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

# Snowflake Embeddings

> Generate embeddings using Snowflake Arctic Embed models

## Overview

Snowflake provides the Arctic Embed family of embedding models optimized for retrieval.

## Quick Start

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import embedding

result = embedding(
    input="Hello world",
    model="snowflake/snowflake-arctic-embed-m"
)
print(f"Dimensions: {len(result.embeddings[0])}")
```

## CLI Usage

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai embed "Hello world" --model snowflake/snowflake-arctic-embed-m
```

## Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
export SNOWFLAKE_JWT="your-snowflake-jwt"
```

## Available Models

| Model                                | Dimensions |
| ------------------------------------ | ---------- |
| `snowflake/snowflake-arctic-embed-m` | 768        |
| `snowflake/snowflake-arctic-embed-l` | 1024       |

## Related

* [Embedding Providers Overview](/docs/embeddings/index)