send_request.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sending Requests\n",
    "This notebook provides a quick-start guide to use SGLang in chat completions after installation.\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",
    "    \"\"\"\n",
    "python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct \\\n",
    " --host 0.0.0.0\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "wait_for_server(f\"http://localhost:{port}\")"
   ]
  },
  {
   "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)"
   ]
  }
 ],
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