eval.ragas_bridge

chunktuner.eval.ragas_bridge

RAGAS Faithfulness + Answer Relevancy (optional chunktuner[ragas] + compatible stack).

RagasBridge 

RagasBridge(llm_client=None)

Thin adapter around RAGAS faithfulness and answer_relevancy (optional extra).

Source code in src/chunktuner/eval/ragas_bridge.py 
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def __init__(self, llm_client: object | None = None):
    self.llm_client = llm_client

is_configured staticmethod 

is_configured()

Return True if an API key RAGAS/LiteLLM typically reads is set.

Includes CHUNKTUNER_API_KEY for the same OpenAI-compatible routing as the rest of chunktuner. RAGAS still does not support local-only (LM Studio / Ollama) judge flows without extra LangChain wiring — a key here means provider-style auth, not api_base alone.

Source code in src/chunktuner/eval/ragas_bridge.py 
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@staticmethod
def is_configured() -> bool:
    """Return True if an API key RAGAS/LiteLLM typically reads is set.

    Includes ``CHUNKTUNER_API_KEY`` for the same OpenAI-compatible routing as the
    rest of chunktuner. RAGAS still does not support local-only (LM Studio / Ollama)
    judge flows without extra LangChain wiring — a key here means provider-style
    auth, not ``api_base`` alone.
    """
    return any(os.environ.get(k) for k in _RAGAS_LLM_ENV_VARS)

compute 

compute(questions, contexts, answers, ground_truths)

Return averaged metrics, or sentinel Nones if RAGAS cannot run.

Source code in src/chunktuner/eval/ragas_bridge.py 
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def compute(
    self,
    questions: list[str],
    contexts: list[list[str]],
    answers: list[str],
    ground_truths: list[str],
) -> dict[str, float | None]:
    """Return averaged metrics, or sentinel Nones if RAGAS cannot run."""
    try:
        from datasets import Dataset
        from ragas import evaluate
        from ragas.metrics import answer_relevancy, faithfulness
    except ImportError as exc:  # pragma: no cover
        logger.warning("RAGAS not installed (%s). Install: uv add 'chunktuner[ragas]'", exc)
        return dict(_UNAVAILABLE)
    except Exception as exc:  # pragma: no cover
        logger.warning("RAGAS import failed (%s); skipping generation metrics", exc)
        return dict(_UNAVAILABLE)

    try:
        rows = []
        for q, ctx, ans, gt in zip(questions, contexts, answers, ground_truths, strict=True):
            rows.append(
                {
                    "user_input": q,
                    "retrieved_contexts": ctx,
                    "response": ans,
                    "reference": gt,
                }
            )
        ds = Dataset.from_list(rows)
        result = evaluate(ds, metrics=[faithfulness, answer_relevancy])
        df = result.to_pandas()
        out: dict[str, float | None] = dict(_UNAVAILABLE)
        if "faithfulness" in df.columns:
            out["faithfulness"] = float(df["faithfulness"].mean())
        if "answer_relevancy" in df.columns:
            out["answer_relevancy"] = float(df["answer_relevancy"].mean())
        return out
    except Exception as exc:  # pragma: no cover
        logger.warning("RAGAS computation failed: %s", exc)
        return dict(_UNAVAILABLE)