Sending Requests#

This notebook provides a quick-start guide to use SGLang in chat completions after installation.

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#

[1]:

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}")

[2025-11-12 15:38:28] INFO utils.py:148: Note: detected 112 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2025-11-12 15:38:28] INFO utils.py:151: Note: NumExpr detected 112 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
[2025-11-12 15:38:28] INFO utils.py:164: NumExpr defaulting to 16 threads.

[2025-11-12 15:38:35] INFO utils.py:148: Note: detected 112 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2025-11-12 15:38:35] INFO utils.py:151: Note: NumExpr detected 112 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
[2025-11-12 15:38:35] INFO utils.py:164: NumExpr defaulting to 16 threads.
[2025-11-12 15:38:38] WARNING server_args.py:1197: Attention backend not explicitly specified. Use fa3 backend by default.
[2025-11-12 15:38:38] INFO trace.py:69: opentelemetry package is not installed, tracing disabled
[2025-11-12 15:38:38] WARNING memory_pool_host.py:36: Current platform not support pin_memory
[2025-11-12 15:38:43] INFO utils.py:148: Note: detected 112 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2025-11-12 15:38:43] INFO utils.py:151: Note: NumExpr detected 112 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
[2025-11-12 15:38:43] INFO utils.py:164: NumExpr defaulting to 16 threads.
[2025-11-12 15:38:45] INFO trace.py:69: opentelemetry package is not installed, tracing disabled
[2025-11-12 15:38:45] WARNING memory_pool_host.py:36: Current platform not support pin_memory
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[2025-11-12 15:38:49] INFO utils.py:148: Note: detected 112 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2025-11-12 15:38:49] INFO utils.py:151: Note: NumExpr detected 112 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
[2025-11-12 15:38:49] INFO utils.py:164: NumExpr defaulting to 16 threads.
[2025-11-12 15:38:51] INFO trace.py:69: opentelemetry package is not installed, tracing disabled
[2025-11-12 15:38:51] WARNING memory_pool_host.py:36: Current platform not support pin_memory
Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  4.47it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  4.46it/s]

Capturing batches (bs=1 avail_mem=44.98 GB): 100%|██████████| 3/3 [00:00<00:00, 11.64it/s]

NOTE: Typically, the server runs in a separate terminal.
In this notebook, we run the server and notebook code together, so their outputs are combined.
To improve clarity, the server logs are displayed in the original black color, while the notebook outputs are highlighted in blue.
To reduce the log length, we set the log level to warning for the server, the default log level is info.
We are running those notebooks in a CI environment, so the throughput is not representative of the actual performance.

Using cURL#

[2]:

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)

{'id': '1a846b8e2e4d47d59533bab24eeff4b2', 'object': 'chat.completion', 'created': 1762961955, 'model': 'qwen/qwen2.5-0.5b-instruct', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': 'The capital of France is Paris.', 'reasoning_content': None, 'tool_calls': None}, 'logprobs': None, 'finish_reason': 'stop', 'matched_stop': 151645}], 'usage': {'prompt_tokens': 36, 'total_tokens': 44, 'completion_tokens': 8, 'prompt_tokens_details': None, 'reasoning_tokens': 0}, 'metadata': {'weight_version': 'default'}}

Using Python Requests#

[3]:

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())

{'id': '8571580a06124006be84b76186ee3220', 'object': 'chat.completion', 'created': 1762961955, 'model': 'qwen/qwen2.5-0.5b-instruct', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': 'The capital of France is Paris.', 'reasoning_content': None, 'tool_calls': None}, 'logprobs': None, 'finish_reason': 'stop', 'matched_stop': 151645}], 'usage': {'prompt_tokens': 36, 'total_tokens': 44, 'completion_tokens': 8, 'prompt_tokens_details': None, 'reasoning_tokens': 0}, 'metadata': {'weight_version': 'default'}}

Using OpenAI Python Client#

[4]:

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)

ChatCompletion(id='d116f9a8792b4daeb7d6661fbbb303a4', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Sure, here are three countries and their respective capitals:\n\n1. **United States** - Washington, D.C.\n2. **Canada** - Ottawa\n3. **Australia** - Canberra', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None, reasoning_content=None), matched_stop=151645)], created=1762961955, model='qwen/qwen2.5-0.5b-instruct', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=39, prompt_tokens=37, total_tokens=76, completion_tokens_details=None, prompt_tokens_details=None, reasoning_tokens=0), metadata={'weight_version': 'default'})

Streaming#

[5]:

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)

Sure, here are three countries and their respective capitals:

1. **United States** - Washington, D.C.
2. **Canada** - Ottawa
3. **Australia** - Canberra

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.

[6]:

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())

{'text': ' Paris. It is the largest city in Europe and the second largest city in the world. It is located in the south of France, on the banks of the', 'output_ids': [12095, 13, 1084, 374, 279, 7772, 3283, 304, 4505, 323, 279, 2086, 7772, 3283, 304, 279, 1879, 13, 1084, 374, 7407, 304, 279, 9806, 315, 9625, 11, 389, 279, 13959, 315, 279], 'meta_info': {'id': '76ef61989b0546d5baec3ad217cdede2', 'finish_reason': {'type': 'length', 'length': 32}, 'prompt_tokens': 5, 'weight_version': 'default', 'total_retractions': 0, 'completion_tokens': 32, 'cached_tokens': 2, 'e2e_latency': 0.05371379852294922, 'response_sent_to_client_ts': 1762961955.8023977}}

Streaming#

[7]:

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)

 Paris. It is the largest city in Europe and the second largest city in the world. It is located in the south of France, on the banks of the

[8]:

terminate_process(server_process)

Sending Requests

Contents

Sending Requests#

Launch A Server#

Using cURL#

Using Python Requests#

Using OpenAI Python Client#

Streaming#

Using Native Generation APIs#

Streaming#