Update Key Check (#8948)

In eval loop. It needn't be done unless you are creating the
corresponding evaluators

libs/langchain/langchain/smith/evaluation/runner_utils.py

@@ -502,6 +502,18 @@ def _construct_run_evaluator(
     return run_evaluator
 
 
+def _get_keys(
+    config: RunEvalConfig,
+    run_inputs: Optional[List[str]],
+    run_outputs: Optional[List[str]],
+    example_outputs: Optional[List[str]],
+) -> Tuple[Optional[str], Optional[str], Optional[str]]:
+    input_key = _determine_input_key(config, run_inputs)
+    prediction_key = _determine_prediction_key(config, run_outputs)
+    reference_key = _determine_reference_key(config, example_outputs)
+    return input_key, prediction_key, reference_key
+
+
 def _load_run_evaluators(
     config: RunEvalConfig,
     run_type: str,
@@ -521,9 +533,13 @@ def _load_run_evaluators(
     """
     eval_llm = config.eval_llm or ChatOpenAI(model="gpt-4", temperature=0.0)
     run_evaluators = []
-    input_key = _determine_input_key(config, run_inputs)
-    prediction_key = _determine_prediction_key(config, run_outputs)
-    reference_key = _determine_reference_key(config, example_outputs)
+    input_key, prediction_key, reference_key = None, None, None
+    if config.evaluators or any(
+        [isinstance(e, EvaluatorType) for e in config.evaluators]
+    ):
+        input_key, prediction_key, reference_key = _get_keys(
+            config, run_inputs, run_outputs, example_outputs
+        )
     for eval_config in config.evaluators:
         run_evaluator = _construct_run_evaluator(
             eval_config,
@@ -1074,15 +1090,15 @@ def _run_on_examples(
         A dictionary mapping example ids to the model outputs.
     """
     results: Dict[str, Any] = {}
-    llm_or_chain_factory = _wrap_in_chain_factory(llm_or_chain_factory)
-    project_name = _get_project_name(project_name, llm_or_chain_factory)
+    wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory)
+    project_name = _get_project_name(project_name, wrapped_model)
     tracer = LangChainTracer(
         project_name=project_name, client=client, use_threading=False
     )
     run_evaluators, examples = _setup_evaluation(
-        llm_or_chain_factory, examples, evaluation, data_type
+        wrapped_model, examples, evaluation, data_type
     )
-    examples = _validate_example_inputs(examples, llm_or_chain_factory, input_mapper)
+    examples = _validate_example_inputs(examples, wrapped_model, input_mapper)
     evalution_handler = EvaluatorCallbackHandler(
         evaluators=run_evaluators or [],
         client=client,
@@ -1091,7 +1107,7 @@ def _run_on_examples(
     for i, example in enumerate(examples):
         result = _run_llm_or_chain(
             example,
-            llm_or_chain_factory,
+            wrapped_model,
             num_repetitions,
             tags=tags,
             callbacks=callbacks,
@@ -1114,8 +1130,8 @@ def _prepare_eval_run(
     llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
     project_name: Optional[str],
 ) -> Tuple[MCF, str, Dataset, Iterator[Example]]:
-    llm_or_chain_factory = _wrap_in_chain_factory(llm_or_chain_factory, dataset_name)
-    project_name = _get_project_name(project_name, llm_or_chain_factory)
+    wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory, dataset_name)
+    project_name = _get_project_name(project_name, wrapped_model)
     try:
         project = client.create_project(project_name)
     except ValueError as e:
@@ -1130,7 +1146,7 @@ def _prepare_eval_run(
     )
     dataset = client.read_dataset(dataset_name=dataset_name)
     examples = client.list_examples(dataset_id=str(dataset.id))
-    return llm_or_chain_factory, project_name, dataset, examples
+    return wrapped_model, project_name, dataset, examples
 
 
 async def arun_on_dataset(
@@ -1256,13 +1272,13 @@ async def arun_on_dataset(
             evaluation=evaluation_config,
         )
     """  # noqa: E501
-    llm_or_chain_factory, project_name, dataset, examples = _prepare_eval_run(
+    wrapped_model, project_name, dataset, examples = _prepare_eval_run(
         client, dataset_name, llm_or_chain_factory, project_name
     )
     results = await _arun_on_examples(
         client,
         examples,
-        llm_or_chain_factory,
+        wrapped_model,
         concurrency_level=concurrency_level,
         num_repetitions=num_repetitions,
         project_name=project_name,
@@ -1423,14 +1439,14 @@ def run_on_dataset(
             evaluation=evaluation_config,
         )
     """  # noqa: E501
-    llm_or_chain_factory, project_name, dataset, examples = _prepare_eval_run(
+    wrapped_model, project_name, dataset, examples = _prepare_eval_run(
         client, dataset_name, llm_or_chain_factory, project_name
     )
     if concurrency_level in (0, 1):
         results = _run_on_examples(
             client,
             examples,
-            llm_or_chain_factory,
+            wrapped_model,
             num_repetitions=num_repetitions,
             project_name=project_name,
             verbose=verbose,
@@ -1444,7 +1460,7 @@ def run_on_dataset(
         coro = _arun_on_examples(
             client,
             examples,
-            llm_or_chain_factory,
+            wrapped_model,
             concurrency_level=concurrency_level,
             num_repetitions=num_repetitions,
             project_name=project_name,