ContextQAEvalChain

libs/langchain/langchain/evaluation/qa/eval_chain.py

@@ -13,7 +13,6 @@ from langchain_core.prompts import PromptTemplate
 from langchain_core.prompts.base import BasePromptTemplate
 from langchain_core.runnables import RunnableConfig
 
-from langchain.chains.llm import LLMChain
 from langchain.evaluation.qa.eval_prompt import CONTEXT_PROMPT, COT_PROMPT, PROMPT
 from langchain.evaluation.schema import LLMEvalChain, StringEvaluator
 from langchain.schema import RUN_KEY
@@ -227,6 +226,7 @@ class QAEvalChain(StringEvaluator, LLMEvalChain):
                 "result": prediction,
             },
             config=config,
+            include_run_info=include_run_info,
         )
         return self._prepare_output(result)
 
@@ -251,13 +251,20 @@ class QAEvalChain(StringEvaluator, LLMEvalChain):
                 "result": prediction,
             },
             config=config,
+            include_run_info=include_run_info,
         )
         return self._prepare_output(result)
 
 
-class ContextQAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
+class ContextQAEvalChain(StringEvaluator, LLMEvalChain):
     """LLM Chain for evaluating QA w/o GT based on context"""
 
+    output_key: str = "text"  #: :meta private:
+    llm: BaseLanguageModel
+    """The language model to use for scoring."""
+    prompt: BasePromptTemplate
+    """The prompt to use for scoring."""
+
     @classmethod
     def is_lc_serializable(cls) -> bool:
         return False
@@ -272,6 +279,22 @@ class ContextQAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
         """Whether the chain requires an input string."""
         return True
 
+    @property
+    def input_keys(self) -> List[str]:
+        """Will be whatever keys the prompt expects.
+
+        :meta private:
+        """
+        return self.prompt.input_variables
+
+    @property
+    def output_keys(self) -> List[str]:
+        """Will always return text key.
+
+        :meta private:
+        """
+        return [self.output_key]
+
     class Config:
         extra = "ignore"
 
@@ -288,6 +311,19 @@ class ContextQAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
     def evaluation_name(self) -> str:
         return "Contextual Accuracy"
 
+    def _call(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        if run_manager:
+            config = RunnableConfig(callbacks=run_manager.get_child())
+        else:
+            config = None
+        chain = self.prompt | self.llm | StrOutputParser()
+        response = chain.invoke(inputs, config=config)
+        return {self.output_key: response}
+
     @classmethod
     def from_llm(
         cls,
@@ -333,8 +369,13 @@ class ContextQAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
             }
             for i, example in enumerate(examples)
         ]
+        if callbacks:
+            config = RunnableConfig(callbacks=callbacks)
+        else:
+            config = None
+        outputs = self.batch(inputs, config=config)
 
-        return self.apply(inputs, callbacks=callbacks)
+        return [{self.output_key: output[self.output_key]} for output in outputs]
 
     def _prepare_output(self, result: dict) -> dict:
         parsed_result = _parse_string_eval_output(result[self.output_key])
@@ -352,13 +393,17 @@ class ContextQAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
         include_run_info: bool = False,
         **kwargs: Any,
     ) -> dict:
-        result = self(
+        if callbacks:
+            config = RunnableConfig(callbacks=callbacks)
+        else:
+            config = None
+        result = self.invoke(
             {
                 "query": input,
                 "context": reference,
                 "result": prediction,
             },
-            callbacks=callbacks,
+            config=config,
             include_run_info=include_run_info,
         )
         return self._prepare_output(result)
@@ -373,9 +418,17 @@ class ContextQAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
         include_run_info: bool = False,
         **kwargs: Any,
     ) -> dict:
-        result = await self.acall(
-            inputs={"query": input, "context": reference, "result": prediction},
-            callbacks=callbacks,
+        if callbacks:
+            config = RunnableConfig(callbacks=callbacks)
+        else:
+            config = None
+        result = await self.ainvoke(
+            {
+                "query": input,
+                "context": reference,
+                "result": prediction,
+            },
+            config=config,
             include_run_info=include_run_info,
         )
         return self._prepare_output(result)