mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
synced 2026-06-17 23:09:35 +00:00
117 lines
3.8 KiB
Python
117 lines
3.8 KiB
Python
"""
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Vertical OS Base — schema for productized sector modules.
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Each vertical bundles: ICP, signals, objections, KPIs, message library,
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proposal template, QBR template, ROI model, compliance notes.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any
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# ── KPI definition ────────────────────────────────────────────────
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@dataclass(frozen=True)
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class KPI:
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metric_id: str
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name_ar: str
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description_ar: str
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unit: str
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higher_is_better: bool = True
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target_p50: float | None = None # sector benchmark
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target_p90: float | None = None
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# ── Message template ─────────────────────────────────────────────
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@dataclass(frozen=True)
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class MessageTemplate:
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template_id: str
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channel: str # whatsapp / email / linkedin
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purpose: str # cold / followup_3d / followup_7d / objection_response
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subject_ar: str | None
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body_ar: str
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variables: tuple[str, ...] = () # {company_name}, {city}, {pain_point}, etc.
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expected_reply_rate: float = 0.0
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# ── Full vertical bundle ─────────────────────────────────────────
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@dataclass(frozen=True)
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class VerticalOS:
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vertical_id: str
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sector_ar: str
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sector_en: str
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# ICP
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icp_company_size: tuple[str, ...]
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icp_cities: tuple[str, ...]
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icp_keywords: tuple[str, ...]
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# Pain & objections
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pain_points_ar: tuple[str, ...]
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top_objection_ids: tuple[str, ...] # ids from revenue_graph.objection_library
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# Signals to watch (from market_intelligence.signal_detectors taxonomy)
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priority_signals: tuple[str, ...]
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# KPIs surfaced on the per-vertical dashboard
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dashboard_kpis: tuple[KPI, ...]
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# Message library
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message_templates: tuple[MessageTemplate, ...]
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# Proposal & QBR templates
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proposal_template_ar: str
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qbr_section_template_ar: str
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# ROI model — what to plug into the simulator
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avg_deal_value_sar: int
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avg_cycle_days: int
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benchmark_reply_rate: float
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benchmark_meeting_rate: float
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benchmark_win_rate: float
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# Compliance notes specific to the sector
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compliance_notes_ar: tuple[str, ...] = ()
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# Recommended channel mix (must sum ~ 1.0)
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recommended_channel_mix: dict[str, float] = field(default_factory=dict)
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# ── Registry helpers ─────────────────────────────────────────────
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_REGISTRY: dict[str, VerticalOS] = {}
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def _register(v: VerticalOS) -> None:
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_REGISTRY[v.vertical_id] = v
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def get_vertical(vertical_id: str) -> VerticalOS | None:
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return _REGISTRY.get(vertical_id)
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def list_vertical_summaries() -> list[dict[str, Any]]:
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"""Compact summary for the verticals overview tile."""
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return [
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{
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"vertical_id": v.vertical_id,
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"sector_ar": v.sector_ar,
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"sector_en": v.sector_en,
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"avg_deal_value_sar": v.avg_deal_value_sar,
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"avg_cycle_days": v.avg_cycle_days,
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"n_pain_points": len(v.pain_points_ar),
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"n_message_templates": len(v.message_templates),
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"n_kpis": len(v.dashboard_kpis),
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"primary_channel": (
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max(v.recommended_channel_mix.items(), key=lambda x: x[1])[0]
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if v.recommended_channel_mix else None
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),
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"benchmark_reply_rate": v.benchmark_reply_rate,
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}
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for v in _REGISTRY.values()
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]
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# Will be populated by clinics.py / real_estate.py / logistics.py imports
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ALL_VERTICALS: dict[str, VerticalOS] = _REGISTRY
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