Add RAGAS initial version

supporting/RAGAS_Service/ragas_service.py

@@ -0,0 +1,53 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from ragas import evaluate
+from ragas.metrics import faithfulness, answer_relevancy, context_precision
+from datasets import Dataset
+from pathlib import Path
+from dotenv import load_dotenv
+import os
+import math
+
+app = FastAPI()
+
+ENV_PATH = Path("../../agent/.env")
+
+load_dotenv(dotenv_path=ENV_PATH)
+
+def sanitize(obj):
+    if isinstance(obj, float) and math.isnan(obj):
+        return None
+    if isinstance(obj, dict):
+        return {k: sanitize(v) for k, v in obj.items()}
+    if isinstance(obj, list):
+        return [sanitize(v) for v in obj]
+    return obj
+
+if not os.getenv("OPENAI_API_KEY"):
+    raise RuntimeError("OPENAI_API_KEY not found")
+
+class EvalRequest(BaseModel):
+    question: str
+    answer: str
+    contexts: list[str]
+
+@app.post("/evaluate")
+def evaluate_rag(req: EvalRequest):
+    dataset = Dataset.from_dict({
+        "question": [req.question],
+        "answer": [req.answer],
+        "contexts": [req.contexts],
+        # "ground_truth": []
+    })
+
+    result = evaluate(
+        dataset,
+        metrics=[
+            faithfulness, 
+            answer_relevancy, 
+            context_precision
+        ],
+    )
+
+    raw = result.to_pandas().to_dict(orient="records")[0]
+    return sanitize(raw)