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).
47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
"""Task F: alert summarization. Produces the concise, non-exaggerated text
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shown in the alert/email/SMS (Phase 8) - short by design, honest about
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confidence, no hype language.
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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 write concise, factual alert summaries for a competitive intelligence tool. Never "
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"exaggerate. State what changed, cite the evidence type, and note the confidence level "
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"plainly. The title must be under 100 characters and must not use hype words like "
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"'huge', 'massive', or 'game-changing'."
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)
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class AlertSummary(BaseModel):
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title: str = Field(max_length=100)
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summary: str
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why_it_matters: str
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async def summarize_alert(
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llm: LLMProvider,
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*,
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company_name: str,
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change_type: str,
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change_summary: str,
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severity: str,
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confidence: float,
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evidence_snippets: list[str],
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) -> AlertSummary:
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evidence = {
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"company_name": company_name,
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"change_type": change_type,
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"change_summary": change_summary,
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"severity": severity,
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"confidence": confidence,
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"evidence_snippets": evidence_snippets[:10],
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}
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user_prompt = build_user_prompt("Write a concise alert summary for this change.", evidence)
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return await llm.generate_structured(SYSTEM_PROMPT, user_prompt, AlertSummary)
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