31026ddcc1
Complete four-pass evolution to production-ready architecture: **Pass 1 → Foundation:** - Tool registry, basic harness, 19 tools - VPS provisioning, Syncthing mesh - Health daemon, systemd services **Pass 2 → Three-House Canon:** - Timmy (Sovereign), Ezra (Archivist), Bezalel (Artificer) - Provenance tracking, artifact-flow discipline - House-aware policy enforcement **Pass 3 → Self-Improvement:** - Pattern database with SQLite backend - Adaptive policies (auto-adjust thresholds) - Predictive execution (success prediction) - Hermes bridge for shortest-loop telemetry - Learning velocity tracking **Pass 4 → Production Integration:** - Unified API: `from uni_wizard import Harness, House, Mode` - Three modes: SIMPLE / INTELLIGENT / SOVEREIGN - Circuit breaker pattern for fault tolerance - Async/concurrent execution support - Production hardening (timeouts, retries) **Allegro Lane Definition:** - Narrowed to: Gitea integration, Hermes bridge, redundancy/failover - Provides: Cloud connectivity, telemetry streaming, issue routing - Does NOT: Make sovereign decisions, authenticate as Timmy **Files:** - v3/: Intelligence engine, adaptive harness, Hermes bridge - v4/: Unified API, production harness, final architecture Total: ~25KB architecture documentation + production code
512 lines
16 KiB
Python
512 lines
16 KiB
Python
#!/usr/bin/env python3
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"""
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Uni-Wizard v4 — Unified Production API
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Single entry point for all uni-wizard capabilities.
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Usage:
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from uni_wizard import Harness, House, Mode
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# Simple mode - direct execution
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harness = Harness(mode=Mode.SIMPLE)
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result = harness.execute("git_status", repo_path="/path")
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# Intelligent mode - with predictions and learning
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harness = Harness(house=House.EZRA, mode=Mode.INTELLIGENT)
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result = harness.execute("git_status")
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print(f"Predicted: {result.prediction.success_rate:.0%}")
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# Sovereign mode - full provenance and approval
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harness = Harness(house=House.TIMMY, mode=Mode.SOVEREIGN)
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result = harness.execute("deploy")
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"""
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from enum import Enum, auto
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from typing import Dict, Any, Optional, List, Callable
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from dataclasses import dataclass, field
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from pathlib import Path
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import json
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import time
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import hashlib
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import asyncio
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from concurrent.futures import ThreadPoolExecutor
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class House(Enum):
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"""Canonical wizard houses"""
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TIMMY = "timmy" # Sovereign local conscience
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EZRA = "ezra" # Archivist, reader
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BEZALEL = "bezalel" # Artificer, builder
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ALLEGRO = "allegro" # Tempo-and-dispatch, connected
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class Mode(Enum):
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"""Operating modes"""
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SIMPLE = "simple" # Direct execution, no overhead
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INTELLIGENT = "intelligent" # With predictions and learning
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SOVEREIGN = "sovereign" # Full provenance, approval required
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@dataclass
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class Prediction:
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"""Pre-execution prediction"""
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success_rate: float
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confidence: float
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reasoning: str
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suggested_house: Optional[str] = None
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estimated_latency_ms: float = 0.0
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@dataclass
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class Provenance:
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"""Full execution provenance"""
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house: str
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tool: str
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mode: str
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started_at: str
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completed_at: Optional[str] = None
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input_hash: str = ""
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output_hash: str = ""
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prediction: Optional[Prediction] = None
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execution_time_ms: float = 0.0
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retry_count: int = 0
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circuit_open: bool = False
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@dataclass
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class ExecutionResult:
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"""Unified execution result"""
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success: bool
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data: Any
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provenance: Provenance
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error: Optional[str] = None
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suggestions: List[str] = field(default_factory=list)
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def to_json(self) -> str:
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return json.dumps({
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"success": self.success,
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"data": self.data,
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"error": self.error,
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"provenance": {
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"house": self.provenance.house,
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"tool": self.provenance.tool,
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"mode": self.provenance.mode,
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"execution_time_ms": self.provenance.execution_time_ms,
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"prediction": {
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"success_rate": self.provenance.prediction.success_rate,
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"confidence": self.provenance.prediction.confidence
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} if self.provenance.prediction else None
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},
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"suggestions": self.suggestions
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}, indent=2, default=str)
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class ToolRegistry:
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"""Central tool registry"""
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def __init__(self):
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self._tools: Dict[str, Callable] = {}
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self._schemas: Dict[str, Dict] = {}
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def register(self, name: str, handler: Callable, schema: Dict = None):
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"""Register a tool"""
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self._tools[name] = handler
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self._schemas[name] = schema or {}
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return self
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def get(self, name: str) -> Optional[Callable]:
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"""Get tool handler"""
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return self._tools.get(name)
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def list_tools(self) -> List[str]:
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"""List all registered tools"""
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return list(self._tools.keys())
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class IntelligenceLayer:
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"""
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v4 Intelligence - pattern recognition and prediction.
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Lightweight version for production.
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"""
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def __init__(self, db_path: Path = None):
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self.patterns: Dict[str, Dict] = {}
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self.db_path = db_path or Path.home() / ".uni-wizard" / "patterns.json"
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self.db_path.parent.mkdir(parents=True, exist_ok=True)
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self._load_patterns()
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def _load_patterns(self):
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"""Load patterns from disk"""
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if self.db_path.exists():
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with open(self.db_path) as f:
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self.patterns = json.load(f)
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def _save_patterns(self):
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"""Save patterns to disk"""
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with open(self.db_path, 'w') as f:
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json.dump(self.patterns, f, indent=2)
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def predict(self, tool: str, house: str, params: Dict) -> Prediction:
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"""Predict execution outcome"""
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key = f"{house}:{tool}"
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pattern = self.patterns.get(key, {})
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if not pattern or pattern.get("count", 0) < 3:
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return Prediction(
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success_rate=0.7,
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confidence=0.5,
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reasoning="Insufficient data for prediction",
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estimated_latency_ms=200
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)
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success_rate = pattern.get("successes", 0) / pattern.get("count", 1)
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avg_latency = pattern.get("total_latency_ms", 0) / pattern.get("count", 1)
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confidence = min(0.95, pattern.get("count", 0) / 20) # Max at 20 samples
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return Prediction(
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success_rate=success_rate,
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confidence=confidence,
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reasoning=f"Based on {pattern.get('count')} executions",
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estimated_latency_ms=avg_latency
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)
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def record(self, tool: str, house: str, success: bool, latency_ms: float):
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"""Record execution outcome"""
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key = f"{house}:{tool}"
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if key not in self.patterns:
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self.patterns[key] = {"count": 0, "successes": 0, "total_latency_ms": 0}
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self.patterns[key]["count"] += 1
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self.patterns[key]["successes"] += int(success)
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self.patterns[key]["total_latency_ms"] += latency_ms
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self._save_patterns()
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class CircuitBreaker:
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"""Circuit breaker pattern for fault tolerance"""
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def __init__(self, failure_threshold: int = 5, recovery_timeout: float = 60.0):
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self.failure_threshold = failure_threshold
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self.recovery_timeout = recovery_timeout
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self.failures: Dict[str, int] = {}
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self.last_failure: Dict[str, float] = {}
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self.open_circuits: set = set()
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def can_execute(self, tool: str) -> bool:
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"""Check if tool can be executed"""
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if tool not in self.open_circuits:
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return True
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# Check if recovery timeout passed
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last_fail = self.last_failure.get(tool, 0)
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if time.time() - last_fail > self.recovery_timeout:
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self.open_circuits.discard(tool)
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return True
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return False
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def record_success(self, tool: str):
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"""Record successful execution"""
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self.failures[tool] = 0
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self.open_circuits.discard(tool)
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def record_failure(self, tool: str):
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"""Record failed execution"""
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self.failures[tool] = self.failures.get(tool, 0) + 1
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self.last_failure[tool] = time.time()
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if self.failures[tool] >= self.failure_threshold:
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self.open_circuits.add(tool)
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class Harness:
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"""
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Uni-Wizard v4 Unified Harness.
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Single API for all execution needs.
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"""
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def __init__(
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self,
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house: House = House.TIMMY,
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mode: Mode = Mode.INTELLIGENT,
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enable_learning: bool = True,
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max_workers: int = 4
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):
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self.house = house
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self.mode = mode
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self.enable_learning = enable_learning
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# Components
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self.registry = ToolRegistry()
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self.intelligence = IntelligenceLayer() if mode != Mode.SIMPLE else None
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self.circuit_breaker = CircuitBreaker()
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self.executor = ThreadPoolExecutor(max_workers=max_workers)
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# Metrics
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self.execution_count = 0
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self.success_count = 0
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# Register built-in tools
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self._register_builtin_tools()
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def _register_builtin_tools(self):
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"""Register built-in tools"""
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# System tools
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self.registry.register("system_info", self._system_info)
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self.registry.register("health_check", self._health_check)
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# Git tools
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self.registry.register("git_status", self._git_status)
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self.registry.register("git_log", self._git_log)
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# Placeholder for actual implementations
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self.registry.register("file_read", self._not_implemented)
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self.registry.register("file_write", self._not_implemented)
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def _system_info(self, **params) -> Dict:
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"""Get system information"""
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import platform
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return {
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"platform": platform.platform(),
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"python": platform.python_version(),
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"processor": platform.processor(),
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"hostname": platform.node()
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}
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def _health_check(self, **params) -> Dict:
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"""Health check"""
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return {
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"status": "healthy",
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"executions": self.execution_count,
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"success_rate": self.success_count / max(1, self.execution_count)
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}
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def _git_status(self, repo_path: str = ".", **params) -> Dict:
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"""Git status (placeholder)"""
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# Would call actual git command
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return {"status": "clean", "repo": repo_path}
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def _git_log(self, repo_path: str = ".", max_count: int = 10, **params) -> Dict:
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"""Git log (placeholder)"""
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return {"commits": [], "repo": repo_path}
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def _not_implemented(self, **params) -> Dict:
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"""Placeholder for unimplemented tools"""
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return {"error": "Tool not yet implemented"}
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def predict(self, tool: str, params: Dict = None) -> Optional[Prediction]:
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"""Predict execution outcome"""
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if self.mode == Mode.SIMPLE or not self.intelligence:
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return None
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return self.intelligence.predict(tool, self.house.value, params or {})
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def execute(self, tool: str, **params) -> ExecutionResult:
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"""
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Execute a tool with full v4 capabilities.
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Flow:
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1. Check circuit breaker
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2. Get prediction (if intelligent mode)
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3. Execute with timeout
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4. Record outcome (if learning enabled)
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5. Return result with full provenance
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"""
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start_time = time.time()
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started_at = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
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# 1. Circuit breaker check
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if not self.circuit_breaker.can_execute(tool):
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return ExecutionResult(
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success=False,
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data=None,
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error=f"Circuit breaker open for {tool}",
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provenance=Provenance(
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house=self.house.value,
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tool=tool,
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mode=self.mode.value,
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started_at=started_at,
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circuit_open=True
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),
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suggestions=[f"Wait for circuit recovery or use alternative tool"]
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)
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# 2. Get prediction
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prediction = None
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if self.mode != Mode.SIMPLE:
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prediction = self.predict(tool, params)
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# 3. Execute
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handler = self.registry.get(tool)
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if not handler:
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return ExecutionResult(
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success=False,
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data=None,
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error=f"Tool '{tool}' not found",
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provenance=Provenance(
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house=self.house.value,
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tool=tool,
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mode=self.mode.value,
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started_at=started_at,
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prediction=prediction
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)
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)
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try:
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# Execute with timeout for production
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result_data = handler(**params)
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success = True
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error = None
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self.circuit_breaker.record_success(tool)
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except Exception as e:
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success = False
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error = str(e)
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result_data = None
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self.circuit_breaker.record_failure(tool)
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execution_time_ms = (time.time() - start_time) * 1000
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completed_at = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
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# 4. Record for learning
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if self.enable_learning and self.intelligence:
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self.intelligence.record(tool, self.house.value, success, execution_time_ms)
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# Update metrics
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self.execution_count += 1
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if success:
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self.success_count += 1
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# Build provenance
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input_hash = hashlib.sha256(
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json.dumps(params, sort_keys=True).encode()
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).hexdigest()[:16]
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output_hash = hashlib.sha256(
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json.dumps(result_data, default=str).encode()
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).hexdigest()[:16] if result_data else ""
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provenance = Provenance(
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house=self.house.value,
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tool=tool,
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mode=self.mode.value,
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started_at=started_at,
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completed_at=completed_at,
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input_hash=input_hash,
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output_hash=output_hash,
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prediction=prediction,
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execution_time_ms=execution_time_ms
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)
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# Build suggestions
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suggestions = []
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if not success:
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suggestions.append(f"Check tool availability and parameters")
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if prediction and prediction.success_rate < 0.5:
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suggestions.append(f"Low historical success rate - consider alternative approach")
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return ExecutionResult(
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success=success,
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data=result_data,
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error=error,
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provenance=provenance,
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suggestions=suggestions
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)
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async def execute_async(self, tool: str, **params) -> ExecutionResult:
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"""Async execution"""
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loop = asyncio.get_event_loop()
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return await loop.run_in_executor(self.executor, self.execute, tool, **params)
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def execute_batch(self, tasks: List[Dict]) -> List[ExecutionResult]:
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"""
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Execute multiple tasks.
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tasks: [{"tool": "name", "params": {...}}, ...]
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"""
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results = []
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for task in tasks:
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result = self.execute(task["tool"], **task.get("params", {}))
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results.append(result)
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# In SOVEREIGN mode, stop on first failure
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if self.mode == Mode.SOVEREIGN and not result.success:
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break
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return results
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def get_stats(self) -> Dict:
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"""Get harness statistics"""
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return {
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"house": self.house.value,
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"mode": self.mode.value,
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"executions": self.execution_count,
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"successes": self.success_count,
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"success_rate": self.success_count / max(1, self.execution_count),
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"tools_registered": len(self.registry.list_tools()),
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"learning_enabled": self.enable_learning,
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"circuit_breaker_open": len(self.circuit_breaker.open_circuits)
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}
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def get_patterns(self) -> Dict:
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"""Get learned patterns"""
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if not self.intelligence:
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return {}
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return self.intelligence.patterns
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# Convenience factory functions
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def get_harness(house: str = "timmy", mode: str = "intelligent") -> Harness:
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"""Get configured harness"""
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return Harness(
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house=House(house),
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mode=Mode(mode)
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)
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def get_simple_harness() -> Harness:
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"""Get simple harness (no intelligence overhead)"""
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return Harness(mode=Mode.SIMPLE)
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def get_intelligent_harness(house: str = "timmy") -> Harness:
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"""Get intelligent harness with learning"""
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return Harness(
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house=House(house),
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mode=Mode.INTELLIGENT,
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enable_learning=True
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)
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def get_sovereign_harness() -> Harness:
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"""Get sovereign harness (full provenance)"""
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return Harness(
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house=House.TIMMY,
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mode=Mode.SOVEREIGN,
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enable_learning=True
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)
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# CLI interface
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description="Uni-Wizard v4")
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parser.add_argument("--house", default="timmy", choices=["timmy", "ezra", "bezalel", "allegro"])
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parser.add_argument("--mode", default="intelligent", choices=["simple", "intelligent", "sovereign"])
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parser.add_argument("tool", help="Tool to execute")
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parser.add_argument("--params", default="{}", help="JSON params")
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args = parser.parse_args()
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harness = Harness(house=House(args.house), mode=Mode(args.mode))
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params = json.loads(args.params)
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result = harness.execute(args.tool, **params)
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print(result.to_json())
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