{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Structured Outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can specify a JSON schema, [regular expression](https://en.wikipedia.org/wiki/Regular_expression) or [EBNF](https://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_form) to constrain the model output. The model output will be guaranteed to follow the given constraints. Only one constraint parameter (`json_schema`, `regex`, or `ebnf`) can be specified for a request.\n",
    "\n",
    "SGLang supports two grammar backends:\n",
    "\n",
    "- [Outlines](https://github.com/dottxt-ai/outlines) (default): Supports JSON schema and regular expression constraints.\n",
    "- [XGrammar](https://github.com/mlc-ai/xgrammar): Supports JSON schema, regular expression, and EBNF constraints.\n",
    "- [Llguidance](https://github.com/guidance-ai/llguidance): Supports JSON schema, regular expression, and EBNF constraints.\n",
    "\n",
    "We suggest using XGrammar for its better performance and utility. XGrammar currently uses the [GGML BNF format](https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md). For more details, see [XGrammar technical overview](https://blog.mlc.ai/2024/11/22/achieving-efficient-flexible-portable-structured-generation-with-xgrammar).\n",
    "\n",
    "To use Xgrammar, simply add `--grammar-backend xgrammar` when launching the server.\n",
    "To use llguidance, add `--grammar-backend llguidance`  when launching the server.\n",
    "If no backend is specified, Outlines will be used as the default.\n",
    "\n",
    "For better output quality, **It's advisable to explicitly include instructions in the prompt to guide the model to generate the desired format.** For example, you can specify, 'Please generate the output in the following JSON format: ...'.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OpenAI Compatible API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openai\n",
    "import os\n",
    "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",
    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
    "\n",
    "\n",
    "server_process, port = launch_server_cmd(\n",
    "    \"python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --host 0.0.0.0 --grammar-backend xgrammar\"\n",
    ")\n",
    "\n",
    "wait_for_server(f\"http://localhost:{port}\")\n",
    "client = openai.Client(base_url=f\"http://127.0.0.1:{port}/v1\", api_key=\"None\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JSON\n",
    "\n",
    "you can directly define a JSON schema or use [Pydantic](https://docs.pydantic.dev/latest/) to define and validate the response."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using Pydantic**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pydantic import BaseModel, Field\n",
    "\n",
    "\n",
    "# Define the schema using Pydantic\n",
    "class CapitalInfo(BaseModel):\n",
    "    name: str = Field(..., pattern=r\"^\\w+$\", description=\"Name of the capital city\")\n",
    "    population: int = Field(..., description=\"Population of the capital city\")\n",
    "\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
    "    messages=[\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": \"Please generate the information of the capital of France in the JSON format.\",\n",
    "        },\n",
    "    ],\n",
    "    temperature=0,\n",
    "    max_tokens=128,\n",
    "    response_format={\n",
    "        \"type\": \"json_schema\",\n",
    "        \"json_schema\": {\n",
    "            \"name\": \"foo\",\n",
    "            # convert the pydantic model to json schema\n",
    "            \"schema\": CapitalInfo.model_json_schema(),\n",
    "        },\n",
    "    },\n",
    ")\n",
    "\n",
    "response_content = response.choices[0].message.content\n",
    "# validate the JSON response by the pydantic model\n",
    "capital_info = CapitalInfo.model_validate_json(response_content)\n",
    "print_highlight(f\"Validated response: {capital_info.model_dump_json()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**JSON Schema Directly**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "json_schema = json.dumps(\n",
    "    {\n",
    "        \"type\": \"object\",\n",
    "        \"properties\": {\n",
    "            \"name\": {\"type\": \"string\", \"pattern\": \"^[\\\\w]+$\"},\n",
    "            \"population\": {\"type\": \"integer\"},\n",
    "        },\n",
    "        \"required\": [\"name\", \"population\"],\n",
    "    }\n",
    ")\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
    "    messages=[\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": \"Give me the information of the capital of France in the JSON format.\",\n",
    "        },\n",
    "    ],\n",
    "    temperature=0,\n",
    "    max_tokens=128,\n",
    "    response_format={\n",
    "        \"type\": \"json_schema\",\n",
    "        \"json_schema\": {\"name\": \"foo\", \"schema\": json.loads(json_schema)},\n",
    "    },\n",
    ")\n",
    "\n",
    "print_highlight(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EBNF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ebnf_grammar = \"\"\"\n",
    "root ::= city | description\n",
    "city ::= \"London\" | \"Paris\" | \"Berlin\" | \"Rome\"\n",
    "description ::= city \" is \" status\n",
    "status ::= \"the capital of \" country\n",
    "country ::= \"England\" | \"France\" | \"Germany\" | \"Italy\"\n",
    "\"\"\"\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
    "    messages=[\n",
    "        {\"role\": \"system\", \"content\": \"You are a helpful geography bot.\"},\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": \"Give me the information of the capital of France.\",\n",
    "        },\n",
    "    ],\n",
    "    temperature=0,\n",
    "    max_tokens=32,\n",
    "    extra_body={\"ebnf\": ebnf_grammar},\n",
    ")\n",
    "\n",
    "print_highlight(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regular expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = client.chat.completions.create(\n",
    "    model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
    "    messages=[\n",
    "        {\"role\": \"user\", \"content\": \"What is the capital of France?\"},\n",
    "    ],\n",
    "    temperature=0,\n",
    "    max_tokens=128,\n",
    "    extra_body={\"regex\": \"(Paris|London)\"},\n",
    ")\n",
    "\n",
    "print_highlight(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Native API and SGLang Runtime (SRT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JSON"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using Pydantic**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "from pydantic import BaseModel, Field\n",
    "\n",
    "\n",
    "# Define the schema using Pydantic\n",
    "class CapitalInfo(BaseModel):\n",
    "    name: str = Field(..., pattern=r\"^\\w+$\", description=\"Name of the capital city\")\n",
    "    population: int = Field(..., description=\"Population of the capital city\")\n",
    "\n",
    "\n",
    "# Make API request\n",
    "response = requests.post(\n",
    "    f\"http://localhost:{port}/generate\",\n",
    "    json={\n",
    "        \"text\": \"Here is the information of the capital of France in the JSON format.\\n\",\n",
    "        \"sampling_params\": {\n",
    "            \"temperature\": 0,\n",
    "            \"max_new_tokens\": 64,\n",
    "            \"json_schema\": json.dumps(CapitalInfo.model_json_schema()),\n",
    "        },\n",
    "    },\n",
    ")\n",
    "print_highlight(response.json())\n",
    "\n",
    "\n",
    "response_data = json.loads(response.json()[\"text\"])\n",
    "# validate the response by the pydantic model\n",
    "capital_info = CapitalInfo.model_validate(response_data)\n",
    "print_highlight(f\"Validated response: {capital_info.model_dump_json()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**JSON Schema Directly**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "json_schema = json.dumps(\n",
    "    {\n",
    "        \"type\": \"object\",\n",
    "        \"properties\": {\n",
    "            \"name\": {\"type\": \"string\", \"pattern\": \"^[\\\\w]+$\"},\n",
    "            \"population\": {\"type\": \"integer\"},\n",
    "        },\n",
    "        \"required\": [\"name\", \"population\"],\n",
    "    }\n",
    ")\n",
    "\n",
    "# JSON\n",
    "response = requests.post(\n",
    "    f\"http://localhost:{port}/generate\",\n",
    "    json={\n",
    "        \"text\": \"Here is the information of the capital of France in the JSON format.\\n\",\n",
    "        \"sampling_params\": {\n",
    "            \"temperature\": 0,\n",
    "            \"max_new_tokens\": 64,\n",
    "            \"json_schema\": json_schema,\n",
    "        },\n",
    "    },\n",
    ")\n",
    "\n",
    "print_highlight(response.json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EBNF"
   ]
  },
  {
   "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\": \"Give me the information of the capital of France.\",\n",
    "        \"sampling_params\": {\n",
    "            \"max_new_tokens\": 128,\n",
    "            \"temperature\": 0,\n",
    "            \"n\": 3,\n",
    "            \"ebnf\": (\n",
    "                \"root ::= city | description\\n\"\n",
    "                'city ::= \"London\" | \"Paris\" | \"Berlin\" | \"Rome\"\\n'\n",
    "                'description ::= city \" is \" status\\n'\n",
    "                'status ::= \"the capital of \" country\\n'\n",
    "                'country ::= \"England\" | \"France\" | \"Germany\" | \"Italy\"'\n",
    "            ),\n",
    "        },\n",
    "        \"stream\": False,\n",
    "        \"return_logprob\": False,\n",
    "    },\n",
    ")\n",
    "\n",
    "print_highlight(response.json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regular expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.post(\n",
    "    f\"http://localhost:{port}/generate\",\n",
    "    json={\n",
    "        \"text\": \"Paris is the capital of\",\n",
    "        \"sampling_params\": {\n",
    "            \"temperature\": 0,\n",
    "            \"max_new_tokens\": 64,\n",
    "            \"regex\": \"(France|England)\",\n",
    "        },\n",
    "    },\n",
    ")\n",
    "print_highlight(response.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "terminate_process(server_process)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Offline Engine API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sglang as sgl\n",
    "\n",
    "llm = sgl.Engine(\n",
    "    model_path=\"meta-llama/Meta-Llama-3.1-8B-Instruct\", grammar_backend=\"xgrammar\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JSON"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using Pydantic**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from pydantic import BaseModel, Field\n",
    "\n",
    "\n",
    "prompts = [\n",
    "    \"Give me the information of the capital of China in the JSON format.\",\n",
    "    \"Give me the information of the capital of France in the JSON format.\",\n",
    "    \"Give me the information of the capital of Ireland in the JSON format.\",\n",
    "]\n",
    "\n",
    "\n",
    "# Define the schema using Pydantic\n",
    "class CapitalInfo(BaseModel):\n",
    "    name: str = Field(..., pattern=r\"^\\w+$\", description=\"Name of the capital city\")\n",
    "    population: int = Field(..., description=\"Population of the capital city\")\n",
    "\n",
    "\n",
    "sampling_params = {\n",
    "    \"temperature\": 0.1,\n",
    "    \"top_p\": 0.95,\n",
    "    \"json_schema\": json.dumps(CapitalInfo.model_json_schema()),\n",
    "}\n",
    "\n",
    "outputs = llm.generate(prompts, sampling_params)\n",
    "for prompt, output in zip(prompts, outputs):\n",
    "    print_highlight(\"===============================\")\n",
    "    print_highlight(f\"Prompt: {prompt}\")  # validate the output by the pydantic model\n",
    "    capital_info = CapitalInfo.model_validate_json(output[\"text\"])\n",
    "    print_highlight(f\"Validated output: {capital_info.model_dump_json()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**JSON Schema Directly**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompts = [\n",
    "    \"Give me the information of the capital of China in the JSON format.\",\n",
    "    \"Give me the information of the capital of France in the JSON format.\",\n",
    "    \"Give me the information of the capital of Ireland in the JSON format.\",\n",
    "]\n",
    "\n",
    "json_schema = json.dumps(\n",
    "    {\n",
    "        \"type\": \"object\",\n",
    "        \"properties\": {\n",
    "            \"name\": {\"type\": \"string\", \"pattern\": \"^[\\\\w]+$\"},\n",
    "            \"population\": {\"type\": \"integer\"},\n",
    "        },\n",
    "        \"required\": [\"name\", \"population\"],\n",
    "    }\n",
    ")\n",
    "\n",
    "sampling_params = {\"temperature\": 0.1, \"top_p\": 0.95, \"json_schema\": json_schema}\n",
    "\n",
    "outputs = llm.generate(prompts, sampling_params)\n",
    "for prompt, output in zip(prompts, outputs):\n",
    "    print_highlight(\"===============================\")\n",
    "    print_highlight(f\"Prompt: {prompt}\\nGenerated text: {output['text']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EBNF\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompts = [\n",
    "    \"Give me the information of the capital of France.\",\n",
    "    \"Give me the information of the capital of Germany.\",\n",
    "    \"Give me the information of the capital of Italy.\",\n",
    "]\n",
    "\n",
    "sampling_params = {\n",
    "    \"temperature\": 0.8,\n",
    "    \"top_p\": 0.95,\n",
    "    \"ebnf\": (\n",
    "        \"root ::= city | description\\n\"\n",
    "        'city ::= \"London\" | \"Paris\" | \"Berlin\" | \"Rome\"\\n'\n",
    "        'description ::= city \" is \" status\\n'\n",
    "        'status ::= \"the capital of \" country\\n'\n",
    "        'country ::= \"England\" | \"France\" | \"Germany\" | \"Italy\"'\n",
    "    ),\n",
    "}\n",
    "\n",
    "outputs = llm.generate(prompts, sampling_params)\n",
    "for prompt, output in zip(prompts, outputs):\n",
    "    print_highlight(\"===============================\")\n",
    "    print_highlight(f\"Prompt: {prompt}\\nGenerated text: {output['text']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regular expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompts = [\n",
    "    \"Please provide information about London as a major global city:\",\n",
    "    \"Please provide information about Paris as a major global city:\",\n",
    "]\n",
    "\n",
    "sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95, \"regex\": \"(France|England)\"}\n",
    "\n",
    "outputs = llm.generate(prompts, sampling_params)\n",
    "for prompt, output in zip(prompts, outputs):\n",
    "    print_highlight(\"===============================\")\n",
    "    print_highlight(f\"Prompt: {prompt}\\nGenerated text: {output['text']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm.shutdown()"
   ]
  }
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