saksham
1a4c80958f
FastAPI + Celery + Next.js + Postgres/Redis app with company monitoring, source collection, LLM-based change analysis, enrichment, and account security (Turnstile, escalating lockout, email verification).
61 lines
2.3 KiB
Python
61 lines
2.3 KiB
Python
"""Task E: change significance narrative.
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Severity itself stays deterministic (app/change_detection/scoring.py) - see
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ARCHITECTURE.md: "the LLM narrates why a change matters; it does not decide
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how much it matters." This task supplies the narrative explanation and a
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second, independent opinion on whether the change looks real/meaningful and
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notification-worthy, which the notification layer (Phase 8) can use as an
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extra signal alongside the deterministic severity - it never overrides it.
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"""
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from __future__ import annotations
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from pydantic import BaseModel, Field
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from app.analysis.llm.base import LLMProvider
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from app.prompts.base import build_user_prompt
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SYSTEM_PROMPT = (
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"You are a competitive intelligence analyst reviewing one detected change between two "
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"snapshots of a company's public information. Explain in plain language why this change "
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"would or wouldn't matter to someone monitoring this company, given their stated focus. "
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"Be honest about uncertainty - do not overstate a routine wording tweak as significant, "
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"and do not undersell a genuine signal."
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)
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class ChangeSignificanceAssessment(BaseModel):
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is_real_change: bool = Field(description="Does this look like a genuine change, not noise?")
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is_meaningful: bool
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why_it_matters: str
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confidence: float = Field(ge=0.0, le=1.0)
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should_notify: bool
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async def assess_change_significance(
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llm: LLMProvider,
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*,
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company_name: str,
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monitoring_focus: str | None,
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change_type: str,
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change_summary: str,
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added_text: list[str],
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removed_text: list[str],
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deterministic_severity: str,
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deterministic_confidence: float,
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) -> ChangeSignificanceAssessment:
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evidence = {
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"company_name": company_name,
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"monitoring_focus": monitoring_focus,
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"change_type": change_type,
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"change_summary": change_summary,
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"text_added": added_text[:20],
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"text_removed": removed_text[:20],
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"deterministic_severity": deterministic_severity,
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"deterministic_confidence": deterministic_confidence,
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}
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user_prompt = build_user_prompt(
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"Assess this detected change and explain why it does or doesn't matter.", evidence
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)
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return await llm.generate_structured(SYSTEM_PROMPT, user_prompt, ChangeSignificanceAssessment)
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