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: "
"<https://python.langchain.com/docs/modules/model_io/chat/structured_output/>. "
"Please follow our extraction use case documentation for more guidelines"
"on how to do information extraction with LLMs."
"<https://python.langchain.com/docs/use_cases/extraction/>. "
"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:"
"<https://github.com/langchain-ai/langchain/discussions/18154>"
),
removal="1.0",
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