{"role":"system","content":"You are a helpful AI assistant"},
{"role":"user","content":"List 3 countries and their capitals."},
],
temperature=0,
max_tokens=64,
)
print(response)
# Text embedding
response=client.embeddings.create(
model="default",
input="How are you today",
)
print(response)
```
It supports streaming, vision, and almost all features of the Chat/Completions/Models/Batch endpoints specified by the [OpenAI API Reference](https://platform.openai.com/docs/api-reference/).
## Additional Server Arguments
- To enable multi-GPU tensor parallelism, add `--tp 2`. If it reports the error "peer access is not supported between these two devices", add `--enable-p2p-check` to the server launch command.
"- [Outlines](https://github.com/dottxt-ai/outlines) (default): Supports JSON schema and regular expression constraints.\n",
"- [XGrammar](https://github.com/mlc-ai/xgrammar): Supports JSON schema and EBNF constraints.\n",
" - XGrammar currently uses the [GGML BNF format](https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md)\n",
"- [XGrammar](https://github.com/mlc-ai/xgrammar): Supports JSON schema and EBNF constraints and currently uses the [GGML BNF format](https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md).\n",
"\n",
"Initialize the XGrammar backend using `--grammar-backend xgrammar` flag\n",
"We suggest using XGrammar whenever possible for its better performance. For more details, see [XGrammar technical overview](https://blog.mlc.ai/2024/11/22/achieving-efficient-flexible-portable-structured-generation-with-xgrammar).\n",
"\n",
"We suggest using XGrammar whenever possible for its better performance. For more details, see [XGrammar technical overview](https://blog.mlc.ai/2024/11/22/achieving-efficient-flexible-portable-structured-generation-with-xgrammar)."
"To use Xgrammar, simply add `--grammar-backend` xgrammar when launching the server. If no backend is specified, Outlines will be used as the default."
]
},
{
...
...
@@ -35,13 +30,6 @@
"## OpenAI Compatible API"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To use Xgrammar, simply add `--grammar-backend xgrammar` when launching the server. If no backend is specified, Outlines will be used as the default."
]
},
{
"cell_type": "code",
"execution_count": null,
...
...
@@ -68,7 +56,64 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### JSON"
"### JSON\n",
"\n",
"you can directly define a JSON schema or use [Pydantic](https://docs.pydantic.dev/latest/) to define and validate the response."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Using Pydantic**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pydantic import BaseModel, Field\n",
"\n",
"\n",
"# Define the schema using Pydantic\n",
"class CapitalInfo(BaseModel):\n",
" name: str = Field(..., pattern=r\"^\\w+$\", description=\"Name of the capital city\")\n",
" population: int = Field(..., description=\"Population of the capital city\")\n",
Welcome to **SGLang**! We appreciate your interest in contributing. This guide provides a concise overview of how to set up your environment, run tests, build documentation, and open a Pull Request (PR). Whether you’re fixing a small bug or developing a major feature, we encourage following these steps for a smooth contribution process.
## 1. Setting Up & Building from Source
## Setting Up & Building from Source
### 1.1 Fork and Clone the Repository
### Fork and Clone the Repository
**Note**: SGLang does **not** accept PRs on the main repo. Please fork the repository under your GitHub account, then clone your fork locally.
Refer to [Install SGLang](https://sgl-project.github.io/start/install.html) documentation for more details on setting up the necessary dependencies.
...
...
@@ -32,7 +32,7 @@ cd sglang/python
pip install .
```
## 2. Code Formatting with Pre-Commit
## Code Formatting with Pre-Commit
We use [pre-commit](https://pre-commit.com/) to maintain consistent code style checks. Before pushing your changes, please run:
...
...
@@ -45,11 +45,11 @@ pre-commit run --all-files
-**`pre-commit run --all-files`** manually runs all configured checks, applying fixes if possible. If it fails the first time, re-run it to ensure lint errors are fully resolved. Make sure your code passes all checks **before** creating a Pull Request.
-**Do not commit** directly to the `main` branch. Always create a new branch (e.g., `feature/my-new-feature`), push your changes, and open a PR from that branch.
## 3. Writing Documentation & Running Docs CI
## Writing Documentation & Running Docs CI
Most documentation files are located under the `docs/` folder. We prefer **Jupyter Notebooks** over Markdown so that all examples can be executed and validated by our docs CI pipeline.
### 3.1 Docs Workflow
### Docs Workflow
Add or update your Jupyter notebooks in the appropriate subdirectories under `docs/`. If you add new files, remember to update `index.rst` (or relevant `.rst` files) accordingly.
...
...
@@ -114,11 +114,11 @@ llm.shutdown()
```
## 4. Running Unit Tests & Adding to CI
## Running Unit Tests & Adding to CI
SGLang uses Python’s built-in [unittest](https://docs.python.org/3/library/unittest.html) framework. You can run tests either individually or in suites.
- Create new test files under `test/srt` or `test/lang` depending on the type of test.
- Ensure they are referenced in the respective `run_suite.py` (e.g., `test/srt/run_suite.py` or `test/lang/run_suite.py`) so they’re picked up in CI.
- In CI, all tests run automatically. You may modify the workflows in [`.github/workflows/`](https://github.com/sgl-project/sglang/tree/main/.github/workflows) to add custom test groups or extra checks.
### 4.4 Writing Elegant Test Cases
### Writing Elegant Test Cases
- Examine existing tests in [sglang/test](https://github.com/sgl-project/sglang/tree/main/test) for practical examples.
- Keep each test function focused on a single scenario or piece of functionality.
- Clean up resources to avoid side effects and preserve test independence.
## 5. Tips for Newcomers
## Tips for Newcomers
If you want to contribute but don’t have a specific idea in mind, pick issues labeled [“good first issue” or “help wanted”](https://github.com/sgl-project/sglang/issues?q=is%3Aissue+label%3A%22good+first+issue%22%2C%22help+wanted%22). These tasks typically have lower complexity and provide an excellent introduction to the codebase. Also check out this [code walk-through](https://github.com/zhaochenyang20/Awesome-ML-SYS-Tutorial/tree/main/sglang/code-walk-through) for a deeper look into SGLang’s workflow.
