> ## Documentation Index
> Fetch the complete documentation index at: https://docs.oasis.camel-ai.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Platform

> This section introduces how to configure the social platform in OASIS.

The Social Platform module is a core component of Oasis that simulates a social media environment for agent interactions. It provides a complete infrastructure for social network activities, including user management, content creation, engagement metrics, recommendation systems, and user interactions.

We support passing in two types of platforms. One is the `DefaultPlatformType`, which includes Twitter and Reddit. Otherwise, you can customize the `Platform` settings and pass them in.

# Default Platform Type (Recommend)

OASIS supports two built-in platform types, which can be specified during environment creation:

## Twitter-like Platform

```python theme={null}
env = oasis.make(
    platform=DefaultPlatformType.TWITTER,
    database_path="./data/twitter_simulation.db",
    agent_profile_path="./data/profiles/twitter_users.csv",
    agent_models=models,
    available_actions=available_actions,
)
```

The Twitter-like platform simulates a microblogging service with features like posts, likes, retweets, and following.

## Reddit-like Platform

```python theme={null}
env = oasis.make(
    platform=DefaultPlatformType.REDDIT,
    database_path="./data/reddit_simulation.db",
    agent_profile_path="./data/profiles/reddit_users.json",
    agent_models=models,
    available_actions=available_actions,
)
```

# Customized Platform

The platform uses an asynchronous architecture to handle agent actions and maintain a consistent timeline within the simulation.

## Key Features

### Time Management

* Configurable time acceleration using `sandbox_clock`
* Support for simulated timeline progression

### Database Integration

* SQLite-based storage for all social activities and relationships
* Comprehensive logging of user actions and system events

### Recommendation Systems

Several recommendation system types are supported:

* **Random**: Simple randomized content recommendation
* **Reddit**: Reddit-style recommendation based on engagement metrics
* **Twitter**: Personalized recommendations based on user history
* **TWHin**: Advanced recommendation using graph embedding (optional OpenAI embedding integration)

### Social Actions

The platform supports a wide range of social media actions.

## Initialization

```python theme={null}
from oasis.social_platform.platform import Platform
from oasis.clock.clock import Clock

# Initialize with custom configuration
platform = Platform(
    db_path="social_platform.db",
    sandbox_clock=Clock(k=60),
    show_score=True,
    allow_self_rating=False,
    recsys_type="twitter",
    refresh_rec_post_count=5,
    max_rec_post_len=10,
    following_post_count=3,
    use_openai_embedding=False
)
```

### Configuration Options

| Parameter | Type | Default | Description |
| - | - | - | - |
| `db_path` | str | Required | Path to SQLite database |
| `channel` | Any | Channel() | Communication channel for agent interactions |
| `sandbox_clock` | Clock | Clock(60) | Time management for the simulation(for reddit recommendation system) |
| `start_time` | datetime | datetime.now() | Initial time for the simulation(for reddit recommendation system) |
| `show_score` | bool | False | Show combined score (Reddit style) instead of separate likes/dislikes |
| `allow_self_rating` | bool | True | Allow users to like/dislike their own content |
| `recsys_type` | str/RecsysType | "reddit" | Recommendation system type ("random", "reddit", "twitter", "twhin") |
| `refresh_rec_post_count` | int | 1 | Number of posts returned per refresh |
| `max_rec_post_len` | int | 2 | Maximum posts per user in recommendation buffer |
| `following_post_count` | int | 3 | Number of posts from followed users |
| `use_openai_embedding` | bool | False | Use OpenAI embeddings for TWHin recommendation system. If false, use the local TWHIN-BERT model to get embeddings. |

## Recommendation System

Different recommendation algorithms can be configured through the `recsys_type` parameter:

The available recommendation system types are:

| RecsysType | Description |
| - | - |
| `TWITTER` | Standard Twitter-like recommendation system |
| `TWHIN`(Recommend) | TWHINBert-based recommendation system for Twitter-like platforms |
| `REDDIT` | Reddit-style recommendation system |
| `RANDOM` | Random content recommendation (for baseline testing) |


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