input (str) – The input to the agent.
reference (Optional[str]) – The reference answer.
Returns
The evaluation result.
Return type
dict
classmethod from_llm(llm: BaseLanguageModel, agent_tools: Optional[Sequence[BaseTool]] = None, output_parser: Optional[TrajectoryOutputParser] = None, **kwargs: Any) → TrajectoryEvalChain[source]¶
Create a TrajectoryEvalChain object from a language model chain.
Parameters
llm (BaseChatModel) – The language model chain.
agent_tools (Optional[Sequence[BaseTool]]) – A list of tools
available to the agent.
output_parser (Optional[TrajectoryOutputParser]) – The output parser
used to parse the chain output into a score.
Returns
The TrajectoryEvalChain object.
Return type
TrajectoryEvalChain
classmethod from_orm(obj: Any) → Model¶
static get_agent_trajectory(steps: Union[str, Sequence[Tuple[AgentAction, str]]]) → str[source]¶
Get the agent trajectory as a formatted string.
Parameters
steps (Union[str, List[Tuple[AgentAction, str]]]) – The agent trajectory.
Returns
The formatted agent trajectory.
Return type
str
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
namespace is [“langchain”, “llms”, “openai”]
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
classmethod is_lc_serializable() → bool¶
Is this class serializable?