate_embeddings) for more details.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.nebulablock.com/core-services/overview/embedding_generation.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.