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OpenAI Functions

These output parsers use OpenAI function calling to structure its outputs. This means they are only usable with models that support function calling. There are a few different variants:

from langchain_community.utils.openai_functions import (
    convert_pydantic_to_openai_function,
)
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field, validator
from langchain_openai import ChatOpenAI
class Joke(BaseModel):
    """Joke to tell user."""

    setup: str = Field(description="question to set up a joke")
    punchline: str = Field(description="answer to resolve the joke")


openai_functions = [convert_pydantic_to_openai_function(Joke)]
model = ChatOpenAI(temperature=0)
prompt = ChatPromptTemplate.from_messages(
    [("system", "You are helpful assistant"), ("user", "{input}")]
)

JsonOutputFunctionsParser​

from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
parser = JsonOutputFunctionsParser()
chain = prompt | model.bind(functions=openai_functions) | parser
chain.invoke({"input": "tell me a joke"})
{'setup': "Why don't scientists trust atoms?",
 'punchline': 'Because they make up everything!'}
for s in chain.stream({"input": "tell me a joke"}):
    print(s)
{}
{'setup': ''}
{'setup': 'Why'}
{'setup': 'Why don'}
{'setup': "Why don't"}
{'setup': "Why don't scientists"}
{'setup': "Why don't scientists trust"}
{'setup': "Why don't scientists trust atoms"}
{'setup': "Why don't scientists trust atoms?", 'punchline': ''}
{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because'}
{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they'}
{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make'}
{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up'}
{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything'}
{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}

JsonKeyOutputFunctionsParser​

This merely extracts a single key from the returned response. This is useful for when you want to return a list of things.

from typing import List

from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser
class Jokes(BaseModel):
    """Jokes to tell user."""

    joke: List[Joke]
    funniness_level: int
parser = JsonKeyOutputFunctionsParser(key_name="joke")
openai_functions = [convert_pydantic_to_openai_function(Jokes)]
chain = prompt | model.bind(functions=openai_functions) | parser
chain.invoke({"input": "tell me two jokes"})
[{'setup': "Why don't scientists trust atoms?",
  'punchline': 'Because they make up everything!'},
 {'setup': 'Why did the scarecrow win an award?',
  'punchline': 'Because he was outstanding in his field!'}]
for s in chain.stream({"input": "tell me two jokes"}):
    print(s)
[]
[{}]
[{'setup': ''}]
[{'setup': 'Why'}]
[{'setup': 'Why don'}]
[{'setup': "Why don't"}]
[{'setup': "Why don't scientists"}]
[{'setup': "Why don't scientists trust"}]
[{'setup': "Why don't scientists trust atoms"}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': ''}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': ''}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did the'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did the scare'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did the scarecrow'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did the scarecrow win'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did the scarecrow win an'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did the scarecrow win an award'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did the scarecrow win an award?', 'punchline': ''}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did the scarecrow win an award?', 'punchline': 'Because'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did the scarecrow win an award?', 'punchline': 'Because he'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did the scarecrow win an award?', 'punchline': 'Because he was'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did the scarecrow win an award?', 'punchline': 'Because he was outstanding'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did the scarecrow win an award?', 'punchline': 'Because he was outstanding in'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did the scarecrow win an award?', 'punchline': 'Because he was outstanding in his'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did the scarecrow win an award?', 'punchline': 'Because he was outstanding in his field'}]
[{'setup': "Why don't scientists trust atoms?", 'punchline': 'Because they make up everything!'}, {'setup': 'Why did the scarecrow win an award?', 'punchline': 'Because he was outstanding in his field!'}]

PydanticOutputFunctionsParser​

This builds on top of JsonOutputFunctionsParser but passes the results to a Pydantic Model. This allows for further validation should you choose.

from langchain.output_parsers.openai_functions import PydanticOutputFunctionsParser
class Joke(BaseModel):
    """Joke to tell user."""

    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


parser = PydanticOutputFunctionsParser(pydantic_schema=Joke)
openai_functions = [convert_pydantic_to_openai_function(Joke)]
chain = prompt | model.bind(functions=openai_functions) | parser
chain.invoke({"input": "tell me a joke"})
Joke(setup="Why don't scientists trust atoms?", punchline='Because they make up everything!')

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