(structured-outputs)=

# Structured Outputs

vLLM supports the generation of structured outputs using [outlines](https://github.com/dottxt-ai/outlines) or [lm-format-enforcer](https://github.com/noamgat/lm-format-enforcer) as backends for the guided decoding.
This document shows you some examples of the different options that are available to generate structured outputs.

## Online Inference (OpenAI API)

You can generate structured outputs using the OpenAI's [Completions](https://platform.openai.com/docs/api-reference/completions) and [Chat](https://platform.openai.com/docs/api-reference/chat) API.

The following parameters are supported, which must be added as extra parameters:

- `guided_choice`: the output will be exactly one of the choices.
- `guided_regex`: the output will follow the regex pattern.
- `guided_json`: the output will follow the JSON schema.
- `guided_grammar`: the output will follow the context free grammar.
- `guided_whitespace_pattern`: used to override the default whitespace pattern for guided json decoding.
- `guided_decoding_backend`: used to select the guided decoding backend to use.

You can see the complete list of supported parameters on the [OpenAI Compatible Server](../serving/openai_compatible_server.md) page.

Now let´s see an example for each of the cases, starting with the `guided_choice`, as it´s the easiest one:

```python
from openai import OpenAI
client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="-",
)

completion = client.chat.completions.create(
    model="Qwen/Qwen2.5-3B-Instruct",
    messages=[
        {"role": "user", "content": "Classify this sentiment: vLLM is wonderful!"}
    ],
    extra_body={"guided_choice": ["positive", "negative"]},
)
print(completion.choices[0].message.content)
```

The next example shows how to use the `guided_regex`. The idea is to generate an email address, given a simple regex template:

```python
completion = client.chat.completions.create(
    model="Qwen/Qwen2.5-3B-Instruct",
    messages=[
        {
            "role": "user",
            "content": "Generate an example 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": "\w+@\w+\.com\n", "stop": ["\n"]},
)
print(completion.choices[0].message.content)
```

One of the most relevant features in structured text generation is the option to generate a valid JSON with pre-defined fields and formats.
For this we can use the `guided_json` parameter in two different ways:

- Using directly a [JSON Schema](https://json-schema.org/)
- Defining a [Pydantic model](https://docs.pydantic.dev/latest/) and then extracting the JSON Schema from it (which is normally an easier option).

The next example shows how to use the `guided_json` parameter with a Pydantic model:

```python
from pydantic import BaseModel
from enum import Enum

class CarType(str, Enum):
    sedan = "sedan"
    suv = "SUV"
    truck = "Truck"
    coupe = "Coupe"


class CarDescription(BaseModel):
    brand: str
    model: str
    car_type: CarType


json_schema = CarDescription.model_json_schema()

completion = client.chat.completions.create(
    model="Qwen/Qwen2.5-3B-Instruct",
    messages=[
        {
            "role": "user",
            "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},
)
print(completion.choices[0].message.content)
```

```{tip}
While not strictly necessary, normally it´s better to indicate in the prompt that a JSON needs to be generated and which fields and how should the LLM fill them.
This can improve the results notably in most cases.
```

Finally we have the `guided_grammar`, which probably is the most difficult one to use but it´s really powerful, as it allows us to define complete languages like SQL queries.
It works by using a context free EBNF grammar, which for example we can use to define a specific format of simplified SQL queries, like in the example below:

```python
simplified_sql_grammar = """
    ?start: select_statement

    ?select_statement: "SELECT " column_list " FROM " table_name

    ?column_list: column_name ("," column_name)*

    ?table_name: identifier

    ?column_name: identifier

    ?identifier: /[a-zA-Z_][a-zA-Z0-9_]*/
"""

completion = client.chat.completions.create(
    model="Qwen/Qwen2.5-3B-Instruct",
    messages=[
        {
            "role": "user",
            "content": "Generate an SQL query to show the 'username' and 'email' from the 'users' table.",
        }
    ],
    extra_body={"guided_grammar": simplified_sql_grammar},
)
print(completion.choices[0].message.content)
```

The complete code of the examples can be found on [examples/openai_chat_completion_structured_outputs.py](https://github.com/vllm-project/vllm/blob/main/examples/openai_chat_completion_structured_outputs.py).

## Experimental Automatic Parsing (OpenAI API)

This section covers the OpenAI beta wrapper over the `client.chat.completions.create()` method that provides richer integrations with Python specific types.

At the time of writing (`openai==1.54.4`), this is a "beta" feature in the OpenAI client library. Code reference can be found [here](https://github.com/openai/openai-python/blob/52357cff50bee57ef442e94d78a0de38b4173fc2/src/openai/resources/beta/chat/completions.py#L100-L104).

For the following examples, vLLM was setup using `vllm serve meta-llama/Llama-3.1-8B-Instruct`

Here is a simple example demonstrating how to get structured output using Pydantic models:

```python
from pydantic import BaseModel
from openai import OpenAI


class Info(BaseModel):
    name: str
    age: int


client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key="dummy")
completion = client.beta.chat.completions.parse(
    model="meta-llama/Llama-3.1-8B-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "My name is Cameron, I'm 28. What's my name and age?"},
    ],
    response_format=Info,
    extra_body=dict(guided_decoding_backend="outlines"),
)

message = completion.choices[0].message
print(message)
assert message.parsed
print("Name:", message.parsed.name)
print("Age:", message.parsed.age)
```

Output:

```console
ParsedChatCompletionMessage[Testing](content='{"name": "Cameron", "age": 28}', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[], parsed=Testing(name='Cameron', age=28))
Name: Cameron
Age: 28
```

Here is a more complex example using nested Pydantic models to handle a step-by-step math solution:

```python
from typing import List
from pydantic import BaseModel
from openai import OpenAI


class Step(BaseModel):
    explanation: str
    output: str


class MathResponse(BaseModel):
    steps: List[Step]
    final_answer: str


client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key="dummy")
completion = client.beta.chat.completions.parse(
    model="meta-llama/Llama-3.1-8B-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful expert math tutor."},
        {"role": "user", "content": "Solve 8x + 31 = 2."},
    ],
    response_format=MathResponse,
    extra_body=dict(guided_decoding_backend="outlines"),
)

message = completion.choices[0].message
print(message)
assert message.parsed
for i, step in enumerate(message.parsed.steps):
    print(f"Step #{i}:", step)
print("Answer:", message.parsed.final_answer)
```

Output:

```console
ParsedChatCompletionMessage[MathResponse](content='{ "steps": [{ "explanation": "First, let\'s isolate the term with the variable \'x\'. To do this, we\'ll subtract 31 from both sides of the equation.", "output": "8x + 31 - 31 = 2 - 31"}, { "explanation": "By subtracting 31 from both sides, we simplify the equation to 8x = -29.", "output": "8x = -29"}, { "explanation": "Next, let\'s isolate \'x\' by dividing both sides of the equation by 8.", "output": "8x / 8 = -29 / 8"}], "final_answer": "x = -29/8" }', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[], parsed=MathResponse(steps=[Step(explanation="First, let's isolate the term with the variable 'x'. To do this, we'll subtract 31 from both sides of the equation.", output='8x + 31 - 31 = 2 - 31'), Step(explanation='By subtracting 31 from both sides, we simplify the equation to 8x = -29.', output='8x = -29'), Step(explanation="Next, let's isolate 'x' by dividing both sides of the equation by 8.", output='8x / 8 = -29 / 8')], final_answer='x = -29/8'))
Step #0: explanation="First, let's isolate the term with the variable 'x'. To do this, we'll subtract 31 from both sides of the equation." output='8x + 31 - 31 = 2 - 31'
Step #1: explanation='By subtracting 31 from both sides, we simplify the equation to 8x = -29.' output='8x = -29'
Step #2: explanation="Next, let's isolate 'x' by dividing both sides of the equation by 8." output='8x / 8 = -29 / 8'
Answer: x = -29/8
```

## Offline Inference

Offline inference allows for the same types of guided decoding.
To use it, we´ll need to configure the guided decoding using the class `GuidedDecodingParams` inside `SamplingParams`.
The main available options inside `GuidedDecodingParams` are:

- `json`
- `regex`
- `choice`
- `grammar`
- `backend`
- `whitespace_pattern`

These parameters can be used in the same way as the parameters from the Online Inference examples above.
One example for the usage of the `choices` parameter is shown below:

```python
from vllm import LLM, SamplingParams
from vllm.sampling_params import GuidedDecodingParams

llm = LLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")

guided_decoding_params = GuidedDecodingParams(choice=["Positive", "Negative"])
sampling_params = SamplingParams(guided_decoding=guided_decoding_params)
outputs = llm.generate(
    prompts="Classify this sentiment: vLLM is wonderful!",
    sampling_params=sampling_params,
)
print(outputs[0].outputs[0].text)
```

A complete example with all options can be found in [examples/offline_inference_structured_outputs.py](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_structured_outputs.py).