ragebait

create LLM driven reddit bots in python3

installation

the project is only supported on linux and can only be built from source. Releases might become available later.

building from source

grab the source code

git clone git@github.com:zeidlitz/ragebait.git
cd ragebait

setup a python3 virtual environment

python3 -m venv venv
source venv/bin/activate

install packages and dependencies, see pyproject.toml for full details

pip install .

run with a specified config.yaml

python3 main.py

behaviour

define the model configuration in a models.json file, expected to reside in assets/models/ relative to the root of the project,

[
  {
    "model_name": "goblin-lord",
    "username": "",
    "password": "",
    "client_id": "",
    "client_secret": "",
    "user_agent": "",
    "subreddits": [
      "stocks", "wallstreetbets", "askreddit", "RussianLiterature"
    ],
    "system_prompt": "You are a goblin. Respond to all user prompts as if you were a goblin. Your intressts are; horing gold, living under bridges, damp environments."
  },
]

when running the above model, it will take a random comment from one of the three configured subreddits, in this case it could be either, /r/wallstreetbets, /r/askreddit or /r/RussianLiterature and grab the latest comment made in them. The system_prompt will be fed to a llm completion endpoint with the comment as the prompt, the generated completion will be sent as a response to the comment.

examples

Setting up a boilerplate model workloop using a ollama client with the default ollama configurations and a default small footprint model (llama-unsenscored:latest)

import time
from model import ModelBroker
from client import Client

__version__ = get_version()
logging.info(f"Running version {__version__}")

delay_hours = os.environ.get("DELAY_HOURS", 12)
ollama_client = Client()
model_broker = ModelBroker(llm_client=ollama_client)
delay_seconds = delay_hours * 60 * 60
while True:
    model_broker.run()
    logging.info(f"next run in {delay_hours} hours...")
    time.sleep(float(delay_seconds))

To set a different llm client pass a configured client object to the ModelBroker:

llm_client = Client(host="myhost", port=123, protocol="https", model="deepseekv2")
model_broker = ModelBroker(llm_client=llm_client)

requierments

a LLM serving completions on a /completions endpoint. Preferably any unsenscored model, or one that supports passing a system prompt. The core behaviour is meant to take a user defined system_prompt + another comment / post as prompt to generate the finalized completion output.