System Message Maker (With Tree-of-Thought for Vastly Improved Quality With Only gpt-3.5-turbo)

With just a SINGLE sentence, this script generates a highly optimized System Message for LLMs using OpenAI's GPT-3 model, with high quality answers on par with ChatGPT 4.

It is cheap, requires running a single command, and has interactivity features, logging, and more.

By employing iterative prompting techniques and tree-of-thought, gpt-3.5-turbo model - which is incredibly cheap to use - produces surprisingly high quality System Messages which can then be used for more powerful models, like gpt-4 or gpt-3.5-turbo-16k.

The results are startling: just one sentence can result in - while slower responses - much cheaper, and equally powerful results as compared to ChatGPT 4, GPT-4-0314, GPT-4 and other state-of-the-art LLMs.

This has been tested with about a sample size of 20, and I'd love to hear feedback!

Usage on Ubuntu or WSL

0. Setup a venv. If you do not know how to do this, skip to the bottom.

1. Run pip install -r requirements.txt

2. Execute export OPENAI_API_KEY=your_actual_key_here in a session (or set the value of OPENAI_API_KEY in the .env.template file and rename it to .env)

3. Run the script without any arguments to generate a new system message:

python system-msg-maker.py

Usage on macOS

1. Run pip install -r requirements.txt or pip3 install -r requirements.txt if you have both Python 2 and Python 3 installed.

2. Execute export OPENAI_API_KEY=your_actual_key_here in a terminal session (or set the value of OPENAI_API_KEY in the .env.template file and rename it to .env)

3. Run the script without any arguments to generate a new system message:

python system-msg-maker.py or python3 system-msg-maker.py if you have both Python 2 and Python 3 installed.

This will prompt you for user input for context generation, then it will generate and print the final system message. The final system message will be printed, but not saved, to a directory.

Logging is soon to come, but the information and answers output are invaluable.

Simple fixes to this will be introduced soon.

TO DO

• Better input validation
• More expansive options
• Integration into langchain
• Portability and overall design integration
• Internet connectivity coming soon
• Further optimizations and selective GPT-4 usage options coming soon! Manual for now
• Binary Search Tree encoding and vectorization to come soon (hosting issues!)
• More steerability and interactivity
• Rotational prompts coming soon
• Add queries/continuation
• Fix flags
• Chat history! Free of training data theft
• Logging & advanced debug output (and suppression)

RECENTLY ADDED

• Better documentation
• Super-charge option (for those with gpt-4 acccess)
• Removed clutter
• Added better instructions

(Please replace your_actual_key_here with your actual OpenAI API key.) (This README.md was optimized with GPT-4)

VENV

## Setting up `venv` for Python

### macOS & Linux

1. **Install Python** (if not already installed):

   • You can download from Python's official website.

2. **Install `venv`** (if not already included with your Python version): ```bash $ sudo apt-get install python3-venv # For Ubuntu/Debian ```

3. **Create a virtual environment**: ```bash $ python3 -m venv myenv ```

4. **Activate the virtual environment**: ```bash $ source myenv/bin/activate ```

5. **Deactivate** (when done): ```bash $ deactivate ```

Windows

1. Install Python:

   • Download from Python's official website.
   • Ensure Python and Pip are added to PATH during installation.

2. Create a virtual environment:

   C:\> python -m venv myenv

3. Activate the virtual environment:

   C:\> myenv\Scripts\activate

4. Deactivate (when done):

   C:\> deactivate

Note: For projects requiring different Python versions or dependencies, repeat the steps to create a new virtual environment.