Create outputs from response.
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, ...}
evaluate_string_pairs(*, prediction: str, prediction_b: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict¶
Evaluate the output string pairs.
Parameters
prediction (str) – The output string from the first model.
prediction_b (str) – The output string from the second model.
reference (Optional[str], optional) – The expected output / reference string.
input (Optional[str], optional) – The input string.
**kwargs – Additional keyword arguments, such as callbacks and optional reference strings.
Returns
A dictionary containing the preference, scores, and/or other information.
Return type
dict
classmethod from_llm(llm: BaseLanguageModel, *, prompt: Optional[PromptTemplate] = None, criteria: Optional[Union[Mapping[str, str], Criteria, ConstitutionalPrinciple, str]] = None, **kwargs: Any) → PairwiseStringEvalChain[source]¶
Initialize the LabeledPairwiseStringEvalChain from an LLM.
Parameters
llm (BaseLanguageModel) – The LLM to use.
prompt (PromptTemplate, optional) – The prompt to use.
criteria (Union[CRITERIA_TYPE, str], optional) – The criteria to use.
**kwargs (Any) – Additional keyword arguments.
Returns
The initialized LabeledPairwiseStringEvalChain.
Return type
LabeledPairwiseStringEvalChain