hallucination-gate

RAG quality system + conservative release gate for RAG and fine-tuned LLMs.

• Eval: claim-level faithfulness / relevancy / context metrics
• Retrieval: hit@k, recall@k, MRR, nDCG@k
• Regression: save baseline → diff → fail CI
• Latency budget: p50 / p95 / p99 / max ceilings
• Modes: ci (heuristic smoke) vs quality (neural); policies strict / balanced
• Gate: pass / rewrite / abstain for production safe_answer

Author: Shreyas G.

Install

pip install -U hallucination-gate
pip install "hallucination-gate[ocr]"   # optional OCR

RAG eval (RAGAS replacement path)

from hallucination_gate import RAGEval, LatencyBudget

evaler = RAGEval(
    use_heuristic=True,  # CI; omit for neural production eval
    latency_budget=LatencyBudget(p95_ms=1500, max_ms=5000),
)
report = evaler.evaluate(
    [
        {
            "query": "What is the warranty?",
            "answer": "The Titan watch has a 2-year warranty.",
            "contexts": [
                "The Titan watch has a 2-year warranty covering defects.",
                "Shipping takes 3-5 days.",
            ],
            "ground_truth": "2-year warranty for manufacturing defects.",
            "relevant_contexts": [
                "The Titan watch has a 2-year warranty covering defects."
            ],
            # or: "relevant_indices": [0],
        }
    ],
    save_baseline_path="baselines/titan.json",
    # baseline_path="baselines/titan.json",
    # fail_on_regression=True,
)
print(report.aggregate)   # faithfulness, answer_relevancy, ...
print(report.retrieval)   # hit_at_k, mrr, ndcg_at_k, ...
print(report.latency)     # p50/p95/p99 + budget ok
report.raise_if_failed()

hallucination-gate eval-dataset samples.jsonl --out report.json \
  --save-baseline baselines/titan.json --p95-ms 1500

# later in CI:
hallucination-gate eval-dataset samples.jsonl \
  --baseline baselines/titan.json --fail-on-regression --p95-ms 1500

Why this beats typical RAGAS setups for grounding: claim-level soft-OR against neighbors, retrieval+latency+regression in one report, false-release oriented gate, multimodal/OCR evidence, and a production safe_answer path — not only a mean score.

Production gate

from hallucination_gate import HallucinationGate, Evidence

gate = HallucinationGate(
    quality_mode="quality",  # or "ci" for heuristic smoke only
    policy="balanced",       # or "strict" for max false-release lock
    warm=True,               # preload models — cuts cold-start tails
)
result = gate.check(query, answer, context=retrieved_docs)
return result.text

Context chunks are aligned/filtered to the query+answer by default (generic overlap/embedding score — no domain lexicon). Metrics expose both context_precision_labeled and context_precision_aligned.

report = gate.evaluate(samples)  # same backends as the gate

OCR

from hallucination_gate import Evidence, ocr_available

ev = Evidence.from_image(path="warranty_card.jpg")
ev = Evidence.from_ocr(path="scanned_policy.pdf")

Drawbacks (honest)

• Latency & cost — neural path adds inference time / GPU·CPU load per sample.
• Over-refusal — conservative gate can abstain on good extractive answers.
• Only as good as evidence — checks support, not world truth; bad retrieval still hurts.
• Hard cases — subtle math/code/reasoning can fool or over-block NLI.
• Heuristic ≠ quality gate — use_heuristic=True is for CI smoke, not calibrated faithfulness.
• Ops surface — HF downloads, torch/sentence-transformers weight, Windows symlink quirks.
• Not magic — still needs your domain labels (relevant_contexts / ground_truth) and human review for hard cases; the stack now covers grounding + retrieval + latency SLOs + regression diffs.

Eval (gate safety)

pip install -e ".[dev]"
set RAG_EVAL_HEURISTIC=1
pytest -q -m "not neural"
hallucination-gate eval-heldout

License

MIT © Shreyas G