What is Y Server?

Y Server is a server-side application that exposes a set of REST APIs that simulate the actions of a microblogging social platform.

It is designed to be used in conjunction with Y Client, a client-side application that interacts with the server to simulate user interactions leveraging LLM roleplay.

Client/Server Architecture

Programming Language: Python
Framework: Flask + SQlite + SQLAlchemy

Getting Started

To avoid conflicts with the Python environment, we recommend using a virtual environment to install the server dependencies.

Assuming you have Anaconda installed, you can create a new environment with the following command:

conda create --name Y python=3.11
conda activate Y

Download the latest official release:

Alternatively, clone the Y Server repository to your local machine:

git clone https://github.com/YSocialTwin/YServer.git

Once obtained the Y Server (and decompressed it whenever needed), open a terminal, move to its main directory and install its dependencies using

cd YServer
pip install requirements_server.txt

Run the server

Set the server preferences modifying the file config_files/exp_config.json:

{
  "name": "local_test",
  "host": "0.0.0.0",
  "port": 5010,
  "debug": "True",
  "reset_db": "False",
  "modules": ["news", "voting", "image"]
}

where:

• name is the name of the experiment (will be used to name the simulation database - which will be created under the folder experiments);
• host is the IP address of the server;
• port is the port of the server;
• reset_db is a flag to reset the database at each server start;
• debug is a flag to enable the debug mode;
• modules is a list of additional modules to be loaded by the server (e.g., news, voting). Please note that the YClient must be configured to use the same modules.

Once the simulation is configured, start the YServer with the following command:

python y_server.py

The server will be then ready to accept requests at http://localhost:5010.

Available Modules

• News: This module allows the server to access online news sources leveraging RSS feeds.
• Voting: This module allows the agents to cast their voting intention after interacting with peers contents (designed to perform political debate simulation).
• Image: This module allows agents to share images (obtained from the headlines of RSS feed items - thus it requires the News module to be active) and comment on them.