File size: 2,513 Bytes
a688aff 5f7dc7e a688aff 5f7dc7e a688aff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | from backend.graph.state import BrainState
from backend.llm import safe_llm_json_call
from backend.sse import emit
MAX_CHUNK_CHARS = 12000
def _cap_chunks(chunks: list[dict]) -> str:
parts = []
chars = 0
for c in chunks:
text = c.get("text", "")
if chars + len(text) > MAX_CHUNK_CHARS:
break
parts.append(text)
chars += len(text)
return "\n\n---\n\n".join(parts)
SYSTEM = """You are a contradiction detection specialist. Your ONLY job is to find CONTRADICTIONS, CONFLICTS, and INCONSISTENCIES across company communications.
Output ONLY a JSON array. No preamble. No explanation. No markdown.
Each item must have exactly these fields:
- id: short snake_case identifier (e.g., "refund_window_conflict")
- domain: the operational domain this contradiction affects
- claim_a: what the first source says
- source_a: which source file claim_a comes from
- claim_b: what the second source says
- source_b: which source file claim_b comes from
- resolution: which claim takes precedence in practice (based on Slack/ticket behavior vs SOP policy)
- severity: "high", "medium", or "low"
If you find no contradictions, output: []
Example: [{"id": "refund_window_conflict", "domain": "Customer Support", "claim_a": "30-day refund window", "source_a": "notion_refund_sop.md", "claim_b": "45-day refund approved for loyal customer", "source_b": "slack_export_support.json", "resolution": "Observed behavior (Slack) shows exceptions beyond SOP — default to SOP, escalate exceptions", "severity": "medium"}]"""
async def detect_contradictions(state: BrainState) -> dict:
job_id = state["job_id"]
chunks = state.get("all_chunks", [])
print(f"[{job_id}] Node detect_contradictions: processing {len(chunks)} chunks")
await emit(
job_id,
"stage",
{
"name": "DETECT_CONTRADICTIONS",
"detail": "Detecting cross-source contradictions...",
},
)
chunk_text = _cap_chunks(chunks)
user = f"Detect contradictions and conflicting instructions across this company data:\n\n{chunk_text}"
results = await safe_llm_json_call(SYSTEM, user, max_tokens=2048)
print(f"[{job_id}] detect_contradictions: found {len(results)} contradictions")
await emit(
job_id,
"stage",
{
"name": "DETECT_CONTRADICTIONS_DONE",
"detail": f"Found {len(results)} contradictions",
},
)
return {"contradictions": results}
|