Explicitly list requires_reference in function (#7357)

langchain/evaluation/criteria/eval_chain.py

@@ -251,17 +251,24 @@ class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
                 requires_reference=True,
             )
         """
+        expected_input_vars = {"input", "output", "criteria"}
         if prompt is None:
             if requires_reference:
                 prompt = PROMPT_WITH_REFERENCES
             else:
                 prompt = PROMPT
+        if requires_reference:
+            expected_input_vars.add("reference")
+        if expected_input_vars != set(prompt.input_variables):
+            raise ValueError(
+                f"Input variables should be {expected_input_vars}, "
+                f"but got {prompt.input_variables}"
+            )
+
         criteria_ = cls.resolve_criteria(criteria)
         criteria_str = " ".join(f"{k}: {v}" for k, v in criteria_.items())
         prompt_ = prompt.partial(criteria=criteria_str)
-        return cls(
+        return cls(llm=llm, prompt=prompt_, **kwargs)
-            llm=llm, prompt=prompt_, requires_reference=requires_reference, **kwargs
-        )
 
     def _get_eval_input(
         self,

langchain/evaluation/run_evaluators/implementations.py

@@ -14,7 +14,6 @@ from langchain.evaluation.criteria.eval_chain import (
     CriteriaEvalChain,
     CriteriaResultOutputParser,
 )
-from langchain.evaluation.criteria.prompt import PROMPT as CRITERIA_PROMPT
 from langchain.evaluation.qa.eval_chain import QAEvalChain
 from langchain.evaluation.qa.eval_prompt import PROMPT as QA_DEFAULT_PROMPT
 from langchain.evaluation.qa.eval_prompt import SQL_PROMPT
@@ -152,8 +151,9 @@ def get_criteria_evaluator(
     *,
     input_key: str = "input",
     prediction_key: str = "output",
-    prompt: BasePromptTemplate = CRITERIA_PROMPT,
+    prompt: Optional[BasePromptTemplate] = None,
     evaluation_name: Optional[str] = None,
+    requires_reference: bool = False,
     **kwargs: Any,
 ) -> RunEvaluatorChain:
     """Get an eval chain for grading a model's response against a map of criteria."""
@@ -174,7 +174,11 @@ def get_criteria_evaluator(
     )
     tags = kwargs.pop("tags", [])
     eval_chain = CriteriaEvalChain.from_llm(
-        llm=llm, criteria=criteria_, prompt=prompt, **kwargs
+        llm=llm,
+        criteria=criteria_,
+        prompt=prompt,
+        requires_reference=requires_reference,
+        **kwargs,
     )
     return RunEvaluatorChain(
         eval_chain=eval_chain,