> ## 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.

# Databricks Embeddings

> Generate embeddings using Databricks Foundation Models

## Overview

Databricks provides embedding models through their Foundation Model APIs.

## Quick Start

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

result = embedding(
    input="Hello world",
    model="databricks/databricks-bge-large-en"
)
print(f"Dimensions: {len(result.embeddings[0])}")
```

## CLI Usage

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai embed "Hello world" --model databricks/databricks-bge-large-en
```

## Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
export DATABRICKS_API_KEY="your-databricks-token"
export DATABRICKS_API_BASE="https://your-workspace.cloud.databricks.com"
```

## Available Models

| Model                                | Dimensions |
| ------------------------------------ | ---------- |
| `databricks/databricks-bge-large-en` | 1024       |
| `databricks/databricks-gte-large-en` | 1024       |

## Related

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