These docs are for version 1.x of Rasa Open Source.

- [Docs for the new version 2.0 can be found here.](/content/docs/rasa/index.html)

# User Guide

- [Installation](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/installation/)
- [Tutorial: Rasa Basics](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/rasa-tutorial/)
- [Tutorial: Building Assistants](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/building-assistants/)
- [Command Line Interface](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/command-line-interface/)
- [Architecture](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/architecture/)
- [Messaging and Voice Channels](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/messaging-and-voice-channels/)
- [Evaluating Models](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/evaluating-models/)
- [Validate Data](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/validate-files/)
- [Configuring the HTTP API](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/configuring-http-api/)
- [Deploying your Rasa Assistant](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/how-to-deploy/)
- [Cloud Storage](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/cloud-storage/)

# NLU

- [About](https://legacy-docs-v1.rasa.com/1.8.2/nlu/about/)
- [Using NLU Only](https://legacy-docs-v1.rasa.com/1.8.2/nlu/using-nlu-only/)
- [Training Data Format](https://legacy-docs-v1.rasa.com/1.8.2/nlu/training-data-format/)
- [Choosing a Pipeline](https://legacy-docs-v1.rasa.com/1.8.2/nlu/choosing-a-pipeline/)
- [Language Support](https://legacy-docs-v1.rasa.com/1.8.2/nlu/language-support/#)
- [Entity Extraction](https://legacy-docs-v1.rasa.com/1.8.2/nlu/entity-extraction/)
- [Components](https://legacy-docs-v1.rasa.com/1.8.2/nlu/components/)

# Core

- [About](https://legacy-docs-v1.rasa.com/1.8.2/core/about/)
- [Stories](https://legacy-docs-v1.rasa.com/1.8.2/core/stories/)
- [Domains](https://legacy-docs-v1.rasa.com/1.8.2/core/domains/)
- [Responses](https://legacy-docs-v1.rasa.com/1.8.2/core/responses/)
- [Actions](https://legacy-docs-v1.rasa.com/1.8.2/core/actions/)
- [Reminders and External Events](https://legacy-docs-v1.rasa.com/1.8.2/core/reminders-and-external-events/)
- [Policies](https://legacy-docs-v1.rasa.com/1.8.2/core/policies/)
- [Slots](https://legacy-docs-v1.rasa.com/1.8.2/core/slots/)
- [Forms](https://legacy-docs-v1.rasa.com/1.8.2/core/forms/)
- [Retrieval Actions](https://legacy-docs-v1.rasa.com/1.8.2/core/retrieval-actions/)
- [Interactive Learning](https://legacy-docs-v1.rasa.com/1.8.2/core/interactive-learning/)
- [Fallback Actions](https://legacy-docs-v1.rasa.com/1.8.2/core/fallback-actions/)
- [Knowledge Base Actions](https://legacy-docs-v1.rasa.com/1.8.2/core/knowledge-bases/)

# Conversation Design

- [Dialogue Elements](https://legacy-docs-v1.rasa.com/1.8.2/dialogue-elements/dialogue-elements/)
- [Small Talk](https://legacy-docs-v1.rasa.com/1.8.2/dialogue-elements/small-talk/)
- [Completing Tasks](https://legacy-docs-v1.rasa.com/1.8.2/dialogue-elements/completing-tasks/)
- [Guiding Users](https://legacy-docs-v1.rasa.com/1.8.2/dialogue-elements/guiding-users/)

# API Reference

- [Action Server](https://legacy-docs-v1.rasa.com/1.8.2/api/action-server/)
- [HTTP API](https://legacy-docs-v1.rasa.com/1.8.2/api/http-api/)
- [Jupyter Notebooks](https://legacy-docs-v1.rasa.com/1.8.2/api/jupyter-notebooks/)
- [Agent](https://legacy-docs-v1.rasa.com/1.8.2/api/agent/)
- [Custom NLU Components](https://legacy-docs-v1.rasa.com/1.8.2/api/custom-nlu-components/)
- [Rasa SDK](https://legacy-docs-v1.rasa.com/1.8.2/api/rasa-sdk/)
- [Events](https://legacy-docs-v1.rasa.com/1.8.2/api/events/)
- [Tracker](https://legacy-docs-v1.rasa.com/1.8.2/api/tracker/)
- [Tracker Stores](https://legacy-docs-v1.rasa.com/1.8.2/api/tracker-stores/)
- [Event Brokers](https://legacy-docs-v1.rasa.com/1.8.2/api/event-brokers/)
- [Lock Stores](https://legacy-docs-v1.rasa.com/1.8.2/api/lock-stores/)
- [Training Data Importers](https://legacy-docs-v1.rasa.com/1.8.2/api/training-data-importers/)
- [Featurization of Conversations](https://legacy-docs-v1.rasa.com/1.8.2/api/core-featurization/)
- [TensorFlow Configuration](https://legacy-docs-v1.rasa.com/1.8.2/api/tensorflow_usage/)
- [Migration Guide](https://legacy-docs-v1.rasa.com/1.8.2/migration-guide/)
- [Rasa Open Source Change Log](https://legacy-docs-v1.rasa.com/1.8.2/changelog/)

# Migrate from (beta)

- [Dialogflow](https://legacy-docs-v1.rasa.com/1.8.2/migrate-from/google-dialogflow-to-rasa/)
- [Wit.ai](https://legacy-docs-v1.rasa.com/1.8.2/migrate-from/facebook-wit-ai-to-rasa/)
- [LUIS](https://legacy-docs-v1.rasa.com/1.8.2/migrate-from/microsoft-luis-to-rasa/)
- [IBM Watson](https://legacy-docs-v1.rasa.com/1.8.2/migrate-from/ibm-watson-to-rasa/)

# Reference

- [Glossary](https://legacy-docs-v1.rasa.com/1.8.2/glossary/)

# Versions

viewing: 1.8.2

# Language Support

You can use Rasa to build assistants in any language you want! Rasa’s `supervised_embeddings` pipeline can be used on training data in **any language**. This pipeline creates word embeddings from scratch with the data you provide.

In addition, we also support pre-trained word embeddings such as spaCy. For information on what pipeline is best for your use case, check out [Choosing a Pipeline](https://legacy-docs-v1.rasa.com/1.8.2/nlu/choosing-a-pipeline/#choosing-a-pipeline).

## Training a Model in Any Language

Rasa’s `supervised_embeddings` pipeline can be used to train models in any language, because it uses your own training data to create custom word embeddings. This means that the vector representation of any specific word will depend on its relationship with the other words in your training data. This customization also means that the pipeline is great for use cases that hinge on domain-specific data.

To train a Rasa model in your preferred language, define the `supervised_embeddings` pipeline as your pipeline in your `config.yml` or other configuration file via the instructions [here](https://legacy-docs-v1.rasa.com/1.8.2/nlu/choosing-a-pipeline/#section-supervised-embeddings-pipeline).

After you define the `supervised_embeddings` processing pipeline and generate some [NLU training data](https://legacy-docs-v1.rasa.com/1.8.2/nlu/training-data-format/#training-data-format) in your chosen language, train the model with `rasa train nlu`. Once the training is finished, you can test your model’s language skills.

```bash
rasa shell nlu
```

Note

Even more so when training word embeddings from scratch, more training data will lead to a better model! If you find your model is having trouble discerning your inputs, try training with more example sentences.

## Pre-trained Word Vectors

If you can find them in your language, pre-trained word vectors are a great way to get started with less data, as the word vectors are trained on large amounts of data such as Wikipedia.

### spaCy

With the `pretrained_embeddings_spacy` [pipeline](https://legacy-docs-v1.rasa.com/1.8.2/nlu/choosing-a-pipeline/#section-pretrained-embeddings-spacy-pipeline), you can use spaCy’s [pre-trained language models](https://spacy.io/usage/models#languages) or load fastText vectors, which are available for [hundreds of languages](https://github.com/facebookresearch/fastText/blob/master/docs/crawl-vectors.md).

### MITIE

You can also pre-train your own word vectors from a language corpus using [MITIE](https://legacy-docs-v1.rasa.com/1.8.2/nlu/choosing-a-pipeline/#section-mitie-pipeline). To do so:

1. Get a clean language corpus (a Wikipedia dump works) as a set of text files.
2. Build and run [MITIE Wordrep Tool](https://github.com/mit-nlp/MITIE/tree/master/tools/wordrep) on your corpus. This can take several hours/days depending on your dataset and your workstation.
3. Set the path of your new `total_word_feature_extractor.dat` as the `model` parameter in your configuration.

For a full example of how to train MITIE word vectors, check out [this blogpost](http://www.crownpku.com/2017/07/27/%E7%94%A8Rasa_NLU%E6%9E%84%E5%BB%BA%E8%87%AA%E5%B7%B1%E7%9A%84%E4%B8%AD%E6%96%87NLU%E7%B3%BB%E7%BB%9F.html) of creating a MITIE model from a Chinese Wikipedia dump.