Creating SQL from natural language
Now you'll write a respond() function that can handle messages like "I want an expensive hotel in the south of town" and respond appropriately according to the number of matching results in a database. This is an important functionality for any database-backed chatbot.
Your find_hotels() function from the previous exercises has already been defined for you, along with a Rasa NLU interpreter object, which can handle hotel queries, and a list of responses, which you can explore in the Shell.
This exercise is part of the course
Building Chatbots in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Define respond()
def respond(message):
# Extract the entities
entities = ____.____(____)["____"]
# Initialize an empty params dictionary
params = {}
# Fill the dictionary with entities
for ent in entities:
params[ent["entity"]] = str(ent["value"])
# Find hotels that match the dictionary
results = ____
# Get the names of the hotels and index of the response
names = [r[0] for r in results]
n = ____(len(results),3)
# Select the nth element of the responses array
return ____[n].____(*names)