| """Isolated test — just explain(). Checks it correctly uses final_verdict |
| (set by verify_dual on agreement) to call /attribute with the right label_id, |
| now that final_verdict is always pre-populated before explain() runs. |
| |
| Requires GROQ_API_KEY not needed here (no LLM call in explain) — only hits |
| the live clAIm /attribute endpoint directly. |
| |
| Run from the veriscite/ root: |
| python -m tests.test_explain_live |
| """ |
|
|
| import asyncio |
| from app.graph.nodes import explain |
|
|
| SAMPLE_CLAIM_CITATION = { |
| "claim": "Attention mechanisms allow transformer models to capture " |
| "long-range dependencies in sequential data without relying on recurrence", |
| "citation_marker": "[1]", |
| "reference_string": "Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., " |
| "Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). " |
| "Attention is all you need. In NeurIPS 2017.", |
| "resolved_paper_id": "204e3073870fae3d05bcbc2f6a8e263d9b72e776", |
| "evidence_text": ( |
| "The dominant sequence transduction models are based on complex " |
| "recurrent or convolutional neural networks in an encoder-decoder " |
| "configuration. We propose a new simple network architecture, the " |
| "Transformer, based solely on attention mechanisms, dispensing with " |
| "recurrence and convolutions entirely." |
| ), |
| } |
|
|
| |
| |
| SAMPLE_AUDIT = { |
| "claim_citation": SAMPLE_CLAIM_CITATION, |
| "winner_sentence": "We propose a new simple network architecture, the " |
| "Transformer, based solely on attention mechanisms, " |
| "dispensing with recurrence and convolutions entirely.", |
| "attribution_available": True, |
| "deberta_verdict": {"label": "SUPPORT", "confidence": 0.9658, "source": "deberta"}, |
| "llm_verdict": {"label": "SUPPORT", "confidence": 1.0, "source": "llm"}, |
| "agreement": True, |
| "escalated": False, |
| "escalation_retry_succeeded": None, |
| "adjudicator_reasoning": None, |
| "final_verdict": {"label": "SUPPORT", "confidence": 0.9658, "source": "deberta"}, |
| "attribution": None, |
| } |
|
|
|
|
| async def main(): |
| state = { |
| "source_text": "", |
| "claims": [SAMPLE_CLAIM_CITATION], |
| "audits": [SAMPLE_AUDIT], |
| "current_index": 0, |
| "report": None, |
| } |
| result = await explain(state) |
| audit = result["audits"][0] |
|
|
| print("attribution_available:", audit["attribution_available"]) |
| print("attribution (first 5 tokens):") |
| for item in (audit["attribution"] or [])[:5]: |
| print(" ", item) |
| print("total tokens:", len(audit["attribution"] or [])) |
|
|
|
|
| if __name__ == "__main__": |
| asyncio.run(main()) |
|
|
