feat: aspect critic with reference

docs/concepts/metrics/available_metrics/general_purpose.md

@@ -6,6 +6,8 @@ General purpose evaluation metrics are used to evaluate any given task.
 
 `AspectCritic` is an evaluation metric that can be used to evaluate responses based on predefined aspects in free form natural language. The output of aspect critiques is binary, indicating whether the submission aligns with the defined aspect or not. 
 
+**Without reference**
+
 ### Example
 
 ```python
@@ -15,7 +17,6 @@ from ragas.metrics import AspectCritic
 sample = SingleTurnSample(
     user_input="Where is the Eiffel Tower located?",
     response="The Eiffel Tower is located in Paris.",
-    reference="The Eiffel Tower is located in Paris.",
 )
 
 scorer =  AspectCritic(
@@ -25,6 +26,31 @@ scorer =  AspectCritic(
 scorer.llm = openai_model
 await scorer.single_turn_ascore(sample)
 ```
+
+**With reference**
+
+### Example
+
+```python
+from ragas.dataset_schema import SingleTurnSample
+from ragas.metrics import AspectCriticWithReference
+
+
+sample = SingleTurnSample(
+    user_input="Where is the Eiffel Tower located?",
+    response="The Eiffel Tower is located in Paris.",
+    reference="The Eiffel Tower is located in Paris.",
+)
+
+scorer =  AspectCritic(
+        name="correctness",
+        definition="Is the response factually similar to the reference?",
+    )
+scorer.llm = openai_model
+await scorer.single_turn_ascore(sample)
+
+```
+
 ### How it works
 
 Critics are essentially basic LLM calls using the defined criteria. For example, let's see how the harmfulness critic works:
@@ -39,6 +65,8 @@ Critics are essentially basic LLM calls using the defined criteria. For example,
 - Step 2: The majority vote from the returned verdicts determines the binary output.
     - Output: Yes
 
+
+
 ## Simple Criteria Scoring
 
 Course graned evaluation method is an evaluation metric that can be used to score (integer) responses based on predefined single free form scoring criteria. The output of course grained evaluation is a integer score between the range specified in the criteria.

src/ragas/metrics/_aspect_critic.py

@@ -105,7 +105,10 @@ class AspectCritic(MetricWithLLM, SingleTurnMetric, MultiTurnMetric):
                 "user_input",
                 "response",
                 "retrieved_contexts:optional",
-            }
+            },
+            MetricType.MULTI_TURN: {
+                "user_input",
+            },
         }
     )
     single_turn_prompt: PydanticPrompt = field(
@@ -114,7 +117,9 @@ class AspectCritic(MetricWithLLM, SingleTurnMetric, MultiTurnMetric):
     multi_turn_prompt: PydanticPrompt = field(
         default_factory=lambda: MultiTurnAspectCriticPrompt()
     )
-    definition: str = field(default="", repr=True)
+    definition: str = field(
+        default="check if the response to the user input is correct", repr=True
+    )
     strictness: int = field(default=1, repr=False)
     max_retries: int = 1
 
@@ -183,13 +188,149 @@ async def _multi_turn_ascore(
         self, sample: MultiTurnSample, callbacks: Callbacks
     ) -> float:
         assert self.llm is not None, "LLM is not set"
-        assert sample.reference is not None, "Reference is not set"
 
         interaction = sample.pretty_repr()
-        reference = sample.reference
-        prompt_input = AspectCriticInput(
+        prompt_input = MultiTurnAspectCriticInput(
+            user_input=interaction,
+            criteria=self.definition,
+        )
+        response = await self.multi_turn_prompt.generate(
+            data=prompt_input,
+            llm=self.llm,
+            callbacks=callbacks,
+        )
+        return self._compute_score([response])
+
+
+class AspectCriticInputWithReference(BaseModel):
+    user_input: str = Field(description="The input to the model")
+    response: str = Field(description="The response from the model")
+    reference: str = Field(description="The reference answer for comparison")
+    criteria: str = Field(description="The criteria to evaluate the response")
+
+
+class MultiTurnAspectCriticInputWithReference(BaseModel):
+    user_input: str = Field(description="The input to the model")
+    reference: str = Field(description="The reference answer for comparison")
+    criteria: str = Field(description="The criteria to evaluate the response")
+
+
+class AspectCriticOutputWithReference(BaseModel):
+    reason: str
+    verdict: int
+
+
+class SingleTurnAspectCriticPromptWithReference(
+    PydanticPrompt[AspectCriticInputWithReference, AspectCriticOutputWithReference]
+):
+    instruction = "Given an input, response, and reference. Evaluate the submission only using the given criteria. Use only 'Yes' (1) and 'No' (0) as verdict."
+    input_model = AspectCriticInputWithReference
+    output_model = AspectCriticOutputWithReference
+    examples = [
+        (
+            AspectCriticInputWithReference(
+                user_input="Who was the director of Los Alamos Laboratory?",
+                response="Einstein was the director of Los Alamos Laboratory.",
+                reference="J. Robert Oppenheimer was the director of Los Alamos Laboratory.",
+                criteria="Is the output written in perfect grammar",
+            ),
+            AspectCriticOutputWithReference(
+                reason="The criteria for evaluation is whether the output is written in perfect grammar. In this case, the output is grammatically correct.",
+                verdict=1,
+            ),
+        )
+    ]
+
+
+@dataclass
+class AspectCriticWithReference(AspectCritic):
+    """
+    AspectCriticWithReference judges the submission to give binary results using the criteria specified
+    It uses user_input, response and reference to evaluate the submission.
+
+    Attributes
+    ----------
+    name: str
+        name of the metrics
+    definition: str
+        criteria to judge the submission, example "Is the submission spreading
+        fake information?"
+    strictness: int
+        The number of times self consistency checks is made. Final judgement is
+        made using majority vote.
+    """
+
+    _required_columns: t.Dict[MetricType, t.Set[str]] = field(
+        default_factory=lambda: {
+            MetricType.SINGLE_TURN: {
+                "user_input",
+                "response",
+                "reference",
+                "retrieved_contexts:optional",
+            },
+            MetricType.MULTI_TURN: {
+                "user_input",
+                "reference",
+            },
+        }
+    )
+    definition: str = field(
+        default="check if response is similar to reference", repr=True
+    )
+    single_turn_prompt: PydanticPrompt = field(
+        default_factory=lambda: SingleTurnAspectCriticPromptWithReference()
+    )
+
+    multi_turn_prompt: PydanticPrompt = field(
+        default_factory=lambda: MultiTurnAspectCriticPrompt()
+    )
+
+    async def _ascore(self, row: t.Dict, callbacks: Callbacks) -> float:
+
+        if self.llm is None:
+            raise ValueError("LLM is not set")
+
+        user_input, context, response, reference = (
+            row["user_input"],
+            row.get("retrieved_contexts"),
+            row["response"],
+            row["reference"],
+        )
+
+        if context is not None:
+            if isinstance(context, list):
+                context = "\n".join(context)
+            user_input = f"`user_input`: {user_input} Answer using `retrieved context`: {context}"
+
+        prompt_input = AspectCriticInputWithReference(
+            user_input=user_input,
+            response=response,
+            reference=reference,
+            criteria=self.definition,
+        )
+
+        response = await self.single_turn_prompt.generate(
+            data=prompt_input,
+            llm=self.llm,
+            callbacks=callbacks,
+        )
+
+        return self._compute_score([response])
+
+    async def _multi_turn_ascore(
+        self, sample: MultiTurnSample, callbacks: Callbacks
+    ) -> float:
+
+        if self.llm is None:
+            raise ValueError("LLM is not set")
+
+        if sample.reference is None:
+            raise ValueError("Reference is not set")
+
+        interaction = sample.pretty_repr()
+        prompt_input = MultiTurnAspectCriticInputWithReference(
             user_input=interaction,
-            response=reference,
+            reference=sample.reference,
             criteria=self.definition,
         )
         response = await self.multi_turn_prompt.generate(