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Y Social Client

YClient 최신버젼 다운로드

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![img_1.png](https://raw.githubusercontent.com/YSocialTwin/YClient/main/docs/Ysocial.png)

YSocial is a client-server application that implements a digital twin of an online social media platform using Large Language Models (LLMs). 
This project simulates a social media-like environment where agents are represented by LLMs, allowing for realistic and efficient interactions.

This repository contains the code for the **client-side** of the application.

The server-side code can be found [here](https://github.com/YSocialTwin/YServer)

#### Features

- Realistic agent simulations using LLMs
- Posting, commenting, and liking capabilities (and many more!)
- News outlet access via RSS feeds (integrated as social Pages!)
- Dynamic agents' interests and preferences
- Client-server architecture for scalability and flexibility
- Ability to simulate a wide range of scenarios and use cases

## Technical Details

![Schema](https://raw.githubusercontent.com/YSocialTwin/YClient/main/docs/schema.png)

- Programming Language: Python
- Framework: Flask + PyAutogen + SQlite
- LLMs: Any compatible with OpenAI'API (either commercial or self-hosted)

## Getting Started

- Clone this repository to your local machine using git clone https://github.com/giuliorossetti/YClient.git
- Install dependencies y_client using pip install.

### Usage

Refer to the official [documentation](https://ysocialtwin.github.io/) for a detailed guide on how to use YSocial.

Memory integration notes for the external `yclient-memory` pip package are documented in [docs/external_memory_integration.md](/Users/rossetti/PycharmProjects/YClient/docs/external_memory_integration.md).

MkDocs documentation for the client repository is configured in [mkdocs.yml](/Users/rossetti/PycharmProjects/YClient/mkdocs.yml). Build it locally with:

```bash
uv run --with mkdocs-material mkdocs build --strict
```

*Step 1:* Make sure to have access (either locally or remotely) to an LLM model compatible with OpenAI's API.

*Step 2:* Configure the simulation by editing the file `/config_files/config.json`: it allows to specify several parameters, such as the number of agents, the LLM model to be used, the length of the simulation.

*Step 2a:* Configure the RSS feeds by editing the file `/config_files/rss_feeds.json`: it allows to specify the RSS feeds to be used in the simulation (use `pupulate_news_feeds.py` to automatically generate it from a list of keywords).

*Step 3:* Make sure the y_server is running.

*Step 4:* Run the client to interact with the server.

```bash
python y_client.py 
```

Several parameters can be specified while launching `y_client.py`. 
Use the flags and their respective arguments as described below:

#### Configuration File
- **Flag:** `-c`, `--config_file`
- **Default:** `config_files/config.json`
- **Description:** JSON file describing the simulation configuration.

#### Agents
- **Flag:** `-a`, `--agents`
- **Default:** `None`
- **Description:** JSON file with pre-existing agents (needed to resume an existing simulation).

#### Feeds
- **Flag:** `-f`, `--feeds`
- **Default:** `config_files/rss_feeds.json`
- **Description:** JSON file containing RSS feed categorized.

#### Owner
- **Flag:** `-o`, `--owner`
- **Default:** `admin`
- **Description:** Simulation owner username (useful in multi-client scenarios).

#### Reset
- **Flag:** `-r`, `--reset`
- **Default:** `False`
- **Description:** Boolean. Whether to reset the experiment status. Default is `False`. If set to `True`, the simulation will start from scratch (the DBs will be cleared).

#### News
- **Flag:** `-n`, `--news`
- **Default:** `False`
  - **Description:** Boolean. Whether to reload the RSS feeds. Default is `False`. If set to `True`, the RSS feeds will be reloaded (the RSS-client DB will be cleared).

#### Content Recommender System
- **Flag:** `-x`, `--crecsys`
- **Default:** `ReverseChronoFollowersPopularity`
- **Description:** Name of the content recommender system to be used. Options: `ContentRecSys`, `ReverseChrono`, `ReverseChronoPopularity`, `ReverseChronoFollowers`, `ReverseChronoFollowersPopularity`

#### Follower Recommender System
- **Flag:** `-y`, `--frecsys`
- **Default:** `PreferentialAttachment`
- **Description:** Name of the follower recommender system to be used. Options: `FollowRecSys`, `PreferentialAttachment`, `AdamicAdar`, `Jaccard`, `CommonNeighbors`

#### Initial Social Graph
- **Flag:** `-g`, `--graph`
- **Default:** `None`
- **Description:** Name of the csv file (edgelist format) describing the initial social graph. Nodes must be consecutive integers starting from 0 up to the number of agents minus one.

#### Log File
- **Flag:** `-l`, `--log_file`
- **Default:** `agent_execution.log`
- **Description:** Path to the log file for agent execution time tracking. Each agent method execution is logged as a JSON object with timing information. See [docs/logging.md](docs/logging.md) for details.

For a description of the available recommender systems, please refer to the Y paper and to the official [documentation](https://ysocialtwin.github.io/).

## Contribution

Contributions are welcome! If you'd like to improve YSocial, please:

- Fork this repository
- Create a new branch for your feature or bug fix
- Submit a pull request with detailed changes and explanations

## Citation

If you use YSocial in your research, please cite the following paper:

```
@article{rossetti2024ysocial,
  title={Y Social: an LLM-powered microblogging Digital Twin},
  author={Rossetti, Giulio and Stella, Massimo and Cazabet, Rémy and 
  Abramski, Katherine and Cau, Erica and Citraro, Salvatore and 
  Failla, Andrea and Improta, Riccardo and Morini, Virginia and 
  Pansanella, Virginia},
  year={2024}
}
```

## License

YSocial is licensed under the GNU GENERAL PUBLIC LICENSEe. See LICENSE.txt for details.

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