from typing import (
Dict,
Optional,
Sequence,
Type,
Union,
)
import google.cloud.aiplatform_v1beta1.types as gapic
from langchain_core.output_parsers import (
BaseGenerationOutputParser,
BaseOutputParser,
StrOutputParser,
)
from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import Runnable
from langchain_google_vertexai.functions_utils import PydanticFunctionsOutputParser
[docs]def get_output_parser(
functions: Sequence[Type[BaseModel]],
) -> Union[BaseOutputParser, BaseGenerationOutputParser]:
"""Get the appropriate function output parser given the user functions.
Args:
functions: Sequence where element is a dictionary, a pydantic.BaseModel class,
or a Python function. If a dictionary is passed in, it is assumed to
already be a valid OpenAI function.
Returns:
A PydanticFunctionsOutputParser
"""
function_names = [f.__name__ for f in functions]
if len(functions) > 1:
pydantic_schema: Union[Dict, Type[BaseModel]] = {
name: fn for name, fn in zip(function_names, functions)
}
else:
pydantic_schema = functions[0]
output_parser: Union[
BaseOutputParser, BaseGenerationOutputParser
] = PydanticFunctionsOutputParser(pydantic_schema=pydantic_schema)
return output_parser
def _create_structured_runnable_extra_step(
functions: Sequence[Type[BaseModel]],
llm: Runnable,
*,
prompt: Optional[BasePromptTemplate] = None,
) -> Runnable:
names = [schema.schema()["title"] for schema in functions]
if hasattr(llm, "is_gemini_advanced") and llm._is_gemini_advanced: # type: ignore
llm_with_functions = llm.bind(
functions=functions,
tool_config={
"function_calling_config": {
"mode": gapic.FunctionCallingConfig.Mode.ANY,
"allowed_function_names": names,
}
},
)
else:
llm_with_functions = llm.bind(
functions=functions,
)
parsing_prompt = ChatPromptTemplate.from_template(
"You are a world class algorithm for recording entities.\nMake calls "
"to the relevant function to record the entities in the following "
"input:\n{output}\nTip: Make sure to answer in the correct format."
)
output_parser = get_output_parser(functions)
if prompt:
initial_chain = (
prompt | llm | StrOutputParser() | parsing_prompt | llm_with_functions
)
else:
initial_chain = parsing_prompt | llm_with_functions
return initial_chain | output_parser
[docs]def create_structured_runnable(
function: Union[Type[BaseModel], Sequence[Type[BaseModel]]],
llm: Runnable,
*,
prompt: Optional[BasePromptTemplate] = None,
use_extra_step: bool = False,
) -> Runnable:
"""Create a runnable sequence that uses OpenAI functions.
Args:
function: Either a single pydantic.BaseModel class or a sequence
of pydantic.BaseModels classes.
For best results, pydantic.BaseModels
should have descriptions of the parameters.
llm: Language model to use,
assumed to support the Google Vertex function-calling API.
prompt: BasePromptTemplate to pass to the model.
use_extra_step: whether to make an extra step to parse output into a function
Returns:
A runnable sequence that will pass in the given functions to the model when run.
Example:
.. code-block:: python
from typing import Optional
from langchain_google_vertexai import ChatVertexAI, create_structured_runnable
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
class RecordPerson(BaseModel):
\"\"\"Record some identifying information about a person.\"\"\"
name: str = Field(..., description="The person's name")
age: int = Field(..., description="The person's age")
fav_food: Optional[str] = Field(None, description="The person's favorite food")
class RecordDog(BaseModel):
\"\"\"Record some identifying information about a dog.\"\"\"
name: str = Field(..., description="The dog's name")
color: str = Field(..., description="The dog's color")
fav_food: Optional[str] = Field(None, description="The dog's favorite food")
llm = ChatVertexAI(model_name="gemini-pro")
prompt = ChatPromptTemplate.from_template(\"\"\"
You are a world class algorithm for recording entities.
Make calls to the relevant function to record the entities in the following input: {input}
Tip: Make sure to answer in the correct format\"\"\"
)
chain = create_structured_runnable([RecordPerson, RecordDog], llm, prompt=prompt)
chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"})
# -> RecordDog(name="Harry", color="brown", fav_food="chicken")
""" # noqa: E501
if not function:
raise ValueError("Need to pass in at least one function. Received zero.")
functions = function if isinstance(function, Sequence) else [function]
if use_extra_step:
return _create_structured_runnable_extra_step(
functions=functions, llm=llm, prompt=prompt
)
output_parser = get_output_parser(functions)
llm_with_functions = llm.bind(functions=functions)
if prompt is None:
initial_chain = llm_with_functions
else:
initial_chain = prompt | llm_with_functions
return initial_chain | output_parser
