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

---

# User Guide

- [Installation](https://legacy-docs-v1.rasa.com/1.10.8/user-guide/installation/)
- [Tutorial: Rasa Basics](https://legacy-docs-v1.rasa.com/1.10.8/user-guide/rasa-tutorial/)
- [Tutorial: Building Assistants](https://legacy-docs-v1.rasa.com/1.10.8/user-guide/building-assistants/)
- [Command Line Interface](https://legacy-docs-v1.rasa.com/1.10.8/user-guide/command-line-interface/)
- [Architecture](https://legacy-docs-v1.rasa.com/1.10.8/user-guide/architecture/)
- [Messaging and Voice Channels](https://legacy-docs-v1.rasa.com/1.10.8/user-guide/messaging-and-voice-channels/)
- [Testing Your Assistant](https://legacy-docs-v1.rasa.com/1.10.8/user-guide/testing-your-assistant/)
- [Setting up CI/CD](https://legacy-docs-v1.rasa.com/1.10.8/user-guide/setting-up-ci-cd/)
- [Validate Data](https://legacy-docs-v1.rasa.com/1.10.8/user-guide/validate-files/)
- [Configuring the HTTP API](https://legacy-docs-v1.rasa.com/1.10.8/user-guide/configuring-http-api/)
- [Deploying Your Rasa Assistant](https://legacy-docs-v1.rasa.com/1.10.8/user-guide/how-to-deploy/)
- [Cloud Storage](https://legacy-docs-v1.rasa.com/1.10.8/user-guide/cloud-storage/)

# NLU

- [About](https://legacy-docs-v1.rasa.com/1.10.8/nlu/about/)
- [Using NLU Only](https://legacy-docs-v1.rasa.com/1.10.8/nlu/using-nlu-only/)
- [Training Data Format](https://legacy-docs-v1.rasa.com/1.10.8/nlu/training-data-format/#)
- [Language Support](https://legacy-docs-v1.rasa.com/1.10.8/nlu/language-support/)
- [Choosing a Pipeline](https://legacy-docs-v1.rasa.com/1.10.8/nlu/choosing-a-pipeline/)
- [Components](https://legacy-docs-v1.rasa.com/1.10.8/nlu/components/)
- [Entity Extraction](https://legacy-docs-v1.rasa.com/1.10.8/nlu/entity-extraction/)

# Core

- [About](https://legacy-docs-v1.rasa.com/1.10.8/core/about/)
- [Stories](https://legacy-docs-v1.rasa.com/1.10.8/core/stories/)
- [Domains](https://legacy-docs-v1.rasa.com/1.10.8/core/domains/)
- [Responses](https://legacy-docs-v1.rasa.com/1.10.8/core/responses/)
- [Actions](https://legacy-docs-v1.rasa.com/1.10.8/core/actions/)
- [Reminders and External Events](https://legacy-docs-v1.rasa.com/1.10.8/core/reminders-and-external-events/)
- [Policies](https://legacy-docs-v1.rasa.com/1.10.8/core/policies/)
- [Slots](https://legacy-docs-v1.rasa.com/1.10.8/core/slots/)
- [Forms](https://legacy-docs-v1.rasa.com/1.10.8/core/forms/)
- [Retrieval Actions](https://legacy-docs-v1.rasa.com/1.10.8/core/retrieval-actions/)
- [Interactive Learning](https://legacy-docs-v1.rasa.com/1.10.8/core/interactive-learning/)
- [Fallback Actions](https://legacy-docs-v1.rasa.com/1.10.8/core/fallback-actions/)
- [Knowledge Base Actions](https://legacy-docs-v1.rasa.com/1.10.8/core/knowledge-bases/)

# Conversation Design

- [Dialogue Elements](https://legacy-docs-v1.rasa.com/1.10.8/dialogue-elements/dialogue-elements/)
- [Small Talk](https://legacy-docs-v1.rasa.com/1.10.8/dialogue-elements/small-talk/)
- [Completing Tasks](https://legacy-docs-v1.rasa.com/1.10.8/dialogue-elements/completing-tasks/)
- [Guiding Users](https://legacy-docs-v1.rasa.com/1.10.8/dialogue-elements/guiding-users/)

# API Reference

- [Action Server](https://legacy-docs-v1.rasa.com/1.10.8/api/action-server/)
- [HTTP API](https://legacy-docs-v1.rasa.com/1.10.8/api/http-api/)
- [Jupyter Notebooks](https://legacy-docs-v1.rasa.com/1.10.8/api/jupyter-notebooks/)
- [Agent](https://legacy-docs-v1.rasa.com/1.10.8/api/agent/)
- [Custom NLU Components](https://legacy-docs-v1.rasa.com/1.10.8/api/custom-nlu-components/)
- [Rasa SDK](https://legacy-docs-v1.rasa.com/1.10.8/api/rasa-sdk/)
- [Events](https://legacy-docs-v1.rasa.com/1.10.8/api/events/)
- [Tracker](https://legacy-docs-v1.rasa.com/1.10.8/api/tracker/)
- [Tracker Stores](https://legacy-docs-v1.rasa.com/1.10.8/api/tracker-stores/)
- [Event Brokers](https://legacy-docs-v1.rasa.com/1.10.8/api/event-brokers/)
- [Lock Stores](https://legacy-docs-v1.rasa.com/1.10.8/api/lock-stores/)
- [Training Data Importers](https://legacy-docs-v1.rasa.com/1.10.8/api/training-data-importers/)
- [Featurization of Conversations](https://legacy-docs-v1.rasa.com/1.10.8/api/core-featurization/)
- [TensorFlow Configuration](https://legacy-docs-v1.rasa.com/1.10.8/api/tensorflow_usage/)
- [Migration Guide](https://legacy-docs-v1.rasa.com/1.10.8/migration-guide/)
- [Rasa Open Source Change Log](https://legacy-docs-v1.rasa.com/1.10.8/changelog/)

# Migrate from (beta)

- [Dialogflow](https://legacy-docs-v1.rasa.com/1.10.8/migrate-from/google-dialogflow-to-rasa/)
- [Wit.ai](https://legacy-docs-v1.rasa.com/1.10.8/migrate-from/facebook-wit-ai-to-rasa/)
- [LUIS](https://legacy-docs-v1.rasa.com/1.10.8/migrate-from/microsoft-luis-to-rasa/)
- [IBM Watson](https://legacy-docs-v1.rasa.com/1.10.8/migrate-from/ibm-watson-to-rasa/)

# Reference

- [Glossary](https://legacy-docs-v1.rasa.com/1.10.8/glossary/)

# Versions

viewing: 1.10.8

# Training Data Format

You can provide training data as Markdown or as JSON, as a single file or as a directory containing multiple files.
Note that Markdown is usually easier to work with.

### Data Formats
- **Markdown Format**

Markdown is the easiest Rasa NLU format for humans to read and write.
Examples are listed using the unordered list syntax, e.g. minus `-`, asterisk `*`, or plus `+`.
Examples are grouped by intent, and entities are annotated as Markdown links.

```markdown
## intent:check_balance
- what is my balance
- how much do I have on my [savings](source_account)

## intent:greet
- hey
- hello
```

### JSON Format

The JSON format consists of a top-level object called `rasa_nlu_data`, with the keys `common_examples`, `entity_synonyms` and `regex_features`.

```json
{
    "rasa_nlu_data": {
        "common_examples": [],
        "regex_features": [],
        "lookup_tables": [],
        "entity_synonyms": []
    }
}
```

### Improving Intent Classification and Entity Recognition

Common examples have three components: `text`, `intent` and `entities`. The first two are strings while the last one is an array.

- The _text_ is the user message [required]
- The _intent_ is the intent that should be associated with the text [optional]
- The _entities_ are specific parts of the text which need to be identified [optional]

---

### Notes

- The common theme here is that common examples, regex features and lookup tables merely act as cues to the final NLU model by providing additional features to the machine learning algorithm during training. Therefore, it must not be assumed that having a single example would be enough for the model to robustly identify intents and/or entities across all variants of that example.

**Images**