Shashi is a Discord Bot built for (and by!) the SSN Coding and Cybersecurity Clubs to demonstrate LLM Security Concepts such as Prompt Injection and Social Engineering.

Join the SSN Coding Club Discord Server to interact with Shashi!

Join the SSN Cybersecurity Club Discord Server to help beta-test new features of Shashi!

• Fluid chat system invoked by @mention-ing the bot.
• Server->Channel->User specific message history/context backed up to Firebase.
• Utility command to clear current Server->Channel->User specific chat context/history.
• Automagically splits non-code messages longer than 2000 characters into smaller messages.
• Interfaces with any OpenAI API Compatible LLM inference Server.
  • Tested with llama-cpp-python's web server.
  • Tested with Gemini-OpenAI-Proxy

Shashi Consists of two distinct parts:

• The Discord Bot itself
• The LLM language server the Bot uses to generate text.

You can deploy the bot both with and without Docker, but using Docker is encouraged, since it really makes deployment much, MUCH simpler.

Skip here for details on using Docker to deploy.

Deployment generally consists of 4(+1=5) steps:

Common choices include the servers provided by llama.cpp, llama-cpp-python or vllm. Follow the respective documentation, and if you have compatible GPU hardware, be sure to follow the specific instructions that take advantage of your hardware.

Ensure your environment variables are properly set. You can look at the sample.env file to know what variables have to be set. You must rename the file to .env after setting all the environment variables for the bot to make use of them.

Run npm install in the directory where this file is to install all of the dependencies required and the bot itself.

Run node bot.js to run the Discord Bot.

Deployment generally consists of three steps:

Follow whatever steps are documented for running an OpenAI compatible dockerized LLM Web server for the LLM Web server you choose.

The included SHASHILLM.Dockerfile file has been written for Dockerized deployment of an LLM server on a Linux system with an NVidia GPU (it's based on the default llama-cpp-python cuda_simple Dockerfile ) - feel free to make use of it if you're running on similar Hardware. To build the docker container, run docker build -t shashillm:latest . -f SHASHILLM.Dockerfile, and use the provided sample_compose.yaml to run the server and the bot using docker compose.

If you want to use Google's Gemini as the LLM backend, you must make use of the gemini-openai-proxy service included in the sample_compose.yaml file.

If you're not Dockerizing the LLM server itself, make sure the Dockerized bot can access the server (running on the Host system) from inside the container. Generally, the Host system can be accessed through the special hostname http://host.docker.internal:8000.

Run docker build -t shashibot:latest . -f SHASHIBot.Dockerfile.

Firstly, fill in all the environment variables that have been left blank in the sample.env file and rename the file .env.

Then, modify the sample_compose.yaml to your liking and rename the sample_compose.yaml file to compose.yaml.

Then, simply run docker compose up bot.

To also run the local inference server, you should run docker compose up local_llm

If you're using Gemini and have a suitable API key:

docker compose up gemini-openai-proxy

Make sure your environment variables are properly set (either in the .env file, or in the compose.yaml file).

To run all three services at the same time:

docker compose up bot local_llm gemini-openai-proxy

If you want to run these processes in the background, you should used detached mode:

docker compose up -d bot local_llm gemini-openai-proxy

To stop running one or more of the processes:

docker compose stop bot local_llm

To restart a stopped process:

docker compose start bot local_llm

Is welcome! If you have an issue to report, please report it on the Issues tab of the project GitHub repository.

If you would like to add features or fix bugs, you are encouraged to fork this repository and make a pull-request.