openai_api_vision.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# OpenAI APIs - Vision\n",
    "\n",
    "SGLang provides OpenAI-compatible APIs to enable a smooth transition from OpenAI services to self-hosted local models.\n",
    "A complete reference for the API is available in the [OpenAI API Reference](https://platform.openai.com/docs/guides/vision).\n",
    "This tutorial covers the vision APIs for vision language models.\n",
    "\n",
    "SGLang supports various vision language models such as Llama 3.2, LLaVA-OneVision, Qwen2.5-VL, Gemma3 and [more](https://docs.sglang.ai/supported_models/multimodal_language_models).\n",
    "\n",
    "As an alternative to the OpenAI API, you can also use the [SGLang offline engine](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/offline_batch_inference_vlm.py)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Launch A Server\n",
    "\n",
    "Launch the server in your terminal and wait for it to initialize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sglang.test.test_utils import is_in_ci\n",
    "\n",
    "if is_in_ci():\n",
    "    from patch import launch_server_cmd\n",
    "else:\n",
    "    from sglang.utils import launch_server_cmd\n",
    "\n",
    "from sglang.utils import wait_for_server, print_highlight, terminate_process\n",
    "\n",
    "vision_process, port = launch_server_cmd(\n",
    "    \"\"\"\n",
    "python3 -m sglang.launch_server --model-path Qwen/Qwen2.5-VL-7B-Instruct\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "wait_for_server(f\"http://localhost:{port}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using cURL\n",
    "\n",
    "Once the server is up, you can send test requests using curl or requests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import subprocess\n",
    "\n",
    "curl_command = f\"\"\"\n",
    "curl -s http://localhost:{port}/v1/chat/completions \\\\\n",
    "  -H \"Content-Type: application/json\" \\\\\n",
    "  -d '{{\n",
    "    \"model\": \"Qwen/Qwen2.5-VL-7B-Instruct\",\n",
    "    \"messages\": [\n",
    "      {{\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [\n",
    "          {{\n",
    "            \"type\": \"text\",\n",
    "            \"text\": \"What’s in this image?\"\n",
    "          }},\n",
    "          {{\n",
    "            \"type\": \"image_url\",\n",
    "            \"image_url\": {{\n",
    "              \"url\": \"https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true\"\n",
    "            }}\n",
    "          }}\n",
    "        ]\n",
    "      }}\n",
    "    ],\n",
    "    \"max_tokens\": 300\n",
    "  }}'\n",
    "\"\"\"\n",
    "\n",
    "response = subprocess.check_output(curl_command, shell=True).decode()\n",
    "print_highlight(response)\n",
    "\n",
    "\n",
    "response = subprocess.check_output(curl_command, shell=True).decode()\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-VL-7B-Instruct\",\n",
    "    \"messages\": [\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": [\n",
    "                {\"type\": \"text\", \"text\": \"What’s in this image?\"},\n",
    "                {\n",
    "                    \"type\": \"image_url\",\n",
    "                    \"image_url\": {\n",
    "                        \"url\": \"https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true\"\n",
    "                    },\n",
    "                },\n",
    "            ],\n",
    "        }\n",
    "    ],\n",
    "    \"max_tokens\": 300,\n",
    "}\n",
    "\n",
    "response = requests.post(url, json=data)\n",
    "print_highlight(response.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using OpenAI Python Client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "\n",
    "client = OpenAI(base_url=f\"http://localhost:{port}/v1\", api_key=\"None\")\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=\"Qwen/Qwen2.5-VL-7B-Instruct\",\n",
    "    messages=[\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": [\n",
    "                {\n",
    "                    \"type\": \"text\",\n",
    "                    \"text\": \"What is in this image?\",\n",
    "                },\n",
    "                {\n",
    "                    \"type\": \"image_url\",\n",
    "                    \"image_url\": {\n",
    "                        \"url\": \"https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true\"\n",
    "                    },\n",
    "                },\n",
    "            ],\n",
    "        }\n",
    "    ],\n",
    "    max_tokens=300,\n",
    ")\n",
    "\n",
    "print_highlight(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple-Image Inputs\n",
    "\n",
    "The server also supports multiple images and interleaved text and images if the model supports it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "\n",
    "client = OpenAI(base_url=f\"http://localhost:{port}/v1\", api_key=\"None\")\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=\"Qwen/Qwen2.5-VL-7B-Instruct\",\n",
    "    messages=[\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": [\n",
    "                {\n",
    "                    \"type\": \"image_url\",\n",
    "                    \"image_url\": {\n",
    "                        \"url\": \"https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true\",\n",
    "                    },\n",
    "                },\n",
    "                {\n",
    "                    \"type\": \"image_url\",\n",
    "                    \"image_url\": {\n",
    "                        \"url\": \"https://raw.githubusercontent.com/sgl-project/sglang/main/assets/logo.png\",\n",
    "                    },\n",
    "                },\n",
    "                {\n",
    "                    \"type\": \"text\",\n",
    "                    \"text\": \"I have two very different images. They are not related at all. \"\n",
    "                    \"Please describe the first image in one sentence, and then describe the second image in another sentence.\",\n",
    "                },\n",
    "            ],\n",
    "        }\n",
    "    ],\n",
    "    temperature=0,\n",
    ")\n",
    "\n",
    "print_highlight(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "terminate_process(vision_process)"
   ]
  }
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