Structured Outputs
Overview
Structured Outputs refer to predefined format constraints that force large language models to generate content strictly following specified structures. This feature significantly improves output controllability and is suitable for scenarios requiring precise format outputs (such as API calls, data parsing, code generation, etc.), while supporting dynamic grammar extensions to balance flexibility and standardization.
FastDeploy supports using the XGrammar backend to generate structured outputs.
Supported output formats:
json
: Output content in standard JSON formatregex
: Precise control over text patterns (e.g., dates, emails, ID numbers), supporting complex regex syntaxchoice
: Output strictly limited to predefined optionsgrammar
: Uses extended BNF grammar to define complex structures, supporting field names, types and logical relationship constraintsstructural tag
: Defines structured outputs through tag trees, supporting nested tags, attribute validation and mixed constraints
Usage
When starting the service, you can specify the desired backend using the --guided-decoding-backend
parameter. If set to auto
, FastDeploy will automatically select the appropriate backend.
OpenAI Interface
FastDeploy supports OpenAI's Completions and Chat Completions APIs. OpenAI specifies structured output formats through the response_format
parameter.
FastDeploy ensures JSON output by specifying json_object
. To define specific parameter names and types within JSON, use json_schema
. Refer to examples for detailed usage.
import openai
port = "8170"
client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="null")
completion = client.chat.completions.create(
model="null",
messages=[
{
"role": "user",
"content": "Generate a JSON object containing: names of China's Four Great Inventions, their dynasties of origin, and brief descriptions (each under 50 characters)",
}
],
response_format={"type": "json_object"}
)
print(completion.choices[0].message.content)
print("\n")
Output:
{"inventions": [{"name": "Paper Making", "dynasty": "Han Dynasty", "description": "Invented during Han Dynasty, revolutionized writing and storage."}, {"name": "Printing", "dynasty": "Tang Dynasty", "description": "Woodblock printing developed in Tang, movable type in Song Dynasty."}, {"name": "Compass", "dynasty": "Han Dynasty (concept)", "description": "Early use in Han Dynasty, refined for navigation by Song Dynasty."}, {"name": "Gunpowder", "dynasty": "Tang Dynasty", "description": "Discovered in Tang Dynasty, later used in weapons and fireworks."}]}
The following example requires the model to return JSON data about a book, which must include author
, title
, and genre
fields, with genre
restricted to given BookType
options.
import openai
from pydantic import BaseModel
from enum import Enum
class BookType(str, Enum):
romance = "Romance"
historical = "Historical"
adventure = "Adventure"
mystery = "Mystery"
dystopian = "Dystopian"
class BookDescription(BaseModel):
author: str
title: str
genre: BookType
port = "8170"
client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="null")
completion = client.chat.completions.create(
model="null",
messages=[
{
"role": "user",
"content": "Generate a JSON describing a literary work, including author, title and book type.",
}
],
response_format={
"type": "json_schema",
"json_schema": {
"name": "book-description",
"schema": BookDescription.model_json_schema()
},
},
)
print(completion.choices[0].message.content)
print("\n")
Output:
{"author": "George Orwell", "title": "1984", "genre": "Dystopian"}
structural_tag
can extract key information from input and call specified methods to return predefined structured output. The following example demonstrates getting a specified timezone using structural_tag
:
import openai
port = "8170"
client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="null")
content_str = """
You have the following function available:
{
"name": "get_current_date",
"description": "Get current date and time for given timezone",
"parameters": {
"type": "object",
"properties": {
"timezone": {
"type": "string",
"description": "Timezone to get current date/time, e.g.: Asia/Shanghai",
}
},
"required": ["timezone"],
}
}
If you choose to call only this function, reply in this format:
<{start_tag}={function_name}>{parameters}{end_tag}
where:
start_tag => `<function`
parameters => JSON dictionary with parameter names as keys
end_tag => `</function>`
Example:
<function=example_function>{"param": "value"}</function>
Note:
- Function call must follow specified format
- Required parameters must be specified
- Only one function can be called at a time
- Place entire function call response on a single line
You are an AI assistant. Answer the following question.
"""
completion = client.chat.completions.create(
model="null",
messages=[
{
"role": "system",
"content": content_str,
},
{
"role": "user",
"content": "You're traveling to Shanghai today",
}
],
response_format={
"type": "structural_tag",
"structures": [
{
"begin": "<function=get_current_date>",
"schema": {
"type": "object",
"properties": {
"timezone": {
"type": "string",
"description": "Timezone to get current date/time, e.g.: Asia/Shanghai",
}
},
"required": ["timezone"],
},
"end": "</function>",
}
],
"triggers": ["<function="],
},
)
print(completion.choices[0].message.content)
print("\n")
Output:
<function=get_current_date>{"timezone": "Asia/Shanghai"}</function>
For choice
, grammar
, and regex
formats, FastDeploy supports them through the extra_body
parameter. choice
is straightforward - the model selects the best option from predefined choices. Example:
import openai
port = "8170"
client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="null")
completion = client.chat.completions.create(
model="null",
messages=[
{"role": "user", "content": "What is the landmark building in Shenzhen?"}
],
extra_body={"guided_choice": ["Ping An Finance Centre", "China Resources Headquarters", "KK100", "Diwang Mansion"]},
)
print(completion.choices[0].message.content)
print("\n")
Output:
Ping An Finance Centre
regex
allows restricting output format via regular expressions. Example for generating a standard web address:
import openai
port = "8170"
client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="null")
completion = client.chat.completions.create(
model="null",
messages=[
{
"role": "user",
"content": "Generate a standard format web address including protocol and domain.\n",
}
],
extra_body={"guided_regex": r"^https:\/\/www\.[a-zA-Z]+\.com\/?$\n"},
)
print(completion.choices[0].message.content)
print("\n")
Output:
https://www.example.com/
grammar
allows defining complex constraints using EBNF syntax. Example for generating HTML code:
import openai
port = "8170"
client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="null")
html_h1_grammar = """
root ::= html_statement
html_statement ::= "<h1" style_attribute? ">" text "</h1>"
style_attribute ::= " style=" dq style_value dq
style_value ::= (font_style ("; " font_weight)?) | (font_weight ("; " font_style)?)
font_style ::= "font-family: '" font_name "'"
font_weight ::= "font-weight: " weight_value
font_name ::= "Arial" | "Times New Roman" | "Courier New"
weight_value ::= "normal" | "bold"
text ::= [A-Za-z0-9 ]+
dq ::= ["]
"""
completion = client.chat.completions.create(
model="null",
messages=[
{
"role": "user",
"content": "Generate HTML code for this heading in bold Times New Roman font: ERNIE Bot",
}
],
extra_body={"guided_grammar": html_h1_grammar},
)
print(completion.choices[0].message.content)
print("\n")
Output:
<h1 style="font-family: 'Times New Roman'; font-weight: bold">ERNIE Bot</h1>
OpenAI Beta Interface
Starting from OpenAI Client v1.54.4, the client.beta.chat.completions.parse
interface provides better support for Python native types. Example using pydantic
:
from pydantic import BaseModel
import openai
class Info(BaseModel):
addr: str
height: int
port = "8170"
client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="null")
completion = client.beta.chat.completions.parse(
model="null",
messages=[
{"role": "user", "content": "The Oriental Pearl Tower is located at No.1 Century Avenue, Pudong New Area, Shanghai, with a height of 468 meters. What is its address and height?"},
],
response_format=Info,
)
message = completion.choices[0].message
print(message)
print("\n")
assert message.parsed
print("Address:", message.parsed.addr)
print("Height:", message.parsed.height)
Output:
ParsedChatCompletionMessage[Info](content='{"addr": "No.1 Century Avenue, Pudong New Area, Shanghai", "height": 468}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None, parsed=Info(addr='No.1 Century Avenue, Pudong New Area, Shanghai', height=468), reasoning_content=None)
Address: No.1 Century Avenue, Pudong New Area, Shanghai
Height: 468