---
title: "DocumentLanguageClassifier"
id: documentlanguageclassifier
slug: "/documentlanguageclassifier"
description: "Use this component to classify documents by language and add language information to metadata."
---

# DocumentLanguageClassifier

Use this component to classify documents by language and add language information to metadata.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before [`MetadataRouter`](../routers/metadatarouter.mdx)                                                                    |
| **Mandatory run variables**            | `documents`:  A list of documents                                                                                  |
| **Output variables**                   | `documents`:  A list of documents                                                                                  |
| **API reference**                      | [Classifiers](/reference/classifiers-api)                                                                                 |
| **GitHub link**                        | https://github.com/deepset-ai/haystack/blob/main/haystack/components/classifiers/document_language_classifier.py |

</div>

## Overview

`DocumentLanguageClassifier` classifies the language of documents and adds the detected language to their metadata. If a document's text does not match any of the languages specified at initialization, it is classified as "unmatched". By default, the classifier classifies for English (”en”) documents, with the rest being classified as “unmatched”.

The set of supported languages can be specified in the init method with the `languages` variable, using ISO codes.

To route your documents to various branches of the pipeline based on the language, use `MetadataRouter` component right after `DocumentLanguageClassifier`.

For classifying and then routing plain text using the same logic, use the `TextLanguageRouter` component instead.

## Usage

Install the `langdetect`package to use the `DocumentLanguageClassifier`component:

```shell shell
pip install langdetect
```

### On its own

Below, we are using the `DocumentLanguageClassifier` to classify English and German documents:

```python
from haystack.components.classifiers import DocumentLanguageClassifier
from haystack import Document

documents = [
    Document(content="Mein Name ist Jean und ich wohne in Paris."),
    Document(content="Mein Name ist Mark und ich wohne in Berlin."),
    Document(content="Mein Name ist Giorgio und ich wohne in Rome."),
    Document(content="My name is Pierre and I live in Paris"),
    Document(content="My name is Paul and I live in Berlin."),
    Document(content="My name is Alessia and I live in Rome."),
]

document_classifier = DocumentLanguageClassifier(languages=["en", "de"])
document_classifier.run(documents=documents)
```

### In a pipeline

Below, we are using the `DocumentLanguageClassifier` in an indexing pipeline that indexes English and German documents into two difference indexes in an `InMemoryDocumentStore`, using embedding models for each language.

```python
from haystack import Pipeline
from haystack import Document
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.classifiers import DocumentLanguageClassifier
from haystack.components.embedders import SentenceTransformersDocumentEmbedder
from haystack.components.writers import DocumentWriter
from haystack.components.routers import MetadataRouter

document_store_en = InMemoryDocumentStore()
document_store_de = InMemoryDocumentStore()

document_classifier = DocumentLanguageClassifier(languages=["en", "de"])
metadata_router = MetadataRouter(
    rules={"en": {"language": {"$eq": "en"}}, "de": {"language": {"$eq": "de"}}},
)
english_embedder = SentenceTransformersDocumentEmbedder()
german_embedder = SentenceTransformersDocumentEmbedder(
    model="PM-AI/bi-encoder_msmarco_bert-base_german",
)
en_writer = DocumentWriter(document_store=document_store_en)
de_writer = DocumentWriter(document_store=document_store_de)

indexing_pipeline = Pipeline()
indexing_pipeline.add_component(
    instance=document_classifier,
    name="document_classifier",
)
indexing_pipeline.add_component(instance=metadata_router, name="metadata_router")
indexing_pipeline.add_component(instance=english_embedder, name="english_embedder")
indexing_pipeline.add_component(instance=german_embedder, name="german_embedder")
indexing_pipeline.add_component(instance=en_writer, name="en_writer")
indexing_pipeline.add_component(instance=de_writer, name="de_writer")

indexing_pipeline.connect("document_classifier.documents", "metadata_router.documents")
indexing_pipeline.connect("metadata_router.en", "english_embedder.documents")
indexing_pipeline.connect("metadata_router.de", "german_embedder.documents")
indexing_pipeline.connect("english_embedder", "en_writer")
indexing_pipeline.connect("german_embedder", "de_writer")

indexing_pipeline.run(
    {
        "document_classifier": {
            "documents": [
                Document(content="This is an English sentence."),
                Document(content="Dies ist ein deutscher Satz."),
            ],
        },
    },
)
```
