> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/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.
</Warning>

## Run the Example

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

  <Step title="Export the API key">
    ```bash theme={null}
    export MISTRAL_API_KEY=your_mistral_api_key_here
    ```
  </Step>

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno mistralai
    ```
  </Step>

  <Step title="Run the example">
    ```bash theme={null}
    python mistral_embedder.py
    ```
  </Step>
</Steps>

## Developer Resources

* [MistralEmbedder reference](/reference/knowledge/embedder/mistral)
* [Mistral embeddings](https://docs.mistral.ai/api/endpoint/embeddings)
* [Embedders overview](/knowledge/concepts/embedder/overview)