A Bot From Scratch¶

Note

This is the place to start if you have a great idea for a bot but you don’t have any conversations to use as training data. We will assume that you’ve already thought of what intents and entities you need (check out the Rasa NLU docs if you don’t know what those are).

The Bot¶

We will build a bot that can recommend concerts to go to. We’ll show how to implement context-aware behaviour without writing a flow chart. For example, if our user asks the question: which of those has better reviews?, our bot should know whether they want to compare musicians or venues.

Let’s go!

The Domain¶

We will keep the concert domain simple, and won’t add any slots just yet. We’ll also only support these intents: "greet", "thankyou", "goodbye", "search_concerts", "search_venues", "compare_reviews" You can see the domain definition in examples/concerts/concert_domain.yml.

Stateless Stories¶

We start by training a stateless model on some simple dialogues in the Rasa story format.

Below is an excerpt of the stories.

In many cases, your bot’s ‘conversations’ are just a single turn and response: “Hello” is always met with a greeting, “goodbye!” is always met with a sign-off, and the correct response to “thank you” is pretty much always “you’re welcome”.

Notice that below, we’ve defined two stories, showing that action_show_venue_reviews and action_show_concert_reviews are both possible responses to the compare_reviews intent, but neither references any context. That comes later.

## greet
* _greet
    - action_greet

## happy
* _thankyou
    - action_youarewelcome
...

## compare_reviews_venues
* _compare_reviews
    - action_show_venue_reviews

## compare_reviews_concerts
* _compare_reviews
    - action_show_concert_reviews

Training¶

We start by training the policy to recognise these input-output pairs independently of any context. ( You can see the definition of the ConcertPolicy class in concert_policy.py. ) Run the script train_init.py. This creates a training set of conversations by randomly combining the stories we’ve provided into longer dialogues, and then trains the policy on that dataset.

Then, run the script run.py to talk to the bot. You should be able to have a conversation similar to the one below

Note

we haven’t connected an NLU tool here, so when you type messages to the bot you have to type the intent starting with a _. If you want to use Rasa NLU / wit.ai / Lex you can just swap the Interpreter class in run.py.

Bot loaded. Type hello and press enter :
_greet
hey there!
_search_concerts
Here's what I found:
Katy Perry, Foo Fighters
_goodbye
goodbye :(

Now we’ll train the bot to use context to respond correctly to the compare_reviews intent.

Interactive Learning¶

Run the script train_online.py. This first repeats the process in the train_init script, creating a stateless policy.

It then runs the bot so that you can provide feedback to train it:

Happy paths

We can start talking to the bot as before, directly entering the intents. For example, if we type _greet, we get the following prompt:

_greet
------
Chat history:

     bot did:        action_listen

     user said:      _greet

                whose intent is:     greet

we currently have slots: {'location': None}

------
The bot wants to [greet] due to the intent. Is this correct?

    1.       Yes
    2.       No, intent is right but the action is wrong
    3.       The intent is wrong

This gives you all the info you should hopefully need to decide what the bot should have done. In this case, the bot chose the right action (‘greet’), so we type 1 and hit enter. We continue this loop until the bot chooses the wrong action.

Providing feedback on errors

We’ve just asked the bot to search for concerts, and now we’re asking it to compare reviews. The bot happens to choose the wrong one out of the two possibilities we wrote in the stories:

_compare_reviews
------
Chat history:

     bot did:        action_search_concerts

     bot did:        action_suggest

     bot did:        action_listen

     user said:      _compare_reviews

                whose intent is:     compare_reviews

we currently have slots: {'location': None}

------
The bot wants to [show_venue_reviews] due to the intent. Is this correct?

    1.       Yes
    2.       No, intent is right but the action is wrong
    3.       The intent is wrong

Now we type 2, because it chose the wrong action, and we get a new prompt asking for the correct one. This also shows the probabilities the model has assigned to each of the actions.

------
what is the next action for the bot?

     default  0.00148131744936
     greet    0.0970264300704
     goodbye  0.0288009047508
     listen   0.00123148341663
     search_cinemas  0.000627864559647
     search_films    0.0367559418082
     suggest         0.0261212754995
    youarewelcome   0.594935178757
    explain_options 0.0516758263111
    store_slot      0.00145904591773
    show_cinema_reviews     0.00887114647776
    show_film_reviews       0.0870243906975

In this case, the bot should show_film_reviews (rather than cinema reviews!) so we type 16 and hit enter.

Note

The policy model will get updated on-the-fly, so that it’s less likely to make the same mistake again. You can also export all of the conversations you have with the bot so you can add these as training stories in the future.

Now we can keep talking to the bot for as long as we like to create a longer conversation. At any point you can type _export and the bot will write the current conversation to a file, which you can then add as a training example for the future.