mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
synced 2026-06-17 23:09:35 +00:00
e34cc729aa
PROBLEM
The codebase used Python 3.11+ stdlib features (`from datetime import UTC`,
`from enum import StrEnum`) in 22 files, breaking local dev on Python 3.10
(Windows users) and any pytest run that imports the affected modules.
SOLUTION
1. New `core/_py_compat.py` providing UTC + StrEnum shims that:
- On 3.11+ re-export the stdlib names (zero overhead)
- On 3.10 fall back to `timezone.utc` and a (str, Enum) backport
2. All 22 affected files patched to import from the shim:
- core/utils.py, core/config/models.py
- api/routers/admin.py
- auto_client_acquisition/{ai/model_router, agents/{intake,icp_matcher},
v3/{memory,agents,compliance_os,market_radar},
personal_operator/{operator,memory,launch_report},
innovation/{proof_ledger_repo,command_feed_live}}.py
- autonomous_growth/agents/sector_intel.py
- dealix/{trust/{approval,tool_verification,policy},
observability/cost_tracker,
contracts/{evidence_pack,event_envelope,audit_log,decision},
classifications/__init__,
governance/approvals}.py
3. Three new test suites for previously-untested layers (54 tests):
- tests/unit/test_business_suite.py — gtm_plan, launch_metrics,
market_positioning, pricing_strategy, proof_pack, unit_economics,
verticals (28 tests covering plan recommendation, performance fee,
ROI math, account health grading, vertical playbook structure)
- tests/unit/test_innovation_suite.py — aeo_radar, command_feed,
deal_rooms, experiments, growth_missions, proof_ledger, ten_in_ten
(18 tests covering deterministic reproducibility, card type taxonomy,
pending-approval invariant, kill-mission visibility)
- tests/unit/test_ai_model_router.py — ModelTask + get_model_route +
estimate_model_cost_class + requires_guardrail (8 tests covering
enum integrity, route round-trip, guardrail bool contract)
VERIFICATION
- ast.parse green on all 22 patched files
- pytest tests/unit/ → 477 passed, 2 skipped (provider smoke needs API keys)
on Python 3.10.12 venv with project requirements installed
- No behavior change on 3.11+: the shim re-exports stdlib symbols
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
376 lines
14 KiB
Python
376 lines
14 KiB
Python
"""
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Sector Intelligence Agent — Saudi sector deep knowledge.
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وكيل ذكاء القطاعات — معرفة عميقة بالقطاعات السعودية.
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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 core._py_compat import StrEnum
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from typing import Any
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from core.agents.base import BaseAgent
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from core.config.models import Task
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from core.llm.base import Message
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from core.prompts import get_prompt
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class SaudiSector(StrEnum):
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REAL_ESTATE = "real_estate"
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HEALTHCARE = "healthcare"
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EDUCATION = "education"
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LOGISTICS = "logistics"
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RETAIL = "retail"
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FINANCE = "finance"
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MANUFACTURING = "manufacturing"
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CONSULTING = "consulting"
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TECHNOLOGY = "technology"
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CONSTRUCTION = "construction"
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OIL_GAS = "oil_gas"
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TOURISM = "tourism"
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@dataclass
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class SectorIntel:
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sector: SaudiSector
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market_size_sar: float = 0.0
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growth_rate: float = 0.0
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key_players: list[str] = field(default_factory=list)
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pain_points: list[str] = field(default_factory=list)
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opportunities: list[str] = field(default_factory=list)
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ai_readiness: float = 0.0 # 0-1
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regulations: list[str] = field(default_factory=list)
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trends: list[str] = field(default_factory=list)
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vision_2030_alignment: str = ""
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def to_dict(self) -> dict[str, Any]:
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return {
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"sector": self.sector.value,
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"market_size_sar": self.market_size_sar,
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"market_size_sar_formatted": self._fmt_money(self.market_size_sar),
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"growth_rate": self.growth_rate,
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"key_players": self.key_players,
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"pain_points": self.pain_points,
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"opportunities": self.opportunities,
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"ai_readiness": self.ai_readiness,
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"regulations": self.regulations,
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"trends": self.trends,
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"vision_2030_alignment": self.vision_2030_alignment,
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}
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@staticmethod
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def _fmt_money(amount: float) -> str:
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if amount >= 1_000_000_000:
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return f"{amount / 1_000_000_000:.1f}B SAR"
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if amount >= 1_000_000:
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return f"{amount / 1_000_000:.1f}M SAR"
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return f"{amount:.0f} SAR"
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# Curated baseline knowledge — enrichable via LLM when needed
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SAUDI_SECTOR_DATA: dict[SaudiSector, SectorIntel] = {
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SaudiSector.REAL_ESTATE: SectorIntel(
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sector=SaudiSector.REAL_ESTATE,
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market_size_sar=150_000_000_000,
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growth_rate=0.08,
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key_players=["Dar Al Arkan", "ROSHN", "Emaar Economic City", "NHC"],
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pain_points=[
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"إدارة العقود والمستأجرين",
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"التسويق العقاري",
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"تحليل السوق",
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"الصيانة والإصلاحات",
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"Lead qualification",
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],
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opportunities=[
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"أتمتة إدارة العقارات",
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"تحليل البيانات العقارية بالذكاء الاصطناعي",
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"AI-powered property matching",
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"Virtual tours at scale",
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"Predictive maintenance",
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],
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ai_readiness=0.7,
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regulations=["REGA", "White Land Tax", "Sakani program"],
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trends=["Giga-projects (NEOM, Red Sea)", "Build-to-rent", "PropTech funding"],
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vision_2030_alignment="Housing program — 70% ownership target",
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),
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SaudiSector.HEALTHCARE: SectorIntel(
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sector=SaudiSector.HEALTHCARE,
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market_size_sar=180_000_000_000,
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growth_rate=0.10,
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key_players=["MOH", "Dr. Sulaiman Al Habib", "Mouwasat", "Dallah"],
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pain_points=[
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"جدولة المواعيد",
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"إدارة السجلات الطبية",
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"التواصل مع المرضى",
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"الفواتير والتأمين",
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"Operational inefficiency",
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],
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opportunities=[
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"مساعد طبي ذكي (Arabic clinical NLP)",
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"تحليل الصور الطبية",
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"التنبؤ بالأمراض",
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"Telemedicine platforms",
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"Claims automation",
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],
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ai_readiness=0.6,
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regulations=["MOH Licensing", "SCFHS", "CCHI", "CDSI data rules"],
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trends=["Privatization wave", "Seha Virtual Hospital", "Medical tourism"],
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vision_2030_alignment="Privatization 25% of services, Health Sector Transformation",
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),
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SaudiSector.EDUCATION: SectorIntel(
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sector=SaudiSector.EDUCATION,
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market_size_sar=50_000_000_000,
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growth_rate=0.12,
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key_players=["Ministry of Education", "Noor", "Madrasati", "Tatweer", "Classera"],
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pain_points=[
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"إدارة الطلاب",
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"التقييم والمتابعة",
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"التواصل مع أولياء الأمور",
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"إعداد المحتوى",
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"Personalization at scale",
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],
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opportunities=[
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"منصات تعليم ذكية",
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"مساعد تدريس AI بالعربية",
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"تحليل أداء الطلاب",
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"AI tutoring",
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"Arabic content generation for curricula",
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],
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ai_readiness=0.8, # highest readiness
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regulations=["Tatweer", "National Curriculum Framework"],
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trends=["EdTech investment", "Gamified learning", "Bilingual K-12"],
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vision_2030_alignment="Human Capability Development Program",
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),
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SaudiSector.LOGISTICS: SectorIntel(
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sector=SaudiSector.LOGISTICS,
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market_size_sar=30_000_000_000,
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growth_rate=0.15,
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key_players=["SALIC", "Aramex", "SMSA", "Naqel", "DHL KSA"],
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pain_points=[
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"تتبع الشحنات",
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"تحسين المسارات",
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"إدارة المخزون",
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"التوصيل الميل الأخير",
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"Customs bottlenecks",
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],
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opportunities=[
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"تحسين المسارات بالذكاء الاصطناعي",
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"التنبؤ بالطلب",
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"أتمتة المستودعات",
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"Last-mile optimization",
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"Customs document automation",
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],
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ai_readiness=0.75,
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regulations=["Zakat & Customs", "Saudi Post", "TGA"],
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trends=["E-commerce growth (30%+)", "Cold chain", "NEOM logistics"],
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vision_2030_alignment="Logistics hub — top 25 globally",
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),
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SaudiSector.FINANCE: SectorIntel(
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sector=SaudiSector.FINANCE,
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market_size_sar=400_000_000_000,
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growth_rate=0.06,
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key_players=["SNB", "Al Rajhi", "Riyad Bank", "STC Pay", "SABB"],
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pain_points=[
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"AML/KYC compliance cost",
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"Fraud detection",
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"Customer service at scale",
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"Manual underwriting",
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],
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opportunities=[
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"AML/KYC automation with Arabic OCR",
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"Conversational banking (Arabic)",
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"Fraud ML",
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"Credit scoring",
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"RegTech",
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],
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ai_readiness=0.75,
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regulations=["SAMA", "CMA", "AML Law", "SAMA Open Banking framework"],
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trends=["Open banking", "BNPL surge", "FinTech sandbox"],
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vision_2030_alignment="Financial Sector Development Program",
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),
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SaudiSector.RETAIL: SectorIntel(
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sector=SaudiSector.RETAIL,
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market_size_sar=250_000_000_000,
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growth_rate=0.09,
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key_players=["Panda", "Othaim", "Noon", "Amazon.sa", "Jarir", "Extra"],
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pain_points=[
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"Inventory forecasting",
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"Customer service in Arabic",
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"Marketing attribution",
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"Shrinkage",
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],
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opportunities=[
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"Demand forecasting",
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"Arabic chatbots / voice",
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"Dynamic pricing",
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"Personalization",
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],
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ai_readiness=0.7,
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regulations=["MoCI", "SASO", "VAT"],
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trends=["Quick commerce", "Q-Pay", "Omnichannel"],
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vision_2030_alignment="Quality of life program — retail experience",
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),
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SaudiSector.TECHNOLOGY: SectorIntel(
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sector=SaudiSector.TECHNOLOGY,
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market_size_sar=130_000_000_000,
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growth_rate=0.14,
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key_players=["STC", "Mobily", "Zain", "Elm", "Thiqah", "SDAIA"],
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pain_points=["Talent gap", "Scaling support", "Localization"],
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opportunities=[
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"Arabic LLM applications",
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"DevOps automation",
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"Sector-specific SaaS",
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"AI-enabled products",
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],
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ai_readiness=0.85,
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regulations=["NCA (cybersecurity)", "SDAIA", "Data privacy law PDPL"],
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trends=["LEAP conference", "Unicorns emerging", "Gov digital transformation"],
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vision_2030_alignment="Digital Transformation Program",
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),
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SaudiSector.CONSTRUCTION: SectorIntel(
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sector=SaudiSector.CONSTRUCTION,
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market_size_sar=180_000_000_000,
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growth_rate=0.11,
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key_players=["SBG", "El Seif", "Al Rashid", "Nesma"],
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pain_points=["Cost overruns", "Delays", "Safety", "Document handling"],
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opportunities=[
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"Computer vision for safety",
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"Procurement optimization",
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"Document extraction",
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"Scheduling AI",
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],
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ai_readiness=0.55,
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regulations=["MoMRA", "Saudi Building Code"],
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trends=["Giga-projects boom", "Modular construction"],
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vision_2030_alignment="NEOM, Red Sea, Diriyah, Qiddiya",
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),
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SaudiSector.OIL_GAS: SectorIntel(
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sector=SaudiSector.OIL_GAS,
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market_size_sar=800_000_000_000,
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growth_rate=0.03,
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key_players=["Saudi Aramco", "SABIC", "Maaden"],
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pain_points=["Predictive maintenance", "Safety incidents", "Document review"],
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opportunities=[
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"Predictive maintenance",
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"Seismic analysis AI",
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"Process optimization",
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"Arabic HSE compliance",
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],
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ai_readiness=0.8,
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regulations=["MoEnergy", "Saudi Aramco standards"],
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trends=["Energy transition", "Hydrogen", "Downstream growth"],
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vision_2030_alignment="Sustainability + downstream localization",
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),
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SaudiSector.TOURISM: SectorIntel(
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sector=SaudiSector.TOURISM,
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market_size_sar=90_000_000_000,
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growth_rate=0.20,
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key_players=["STA", "Red Sea Global", "Neom", "Diriyah Gate"],
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pain_points=["Multilingual support", "Demand forecasting", "Experience personalization"],
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opportunities=[
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"Multilingual AI concierge",
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"Dynamic pricing",
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"Itinerary AI",
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"Review analysis",
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],
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ai_readiness=0.65,
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regulations=["STA", "SAGIA"],
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trends=["100M visitors target", "Giga-projects", "Religious tourism tech"],
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vision_2030_alignment="Tourism — 10% GDP target",
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),
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SaudiSector.MANUFACTURING: SectorIntel(
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sector=SaudiSector.MANUFACTURING,
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market_size_sar=220_000_000_000,
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growth_rate=0.07,
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key_players=["SABIC", "Maaden", "Al-Yamamah Steel", "Zamil"],
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pain_points=["Predictive maintenance", "Quality control", "Supply chain"],
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opportunities=["Computer vision QC", "Predictive maintenance", "Demand sensing"],
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ai_readiness=0.6,
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regulations=["SASO", "MODON", "Made in Saudi program"],
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trends=["Localization drive", "Industry 4.0 push"],
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vision_2030_alignment="NIDLP — National Industrial Development",
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),
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SaudiSector.CONSULTING: SectorIntel(
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sector=SaudiSector.CONSULTING,
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market_size_sar=15_000_000_000,
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growth_rate=0.10,
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key_players=["Big4 KSA offices", "Strategy&", "Oliver Wyman", "Elixir"],
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pain_points=["Report turnaround", "Research efficiency", "Proposal writing"],
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opportunities=[
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"Research co-pilots",
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"Proposal generation",
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"Deck automation",
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"Knowledge management",
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],
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ai_readiness=0.8,
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regulations=["SAGIA licensing"],
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trends=["Gov consulting boom", "Vision 2030 PMO work"],
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vision_2030_alignment="Serves all VRPs (Vision Realization Programs)",
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),
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}
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class SectorIntelAgent(BaseAgent):
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"""Deep knowledge + LLM-enriched analysis for Saudi sectors."""
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name = "sector_intel"
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async def run(
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self,
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*,
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sector: SaudiSector | str,
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enrich_with_llm: bool = False,
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locale: str = "ar",
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**_: Any,
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) -> SectorIntel:
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"""Return baseline sector intel, optionally enriched by LLM."""
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if isinstance(sector, str):
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sector = SaudiSector(sector)
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base = SAUDI_SECTOR_DATA.get(sector)
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if base is None:
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base = SectorIntel(sector=sector)
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if not enrich_with_llm:
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return base
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try:
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prompt = get_prompt("sector_analysis", sector=sector.value)
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response = await self.router.run(
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task=Task.RESEARCH,
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messages=[Message(role="user", content=prompt)],
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max_tokens=1500,
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temperature=0.3,
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)
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extra = self.parse_json_response(response.content)
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# Merge — LLM adds fresh items without losing baseline
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base.pain_points = list(set(base.pain_points + list(extra.get("pain_points", []))))
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base.opportunities = list(
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set(base.opportunities + list(extra.get("opportunities", [])))
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)
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if extra.get("market_size_sar"):
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base.market_size_sar = float(extra["market_size_sar"])
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if extra.get("growth_rate"):
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base.growth_rate = float(extra["growth_rate"])
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if extra.get("ai_readiness"):
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base.ai_readiness = float(extra["ai_readiness"])
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except Exception as e:
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self.log.warning("sector_enrich_failed", error=str(e))
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return base
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async def best_opportunity(self) -> SectorIntel:
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"""Return the sector with the highest (growth × AI readiness) product."""
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scored = [(s.growth_rate * s.ai_readiness, s) for s in SAUDI_SECTOR_DATA.values()]
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scored.sort(key=lambda x: x[0], reverse=True)
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return scored[0][1]
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def target_sectors(self) -> list[SectorIntel]:
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"""Return our priority target sectors (top 5 by opportunity)."""
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scored = sorted(
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SAUDI_SECTOR_DATA.values(),
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key=lambda s: s.growth_rate * s.ai_readiness,
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reverse=True,
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)
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return scored[:5]
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