.. _tutorial_scratch: 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. .. code-block:: md ## 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`. .. code-block:: text 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: .. code-block:: text _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: .. code-block:: text _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. .. code-block:: text ------ what is the next action for the bot? 0 default 0.00148131744936 1 greet 0.0970264300704 2 goodbye 0.0288009047508 3 listen 0.00123148341663 6 search_cinemas 0.000627864559647 8 search_films 0.0367559418082 9 suggest 0.0261212754995 11 youarewelcome 0.594935178757 13 explain_options 0.0516758263111 14 store_slot 0.00145904591773 15 show_cinema_reviews 0.00887114647776 16 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.