Source code for langchain.chains.openai_functions.tagging
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers.openai_functions import (
JsonOutputFunctionsParser,
PydanticOutputFunctionsParser,
)
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import _convert_schema, get_llm_kwargs
def _get_tagging_function(schema: dict) -> dict:
return {
"name": "information_extraction",
"description": "Extracts the relevant information from the passage.",
"parameters": _convert_schema(schema),
}
_TAGGING_TEMPLATE = """Extract the desired information from the following passage.
Only extract the properties mentioned in the 'information_extraction' function.
Passage:
{input}
"""
[docs]@deprecated(
since="0.2.13",
message=(
"LangChain has introduced a method called `with_structured_output` that "
"is available on ChatModels capable of tool calling. "
"See API reference for this function for replacement: "
"<https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.tagging.create_tagging_chain.html> " # noqa: E501
"You can read more about `with_structured_output` here: "
"<https://python.langchain.com/v0.2/docs/how_to/structured_output/>. "
"If you notice other issues, please provide "
"feedback here: "
"<https://github.com/langchain-ai/langchain/discussions/18154>"
),
removal="1.0",
)
def create_tagging_chain(
schema: dict,
llm: BaseLanguageModel,
prompt: Optional[ChatPromptTemplate] = None,
**kwargs: Any,
) -> Chain:
"""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:
.. code-block:: python
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 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_llm = model.with_structured_output(Joke)
structured_llm.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://python.langchain.com/v0.2/docs/how_to/structured_output/
Args:
schema: The schema of the entities to extract.
llm: The language model to use.
Returns:
Chain (LLMChain) that can be used to extract information from a passage.
"""
function = _get_tagging_function(schema)
prompt = prompt or ChatPromptTemplate.from_template(_TAGGING_TEMPLATE)
output_parser = JsonOutputFunctionsParser()
llm_kwargs = get_llm_kwargs(function)
chain = LLMChain(
llm=llm,
prompt=prompt,
llm_kwargs=llm_kwargs,
output_parser=output_parser,
**kwargs,
)
return chain
[docs]@deprecated(
since="0.2.13",
message=(
"LangChain has introduced a method called `with_structured_output` that "
"is available on ChatModels capable of tool calling. "
"See API reference for this function for replacement: "
"<https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.tagging.create_tagging_chain_pydantic.html> " # noqa: E501
"You can read more about `with_structured_output` here: "
"<https://python.langchain.com/v0.2/docs/how_to/structured_output/>. "
"If you notice other issues, please provide "
"feedback here: "
"<https://github.com/langchain-ai/langchain/discussions/18154>"
),
removal="1.0",
)
def create_tagging_chain_pydantic(
pydantic_schema: Any,
llm: BaseLanguageModel,
prompt: Optional[ChatPromptTemplate] = None,
**kwargs: Any,
) -> Chain:
"""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:
.. code-block:: python
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(
"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://python.langchain.com/v0.2/docs/how_to/structured_output/
Args:
pydantic_schema: The pydantic schema of the entities to extract.
llm: The language model to use.
Returns:
Chain (LLMChain) that can be used to extract information from a passage.
"""
openai_schema = pydantic_schema.schema()
function = _get_tagging_function(openai_schema)
prompt = prompt or ChatPromptTemplate.from_template(_TAGGING_TEMPLATE)
output_parser = PydanticOutputFunctionsParser(pydantic_schema=pydantic_schema)
llm_kwargs = get_llm_kwargs(function)
chain = LLMChain(
llm=llm,
prompt=prompt,
llm_kwargs=llm_kwargs,
output_parser=output_parser,
**kwargs,
)
return chain
