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1. Project Title

Building a Chatbot with OpenAI

2. Project Instructions

The Head of Innovation at TechNova Industries has enlisted your help in integrating AI into their customer service! They would like you to develop a functional chatbot using OpenAI's GPT model that can interact with users, handle user commands, and maintain a continuous conversation.

Below are the specific tasks that you are required to accomplish:

  1. Set up the OpenAI API and initialize the GPT model. Make sure to replace key_goes_here with your actual API key and define the correct model to be used.

  2. Import the appropriate transformer and initialize the GPT2TokenizerFast from the gpt2 model. This will be used to tokenize the input queries and trim them as required.

  3. Develop a function, trim_prompt, to manage the length of the conversation. This function should take in a prompt and a token limit, tokenizing the prompt, trimming it to the last n tokens, and converting it back into a string.

  4. Define a user prompt for the model to initiate a conversation. An initial prompt can be entered by the user to kick-start the conversation.

  5. Generate the response from the model using the OpenAI's Completion.create method, passing the appropriate parameters such as model, prompt, temperature, and max_tokens. Print the model's response to the console.

  6. Create a list, conversation, to store each interaction (user input and chatbot response).

  7. Implement a while-loop to continuously interact with the chatbot. The loop should handle user commands such as 'back' (which moves the conversation back to the previous response) and 'exit' (which terminates the conversation), as well as appending each interaction to the conversation list.

  8. For each iteration, construct the next prompt by joining all previous inputs and responses in the conversation list.

  9. Trim the memory of the conversation to the last 2048 tokens using the trim_prompt function before feeding it into the next API call.

  10. Create a DataFrame called performance_df, containing columns named "user_input", "bot_response", and "performance_score", containing the query, the response, and a score reflecting the chatbot's performance.

Remember to hit the "Submit Project" button once you're satisfied with your chatbot's functionality and performance.

3. Functions

Packages:

  1. openai: This package allows interaction with the OpenAI API.
  2. transformers: This package provides state-of-the-art machine learning models like GPT-2 and utilities for natural language processing tasks.

Functions and Methods:

  1. openai.api_key: Used to set your OpenAI API key.
  2. openai.Completion.create: This function is used to generate text completions for a given prompt.
  3. GPT2TokenizerFast.from_pretrained: This function from the transformers package is used to load the GPT2 tokenizer.
  4. tokenizer.tokenize: This method tokenizes a given input string into tokens.
  5. tokenizer.convert_tokens_to_ids: This method converts a list of tokens into their corresponding IDs.
  6. tokenizer.decode: This method is used to convert a list of token IDs back into a string.
  7. input: A built-in Python function that allows user input.
  8. print: A built-in Python function that prints the specified message to the screen.
  9. list.append: This method adds an element to the end of a list.
  10. del: A Python keyword used to delete objects.
  11. str.lower: This method returns a lowercase version of a string.
  12. str.join: This method concatenates a list of strings into a single string.
  13. dict.get: This method returns the value for a given key in a dictionary.
  14. .choices: A property of the Completion object that returns a list of choices made by the model for the completion.
  15. break: A Python statement used to exit the current loop.

4. Dataset

As per the project requirements and the provided code, there is no need for a traditional dataset like CSV, JSON, parquet, Excel, or SQL. The project revolves around creating an interactive chatbot using the OpenAI's GPT model which generates responses dynamically based on user input, not on a predefined dataset.

The nature of this project is interactive and real-time. The user provides inputs (questions or commands), to which the chatbot will respond, creating an on-the-fly dialogue. The code provided includes functionality for the user to control the conversation's flow, including moving backwards in the conversation or exiting it altogether.

However, for the sake of evaluation and potential improvements to the chatbot, the conversation history can be stored in a structured format. In the provided code, each user input and corresponding bot response are stored in a list of dictionaries called conversation. This allows the ongoing conversation to be tracked and potentially saved for later analysis, if required.

Please note that all data handling should comply with relevant data protection and privacy laws, ensuring that user inputs are treated as confidential and not misused or stored without appropriate security measures.