Source code for langchain.chains.openai_tools.extraction

from typing import List, Type, Union

from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers.openai_tools import PydanticToolsParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import Runnable
from langchain_core.utils.function_calling import convert_pydantic_to_openai_function

_EXTRACTION_TEMPLATE = """Extract 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."""  # noqa: E501

[docs]@deprecated( since="0.1.14", message=( "LangChain has introduced a method called `with_structured_output` that" "is available on ChatModels capable of tool calling." "You can read more about the method here: " "" "Please follow our extraction use case documentation for more guidelines" "on 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:" "" ), removal="0.3.0", alternative=( """ from langchain_core.pydantic_v1 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.") """ ), ) def create_extraction_chain_pydantic( pydantic_schemas: Union[List[Type[BaseModel]], Type[BaseModel]], llm: BaseLanguageModel, system_message: str = _EXTRACTION_TEMPLATE, ) -> Runnable: """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. """ if not isinstance(pydantic_schemas, list): pydantic_schemas = [pydantic_schemas] prompt = ChatPromptTemplate.from_messages( [("system", system_message), ("user", "{input}")] ) functions = [convert_pydantic_to_openai_function(p) for p in pydantic_schemas] tools = [{"type": "function", "function": d} for d in functions] model = llm.bind(tools=tools) chain = prompt | model | PydanticToolsParser(tools=pydantic_schemas) return chain