readme.md

# SGLang Engine

## Introduction
SGLang provides a direct inference engine without the need for an HTTP server. There are generally two use cases:

1. **Offline Batch Inference**
2. **Embedding Generation**
3. **Custom Server on Top of the Engine**
4. **Inference Using FastAPI**

## Examples

### [Offline Batch Inference](./offline_batch_inference.py)

In this example, we launch an SGLang engine and feed a batch of inputs for inference. If you provide a very large batch, the engine will intelligently schedule the requests to process efficiently and prevent OOM (Out of Memory) errors.

### [Embedding Generation](./embedding.py)

In this example, we launch an SGLang engine and feed a batch of inputs for embedding generation.

### [Custom Server](./custom_server.py)

This example demonstrates how to create a custom server on top of the SGLang Engine. We use [Sanic](https://sanic.dev/en/) as an example. The server supports both non-streaming and streaming endpoints.

#### Steps:

1. Install Sanic:

```bash
pip install sanic
```

2. Run the server:

```bash
python custom_server
```

3. Send requests:

```bash
curl -X POST http://localhost:8000/generate  -H "Content-Type: application/json"  -d '{"prompt": "The Transformer architecture is..."}'
curl -X POST http://localhost:8000/generate_stream  -H "Content-Type: application/json"  -d '{"prompt": "The Transformer architecture is..."}' --no-buffer
```

This will send both non-streaming and streaming requests to the server.

### [Token-In-Token-Out for RLHF](../token_in_token_out)

In this example, we launch an SGLang engine, feed tokens as input and generate tokens as output.

### [Inference Using FastAPI](fastapi_engine_inference.py)

This example demonstrates how to create a FastAPI server that uses the SGLang engine for text generation.