README.md

Psylink

A backend and minimal protocol for distributing local LLM prompts. Built using Llama CPP for Python, and Redis.

┌────────┐                                                 ┌────────────────┐
│ Client ├──────┐                                    ┌─────┤ Psylink Worker │
└────────┘      │  ┌──────────────────────────────┐  │     └────────────────┘
                └──┤                              ├──┘
┌────────┐         │ Redis Message Broker Server  │        ┌────────────────┐
│ Client ├─────────┤                              ├────────┤ Psylink Worker │
└────────┘         ├──────────────────────────────┤        └────────────────┘
                   │                              │
┌────────┐         │ Prompt Input/ Output Queues  │        ┌────────────────┐
│ Client ├─────────┤                              ├────────┤ Psylink Worker │
└────────┘         │ Command Input/ Output Queues │        └────────────────┘
               ┌───┤                              ├──┐
┌────────┐     │   └──────────────────────────────┘  │     ┌────────────────┐
│ Client ├─────┘                                     └─────┤ Psylink Worker │
└────────┘                                                 └────────────────┘

Requirements

• Python >= 3.11
• A Redis server
• A Llama CPP-compatible LLM model

Create a virtual environment and run

pip install -r requirements.txt

to install the required python packages.

Psylink Message Format

Client input queue message format:

{
    "message_id": str,
    "client_id": str,
    "priority": int,
    "input": str,
    "timestamp": int
}

Client command queue message format:

{
    "message_id": str,
    "client_id": str,
    "worker_id": str,
    "input": {
        "command": str,
        "args": dict | None
    },
    "timestamp": int
}

Worker output queue message format:

{
    "message_id": str,
    "client_id": str,
    "worker_id": str,
    "input": str,
    "output": str,
    "timestamp": int
}

Example config.toml:

[redis]
host = "localhost"
port = 6379
password = "supersecretpassword"
db = 0
prompt_input_queue = "psy_prompt_input_queue"
prompt_output_queue = "psy_prompt_output_queue"
command_input_queue = "psy_command_input_queue"
command_output_queue = "psy_command_output_queue"

[model]
model_path = "models/codellama-13b-instruct.Q8_0.gguf"

[llama_params]
max_tokens = 32
echo = true
temperature = 0.85

[properties]
min_priority = 1
max_priority = 5