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    Pythonlangchain-classicchainsopenai_functionstagging
    Module●Since v1.0

    tagging

    Functions

    function
    get_llm_kwargs

    Return the kwargs for the LLMChain constructor.

    deprecatedfunction
    create_tagging_chain

    Create tagging chain from schema.

    Create a chain that extracts information from a passage based on a schema.

    This function is deprecated. Please use with_structured_output instead. See example usage below:

    from typing_extensions import Annotated, TypedDict
    from langchain_anthropic import ChatAnthropic
    
    class Joke(TypedDict):
        """Tagged joke."""
    
        setup: Annotated[str, ..., "The setup of the joke"]
        punchline: Annotated[str, ..., "The punchline of the joke"]
    
    # Or any other chat model that supports tools.
    # Please reference 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-haiku-20240307", temperature=0)
    structured_model = model.with_structured_output(Joke)
    structured_model.invoke(
        "Why did the cat cross the road? To get to the other "
        "side... and then lay down in the middle of it!"
    )

    Read more here: https://docs.langchain.com/oss/python/langchain/models#structured-outputs

    deprecatedfunction
    create_tagging_chain_pydantic

    Create tagging chain from Pydantic schema.

    Create a chain that extracts information from a passage based on a Pydantic schema.

    This function is deprecated. Please use with_structured_output instead. See example usage below:

    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 the documentation of structured_output
    # to see an up to date list of which models support
    # with_structured_output.
    model = ChatAnthropic(model="claude-opus-4-1-20250805", temperature=0)
    structured_model = model.with_structured_output(Joke)
    structured_model.invoke(
        "Why did the cat cross the road? To get to the other "
        "side... and then lay down in the middle of it!"
    )

    Read more here: https://docs.langchain.com/oss/python/langchain/models#structured-outputs

    Classes

    class
    Chain

    Abstract base class for creating structured sequences of calls to components.

    Chains should be used to encode a sequence of calls to components like models, document retrievers, other chains, etc., and provide a simple interface to this sequence.

    deprecatedclass
    LLMChain

    Chain to run queries against LLMs.

    This class is deprecated. See below for an example implementation using LangChain runnables:

    from langchain_core.output_parsers import StrOutputParser
    from langchain_core.prompts import PromptTemplate
    from langchain_openai import OpenAI
    
    prompt_template = "Tell me a {adjective} joke"
    prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
    model = OpenAI()
    chain = prompt | model | StrOutputParser()
    
    chain.invoke("your adjective here")
    View source on GitHub