Pending state transitions

You'll often need to briefly deviate from the flow of a conversation, for example to authenticate a user, before returning to the topic of discussion.

In these cases, it's often simpler - and easier to debug - if you save some actions/states as pending rather than adding ever more complicated rules.

Here, you're going to define a policy_rules dictionary, where the keys are tuples of the current state and the received intent, and the values are tuples of the next state, the bot's response, and a state for which to set a pending transition.

This exercise is part of the course

Building Chatbots in Python

Exercise instructions

Complete the policy_rules dictionary by filling in the values:
- A user starts in the INIT state.
- If the user is in the INIT state and tries to place an order, you should ask for their number and create a pending transition to the AUTHED state.
- This is the only policy rule which creates a pending transition, so the others simply have a pending state value of None.
The pending state has been added as the second argument of the send_message() function, which now returns the new state as well as the pending state. Call this send_message() function inside send_messages(), unpacking the output into the variables state and pending.
Hit 'Submit Answer' to send the messages to the bot!

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Define the states
INIT=0
AUTHED=1
CHOOSE_COFFEE=2
ORDERED=3

# Define the policy rules
policy_rules = {
    (INIT, "order"): (____, "you'll have to log in first, what's your phone number?", ____),
    (INIT, "number"): (____, "perfect, welcome back!", None),
    (AUTHED, "order"): (____, "would you like Colombian or Kenyan?", None),    
    (CHOOSE_COFFEE, "specify_coffee"): (____, "perfect, the beans are on their way!", None)
}

# Define send_messages()
def send_messages(messages):
    state = INIT
    pending = None
    for msg in messages:
        state, pending = ____(____, ____, ____)

# Send the messages
send_messages([
    "I'd like to order some coffee",
    "555-1234",
    "kenyan"
])

Edit and Run Code

This exercise is part of the course

Building Chatbots in Python

IntermediateSkill Level

4.7+

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In this chapter, you'll learn how to build your first chatbot. After gaining a bit of historical context, you'll set up a basic structure for receiving text and responding to users, and then learn how to add the basic elements of personality. You'll then build rule-based systems for parsing text.

Exercise 1: Introduction to conversational software Exercise 2: EchoBot I Exercise 3: EchoBot II Exercise 4: Creating a personality Exercise 5: Chitchat Exercise 6: Adding variety Exercise 7: ELIZA I: asking questions Exercise 8: Text processing with regular expressions Exercise 9: ELIZA II: Extracting key phrases Exercise 10: ELIZA III: Pronouns Exercise 11: ELIZA IV: Putting it all together

Here, you'll use machine learning to turn natural language into structured data using spaCy, scikit-learn, and rasa NLU. You'll start with a refresher on the theoretical foundations and then move onto building models using the ATIS dataset, which contains thousands of sentences from real people interacting with a flight booking system.

Exercise 1: Understanding intents and entities Exercise 2: Intent classification with regex I Exercise 3: Intent classification with regex II Exercise 4: Entity extraction with regex Exercise 5: Word vectors Exercise 6: word vectors with spaCy Exercise 7: Intents and classification Exercise 8: Intent classification with sklearn Exercise 9: Entity extraction Exercise 10: Using spaCy's entity recognizer Exercise 11: Assigning roles using spaCy's parser Exercise 12: Robust language understanding with rasa NLU Exercise 13: Rasa NLU Exercise 14: Data-efficient entity recognition

In this chapter, you'll build a personal assistant to help you plan a trip. It will be able to respond to questions like "are there any cheap hotels in the north of town?" by looking inside a hotel’s database for matching results.

Exercise 1: Virtual assistants and accessing data Exercise 2: SQL basics Exercise 3: SQL statements in Python Exercise 4: Exploring a DB with natural language Exercise 5: Creating queries from parameters Exercise 6: Using your custom function to find hotels Exercise 7: Creating SQL from natural language Exercise 8: Incremental slot filling and negation Exercise 9: Refining your search Exercise 10: Basic negation Exercise 11: Filtering with excluded slots

Everything you've built so far has statelessly mapped intents to actions and responses. It's amazing how far you can get with that! But to build more sophisticated bots you will always want to add some statefulness. That's what you'll do here, as you build a chatbot that helps users order coffee.

Exercise 1: Why statefulness is key Exercise 2: Form filling Exercise 3: Asking contextual questions Exercise 4: Dealing with rejection Exercise 5: Asking questions & queuing answers Exercise 6: Pending actions I Exercise 7: Pending actions II Exercise 8: Pending state transitions

Current Exercise

Exercise 9: Putting it all together I Exercise 10: Putting it all together II Exercise 11: Frontiers of dialogue research Exercise 12: Generating text with neural networks Exercise 13: Congratulations!