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OutputFixingParser
OutputFixingParser#
This output parser wraps another output parser and tries to fix any mistakes
The Pydantic guardrail simply tries to parse the LLM response. If it does not parse correctly, then it errors.
But we can do other things besides throw errors. Specifically, we can pass the misformatted output, along with the formatted instructions, to the model and ask it to fix it.
For this example, we’ll use the above OutputParser. Here’s what happens if we pass it a result that does not comply with the schema:
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
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)
misformatted = "{'name': 'Tom Hanks', 'film_names': ['Forrest Gump']}"
parser.parse(misformatted)
---------------------------------------------------------------------------
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)
rtdocs_stable/api.python.langchain.com/en/stable/modules/prompts/output_parsers/examples/output_fixing_parser.html