QnA Chatbot using sentence simi. Introduction | by AI Brewery

When the user enters the query:
1. Calculate the sentence vector of the query
2. Identify the most matching sentence from the knowledge base using cosine similarity

def calculate_similarity(query):
mean_pooled = get_embedding([query])  question = []
similarity = []
intent = []  for rec in collection.find():
question.append(rec[‘Text’])
intent.append(rec[‘Intent’])
cos_sim = cosine_similarity([mean_pooled[0]],
[np.fromiter(rec[‘Embedding’], dtype=np.float32)]) 
similarity.append(cos_sim)  index = np.argmax(similarity)
recognized_intent = intent[index]
return recognized_intent

Intents JSON file

Here we set up an intents JSON file that defines the intentions of the chatbot user.
For example:
A user may wish to know the name of our chatbot; therefore, we have created an intent called name.
A user may wish to know the age of our chatbot; therefore, we have created an intent called age.

In this chatbot, we have used 5 intents: name, age, date, greeting, and goodbye. When the user enters any input, the intent will be recognized by the bot.

Within this intents JSON file, alongside each intents tag, there are responses. For our chatbot, once the intent is recognized the response will be randomly selected from the static set of responses associated with each intent.

# used a dictionary to represent an intents JSON data = {"intents": [
{"tag": "greeting",
"responses": ["Howdy Partner!", "Hello", "How are you doing?", "Greetings!", "How do you do?"],
},
{"tag": "age",
"responses": ["I am 24 years old", "I was born in 1996", "My birthday is July 3rd and I was born in 1996", "03/07/1996"]
},
{"tag": "date",
"responses": ["I am available all week", "I don't have any plans", "I am not busy"]
},
{"tag": "name",
"responses": ["My name is Kippi", "I'm Kippi", "Kippi"]
},
{"tag": "goodbye",
"responses": ["It was nice speaking to you", "See you later", "Speak soon!"]
}
]}

Generate the response to the user’s query

def get_response(intent): 
for i in data[‘intents’]: 
if i[“tag”] == intent:
result = random.choice(i[“responses”])
break
return result

Now its time to test our chatbot

recognized_intent = calculate_similarity('Hello my friend')
print(f"Intent: {recognized_intent}")
print(f"Response: {get_response(recognized_intent)}")

recognized_intent = calculate_similarity('What do people call you')
print(f"Intent: {recognized_intent}")
print(f"Response: {get_response(recognized_intent)}")

recognized_intent = calculate_similarity(‘How old are you’)
print(f”Intent: {recognized_intent}”)
print(f”Response: {get_response(recognized_intent)}”)

recognized_intent = calculate_similarity(‘what are your weekend plans’)
print(f”Intent: {recognized_intent}”)
print(f”Response: {get_response(recognized_intent)}”)

recognized_intent = calculate_similarity(‘Catch you later’)
print(f”Intent: {recognized_intent}”)
print(f”Response: {get_response(recognized_intent)}”)