The distance metric to use for comparing the embeddings.
Optional embeddingThe embedding objects to vectorize the outputs.
Optional evaluationThe name of the evaluation.
Optional memoryOptional skipOptional skipOptional config: any[]Use .batch() instead. Will be removed in 0.2.0.
This feature is deprecated and will be removed in the future.
It is not recommended for use.
Call the chain on all inputs in the list
Optional config: anyOptional tags: string[]Use .invoke() instead. Will be removed in 0.2.0.
Run the core logic of this chain and add to output if desired.
Wraps _call and handles memory.
Check if the evaluation arguments are valid.
Optional reference: stringThe reference label.
Optional input: stringThe input string.
If the evaluator requires an input string but none is provided, or if the evaluator requires a reference label but none is provided.
Evaluate Chain or LLM output, based on optional input and label.
Optional config: anyThe evaluation results containing the score or value. It is recommended that the dictionary contain the following keys:
Invoke the chain with the provided input and returns the output.
Input values for the chain run.
Optional config: anyOptional configuration for the Runnable.
Promise that resolves with the output of the chain run.
Return a json-like object representing this chain.
Static deserializeLoad a chain from a json-like object describing it.
Generated using TypeDoc
Use embedding distances to score semantic difference between a prediction and reference.