This repository is a ready-to-run basic Slack AI solution you can host yourself and unlock the ability to summarize threads and channels on demand using OpenAI (support for alternative and open source LLMs will be added if there's demand). The official Slack AI product looks great, but with limited access and add-on pricing, I decided to open-source the version I built in September 2023. Learn more about how and why I built an open-source Slack AI.

Once up and running (instructions for the whole process are provided below), all your Slack users will be able to generate to both public and private:

1. Thread summaries - Generate a detailed summary of any Slack thread (powered by GPT-3.5-Turbo)
2. Channel overviews - Generate an outline of the channel's purpose based on the extended message history (powered by an ensemble of NLP models and a little GPT-4 to explain the analysis in natural language)
3. Channel summaries (beta) - Generate a detailed summary of a channel's extended history (powered by GPT-3.5-Turbo). Note: this can get very long!

• Getting Started
  • Prerequisites
  • Installation
  • Usage
  • Customization
• Testing
• Future Enhancements
• Contributing

Follow these instructions to get a copy of the project up and running on your local machine for development and testing purposes.

Ensure you have the following preconfigured or installed on your local development machine:

• Python 3.8 or higher
• OpenAI API key
• Slack App & associated API tokens
• Poetry package manager
• ngrok (recommended)

1. Clone the repository to your local machine.
2. Navigate to the project directory.
3. Install the required Python packages using Poetry:

poetry install

4. Create a .env file in the root directory of the project, and fill it with your API keys and tokens. Use the example.env file as a template.

cp example.env .env && open .env

TODO

To run the application, run the FastAPI server:

uvicorn slack_server:fast_app --reload

You'll then need to expose the server to the internet using ngrok.

Run ngrok with the following command: ngrok http 8000

Then add the ngrok URL to your Slack app's settings.

The main customization options are:

• Channel Summary: customize the ChatGPT prompt in topic_analysis.py
• Thread Summary: customize the ChatGPT prompt in summarizer.py

This project uses pytest and pytest-cov to run tests and measure test coverage.

Follow these steps to run the tests with coverage:

1. Navigate to the project root directory.

2. Run the following command to execute the tests with coverage:

   pytest --cov=hackathon_2023 tests/

   This command will run all the tests in the tests/ directory and generate a coverage report for the hackathon_2023 module.

3. After running the tests, you will see a report in your terminal that shows the percentage of code covered by tests and highlights any lines that are not covered.

Please note that if you're using a virtual environment, make sure it's activated before running these commands.

• Add support for alternative and open-source LLMs
• Add support for anonymized message summaries
• Leverage prompt tools like Chain of Destiny
• Add support for pulling supporting context from external sources like company knowledge bases
• Explore caching and other performance optimizations
• Explore sentiment analysis
• Explore migrating to LangChain for more extensibility & control

I more than welcome contributions! Please read CONTRIBUTING.md for details on how to submit feedback, bugs, feature requests, enhancements, or your own pull requests.

This project is licensed under the GPL-3.0 License - see the LICENSE.md file for details.