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deprecated)BaseLanguageModel)PydanticToolsParser)ChatPromptTemplate)Runnable)#convert_pydantic_to_openai_function) BaseModelzExtract and save the relevant entities mentioned in the following passage together with their properties.
If a property is not present and is not required in the function parameters, do not include it in the output.z0.1.14a| LangChain has introduced a method called `with_structured_output` thatis available on ChatModels capable of tool calling.You can read more about the method here: . Please follow our extraction use case documentation for more guidelineson how to do information extraction with LLMs.. with_structured_output does not currently support a list of pydantic schemas. If this is a blocker or if you notice other issues, please provide feedback here:z1.0a)
from pydantic import BaseModel, Field
from langchain_anthropic import ChatAnthropic
class Joke(BaseModel):
setup: str = Field(description="The setup of the joke")
punchline: str = Field(description="The punchline to the joke")
# Or any other chat model that supports tools.
# Please reference to to the documentation of structured_output
# to see an up to date list of which models support
# with_structured_output.
model = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
structured_llm = model.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats.
Make sure to call the Joke function.")
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| z S c c}w c c}w )a? Creates a chain that extracts information from a passage.
Args:
pydantic_schemas: The schema of the entities to extract.
llm: The language model to use.
system_message: The system message to use for extraction.
Returns:
A runnable that extracts information from a passage.
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