# Language Detection

Language detection is a necessary first step for businesses that deal with multilingual user bases. Whether you are working with a single multi-lingual model or a multi-model environment, understanding the language of a request (e.g. query, input) is paramount to a good user experience.

[Detect Language API](/api) identifies the language used in each of the provided `texts`. For each input, the API will come back with the following:

- `language_name` - the full name of the language the input is in.
- `language_code` - the ISO code of the language. For example, the language code for English is `en`.

## Use Cases

### Single Model Environment

Use a single multilingual model to handle both English and non-English queries. Identify the language of an incoming query and filter your results by matching languages for monolingual retrieval with a multilingual model - in addition, you can specify which languages you want to filter for in a cross-lingual retrieval setup.

### Multi Model Environment

Use multiple models for English and non-English embeddings. Identify a query in its respective language and route the request to different models depending on your setup. For example, if a query is in identified as English, route it to our default English embed model, if not, route it to our [multilingual embed model](/guides/embeddings-vectors-search-retrieval-text-embeddings-multilingual-language-models).

## Examples

### Input

## Related pages

- [Changelog](../changelog.md)
- [Cohere](../index.md)
- [Cohere API](./cohere-api-index.md)
- [Cohere Labs](./cohere-labs-index.md)
- [Cohere Platform](./cohere-platform-index.md)
- [Cookbooks](./cookbooks-index.md)
- [Deployment Options](./deployment-options-index.md)
- [Embeddings (Vectors, Search, Retrieval)](./embeddings-vectors-search-retrieval-index.md)
- [Get Started](./get-started-index.md)
- [Going to Production](./going-to-production-index.md)

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