langchain/docs/snippets/modules/model_io/output_parsers/get_started.mdx

---
sidebar_position: 2
---
Below we go over the main type of output parser, the `PydanticOutputParser`.

```python
from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI

from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field, validator
from typing import List
```


```python
model_name = 'text-davinci-003'
temperature = 0.0
model = OpenAI(model_name=model_name, temperature=temperature)
```


```python
# Define your desired data structure.
class Joke(BaseModel):
    setup: str = Field(description="question to set up a joke")
    punchline: str = Field(description="answer to resolve the joke")
    
    # You can add custom validation logic easily with Pydantic.
    @validator('setup')
    def question_ends_with_question_mark(cls, field):
        if field[-1] != '?':
            raise ValueError("Badly formed question!")
        return field
```


```python
# Set up a parser + inject instructions into the prompt template.
parser = PydanticOutputParser(pydantic_object=Joke)
```


```python
prompt = PromptTemplate(
    template="Answer the user query.\n{format_instructions}\n{query}\n",
    input_variables=["query"],
    partial_variables={"format_instructions": parser.get_format_instructions()}
)
```


```python
# And a query intented to prompt a language model to populate the data structure.
joke_query = "Tell me a joke."
_input = prompt.format_prompt(query=joke_query)
```


```python
output = model(_input.to_string())
```


```python
parser.parse(output)
```

<CodeOutputBlock lang="python">

```
    Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!')
```

</CodeOutputBlock>