create_structured_output_runnable

OpenAI tools example with Pydantic schema (mode='openai-tools'):

from typing import Optional

from langchain_classic.chains import create_structured_output_runnable
from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field

class RecordDog(BaseModel):
    '''Record some identifying information about a dog.'''

    name: str = Field(..., description="The dog's name")
    color: str = Field(..., description="The dog's color")
    fav_food: str | None = Field(None, description="The dog's favorite food")

model = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are an extraction algorithm. Please extract every possible instance"),
        ('human', '{input}')
    ]
)
structured_model = create_structured_output_runnable(
    RecordDog,
    model,
    mode="openai-tools",
    enforce_function_usage=True,
    return_single=True
)
structured_model.invoke({"input": "Harry was a chubby brown beagle who loved chicken"})
# -> RecordDog(name="Harry", color="brown", fav_food="chicken")

OpenAI tools example with dict schema (mode="openai-tools"):

from typing import Optional

from langchain_classic.chains import create_structured_output_runnable
from langchain_openai import ChatOpenAI

dog_schema = {
    "type": "function",
    "function": {
        "name": "record_dog",
        "description": "Record some identifying information about a dog.",
        "parameters": {
            "type": "object",
            "properties": {
                "name": {
                    "description": "The dog's name",
                    "type": "string"
                },
                "color": {
                    "description": "The dog's color",
                    "type": "string"
                },
                "fav_food": {
                    "description": "The dog's favorite food",
                    "type": "string"
                }
            },
            "required": ["name", "color"]
        }
    }
}

model = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_model = create_structured_output_runnable(
    dog_schema,
    model,
    mode="openai-tools",
    enforce_function_usage=True,
    return_single=True
)
structured_model.invoke("Harry was a chubby brown beagle who loved chicken")
# -> {'name': 'Harry', 'color': 'brown', 'fav_food': 'chicken'}

OpenAI functions example (mode="openai-functions"):

from typing import Optional

from langchain_classic.chains import create_structured_output_runnable
from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field

class Dog(BaseModel):
    '''Identifying information about a dog.'''

    name: str = Field(..., description="The dog's name")
    color: str = Field(..., description="The dog's color")
    fav_food: str | None = Field(None, description="The dog's favorite food")

model = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_model = create_structured_output_runnable(Dog, model, mode="openai-functions")
structured_model.invoke("Harry was a chubby brown beagle who loved chicken")
# -> Dog(name="Harry", color="brown", fav_food="chicken")

OpenAI functions with prompt example:

from typing import Optional

from langchain_classic.chains import create_structured_output_runnable
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from pydantic import BaseModel, Field

class Dog(BaseModel):
    '''Identifying information about a dog.'''

    name: str = Field(..., description="The dog's name")
    color: str = Field(..., description="The dog's color")
    fav_food: str | None = Field(None, description="The dog's favorite food")

model = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_model = create_structured_output_runnable(Dog, model, mode="openai-functions")
system = '''Extract information about any dogs mentioned in the user input.'''
prompt = ChatPromptTemplate.from_messages(
    [("system", system), ("human", "{input}"),]
)
chain = prompt | structured_model
chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"})
# -> Dog(name="Harry", color="brown", fav_food="chicken")

OpenAI json response format example (mode="openai-json"):

from typing import Optional

from langchain_classic.chains import create_structured_output_runnable
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from pydantic import BaseModel, Field

class Dog(BaseModel):
    '''Identifying information about a dog.'''

    name: str = Field(..., description="The dog's name")
    color: str = Field(..., description="The dog's color")
    fav_food: str | None = Field(None, description="The dog's favorite food")

model = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_model = create_structured_output_runnable(Dog, model, mode="openai-json")
system = '''You are a world class assistant for extracting information in structured JSON formats. 
Extract a valid JSON blob from the user input that matches the following JSON Schema:

{output_schema}'''
prompt = ChatPromptTemplate.from_messages(
    [("system", system), ("human", "{input}"),]
)
chain = prompt | structured_model
chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"})