> ## 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.

# Mistral Embeddings

> Generate embeddings using Mistral AI's embedding model

## Overview

Mistral AI provides a high-quality embedding model optimized for retrieval and semantic search.

## Quick Start

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

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

## CLI Usage

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

## Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
export MISTRAL_API_KEY="your-mistral-api-key"
```

## Available Models

| Model                   | Dimensions | Use Case        |
| ----------------------- | ---------- | --------------- |
| `mistral/mistral-embed` | 1024       | General purpose |

## Batch Embeddings

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

texts = ["Document 1", "Document 2", "Document 3"]
result = embedding(
    input=texts,
    model="mistral/mistral-embed"
)
print(f"Generated {len(result.embeddings)} embeddings")
```

## Related

* [Embedding Providers Overview](/docs/embeddings/index)
* [Cohere Embeddings](/docs/embeddings/providers/cohere)