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Allow not enforcing function usage when a single function is passed to openai function executable (#14308)

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- **Description:** allows not enforcing function usage when a single
function is passed to an openAI function executable (or corresponding
legacy chain). This is a desired feature in the case where the model
does not have enough information to call a function, and needs to get
back to the user.
  - **Issue:** N/A
  - **Dependencies:** N/A
  - **Tag maintainer:** N/A
This commit is contained in:
Karim Assi 2023-12-06 00:56:31 +01:00 committed by GitHub
parent d22c13ec48
commit 9401539e43
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1 changed files with 10 additions and 2 deletions
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12
libs/langchain/langchain/chains/openai_functions/base.py
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@ -204,6 +204,7 @@ def create_openai_fn_runnable(
llm: Runnable,
prompt: BasePromptTemplate,
*,
enforce_single_function_usage: bool = True,
output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None,
**kwargs: Any,
) -> Runnable:
@ -222,6 +223,9 @@ def create_openai_fn_runnable(
pydantic.BaseModels for arguments.
llm: Language model to use, assumed to support the OpenAI function-calling API.
prompt: BasePromptTemplate to pass to the model.
enforce_single_function_usage: only used if a single function is passed in. If
True, then the model will be forced to use the given function. If False,
then the model will be given the option to use the given function or not.
output_parser: BaseLLMOutputParser to use for parsing model outputs. By default
will be inferred from the function types. If pydantic.BaseModels are passed
in, then the OutputParser will try to parse outputs using those. Otherwise
@ -276,7 +280,7 @@ def create_openai_fn_runnable(
raise ValueError("Need to pass in at least one function. Received zero.")
openai_functions = [convert_to_openai_function(f) for f in functions]
llm_kwargs: Dict[str, Any] = {"functions": openai_functions, **kwargs}
if len(openai_functions) == 1:
if len(openai_functions) == 1 and enforce_single_function_usage:
llm_kwargs["function_call"] = {"name": openai_functions[0]["name"]}
output_parser = output_parser or get_openai_output_parser(functions)
return prompt | llm.bind(**llm_kwargs) | output_parser
@ -373,6 +377,7 @@ def create_openai_fn_chain(
llm: BaseLanguageModel,
prompt: BasePromptTemplate,
*,
enforce_single_function_usage: bool = True,
output_key: str = "function",
output_parser: Optional[BaseLLMOutputParser] = None,
**kwargs: Any,
@ -392,6 +397,9 @@ def create_openai_fn_chain(
pydantic.BaseModels for arguments.
llm: Language model to use, assumed to support the OpenAI function-calling API.
prompt: BasePromptTemplate to pass to the model.
enforce_single_function_usage: only used if a single function is passed in. If
True, then the model will be forced to use the given function. If False,
then the model will be given the option to use the given function or not.
output_key: The key to use when returning the output in LLMChain.__call__.
output_parser: BaseLLMOutputParser to use for parsing model outputs. By default
will be inferred from the function types. If pydantic.BaseModels are passed
@ -451,7 +459,7 @@ def create_openai_fn_chain(
llm_kwargs: Dict[str, Any] = {
"functions": openai_functions,
}
if len(openai_functions) == 1:
if len(openai_functions) == 1 and enforce_single_function_usage:
llm_kwargs["function_call"] = {"name": openai_functions[0]["name"]}
llm_chain = LLMChain(
llm=llm,