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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sending Requests\n",
"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.\n",
"\n",
"- For Vision Language Models, see [OpenAI APIs - Vision](openai_api_vision.ipynb).\n",
"- For Embedding Models, see [OpenAI APIs - Embedding](openai_api_embeddings.ipynb) and [Encode (embedding model)](native_api.html#Encode-(embedding-model)).\n",
"- For Reward Models, see [Classify (reward model)](native_api.html#Classify-(reward-model))."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Launch A Server"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sglang.test.doc_patch import launch_server_cmd\n",
"from sglang.utils import wait_for_server, print_highlight, terminate_process\n",
"\n",
"# This is equivalent to running the following command in your terminal\n",
"# python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct --host 0.0.0.0\n",
"\n",
"server_process, port = launch_server_cmd(\"\"\"\n",
"python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct \\\n",
" --host 0.0.0.0 --log-level warning\n",
"\"\"\")\n",
"\n",
"wait_for_server(f\"http://localhost:{port}\", process=server_process)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using cURL\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import subprocess, json\n",
"\n",
"curl_command = f\"\"\"\n",
"curl -s http://localhost:{port}/v1/chat/completions \\\n",
" -H \"Content-Type: application/json\" \\\n",
" -d '{{\"model\": \"qwen/qwen2.5-0.5b-instruct\", \"messages\": [{{\"role\": \"user\", \"content\": \"What is the capital of France?\"}}]}}'\n",
"\"\"\"\n",
"\n",
"response = json.loads(subprocess.check_output(curl_command, shell=True))\n",
"print_highlight(response)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using Python Requests"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"\n",
"url = f\"http://localhost:{port}/v1/chat/completions\"\n",
"\n",
"data = {\n",
" \"model\": \"qwen/qwen2.5-0.5b-instruct\",\n",
" \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of France?\"}],\n",
"}\n",
"\n",
"response = requests.post(url, json=data)\n",
"print_highlight(response.json())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using OpenAI Python Client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import openai\n",
"\n",
"client = openai.Client(base_url=f\"http://127.0.0.1:{port}/v1\", api_key=\"None\")\n",
"\n",
"response = client.chat.completions.create(\n",
" model=\"qwen/qwen2.5-0.5b-instruct\",\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": \"List 3 countries and their capitals.\"},\n",
" ],\n",
" temperature=0,\n",
" max_tokens=64,\n",
")\n",
"print_highlight(response)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Streaming"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import openai\n",
"\n",
"client = openai.Client(base_url=f\"http://127.0.0.1:{port}/v1\", api_key=\"None\")\n",
"\n",
"# Use stream=True for streaming responses\n",
"response = client.chat.completions.create(\n",
" model=\"qwen/qwen2.5-0.5b-instruct\",\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": \"List 3 countries and their capitals.\"},\n",
" ],\n",
" temperature=0,\n",
" max_tokens=64,\n",
" stream=True,\n",
")\n",
"\n",
"# Handle the streaming output\n",
"for chunk in response:\n",
" if chunk.choices[0].delta.content:\n",
" print(chunk.choices[0].delta.content, end=\"\", flush=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using Native Generation APIs\n",
"\n",
"You can also use the native `/generate` endpoint with requests, which provides more flexibility. An API reference is available at [Sampling Parameters](sampling_params.md)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"\n",
"response = requests.post(\n",
" f\"http://localhost:{port}/generate\",\n",
" json={\n",
" \"text\": \"The capital of France is\",\n",
" \"sampling_params\": {\n",
" \"temperature\": 0,\n",
" \"max_new_tokens\": 32,\n",
" },\n",
" },\n",
")\n",
"\n",
"print_highlight(response.json())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Streaming"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests, json\n",
"\n",
"response = requests.post(\n",
" f\"http://localhost:{port}/generate\",\n",
" json={\n",
" \"text\": \"The capital of France is\",\n",
" \"sampling_params\": {\n",
" \"temperature\": 0,\n",
" \"max_new_tokens\": 32,\n",
" },\n",
" \"stream\": True,\n",
" },\n",
" stream=True,\n",
")\n",
"\n",
"prev = 0\n",
"for chunk in response.iter_lines(decode_unicode=False):\n",
" chunk = chunk.decode(\"utf-8\")\n",
" if chunk and chunk.startswith(\"data:\"):\n",
" if chunk == \"data: [DONE]\":\n",
" break\n",
" data = json.loads(chunk[5:].strip(\"\\n\"))\n",
" output = data[\"text\"]\n",
" print(output[prev:], end=\"\", flush=True)\n",
" prev = len(output)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"terminate_process(server_process)"
]
}
],
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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