> ## Documentation Index
> Fetch the complete documentation index at: https://docs.leapx-hub.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Embeddings

## Introduction

Convert text to vector embeddings for semantic search, similarity calculation, and clustering.

## Authentication

<ParamField header="Authorization" type="string" required>
  Bearer Token, e.g. `Bearer sk-xxxxxxxxxx`
</ParamField>

## Request Parameters

<ParamField body="model" type="string" required>
  Model name, e.g. `text-embedding-3-small`, `text-embedding-3-large`
</ParamField>

<ParamField body="input" type="string | array" required>
  Text to embed, string or array of strings
</ParamField>

<ParamField body="encoding_format" type="string" default="float">
  Return format: `float` or `base64`
</ParamField>

<ParamField body="dimensions" type="integer">
  Output dimensions (supported by some models)
</ParamField>

## cURL Example

```bash theme={null}
curl https://api.leapx-hub.com/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-XyLy**************************mIqSt" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "Hello, world"
  }'
```

## Python Example

```python theme={null}
from openai import OpenAI

client = OpenAI(
    api_key="sk-XyLy**************************mIqSt",
    base_url="https://api.leapx-hub.com/v1"
)

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="Hello, world"
)

print(response.data[0].embedding)
print(f"Dimensions: {len(response.data[0].embedding)}")
```

<ResponseExample>
  ```json theme={null}
  {
    "object": "list",
    "data": [
      {
        "object": "embedding",
        "index": 0,
        "embedding": [0.0023064255, -0.009327292, 0.015797347, ...]
      }
    ],
    "model": "text-embedding-3-small",
    "usage": {
      "prompt_tokens": 5,
      "total_tokens": 5
    }
  }
  ```
</ResponseExample>

## Supported Models

| Model                  | Dimensions | Description                               |
| ---------------------- | ---------- | ----------------------------------------- |
| text-embedding-3-small | 1536       | Cost-effective for most use cases         |
| text-embedding-3-large | 3072       | High precision for demanding applications |
| text-embedding-ada-002 | 1536       | Legacy model                              |

## Notes

<Note>
  * For batch embedding, pass an array of strings to `input`
  * Some models support custom dimensions via `dimensions` parameter
  * Requires `openai` library: `pip install openai`
</Note>