sglang/docs_new/docs/basic_usage/send_request.ipynb

Sending Requests

This notebook provides a quick-start guide to use SGLang in chat completions after installation. Once your server is running, API documentation is available at http://localhost:30000/docs (Swagger UI), http://localhost:30000/redoc (ReDoc), or http://localhost:30000/openapi.json (OpenAPI spec, useful for AI agents). Replace 30000 with your port if using a different one.

• For Vision Language Models, see OpenAI APIs - Vision.
• For Embedding Models, see OpenAI APIs - Embedding and Encode (embedding model).
• For Reward Models, see Classify (reward model).

Launch A Server

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from sglang.test.doc_patch import launch_server_cmd
from sglang.utils import wait_for_server, print_highlight, terminate_process

# This is equivalent to running the following command in your terminal
# python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct --host 0.0.0.0

server_process, port = launch_server_cmd("""
python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct \
 --host 0.0.0.0 --log-level warning
""")

wait_for_server(f"http://localhost:{port}")

Using cURL

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import subprocess, json

curl_command = f"""
curl -s http://localhost:{port}/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{{"model": "qwen/qwen2.5-0.5b-instruct", "messages": [{{"role": "user", "content": "What is the capital of France?"}}]}}'
"""

response = json.loads(subprocess.check_output(curl_command, shell=True))
print_highlight(response)

Using Python Requests

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import requests

url = f"http://localhost:{port}/v1/chat/completions"

data = {
    "model": "qwen/qwen2.5-0.5b-instruct",
    "messages": [{"role": "user", "content": "What is the capital of France?"}],
}

response = requests.post(url, json=data)
print_highlight(response.json())

Using OpenAI Python Client

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import openai

client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="None")

response = client.chat.completions.create(
    model="qwen/qwen2.5-0.5b-instruct",
    messages=[
        {"role": "user", "content": "List 3 countries and their capitals."},
    ],
    temperature=0,
    max_tokens=64,
)
print_highlight(response)

Streaming

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import openai

client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="None")

# Use stream=True for streaming responses
response = client.chat.completions.create(
    model="qwen/qwen2.5-0.5b-instruct",
    messages=[
        {"role": "user", "content": "List 3 countries and their capitals."},
    ],
    temperature=0,
    max_tokens=64,
    stream=True,
)

# Handle the streaming output
for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Using Native Generation APIs

You can also use the native /generate endpoint with requests, which provides more flexibility. An API reference is available at Sampling Parameters.

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import requests

response = requests.post(
    f"http://localhost:{port}/generate",
    json={
        "text": "The capital of France is",
        "sampling_params": {
            "temperature": 0,
            "max_new_tokens": 32,
        },
    },
)

print_highlight(response.json())

Streaming

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import requests, json

response = requests.post(
    f"http://localhost:{port}/generate",
    json={
        "text": "The capital of France is",
        "sampling_params": {
            "temperature": 0,
            "max_new_tokens": 32,
        },
        "stream": True,
    },
    stream=True,
)

prev = 0
for chunk in response.iter_lines(decode_unicode=False):
    chunk = chunk.decode("utf-8")
    if chunk and chunk.startswith("data:"):
        if chunk == "data: [DONE]":
            break
        data = json.loads(chunk[5:].strip("\n"))
        output = data["text"]
        print(output[prev:], end="", flush=True)
        prev = len(output)

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terminate_process(server_process)