adds basic ragas eval

Signed-off-by: Oleg S <97077423+RobotSail@users.noreply.github.com>

Author: RobotSail

requirements.txt

@@ -10,3 +10,5 @@ pandas
 pandas-stubs
 lm-eval>=0.4.4
 httpx
+
+ragas

src/instructlab/eval/ragas.py

@@ -0,0 +1,78 @@
+from .evaluator import Evaluator
+from typing import List, TypedDict
+from ragas.metrics import RubricsScore
+from ragas.evaluation import EvaluationResult, EvaluationDataset, RunConfig, evaluate
+from langchain_community.chat_models import ChatOpenAI
+
+
+class Sample(TypedDict):
+    # question
+    user_input: str
+
+    # model answer
+    response: str
+
+    # golden answer
+    reference: str
+
+
+DEFAULT_RUBRICS = {
+    "score1_description": "The response is completely incorrect, inaccurate, and/or not factual.",
+    "score2_description": "The response is mostly incorrect, inaccurate, and/or not factual.",
+    "score3_description": "The response is somewhat correct, accurate, and/or factual.",
+    "score4_description": "The response is mostly correct, accurate, and factual.",
+    "score5_description": "The response is completely correct, accurate, and factual.",
+}
+
+
+class RagasEvaluator(Evaluator):
+    # most basic implementation, we just assume that the user will bring the existing model responses
+    name = "ragas"
+
+    def __init__(self):
+        pass
+
+    def run(
+        self, dataset: List[Sample], run_config: RunConfig | None = None
+    ) -> EvaluationResult:
+        """
+        Evaluates the quality of model responses against a graded rubric.
+
+        Args:
+            dataset (List[Sample]): List of model questions and ans
+            run_config (RunConfig | None, optional): _description_. Defaults to None.
+
+        Returns:
+            EvaluationResult: _description_
+        """
+        if not run_config:
+            # we set extreme timeout/retry values by default since OpenAI tier-1 rate limits
+            # are horrible and will result in half of our evaluation results being NaN or 0
+            run_config = RunConfig(
+                max_retries=120,
+                max_wait=7200,
+                seed=42,
+                timeout=3600,
+            )
+
+        # we will be using gpt-4o for the foreseeable future, we hardcode this
+        # for consistency of answers
+        input_ds = EvaluationDataset.from_list(dataset)
+
+        # default set of metrics
+        metrics = [
+            RubricsScore(
+                rubrics=DEFAULT_RUBRICS,
+            )
+        ]
+
+        critic_lm = ChatOpenAI(model="gpt-4o")
+        results = evaluate(
+            dataset=input_ds,
+            batch_size=4,
+            run_config=run_config,
+            llm=critic_lm,
+            metrics=metrics,
+            show_progress=True,
+        )
+        return results