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51 lines
2.0 KiB
Markdown
51 lines
2.0 KiB
Markdown
# Formbot
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The `formbot` example is designed to help you understand how the `FormAction` works and how
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to implement it in practice. Using the code and data files in this directory, you
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can build a simple restaurant search assistant capable of recommending
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restaurants based on user preferences.
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## What’s inside this example?
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This example contains some training data and the main files needed to build an
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assistant on your local machine. The `formbot` consists of the following files:
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- **data/nlu.yml** contains training examples for the NLU model
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- **data/stories.yml** contains training stories for the Core model
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- **actions/actions.py** contains the implementation of a custom `FormAction`
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- **config.yml** contains the model configuration
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- **domain.yml** contains the domain of the assistant
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- **endpoints.yml** contains the webhook configuration for the custom actions
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## How to use this example?
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Using this example you can build an actual assistant which demonstrates the
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functionality of the `FormAction`. You can test the example using the following
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steps:
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1. Train a Rasa model containing the Rasa NLU and Rasa Core models by running:
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```
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rasa train
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```
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The model will be stored in the `/models` directory as a zipped file.
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2. Run an instance of [duckling](https://rasa.com/docs/rasa/nlu/components/#ducklingentityextractor)
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on port 8000 by either running the docker command
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```
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docker run -p 8000:8000 rasa/duckling
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```
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or [installing duckling](https://github.com/facebook/duckling#requirements) directly on your machine and starting the server.
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3. Test the assistant by running:
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```
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rasa run actions&
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rasa shell -m models --endpoints endpoints.yml
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```
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This will load the assistant in your command line for you to chat.
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For more information about the individual commands, please check out our
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[documentation](http://rasa.com/docs/rasa/command-line-interface).
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## Encountered any issues?
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Let us know about it by posting on [Rasa Community Forum](https://forum.rasa.com)!
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