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[Doc] Unify structured outputs examples (#18196)

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Signed-off-by: Aaron Pham <contact@aarnphm.xyz>
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45
docs/features/reasoning_outputs.md
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@ -142,51 +142,6 @@ for chunk in stream:
Remember to check whether the `reasoning_content` exists in the response before accessing it. You could checkout the [example](https://github.com/vllm-project/vllm/blob/main/examples/online_serving/openai_chat_completion_with_reasoning_streaming.py). Remember to check whether the `reasoning_content` exists in the response before accessing it. You could checkout the [example](https://github.com/vllm-project/vllm/blob/main/examples/online_serving/openai_chat_completion_with_reasoning_streaming.py).
## Structured output
The reasoning content is also available in the structured output. The structured output engine like `xgrammar` will use the reasoning content to generate structured output. It is only supported in v0 engine now.
```bash
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --reasoning-parser deepseek_r1
```
The following is an example client:
```python
from openai import OpenAI
from pydantic import BaseModel
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
class People(BaseModel):
name: str
age: int
json_schema = People.model_json_schema()
prompt = ("Generate a JSON with the name and age of one random person.")
completion = client.chat.completions.create(
model=model,
messages=[{
"role": "user",
"content": prompt,
}],
extra_body={"guided_json": json_schema},
)
print("reasoning_content: ", completion.choices[0].message.reasoning_content)
print("content: ", completion.choices[0].message.content)
```
## Tool Calling ## Tool Calling
The reasoning content is also available when both tool calling and the reasoning parser are enabled. Additionally, tool calling only parses functions from the `content` field, not from the `reasoning_content`. The reasoning content is also available when both tool calling and the reasoning parser are enabled. Additionally, tool calling only parses functions from the `content` field, not from the `reasoning_content`.

80
docs/features/structured_outputs.md
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@ -39,9 +39,10 @@ client = OpenAI(
base_url="http://localhost:8000/v1", base_url="http://localhost:8000/v1",
api_key="-", api_key="-",
) )
model = client.models.list().data[0].id
completion = client.chat.completions.create( completion = client.chat.completions.create(
model="Qwen/Qwen2.5-3B-Instruct", model=model,
messages=[ messages=[
{"role": "user", "content": "Classify this sentiment: vLLM is wonderful!"} {"role": "user", "content": "Classify this sentiment: vLLM is wonderful!"}
], ],
@ -54,7 +55,7 @@ The next example shows how to use the `guided_regex`. The idea is to generate an
```python ```python
completion = client.chat.completions.create( completion = client.chat.completions.create(
model="Qwen/Qwen2.5-3B-Instruct", model=model,
messages=[ messages=[
{ {
"role": "user", "role": "user",
@ -92,26 +93,32 @@ class CarDescription(BaseModel):
json_schema = CarDescription.model_json_schema() json_schema = CarDescription.model_json_schema()
completion = client.chat.completions.create( completion = client.chat.completions.create(
model="Qwen/Qwen2.5-3B-Instruct", model=model,
messages=[ messages=[
{ {
"role": "user", "role": "user",
"content": "Generate a JSON with the brand, model and car_type of the most iconic car from the 90's", "content": "Generate a JSON with the brand, model and car_type of the most iconic car from the 90's",
} }
], ],
extra_body={"guided_json": json_schema}, "response_format": {
"type": "json_schema",
"json_schema": {
"name": "car-description",
"schema": CarDescription.model_json_schema()
},
},
) )
print(completion.choices[0].message.content) print(completion.choices[0].message.content)
``` ```
!!! tip !!! tip
While not strictly necessary, normally it´s better to indicate in the prompt the While not strictly necessary, normally it´s better to indicate in the prompt the
JSON schema and how the fields should be populated. This can improve the JSON schema and how the fields should be populated. This can improve the
results notably in most cases. results notably in most cases.
Finally we have the `guided_grammar` option, which is probably the most Finally we have the `guided_grammar` option, which is probably the most
difficult to use, but it´s really powerful. It allows us to define complete difficult to use, but it´s really powerful. It allows us to define complete
languages like SQL queries. It works by using a context free EBNF grammar. languages like SQL queries. It works by using a context free EBNF grammar.
As an example, we can use to define a specific format of simplified SQL queries: As an example, we can use to define a specific format of simplified SQL queries:
```python ```python
@ -130,7 +137,7 @@ simplified_sql_grammar = """
""" """
completion = client.chat.completions.create( completion = client.chat.completions.create(
model="Qwen/Qwen2.5-3B-Instruct", model=model,
messages=[ messages=[
{ {
"role": "user", "role": "user",
@ -142,7 +149,48 @@ completion = client.chat.completions.create(
print(completion.choices[0].message.content) print(completion.choices[0].message.content)
``` ```
Full example: <gh-file:examples/online_serving/openai_chat_completion_structured_outputs.py> See also: [full example](../../examples/online_serving/structured_outputs)
## Reasoning Outputs
You can also use structured outputs with <project:#reasoning-outputs> for reasoning models.
```bash
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-7B --reasoning-parser deepseek_r1
```
Note that you can use reasoning with any provided structured outputs feature. The following uses one with JSON schema:
```python
from pydantic import BaseModel
class People(BaseModel):
name: str
age: int
completion = client.chat.completions.create(
model=model,
messages=[
{
"role": "user",
"content": "Generate a JSON with the name and age of one random person.",
}
],
response_format={
"type": "json_schema",
"json_schema": {
"name": "people",
"schema": People.model_json_schema()
}
},
)
print("reasoning_content: ", completion.choices[0].message.reasoning_content)
print("content: ", completion.choices[0].message.content)
```
See also: [full example](../../examples/online_serving/structured_outputs)
## Experimental Automatic Parsing (OpenAI API) ## Experimental Automatic Parsing (OpenAI API)
@ -163,14 +211,14 @@ class Info(BaseModel):
age: int age: int
client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key="dummy") client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key="dummy")
model = client.models.list().data[0].id
completion = client.beta.chat.completions.parse( completion = client.beta.chat.completions.parse(
model="meta-llama/Llama-3.1-8B-Instruct", model=model,
messages=[ messages=[
{"role": "system", "content": "You are a helpful assistant."}, {"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "My name is Cameron, I'm 28. What's my name and age?"}, {"role": "user", "content": "My name is Cameron, I'm 28. What's my name and age?"},
], ],
response_format=Info, response_format=Info,
extra_body=dict(guided_decoding_backend="outlines"),
) )
message = completion.choices[0].message message = completion.choices[0].message
@ -203,15 +251,13 @@ class MathResponse(BaseModel):
steps: list[Step] steps: list[Step]
final_answer: str final_answer: str
client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key="dummy")
completion = client.beta.chat.completions.parse( completion = client.beta.chat.completions.parse(
model="meta-llama/Llama-3.1-8B-Instruct", model=model,
messages=[ messages=[
{"role": "system", "content": "You are a helpful expert math tutor."}, {"role": "system", "content": "You are a helpful expert math tutor."},
{"role": "user", "content": "Solve 8x + 31 = 2."}, {"role": "user", "content": "Solve 8x + 31 = 2."},
], ],
response_format=MathResponse, response_format=MathResponse,
extra_body=dict(guided_decoding_backend="outlines"),
) )
message = completion.choices[0].message message = completion.choices[0].message
@ -232,11 +278,11 @@ Step #2: explanation="Next, let's isolate 'x' by dividing both sides of the equa
Answer: x = -29/8 Answer: x = -29/8
``` ```
An example of using `structural_tag` can be found here: <gh-file:examples/online_serving/openai_chat_completion_structured_outputs_structural_tag.py> An example of using `structural_tag` can be found here: <gh-file:examples/online_serving/structured_outputs>
## Offline Inference ## Offline Inference
Offline inference allows for the same types of guided decoding. Offline inference allows for the same types of structured outputs.
To use it, we´ll need to configure the guided decoding using the class `GuidedDecodingParams` inside `SamplingParams`. To use it, we´ll need to configure the guided decoding using the class `GuidedDecodingParams` inside `SamplingParams`.
The main available options inside `GuidedDecodingParams` are: The main available options inside `GuidedDecodingParams` are:
@ -247,7 +293,7 @@ The main available options inside `GuidedDecodingParams` are:
- `structural_tag` - `structural_tag`
These parameters can be used in the same way as the parameters from the Online These parameters can be used in the same way as the parameters from the Online
Serving examples above. One example for the usage of the `choice` parameter is Serving examples above. One example for the usage of the `choice` parameter is
shown below: shown below:
```python ```python
@ -265,4 +311,4 @@ outputs = llm.generate(
print(outputs[0].outputs[0].text) print(outputs[0].outputs[0].text)
``` ```
Full example: <gh-file:examples/offline_inference/structured_outputs.py> See also: [full example](../../examples/online_serving/structured_outputs)

175
examples/online_serving/openai_chat_completion_structured_outputs.py
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@ -1,175 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
To run this example, you need to start the vLLM server:
```bash
vllm serve Qwen/Qwen2.5-3B-Instruct
```
"""
from enum import Enum
from openai import BadRequestError, OpenAI
from pydantic import BaseModel
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
# Guided decoding by Choice (list of possible options)
def guided_choice_completion(client: OpenAI, model: str):
completion = client.chat.completions.create(
model=model,
messages=[
{"role": "user", "content": "Classify this sentiment: vLLM is wonderful!"}
],
extra_body={"guided_choice": ["positive", "negative"]},
)
return completion.choices[0].message.content
# Guided decoding by Regex
def guided_regex_completion(client: OpenAI, model: str):
prompt = (
"Generate an email address for Alan Turing, who works in Enigma."
"End in .com and new line. Example result:"
"alan.turing@enigma.com\n"
)
completion = client.chat.completions.create(
model=model,
messages=[
{
"role": "user",
"content": prompt,
}
],
extra_body={"guided_regex": r"\w+@\w+\.com\n", "stop": ["\n"]},
)
return completion.choices[0].message.content
# Guided decoding by JSON using Pydantic schema
class CarType(str, Enum):
sedan = "sedan"
suv = "SUV"
truck = "Truck"
coupe = "Coupe"
class CarDescription(BaseModel):
brand: str
model: str
car_type: CarType
def guided_json_completion(client: OpenAI, model: str):
json_schema = CarDescription.model_json_schema()
prompt = (
"Generate a JSON with the brand, model and car_type of"
"the most iconic car from the 90's"
)
completion = client.chat.completions.create(
model=model,
messages=[
{
"role": "user",
"content": prompt,
}
],
extra_body={"guided_json": json_schema},
)
return completion.choices[0].message.content
# Guided decoding by Grammar
def guided_grammar_completion(client: OpenAI, model: str):
simplified_sql_grammar = """
root ::= select_statement
select_statement ::= "SELECT " column " from " table " where " condition
column ::= "col_1 " | "col_2 "
table ::= "table_1 " | "table_2 "
condition ::= column "= " number
number ::= "1 " | "2 "
"""
prompt = (
"Generate an SQL query to show the 'username' and 'email'"
"from the 'users' table."
)
completion = client.chat.completions.create(
model=model,
messages=[
{
"role": "user",
"content": prompt,
}
],
extra_body={"guided_grammar": simplified_sql_grammar},
)
return completion.choices[0].message.content
# Extra backend options
def extra_backend_options_completion(client: OpenAI, model: str):
prompt = (
"Generate an email address for Alan Turing, who works in Enigma."
"End in .com and new line. Example result:"
"alan.turing@enigma.com\n"
)
try:
# The guided_decoding_disable_fallback option forces vLLM to use
# xgrammar, so when it fails you get a 400 with the reason why
completion = client.chat.completions.create(
model=model,
messages=[
{
"role": "user",
"content": prompt,
}
],
extra_body={
"guided_regex": r"\w+@\w+\.com\n",
"stop": ["\n"],
"guided_decoding_disable_fallback": True,
},
)
return completion.choices[0].message.content
except BadRequestError as e:
print("This error is expected:", e)
def main():
client: OpenAI = OpenAI(
base_url=openai_api_base,
api_key=openai_api_key,
)
model = client.models.list().data[0].id
print("Guided Choice Completion:")
print(guided_choice_completion(client, model))
print("\nGuided Regex Completion:")
print(guided_regex_completion(client, model))
print("\nGuided JSON Completion:")
print(guided_json_completion(client, model))
print("\nGuided Grammar Completion:")
print(guided_grammar_completion(client, model))
print("\nExtra Backend Options Completion:")
print(extra_backend_options_completion(client, model))
if __name__ == "__main__":
main()

87
examples/online_serving/openai_chat_completion_structured_outputs_structural_tag.py
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@ -1,87 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from openai import OpenAI
# This example demonstrates the `structural_tag` response format.
# It can be used to specify a structured output format that occurs between
# specific tags in the response. This example shows how it could be used
# to enforce the format of a tool call response, but it could be used for
# any structured output within a subset of the response.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
def main():
client = OpenAI(
base_url=openai_api_base,
api_key=openai_api_key,
)
messages = [
{
"role": "user",
"content": """
You have access to the following function to retrieve the weather in a city:
{
"name": "get_weather",
"parameters": {
"city": {
"param_type": "string",
"description": "The city to get the weather for",
"required": True
}
}
}
If a you choose to call a function ONLY reply in the following format:
<{start_tag}={function_name}>{parameters}{end_tag}
where
start_tag => `<function`
parameters => a JSON dict with the function argument name as key and function
argument value as value.
end_tag => `</function>`
Here is an example,
<function=example_function_name>{"example_name": "example_value"}</function>
Reminder:
- Function calls MUST follow the specified format
- Required parameters MUST be specified
- Only call one function at a time
- Put the entire function call reply on one line
- Always add your sources when using search results to answer the user query
You are a helpful assistant.
Given the previous instructions, what is the weather in New York City, Boston,
and San Francisco?
""",
}
]
response = client.chat.completions.create(
model=client.models.list().data[0].id,
messages=messages,
response_format={
"type": "structural_tag",
"structures": [
{
"begin": "<function=get_weather>",
"schema": {
"type": "object",
"properties": {"city": {"type": "string"}},
},
"end": "</function>",
}
],
"triggers": ["<function="],
},
)
print(response)
if __name__ == "__main__":
main()

167
examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py
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@ -1,167 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
An example shows how to generate structured outputs from reasoning models
like DeepSeekR1. The thinking process will not be guided by the JSON
schema provided by the user. Only the final output will be structured.
To run this example, you need to start the vLLM server with the reasoning
parser:
```bash
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B \
--reasoning-parser deepseek_r1
```
This example demonstrates how to generate chat completions from reasoning models
using the OpenAI Python client library.
"""
from enum import Enum
from openai import OpenAI
from pydantic import BaseModel
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
def print_completion_details(completion):
print("reasoning_content: ", completion.choices[0].message.reasoning_content)
print("content: ", completion.choices[0].message.content)
# Guided decoding by Regex
def guided_regex_completion(client: OpenAI, model: str):
prompt = "What is the capital of France?"
completion = client.chat.completions.create(
model=model,
messages=[
{
"role": "user",
"content": prompt,
}
],
extra_body={
"guided_regex": "(Paris|London)",
},
)
print_completion_details(completion)
class People(BaseModel):
name: str
age: int
def guided_json_completion(client: OpenAI, model: str):
json_schema = People.model_json_schema()
prompt = "Generate a JSON with the name and age of one random person."
completion = client.chat.completions.create(
model=model,
messages=[
{
"role": "user",
"content": prompt,
}
],
extra_body={"guided_json": json_schema},
)
print_completion_details(completion)
# Guided decoding by JSON using Pydantic schema
class CarType(str, Enum):
sedan = "sedan"
suv = "SUV"
truck = "Truck"
coupe = "Coupe"
class CarDescription(BaseModel):
brand: str
model: str
car_type: CarType
def guided_car_json_completion(client: OpenAI, model: str):
json_schema = CarDescription.model_json_schema()
prompt = (
"Generate a JSON with the brand, model and car_type of"
"the most iconic car from the 90's"
)
completion = client.chat.completions.create(
model=model,
messages=[
{
"role": "user",
"content": prompt,
}
],
extra_body={"guided_json": json_schema},
)
print_completion_details(completion)
# Guided decoding by Grammar
def guided_grammar_completion(client: OpenAI, model: str):
simplified_sql_grammar = """
root ::= select_statement
select_statement ::= "SELECT " column " from " table " where " condition
column ::= "col_1 " | "col_2 "
table ::= "table_1 " | "table_2 "
condition ::= column "= " number
number ::= "1 " | "2 "
"""
# This may be very slow https://github.com/vllm-project/vllm/issues/12122
prompt = (
"Generate an SQL query to show the 'username' and 'email'"
"from the 'users' table."
)
completion = client.chat.completions.create(
model=model,
messages=[
{
"role": "user",
"content": prompt,
}
],
extra_body={"guided_grammar": simplified_sql_grammar},
)
print_completion_details(completion)
def main():
client: OpenAI = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model: str = models.data[0].id
print("Guided Regex Completion:")
guided_regex_completion(client, model)
print("\nGuided JSON Completion (People):")
guided_json_completion(client, model)
print("\nGuided JSON Completion (CarDescription):")
guided_car_json_completion(client, model)
print("\nGuided Grammar Completion:")
guided_grammar_completion(client, model)
if __name__ == "__main__":
main()

54
examples/online_serving/structured_outputs/README.md Normal file
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@ -0,0 +1,54 @@
# Structured Outputs
This script demonstrates various structured output capabilities of vLLM's OpenAI-compatible server.
It can run individual constraint type or all of them.
It supports both streaming responses and concurrent non-streaming requests.
To use this example, you must start an vLLM server with any model of your choice.
```bash
vllm serve Qwen/Qwen2.5-3B-Instruct
```
To serve a reasoning model, you can use the following command:
```bash
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-7B --reasoning-parser deepseek_r1
```
If you want to run this script standalone with `uv`, you can use the following:
```bash
uvx --from git+https://github.com/vllm-project/vllm#subdirectory=examples/online_serving/structured_outputs structured-output
```
See [feature docs](../../../features/structured_outputs.md) for more information.
!!! tip
If vLLM is running remotely, then set `OPENAI_BASE_URL=<remote_url>` before running the script.
## Usage
Run all constraints, non-streaming:
```bash
uv run structured_outputs.py
```
Run all constraints, streaming:
```bash
uv run structured_outputs.py --stream
```
Run certain constraints, for example `structural_tag` and `regex`, streaming:
```bash
uv run structured_outputs.py --constraint structural_tag regex --stream
```
Run all constraints, with reasoning models and streaming:
```bash
uv run structured_outputs.py --reasoning --stream
```

8
examples/online_serving/structured_outputs/pyproject.toml Normal file
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@ -0,0 +1,8 @@
[project]
name = "examples-online-structured-outputs"
requires-python = ">=3.9, <3.13"
dependencies = ["openai==1.78.1", "pydantic==2.11.4"]
version = "0.0.0"
[project.scripts]
structured-outputs = "structured_outputs:main"

272
examples/online_serving/structured_outputs/structured_outputs.py Normal file
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@ -0,0 +1,272 @@
# ruff: noqa: E501
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from __future__ import annotations
import argparse
import asyncio
import enum
import os
from typing import TYPE_CHECKING, Any, Literal
import openai
import pydantic
if TYPE_CHECKING:
from openai.types.chat import ChatCompletionChunk
ConstraintsFormat = Literal[
"choice",
"regex",
"json",
"grammar",
"structural_tag",
]
async def print_stream_response(
stream_response: openai.AsyncStream[ChatCompletionChunk],
title: str,
args: argparse.Namespace,
):
print(f"\n\n{title} (Streaming):")
local_reasoning_header_printed = False
local_content_header_printed = False
async for chunk in stream_response:
delta = chunk.choices[0].delta
reasoning_chunk_text: str | None = getattr(delta, "reasoning_content", None)
content_chunk_text = delta.content
if args.reasoning:
if reasoning_chunk_text:
if not local_reasoning_header_printed:
print(" Reasoning: ", end="")
local_reasoning_header_printed = True
print(reasoning_chunk_text, end="", flush=True)
if content_chunk_text:
if not local_content_header_printed:
if local_reasoning_header_printed:
print()
print(" Content: ", end="")
local_content_header_printed = True
print(content_chunk_text, end="", flush=True)
else:
if content_chunk_text:
if not local_content_header_printed:
print(" Content: ", end="")
local_content_header_printed = True
print(content_chunk_text, end="", flush=True)
print()
class CarType(str, enum.Enum):
SEDAN = "SEDAN"
SUV = "SUV"
TRUCK = "TRUCK"
COUPE = "COUPE"
class CarDescription(pydantic.BaseModel):
brand: str
model: str
car_type: CarType
PARAMS: dict[ConstraintsFormat, dict[str, Any]] = {
"choice": {
"messages": [
{
"role": "user",
"content": "Classify this sentiment: vLLM is wonderful!",
}
],
"extra_body": {"guided_choice": ["positive", "negative"]},
},
"regex": {
"messages": [
{
"role": "user",
"content": "Generate an email address for Alan Turing, who works in Enigma. End in .com and new line. Example result: 'alan.turing@enigma.com\n'",
}
],
"extra_body": {
"guided_regex": r"[a-z0-9.]{1,20}@\w{6,10}\.com\n",
},
},
"json": {
"messages": [
{
"role": "user",
"content": "Generate a JSON with the brand, model and car_type of the most iconic car from the 90's",
}
],
"response_format": {
"type": "json_schema",
"json_schema": {
"name": "car-description",
"schema": CarDescription.model_json_schema(),
},
},
},
"grammar": {
"messages": [
{
"role": "user",
"content": "Generate an SQL query to show the 'username' and 'email'from the 'users' table.",
}
],
"extra_body": {
"guided_grammar": """
root ::= select_statement
select_statement ::= "SELECT " column " from " table " where " condition
column ::= "col_1 " | "col_2 "
table ::= "table_1 " | "table_2 "
condition ::= column "= " number
number ::= "1 " | "2 "
""",
},
},
"structural_tag": {
"messages": [
{
"role": "user",
"content": """
You have access to the following function to retrieve the weather in a city:
{
"name": "get_weather",
"parameters": {
"city": {
"param_type": "string",
"description": "The city to get the weather for",
"required": True
}
}
}
If a you choose to call a function ONLY reply in the following format:
<{start_tag}={function_name}>{parameters}{end_tag}
where
start_tag => `<function`
parameters => a JSON dict with the function argument name as key and function
argument value as value.
end_tag => `</function>`
Here is an example,
<function=example_function_name>{"example_name": "example_value"}</function>
Reminder:
- Function calls MUST follow the specified format
- Required parameters MUST be specified
- Only call one function at a time
- Put the entire function call reply on one line
- Always add your sources when using search results to answer the user query
You are a helpful assistant.
Given the previous instructions, what is the weather in New York City, Boston,
and San Francisco?""",
},
],
"response_format": {
"type": "structural_tag",
"structures": [
{
"begin": "<function=get_weather>",
"schema": {
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"],
},
"end": "</function>",
}
],
"triggers": ["<function="],
},
},
}
async def cli():
parser = argparse.ArgumentParser(
description="Run OpenAI Chat Completion with various structured outputs capabilities",
)
_ = parser.add_argument(
"--constraint",
type=str,
nargs="+",
choices=[*list(PARAMS), "*"],
default=["*"],
help="Specify which constraint(s) to run.",
)
_ = parser.add_argument(
"--stream",
action=argparse.BooleanOptionalAction,
default=False,
help="Enable streaming output",
)
_ = parser.add_argument(
"--reasoning",
action=argparse.BooleanOptionalAction,
default=False,
help="Enable printing of reasoning traces if available.",
)
args = parser.parse_args()
base_url = os.getenv("OPENAI_BASE_URL", "http://localhost:8000/v1")
client = openai.AsyncOpenAI(base_url=base_url, api_key="EMPTY")
constraints = list(PARAMS) if "*" in args.constraint else list(set(args.constraint))
model = (await client.models.list()).data[0].id
if args.stream:
results = await asyncio.gather(
*[
client.chat.completions.create(
model=model,
max_tokens=1024,
stream=True,
**PARAMS[name],
)
for name in constraints
]
)
for constraint, stream in zip(constraints, results):
await print_stream_response(stream, constraint, args)
else:
results = await asyncio.gather(
*[
client.chat.completions.create(
model=model,
max_tokens=1024,
stream=False,
**PARAMS[name],
)
for name in constraints
]
)
for constraint, response in zip(constraints, results):
print(f"\n\n{constraint}:")
message = response.choices[0].message
if args.reasoning and hasattr(message, "reasoning_content"):
print(f" Reasoning: {message.reasoning_content or ''}")
print(f" Content: {message.content!r}")
def main():
asyncio.run(cli())
if __name__ == "__main__":
main()