new model instance
dict(**kwargs: Any) → Dict¶
Dictionary representation of chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
**kwargs – Keyword arguments passed to default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
classmethod from_colored_object_prompt(llm: BaseLanguageModel, **kwargs: Any) → PALChain[source]¶
Load PAL from colored object prompt.
Parameters
llm (BaseLanguageModel) – The language model to use for generating code.
Returns
An instance of PALChain.
Return type
PALChain
classmethod from_math_prompt(llm: BaseLanguageModel, **kwargs: Any) → PALChain[source]¶
Load PAL from math prompt.
Parameters
llm (BaseLanguageModel) – The language model to use for generating code.
Returns
An instance of PALChain.
Return type
PALChain
classmethod from_orm(obj: Any) → Model¶
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic input schema that depends on which
configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the