828 lines
26 KiB
Plaintext
828 lines
26 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Structured Outputs"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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",
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"\n",
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"SGLang supports three grammar backends:\n",
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"\n",
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"- [Outlines](https://github.com/dottxt-ai/outlines): Supports JSON schema and regular expression constraints.\n",
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"- [XGrammar](https://github.com/mlc-ai/xgrammar)(default): Supports JSON schema, regular expression, and EBNF constraints.\n",
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"- [Llguidance](https://github.com/guidance-ai/llguidance): Supports JSON schema, regular expression, and EBNF constraints.\n",
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"\n",
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"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",
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"\n",
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"To use Outlines, simply add `--grammar-backend outlines` when launching the server.\n",
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"To use llguidance, add `--grammar-backend llguidance` when launching the server.\n",
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"If no backend is specified, XGrammar will be used as the default.\n",
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"\n",
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"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"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## OpenAI Compatible API"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import openai\n",
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"import os\n",
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"from sglang.test.test_utils import is_in_ci\n",
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"\n",
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"if is_in_ci():\n",
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" from patch import launch_server_cmd\n",
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"else:\n",
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" from sglang.utils import launch_server_cmd\n",
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"\n",
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"from sglang.utils import wait_for_server, print_highlight, terminate_process\n",
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"\n",
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"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
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"\n",
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"\n",
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"server_process, port = launch_server_cmd(\n",
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" \"python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --host 0.0.0.0\"\n",
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")\n",
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"\n",
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"wait_for_server(f\"http://localhost:{port}\")\n",
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"client = openai.Client(base_url=f\"http://127.0.0.1:{port}/v1\", api_key=\"None\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### JSON\n",
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"\n",
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"you can directly define a JSON schema or use [Pydantic](https://docs.pydantic.dev/latest/) to define and validate the response."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Using Pydantic**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pydantic import BaseModel, Field\n",
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"\n",
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"\n",
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"# Define the schema using Pydantic\n",
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"class CapitalInfo(BaseModel):\n",
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" name: str = Field(..., pattern=r\"^\\w+$\", description=\"Name of the capital city\")\n",
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" population: int = Field(..., description=\"Population of the capital city\")\n",
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"\n",
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"\n",
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"response = client.chat.completions.create(\n",
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" model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
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" messages=[\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"Please generate the information of the capital of France in the JSON format.\",\n",
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" },\n",
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" ],\n",
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" temperature=0,\n",
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" max_tokens=128,\n",
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" response_format={\n",
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" \"type\": \"json_schema\",\n",
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" \"json_schema\": {\n",
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" \"name\": \"foo\",\n",
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" # convert the pydantic model to json schema\n",
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" \"schema\": CapitalInfo.model_json_schema(),\n",
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" },\n",
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" },\n",
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")\n",
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"\n",
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"response_content = response.choices[0].message.content\n",
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"# validate the JSON response by the pydantic model\n",
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"capital_info = CapitalInfo.model_validate_json(response_content)\n",
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"print_highlight(f\"Validated response: {capital_info.model_dump_json()}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**JSON Schema Directly**\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"\n",
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"json_schema = json.dumps(\n",
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" {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"name\": {\"type\": \"string\", \"pattern\": \"^[\\\\w]+$\"},\n",
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" \"population\": {\"type\": \"integer\"},\n",
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" },\n",
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" \"required\": [\"name\", \"population\"],\n",
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" }\n",
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")\n",
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"\n",
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"response = client.chat.completions.create(\n",
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" model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
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" messages=[\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"Give me the information of the capital of France in the JSON format.\",\n",
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" },\n",
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" ],\n",
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" temperature=0,\n",
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" max_tokens=128,\n",
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" response_format={\n",
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" \"type\": \"json_schema\",\n",
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" \"json_schema\": {\"name\": \"foo\", \"schema\": json.loads(json_schema)},\n",
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" },\n",
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")\n",
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"\n",
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"print_highlight(response.choices[0].message.content)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### EBNF"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ebnf_grammar = \"\"\"\n",
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"root ::= city | description\n",
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"city ::= \"London\" | \"Paris\" | \"Berlin\" | \"Rome\"\n",
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"description ::= city \" is \" status\n",
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"status ::= \"the capital of \" country\n",
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"country ::= \"England\" | \"France\" | \"Germany\" | \"Italy\"\n",
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"\"\"\"\n",
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"\n",
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"response = client.chat.completions.create(\n",
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" model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": \"You are a helpful geography bot.\"},\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"Give me the information of the capital of France.\",\n",
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" },\n",
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" ],\n",
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" temperature=0,\n",
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" max_tokens=32,\n",
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" extra_body={\"ebnf\": ebnf_grammar},\n",
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")\n",
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"\n",
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"print_highlight(response.choices[0].message.content)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Regular expression"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"response = client.chat.completions.create(\n",
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" model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
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" messages=[\n",
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" {\"role\": \"user\", \"content\": \"What is the capital of France?\"},\n",
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" ],\n",
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" temperature=0,\n",
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" max_tokens=128,\n",
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" extra_body={\"regex\": \"(Paris|London)\"},\n",
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")\n",
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"\n",
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"print_highlight(response.choices[0].message.content)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Structural Tag"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"tool_get_current_weather = {\n",
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" \"type\": \"function\",\n",
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" \"function\": {\n",
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" \"name\": \"get_current_weather\",\n",
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" \"description\": \"Get the current weather in a given location\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"city\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The city to find the weather for, e.g. 'San Francisco'\",\n",
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" },\n",
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" \"state\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"the two-letter abbreviation for the state that the city is\"\n",
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" \" in, e.g. 'CA' which would mean 'California'\",\n",
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" },\n",
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" \"unit\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The unit to fetch the temperature in\",\n",
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" \"enum\": [\"celsius\", \"fahrenheit\"],\n",
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" },\n",
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" },\n",
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" \"required\": [\"city\", \"state\", \"unit\"],\n",
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" },\n",
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" },\n",
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"}\n",
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"\n",
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"tool_get_current_date = {\n",
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" \"type\": \"function\",\n",
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" \"function\": {\n",
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" \"name\": \"get_current_date\",\n",
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" \"description\": \"Get the current date and time for a given timezone\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"timezone\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The timezone to fetch the current date and time for, e.g. 'America/New_York'\",\n",
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" }\n",
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" },\n",
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" \"required\": [\"timezone\"],\n",
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" },\n",
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" },\n",
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"}\n",
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"\n",
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"schema_get_current_weather = tool_get_current_weather[\"function\"][\"parameters\"]\n",
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"schema_get_current_date = tool_get_current_date[\"function\"][\"parameters\"]\n",
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"\n",
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"\n",
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"def get_messages():\n",
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" return [\n",
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" {\n",
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" \"role\": \"system\",\n",
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" \"content\": f\"\"\"\n",
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"# Tool Instructions\n",
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"- Always execute python code in messages that you share.\n",
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"- When looking for real time information use relevant functions if available else fallback to brave_search\n",
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"You have access to the following functions:\n",
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"Use the function 'get_current_weather' to: Get the current weather in a given location\n",
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"{tool_get_current_weather[\"function\"]}\n",
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"Use the function 'get_current_date' to: Get the current date and time for a given timezone\n",
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"{tool_get_current_date[\"function\"]}\n",
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"If a you choose to call a function ONLY reply in the following format:\n",
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"<{{start_tag}}={{function_name}}>{{parameters}}{{end_tag}}\n",
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"where\n",
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"start_tag => `<function`\n",
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"parameters => a JSON dict with the function argument name as key and function argument value as value.\n",
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"end_tag => `</function>`\n",
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"Here is an example,\n",
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"<function=example_function_name>{{\"example_name\": \"example_value\"}}</function>\n",
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"Reminder:\n",
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"- Function calls MUST follow the specified format\n",
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"- Required parameters MUST be specified\n",
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"- Only call one function at a time\n",
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"- Put the entire function call reply on one line\n",
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"- Always add your sources when using search results to answer the user query\n",
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"You are a helpful assistant.\"\"\",\n",
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" },\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"You are in New York. Please get the current date and time, and the weather.\",\n",
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" },\n",
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" ]\n",
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"\n",
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"\n",
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"messages = get_messages()\n",
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"\n",
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"response = client.chat.completions.create(\n",
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" model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
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" messages=messages,\n",
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" response_format={\n",
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" \"type\": \"structural_tag\",\n",
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" \"structures\": [\n",
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" {\n",
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" \"begin\": \"<function=get_current_weather>\",\n",
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" \"schema\": schema_get_current_weather,\n",
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" \"end\": \"</function>\",\n",
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" },\n",
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" {\n",
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" \"begin\": \"<function=get_current_date>\",\n",
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" \"schema\": schema_get_current_date,\n",
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" \"end\": \"</function>\",\n",
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" },\n",
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" ],\n",
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" \"triggers\": [\"<function=\"],\n",
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" },\n",
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")\n",
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"\n",
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"print_highlight(response.choices[0].message.content)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Native API and SGLang Runtime (SRT)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### JSON"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Using Pydantic**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import requests\n",
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"import json\n",
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"from pydantic import BaseModel, Field\n",
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"\n",
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"\n",
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"# Define the schema using Pydantic\n",
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"class CapitalInfo(BaseModel):\n",
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" name: str = Field(..., pattern=r\"^\\w+$\", description=\"Name of the capital city\")\n",
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" population: int = Field(..., description=\"Population of the capital city\")\n",
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"\n",
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"\n",
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"# Make API request\n",
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"response = requests.post(\n",
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" f\"http://localhost:{port}/generate\",\n",
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" json={\n",
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" \"text\": \"Here is the information of the capital of France in the JSON format.\\n\",\n",
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" \"sampling_params\": {\n",
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" \"temperature\": 0,\n",
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" \"max_new_tokens\": 64,\n",
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" \"json_schema\": json.dumps(CapitalInfo.model_json_schema()),\n",
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" },\n",
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" },\n",
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")\n",
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"print_highlight(response.json())\n",
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"\n",
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"\n",
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"response_data = json.loads(response.json()[\"text\"])\n",
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"# validate the response by the pydantic model\n",
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"capital_info = CapitalInfo.model_validate(response_data)\n",
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"print_highlight(f\"Validated response: {capital_info.model_dump_json()}\")"
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]
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},
|
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{
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"cell_type": "markdown",
|
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"metadata": {},
|
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"source": [
|
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"**JSON Schema Directly**"
|
|
]
|
|
},
|
|
{
|
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"json_schema = json.dumps(\n",
|
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" {\n",
|
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"name\": {\"type\": \"string\", \"pattern\": \"^[\\\\w]+$\"},\n",
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" \"population\": {\"type\": \"integer\"},\n",
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" },\n",
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" \"required\": [\"name\", \"population\"],\n",
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" }\n",
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")\n",
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"\n",
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"# JSON\n",
|
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"response = requests.post(\n",
|
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" f\"http://localhost:{port}/generate\",\n",
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" json={\n",
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" \"text\": \"Here is the information of the capital of France in the JSON format.\\n\",\n",
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" \"sampling_params\": {\n",
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" \"temperature\": 0,\n",
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" \"max_new_tokens\": 64,\n",
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" \"json_schema\": json_schema,\n",
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" },\n",
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" },\n",
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")\n",
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"\n",
|
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"print_highlight(response.json())"
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]
|
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},
|
|
{
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"cell_type": "markdown",
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|
"metadata": {},
|
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"source": [
|
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"### EBNF"
|
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]
|
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},
|
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{
|
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"cell_type": "code",
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"execution_count": null,
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|
"metadata": {},
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"outputs": [],
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"source": [
|
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"response = requests.post(\n",
|
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" f\"http://localhost:{port}/generate\",\n",
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" json={\n",
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" \"text\": \"Give me the information of the capital of France.\",\n",
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" \"sampling_params\": {\n",
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" \"max_new_tokens\": 128,\n",
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" \"temperature\": 0,\n",
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" \"n\": 3,\n",
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" \"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": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Structural Tag"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from transformers import AutoTokenizer\n",
|
|
"\n",
|
|
"# generate an answer\n",
|
|
"tokenizer = AutoTokenizer.from_pretrained(\"meta-llama/Meta-Llama-3.1-8B-Instruct\")\n",
|
|
"\n",
|
|
"text = tokenizer.apply_chat_template(\n",
|
|
" messages, tokenize=False, add_generation_prompt=True\n",
|
|
")\n",
|
|
"payload = {\n",
|
|
" \"text\": text,\n",
|
|
" \"sampling_params\": {\n",
|
|
" \"structural_tag\": json.dumps(\n",
|
|
" {\n",
|
|
" \"type\": \"structural_tag\",\n",
|
|
" \"structures\": [\n",
|
|
" {\n",
|
|
" \"begin\": \"<function=get_current_weather>\",\n",
|
|
" \"schema\": schema_get_current_weather,\n",
|
|
" \"end\": \"</function>\",\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"begin\": \"<function=get_current_date>\",\n",
|
|
" \"schema\": schema_get_current_date,\n",
|
|
" \"end\": \"</function>\",\n",
|
|
" },\n",
|
|
" ],\n",
|
|
" \"triggers\": [\"<function=\"],\n",
|
|
" }\n",
|
|
" )\n",
|
|
" },\n",
|
|
"}\n",
|
|
"\n",
|
|
"\n",
|
|
"# Send POST request to the API endpoint\n",
|
|
"response = requests.post(f\"http://localhost:{port}/generate\", json=payload)\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": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Structural Tag"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"text = tokenizer.apply_chat_template(\n",
|
|
" messages, tokenize=False, add_generation_prompt=True\n",
|
|
")\n",
|
|
"prompts = [text]\n",
|
|
"\n",
|
|
"\n",
|
|
"sampling_params = {\n",
|
|
" \"temperature\": 0.8,\n",
|
|
" \"top_p\": 0.95,\n",
|
|
" \"structural_tag\": json.dumps(\n",
|
|
" {\n",
|
|
" \"type\": \"structural_tag\",\n",
|
|
" \"structures\": [\n",
|
|
" {\n",
|
|
" \"begin\": \"<function=get_current_weather>\",\n",
|
|
" \"schema\": schema_get_current_weather,\n",
|
|
" \"end\": \"</function>\",\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"begin\": \"<function=get_current_date>\",\n",
|
|
" \"schema\": schema_get_current_date,\n",
|
|
" \"end\": \"</function>\",\n",
|
|
" },\n",
|
|
" ],\n",
|
|
" \"triggers\": [\"<function=\"],\n",
|
|
" }\n",
|
|
" ),\n",
|
|
"}\n",
|
|
"\n",
|
|
"\n",
|
|
"# Send POST request to the API endpoint\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()"
|
|
]
|
|
}
|
|
],
|
|
"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
|
|
}
|