快速指引¶

What is it?¶

Botfront is an open source platform to build chatbots with Rasa. With Botfront you can: - Build, train and evaluate NLU models - Edit and store bot responses - Do simple Q&A (no code) and mix them with more complex stories (in code) - Monitor conversations - Review and annotate incoming NLU utterances - Build multilingual chatbots

This tutorial will guide you through the installation and the development of your first chatbot with Botfront/Rasa. You will run Botfront with docker-compose which makes running all the applications needed for your project (Botfront, Rasa, Mongo) very easy.

Setup¶

Install Docker¶

• Install Docker for Mac
• Install Docker for Windows

If you already have Docker installed, make sure it's up to date.

::: warning Linux users host.docker.internal does not resolve on Docker for linux, see this issue for possible solutions. :::

Install Botfront¶

Open your terminal ::: tip Beginner tip On a Mac, press Cmd+Space, look up terminal and select Terminal.app :::

Copy the following lines, paste them in your terminal window and type Enter.

git clone https://github.com/botfront/botfront-starter
cd botfront-starter
docker-compose up

First, it's going to download all the Docker images required (it's going to take a while, be patient). Then it's going to start all the services. ::: tip Note You can safely ignore the errors in the logs: we just need to setup Botfront and it's all going to be fixed. :::

Start Botfront¶

Open http://localhost:8888 and follow the steps to create your first project. (If it doesn't work just wait a few more seconds for the service to be ready)

::: tip If you're following the tutorial Name your project "My First Project" and choose the English language if you want to follow the rest of this tutorial :::

Congratulations, Botfront is installed. You should see a screen with your First NLU Model.

Add training data¶

Let's just teach our NLU model to recognize simple things like "Hi", "Thanks", "Bye". We'll do more advanced stuff later. Botfront comes with pre-trained intents for general conversation (Chit Chat). The following video shows how to: 1. Import Chit Chat intents to your model 2. train and test your model

Create a Bot response¶

Now that our bot can understand a few things, let's see how we can get it to respond. The following video shows how to apply create a bot response and to assign it to an intent.

::: tip Botfront adds a special behaviour to intents prefixed with chitchat. or faq. This allows to map a response without having to retrain Rasa on stories. We are using this feature here. \ For more information, see Q&A and FAQ Bots or Rules :::

This example is minimal, but you can do more advanced assignments such as combinations of intent and entities.

Monitor and improve¶

You can follow the conversations from the conversations menu item, and monitor NLU from the Activity tab in your NLU model.

What's next¶

You learned:

1. How to setup Botfront on your machine
2. How to add data and train your first NLU mode,
3. How to create a simple Q&A bot without coding.

There's a lot you can do with this already, but there's way more. You could read the NLU guide and build a more advanced NLU model