create_structured_output_chain(
output_schema: dict[str, Any] | type[BaseModel],
llm: BaseLanguageModel| Name | Type | Description |
|---|---|---|
output_schema* | dict[str, Any] | type[BaseModel] | Either a dictionary or pydantic.BaseModel class. If a dictionary is passed in, it's assumed to already be a valid JsonSchema. For best results, pydantic.BaseModels should have docstrings describing what the schema represents and descriptions for the parameters. |
llm* | BaseLanguageModel | Language model to use, assumed to support the OpenAI function-calling API. |
prompt* | BasePromptTemplate | |
output_key | str | Default: 'function' |
output_parser | BaseLLMOutputParser | None | Default: None |
**kwargs | Any | Default: {} |
[Legacy] Create an LLMChain that uses an OpenAI function to get a structured output.
Example:
from typing import Optional
from langchain_classic.chains.openai_functions import create_structured_output_chain
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-0613", temperature=0)
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a world class algorithm for extracting information in structured formats."),
("human", "Use the given format to extract information from the following input: {input}"),
("human", "Tip: Make sure to answer in the correct format"),
]
)
chain = create_structured_output_chain(Dog, model, prompt)
chain.run("Harry was a chubby brown beagle who loved chicken")
# -> Dog(name="Harry", color="brown", fav_food="chicken")
BasePromptTemplate to pass to the model.
The key to use when returning the output in LLMChain.call.
BaseLLMOutputParser to use for parsing model outputs. By default will be inferred from the function types. If pydantic.BaseModels are passed in, then the OutputParser will try to parse outputs using those. Otherwise model outputs will simply be parsed as JSON.
Additional keyword arguments to pass to LLMChain.