openai_api_vision.ipynb

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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/vision_language_models): \n",
    "- [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct)  \n",
    "- [lmms-lab/llava-onevision-qwen2-72b-ov-chat](https://huggingface.co/lmms-lab/llava-onevision-qwen2-72b-ov-chat)  \n",
    "- [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)\n",
    "- [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it)\n",
    "- [openbmb/MiniCPM-V](https://huggingface.co/openbmb/MiniCPM-V)\n",
    "- [deepseek-ai/deepseek-vl2](https://huggingface.co/deepseek-ai/deepseek-vl2)\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.\n",
    "\n",
    "**Remember to add** `--chat-template llama_3_vision` **to specify the [vision chat template](https://docs.sglang.ai/backend/openai_api_vision.html#Chat-Template), otherwise, the server will only support text (images won’t be passed in), which can lead to degraded performance.**\n",
    "\n",
    "We need to specify `--chat-template` for vision language models because the chat template provided in Hugging Face tokenizer only supports text."
   ]
  },
  {
   "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 meta-llama/Llama-3.2-11B-Vision-Instruct \\\n",
    "    --chat-template=llama_3_vision\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",
    "  -d '{{\n",
    "    \"model\": \"meta-llama/Llama-3.2-11B-Vision-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\": \"meta-llama/Llama-3.2-11B-Vision-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=\"meta-llama/Llama-3.2-11B-Vision-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=\"meta-llama/Llama-3.2-11B-Vision-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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chat Template\n",
    "\n",
    "As mentioned before, if you do not specify a vision model's `--chat-template`, the server uses Hugging Face's default template, which only supports text.\n",
    "\n",
    "We list popular vision models with their chat templates:\n",
    "\n",
    "- [meta-llama/Llama-3.2-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) uses `llama_3_vision`.\n",
    "- [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) uses `qwen2-vl`.\n",
    "- [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it) uses `gemma-it`.\n",
    "- [openbmb/MiniCPM-V](https://huggingface.co/openbmb/MiniCPM-V) uses `minicpmv`.\n",
    "- [deepseek-ai/deepseek-vl2](https://huggingface.co/deepseek-ai/deepseek-vl2) uses `deepseek-vl2`.\n",
    "- [LlaVA-OneVision](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov) uses `chatml-llava`.\n",
    "- [LLaVA-NeXT](https://huggingface.co/collections/lmms-lab/llava-next-6623288e2d61edba3ddbf5ff) uses `chatml-llava`.\n",
    "- [Llama3-LLaVA-NeXT](https://huggingface.co/lmms-lab/llama3-llava-next-8b) uses `llava_llama_3`.\n",
    "- [LLaVA-v1.5 / 1.6](https://huggingface.co/liuhaotian/llava-v1.6-34b) uses `vicuna_v1.1`."
   ]
  }
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