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