> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Mistral Embedder

> Generate 1024-dimensional embeddings with MistralEmbedder and mistral-embed.

`MistralEmbedder` uses `mistral-embed` by default. The model returns 1024-dimensional vectors.

```python mistral_embedder.py theme={null}
from agno.knowledge.embedder.mistral import MistralEmbedder

embedder = MistralEmbedder()
embedding = embedder.get_embedding("The quick brown fox jumps over the lazy dog.")

print(embedding[:5])
print(len(embedding))
```

> [!WARNING]
>
> In v2.7.2, `endpoint` and `max_retries` use parameter names rejected by the current `mistralai` client. Pass a preconfigured client through `mistral_client` when you need either setting.

## Run the Example

<Snippet file="create-venv-step.mdx" />

**1. Export the API key**

```bash theme={null}
export MISTRAL_API_KEY=your_mistral_api_key_here
```

**2. Install dependencies**

```bash theme={null}
uv pip install -U agno mistralai
```

**3. Run the example**

```bash theme={null}
python mistral_embedder.py
```

## Developer Resources

* [MistralEmbedder reference](https://agno-v2.mintlify.site/reference/knowledge/embedder/mistral.md)
* [Mistral embeddings](https://docs.mistral.ai/api/endpoint/embeddings)
* [Embedders overview](https://agno-v2.mintlify.site/knowledge/concepts/embedder/overview.md)


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.