For this example, we'll use the above Pydantic output parser. Here's what happens if we pass it a result that does not comply with the schema:

```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
class Actor(BaseModel):
    name: str = Field(description="name of an actor")
    film_names: List[str] = Field(description="list of names of films they starred in")
        
actor_query = "Generate the filmography for a random actor."

parser = PydanticOutputParser(pydantic_object=Actor)
```


```python
misformatted = "{'name': 'Tom Hanks', 'film_names': ['Forrest Gump']}"
```


```python
parser.parse(misformatted)
```

<CodeOutputBlock lang="python">

```
    ---------------------------------------------------------------------------

    JSONDecodeError                           Traceback (most recent call last)

    File ~/workplace/langchain/langchain/output_parsers/pydantic.py:23, in PydanticOutputParser.parse(self, text)
         22     json_str = match.group()
    ---> 23 json_object = json.loads(json_str)
         24 return self.pydantic_object.parse_obj(json_object)


    File ~/.pyenv/versions/3.9.1/lib/python3.9/json/__init__.py:346, in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
        343 if (cls is None and object_hook is None and
        344         parse_int is None and parse_float is None and
        345         parse_constant is None and object_pairs_hook is None and not kw):
    --> 346     return _default_decoder.decode(s)
        347 if cls is None:


    File ~/.pyenv/versions/3.9.1/lib/python3.9/json/decoder.py:337, in JSONDecoder.decode(self, s, _w)
        333 """Return the Python representation of ``s`` (a ``str`` instance
        334 containing a JSON document).
        335 
        336 """
    --> 337 obj, end = self.raw_decode(s, idx=_w(s, 0).end())
        338 end = _w(s, end).end()


    File ~/.pyenv/versions/3.9.1/lib/python3.9/json/decoder.py:353, in JSONDecoder.raw_decode(self, s, idx)
        352 try:
    --> 353     obj, end = self.scan_once(s, idx)
        354 except StopIteration as err:


    JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)

    
    During handling of the above exception, another exception occurred:


    OutputParserException                     Traceback (most recent call last)

    Cell In[6], line 1
    ----> 1 parser.parse(misformatted)


    File ~/workplace/langchain/langchain/output_parsers/pydantic.py:29, in PydanticOutputParser.parse(self, text)
         27 name = self.pydantic_object.__name__
         28 msg = f"Failed to parse {name} from completion {text}. Got: {e}"
    ---> 29 raise OutputParserException(msg)


    OutputParserException: Failed to parse Actor from completion {'name': 'Tom Hanks', 'film_names': ['Forrest Gump']}. Got: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)
```

</CodeOutputBlock>

Now we can construct and use a `OutputFixingParser`. This output parser takes as an argument another output parser but also an LLM with which to try to correct any formatting mistakes.


```python
from langchain.output_parsers import OutputFixingParser

new_parser = OutputFixingParser.from_llm(parser=parser, llm=ChatOpenAI())
```


```python
new_parser.parse(misformatted)
```

<CodeOutputBlock lang="python">

```
    Actor(name='Tom Hanks', film_names=['Forrest Gump'])
```

</CodeOutputBlock>
