Add Input Mapper in run_on_dataset #6894
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| @@ -651,6 +694,7 @@ def run_on_dataset( | |||
| client: Optional[LangChainPlusClient] = None, | |||
| tags: Optional[List[str]] = None, | |||
| run_evaluators: Optional[Sequence[RunEvaluator]] = None, | |||
| input_mapper: Optional[Callable[[Dict], Any]] = None, | |||
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This is a lot of arguments. Also may as well make the other handlers an input mapper at that point?
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If you create a dataset from runs and run the same chain or llm on it later, it usually works great. If you have an agent dataset and want to run a different agent on it, or have more complex schema, it's hard for us to automatically map these values every time. This PR lets you pass in an input_mapper function that converts the example inputs to whatever format your model expects
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If you create a dataset from runs and run the same chain or llm on it later, it usually works great. If you have an agent dataset and want to run a different agent on it, or have more complex schema, it's hard for us to automatically map these values every time. This PR lets you pass in an input_mapper function that converts the example inputs to whatever format your model expects
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If you create a dataset from runs and run the same chain or llm on it later, it usually works great.
If you have an agent dataset and want to run a different agent on it, or have more complex schema, it's hard for us to automatically map these values every time. This PR lets you pass in an input_mapper function that converts the example inputs to whatever format your model expects