Handling User Input

In custom ChatGPT models, user input is a crucial component in the conversation flow. Therefore, it is important to handle it effectively in order to generate accurate and relevant responses. In this chapter, we will explore various techniques for handling user inputs.

Tokenization

Tokenization is the process of breaking down a sentence into individual words or tokens. In the case of user input, we need to tokenize the input to convert it into a suitable format for the chatbot to process.

An effective way to tokenize user input is to use a tokenizer such as the NLTK library or the spaCy library. These libraries can be used to tokenize a sentence into individual words or phrases.

For example, let's say the user inputs the sentence "I want to order a pizza". We can use a tokenizer to break the sentence down into individual words: "I", "want", "to", "order", "a", "pizza".

Text Pre-processing

Text pre-processing involves cleaning and manipulating the text data to remove unnecessary elements and prepare it for further analysis. In the case of user input, pre-processing can involve removing stop words, stemming, and removing special characters.

For example, let's say the user inputs the sentence "Can I please order a large pepperoni pizza?". We can use pre-processing techniques to remove the stop words "Can", "I", "please", and "a", as well as removing the question mark and converting the input to lowercase. This transforms the input into "order large pepperoni pizza".

Intent and Entity Recognition

Intent recognition involves identifying the intention behind a user's input. This is crucial for generating relevant responses. For example, if the user inputs "Can you give me directions to the nearest restaurant?", the intent would be to get directions.

Entity recognition involves identifying important elements in the user's input, such as dates, names, and locations. This can be used to provide more accurate and specific responses. For example, if the user inputs "I want to book a flight to Paris", entity recognition can identify "Paris" as a location.

Intent and entity recognition can be achieved using natural language processing techniques such as named entity recognition or intent classifiers. Tools such as RASA or Dialogflow provide these features for free.

Examples

Here are some examples of how user input can be handled effectively:

1. User Input: "What is the weather like today?"

Tokenization: ["What", "is", "the", "weather", "like", "today", "?"]

Pre-processing: ["weather", "like", "today"]

Intent: "get_weather"

1. User Input: "Can you give me directions to the nearest coffee shop?"

Tokenization: ["Can", "you", "give", "me", "directions", "to", "the", "nearest", "coffee", "shop", "?"]

Pre-processing: ["give", "directions", "nearest", "coffee", "shop"]

Intent: "get_directions"

Entity: coffee_shop

1. User Input: "Is there a flight from New York to Los Angeles on Friday?"

Tokenization: ["Is", "there", "a", "flight", "from", "New", "York", "to", "Los", "Angeles", "on", "Friday", "?"]

Pre-processing: ["flight", "New", "York", "Los", "Angeles", "Friday"]

Intent: "get_flight"

Entity: From: "New York", To: "Los Angeles", Date: "Friday"