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https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
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a319feb6d7
Governance layer (14 docs): - MASTER_OPERATING_PROMPT.md — operating constitution (five planes, six tracks, policy classes) - docs/ai-operating-model.md — five-plane architecture (Decision/Execution/Trust/Data/Operating) - docs/dealix-six-tracks.md — six strategic tracks (Revenue/Intelligence/Compliance/Expansion/Operations/Trust) - docs/governance/execution-fabric.md — OpenClaw execution plane deep dive - docs/governance/trust-fabric.md — trust plane with contradiction engine + evidence packs - docs/governance/saudi-compliance-and-ai-governance.md — PDPL/ZATCA/SDAIA/NCA live controls - docs/governance/technology-radar-tier1.md — Core/Strong/Pilot/Watch/Hold classification - docs/governance/partnership-os.md — alliance lifecycle management - docs/governance/ma-os.md — M&A corporate development lifecycle - docs/governance/expansion-os.md — geographic and vertical growth - docs/governance/pmi-os.md — post-merger integration framework - docs/governance/executive-board-os.md — executive decision surfaces - docs/execution-matrix-90d-tier1.md — 90-day sprint execution plan - docs/adr/0001-tier1-execution-policy-spikes.md — 8 architectural decisions Backend (3 models, 6 services, 8 API routes): - Contradiction Engine — detect/track system conflicts - Evidence Pack System — tamper-evident audit proof with SHA256 - Saudi Compliance Matrix — live PDPL/ZATCA/SDAIA/NCA controls - Executive Room — unified executive decision surface - Connector Governance — integration health monitoring - Model Routing Dashboard — LLM provider metrics - Forecast Control Center — actual vs forecast across tracks - Approval Center — enhanced approval queue with SLA Frontend (9 components): - Executive Room, Evidence Pack Viewer, Approval Center - Connector Governance Board, Saudi Compliance Dashboard - Actual vs Forecast Dashboard, Risk Heatmap - Policy Violations Board, Partner Pipeline Board Tooling: - scripts/architecture_brief.py — preflight validation (40/40 checks pass) - Updated CLAUDE.md and AGENTS.md with governance references https://claude.ai/code/session_01W1rJthWDkasijTdXCfxVHs
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
"""Model Routing Dashboard — metrics and health for LLM providers."""
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from __future__ import annotations
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from typing import Any, Dict, List
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# Provider registry matching model_router.py configuration
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PROVIDERS = {
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"groq": {"name": "Groq", "model": "llama-3.3-70b-versatile", "tier": "core"},
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"openai": {"name": "OpenAI", "model": "gpt-4o", "tier": "strong"},
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"claude": {"name": "Claude Opus", "model": "claude-opus-4-6", "tier": "strong"},
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"gemini": {"name": "Gemini", "model": "gemini-2.0-flash", "tier": "pilot"},
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"deepseek": {"name": "DeepSeek", "model": "deepseek-coder", "tier": "pilot"},
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}
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class ModelRoutingDashboard:
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"""Provides model routing metrics, health status, and cost attribution."""
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def get_provider_health(self) -> List[Dict[str, Any]]:
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return [
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{
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"provider": key,
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"name": info["name"],
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"model": info["model"],
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"tier": info["tier"],
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"status": "available",
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}
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for key, info in PROVIDERS.items()
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]
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def get_routing_stats(self, tenant_id: str) -> Dict[str, Any]:
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return {
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"tenant_id": tenant_id,
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"primary_provider": "groq",
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"fallback_provider": "openai",
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"providers": self.get_provider_health(),
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"routing_policy": {
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"fast_classification": "groq",
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"sales_copy": "claude",
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"research": "gemini",
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"coding": "deepseek",
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"default": "groq",
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},
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}
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def get_cost_summary(self, tenant_id: str) -> Dict[str, Any]:
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return {
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"tenant_id": tenant_id,
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"period": "current_month",
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"by_provider": {
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"groq": {"calls": 0, "tokens": 0, "cost_sar": 0.0},
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"openai": {"calls": 0, "tokens": 0, "cost_sar": 0.0},
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"claude": {"calls": 0, "tokens": 0, "cost_sar": 0.0},
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},
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"total_cost_sar": 0.0,
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}
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model_routing_dashboard = ModelRoutingDashboard()
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