feat: add InlineScorer service with LLM client caching

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

webapp/services/inline_scorer.py

@@ -0,0 +1,109 @@
+"""LLM-cached inline RAGAS scorer for the real-time /api/score endpoint.
+
+A module-level InlineScorer singleton caches (llm, embeddings) pairs keyed by
+(judge_model, embedding_model), so repeated Dify Tool calls with the same
+models reuse existing AsyncOpenAI connections instead of creating new ones.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import math
+import threading
+from typing import Any
+
+from rag_eval.compat import ensure_ragas_import_compat
+from rag_eval.metrics.factory import build_models
+from rag_eval.metrics.pipeline import MetricPipeline
+from rag_eval.settings import EvaluationSettings
+from rag_eval.shared.models import NormalizedSample
+
+ensure_ragas_import_compat()
+
+from ragas.metrics.collections import (  # noqa: E402
+    AnswerRelevancy,
+    ContextPrecision,
+    ContextRecall,
+    FactualCorrectness,
+    Faithfulness,
+    NoiseSensitivity,
+    SemanticSimilarity,
+)
+
+
+def _build_metric_instances(metrics: list[str], llm: Any, embeddings: Any) -> dict[str, Any]:
+    """Instantiate only the RAGAS metric objects requested."""
+    registry: dict[str, Any] = {
+        "faithfulness": Faithfulness(llm=llm),
+        "answer_relevancy": AnswerRelevancy(llm=llm, embeddings=embeddings),
+        "context_recall": ContextRecall(llm=llm),
+        "context_precision": ContextPrecision(llm=llm),
+        "noise_sensitivity": NoiseSensitivity(llm=llm),
+        "factual_correctness": FactualCorrectness(llm=llm),
+        "semantic_similarity": SemanticSimilarity(embeddings=embeddings),
+    }
+    return {name: registry[name] for name in metrics if name in registry}
+
+
+class InlineScorer:
+    """Thread-safe single-sample RAGAS scorer with LLM client caching."""
+
+    def __init__(self) -> None:
+        """Initialize the scorer cache and synchronization primitives."""
+        # Cache keyed by (judge_model, embedding_model) -> (llm, embeddings)
+        self._model_cache: dict[tuple[str, str], tuple[Any, Any]] = {}
+        self._lock = threading.Lock()
+
+    def _get_models(
+        self,
+        judge_model: str,
+        embedding_model: str,
+        settings: EvaluationSettings,
+    ) -> tuple[Any, Any]:
+        """Return cached LLM/embedding clients, building them on first use."""
+        cache_key = (judge_model, embedding_model)
+        with self._lock:
+            if cache_key not in self._model_cache:
+                llm, embeddings = build_models(judge_model, embedding_model, settings)
+                self._model_cache[cache_key] = (llm, embeddings)
+            return self._model_cache[cache_key]
+
+    def score(
+        self,
+        question: str,
+        answer: str,
+        contexts: list[str],
+        ground_truth: str | None,
+        metrics: list[str],
+        judge_model: str,
+        embedding_model: str,
+        settings: EvaluationSettings,
+    ) -> dict[str, float | None]:
+        """Score one sample synchronously and return {metric_name: score | None}."""
+        llm, embeddings = self._get_models(judge_model, embedding_model, settings)
+        metric_instances = _build_metric_instances(metrics, llm, embeddings)
+
+        pipeline = MetricPipeline(
+            metrics=metric_instances,
+            metric_timeout_seconds=settings.ragas_metric_timeout_seconds,
+        )
+
+        sample = NormalizedSample(
+            sample_id="inline-score",
+            question=question,
+            answer=answer,
+            contexts=contexts,
+            ground_truth=ground_truth or "",
+        )
+
+        metric_score = asyncio.run(pipeline.score_sample(sample))
+
+        # Convert NaN and Inf into None for clean JSON output.
+        return {
+            name: (None if math.isnan(value) or math.isinf(value) else round(value, 4))
+            for name, value in metric_score.metrics.items()
+        }
+
+
+# Module-level singleton shared by FastAPI routes.
+inline_scorer = InlineScorer()