SkyNET, here we come.

What do you get when you ask AI to make a cognitive system. 

This …

Sure, let’s construct a high-level outline for a Python-based cognitive chatbot that utilizes both ChatGPT and ChromaDB. Keep in mind that this is just a general outline and does not include actual code. You’ll need to have access to both GPT (OpenAI’s API) and ChromaDB’s APIs or libraries.

  1. Import necessary libraries
  • Import libraries needed for the program like OpenAI’s GPT Python library, ChromaDB library, necessary web framework libraries if the chatbot is to be deployed on the web, and other necessary standard Python libraries.
  1. API key Configuration
  • Initialize your API keys for both OpenAI GPT and ChromaDB securely.
  1. Setup ChromaDB
  • Connect to your ChromaDB instance.
  • Define functions to write, read, and update data in ChromaDB. You will use this to store and retrieve past interactions, user data or any other information relevant to the chatbot’s functioning.
  1. Setup OpenAI GPT
  • Initialize a function to communicate with the GPT model. The function will receive a string input (the user’s message) and will return the model’s response.
  1. Main Chatbot Function
  • Create a main function to run the chatbot. This should receive user input, process the message, generate a response, and deliver the response back to the user. i. Receive User Input
    • Get input from the user. This could be via a web interface, command line interface, or other method.
    ii. Process User Input
    • Before sending the message to the model, you may want to perform any preprocessing necessary, like cleaning the text, managing context from ChromaDB, etc.
    iii. Generate Response
    • Use the function you created to get a response from the GPT model.
    iv. Post-Processing
    • After getting the response, you may want to post-process the response (e.g., censoring certain words, refining output).
    v. Storing Interaction
    • Using the ChromaDB functions you created, store the interaction for future reference. This could include the user’s message, the model’s response, and any other information.
    vi. Deliver Response
    • Finally, return the model’s response to the user. The method for this will depend on how you’re interfacing with the user.
  1. User Interface
  • This part will vary greatly depending on your specific needs. If you’re creating a web chatbot, you might need a server (like Flask or Django) to manage web requests and deliver responses. For a console application, you’ll need a loop to continuously receive user input and print the chatbot’s responses.
  1. Exception Handling
  • Define exception handlers to handle any errors that might occur during the chatbot’s operation, ensuring that the program can fail gracefully.
  1. Main Program
  • Call the main chatbot function and run the chatbot.

Remember, while GPT-4 is able to understand and generate human-like text, it still has limitations in terms of understanding context, and doesn’t have real world knowledge post its last training cut-off. Similarly, always be mindful of the privacy and security implications when handling user data with ChromaDB.

Now the questions is should I have it start making the code. You know it will do it.