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Add complete QLM (Quantum Language Model) implementation
This commit introduces the QLM system - a stateful semantic layer for
tracking HI (Human Intelligence), AI (Agent Intelligence), and QI
(Quantum/Emergent Intelligence) in BlackRoad OS.
Core Features:
- HI/AI/QI intelligence layer modeling
- Event-driven state management
- QI emergence detection (agent self-correction, feedback loops, etc.)
- HI-AI alignment scoring
- Operator-facing query interface
- Reality ingestion (git, CI, agent logs)
Components Added:
- qlm_lab/models.py: Core data models (Actor, QLMEvent, QIEmergence, etc.)
- qlm_lab/state.py: State management and transition tracking
- qlm_lab/api.py: Public QLMInterface API
- qlm_lab/ingestion/: Git, CI, and agent log connectors
- qlm_lab/experiments/: Alignment and emergence validation
- qlm_lab/visualization.py: Timeline, actor graph, alignment plots
- qlm_lab/demo.py: Interactive demo script
- tests/test_qlm_core.py: Comprehensive test suite
- docs/QLM.md: Complete documentation (concepts, API, integration)
Usage:
from qlm_lab.api import QLMInterface
qlm = QLMInterface()
qlm.record_operator_intent("Build feature X")
qlm.record_agent_execution("agent-1", "Implement X", "task-1")
summary = qlm.get_summary(days=7)
Run:
python -m qlm_lab.demo
python -m qlm_lab.experiments.alignment_detection
pytest tests/test_qlm_core.py -v
Integrates with:
- cognitive/intent_graph.py (intent tracking)
- cognitive/agent_coordination.py (multi-agent coordination)
- operator_engine/scheduler.py (background analysis)
Next steps: Integrate with FastAPI backend, add Prism Console UI,
implement Lucidia language runtime.
This commit is contained in:
Claude
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qlm_lab/demo.py
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qlm_lab/demo.py
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"""
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QLM Demo Script - Shows QLM in action
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This script demonstrates the core QLM functionality:
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1. Recording Operator intent
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2. Recording agent executions
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3. Detecting QI emergence
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4. Querying state
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5. Generating Operator summaries
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Run: python -m qlm_lab.demo
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"""
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import logging
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from datetime import datetime, timedelta
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from qlm_lab.api import QLMInterface
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from qlm_lab.models import ActorRole, EventType
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# Setup logging
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logging.basicConfig(
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level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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)
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def demo_basic_workflow():
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"""Demonstrate basic QLM workflow"""
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print("=" * 60)
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print("QLM Demo: Basic Workflow")
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print("=" * 60)
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print()
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# Initialize QLM
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qlm = QLMInterface()
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print("✓ QLM initialized")
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print()
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# Register agents
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agent1 = qlm.register_agent(
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agent_id="agent-coder-001",
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name="CodeWriter",
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role=ActorRole.CODER,
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capabilities=["python", "javascript", "testing"],
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)
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print(f"✓ Registered agent: {agent1.name}")
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agent2 = qlm.register_agent(
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agent_id="agent-reviewer-001",
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name="CodeReviewer",
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role=ActorRole.REVIEWER,
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capabilities=["code_review", "security_audit"],
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)
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print(f"✓ Registered agent: {agent2.name}")
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print()
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# Operator defines intent
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print("👤 Operator: 'Build authentication feature'")
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intent_event = qlm.record_operator_intent(
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intent="Build authentication feature",
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description="Implement login, signup, and password reset",
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intent_node_id="intent-auth-001",
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)
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print(f"✓ Intent recorded (event: {intent_event.id[:8]}...)")
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print()
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# Agent executes
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print("🤖 Agent CodeWriter: Starting implementation...")
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exec_event = qlm.record_agent_execution(
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agent_id="agent-coder-001",
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task_description="Implement login endpoint",
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task_id="task-login-001",
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intent_node_id="intent-auth-001",
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)
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print(f"✓ Execution recorded (event: {exec_event.id[:8]}...)")
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print()
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# Agent completes
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print("🤖 Agent CodeWriter: Completed!")
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completion_event = qlm.record_agent_completion(
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agent_id="agent-coder-001",
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task_id="task-login-001",
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success=True,
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result={"files_modified": ["auth.py", "routes.py"], "tests_added": 5},
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intent_node_id="intent-auth-001",
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)
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print(f"✓ Completion recorded (event: {completion_event.id[:8]}...)")
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print()
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# Operator approves
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print("👤 Operator: 'Looks good!'")
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approval_event = qlm.record_operator_approval(
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what_approved="Login implementation",
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intent_node_id="intent-auth-001",
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task_id="task-login-001",
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)
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print(f"✓ Approval recorded (event: {approval_event.id[:8]}...)")
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print()
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# Query state
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print("📊 Query: Active actors")
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active = qlm.get_active_actors()
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print(f" Active actors: {len(active)}")
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for actor in active:
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print(f" - {actor.name} ({actor.role.value})")
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print()
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# Alignment
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print("🎯 Calculating HI-AI alignment...")
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alignment = qlm.get_alignment_score()
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print(f" Alignment: {alignment:.1%}")
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print()
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# Summary
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print("📝 Operator Summary:")
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print("-" * 60)
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summary = qlm.get_summary(days=1)
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print(summary)
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print()
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def demo_qi_emergence():
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"""Demonstrate QI emergence detection"""
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print("=" * 60)
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print("QLM Demo: QI Emergence Detection")
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print("=" * 60)
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print()
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qlm = QLMInterface()
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qlm.register_agent("agent-001", "Agent1", ActorRole.EXECUTOR)
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# Operator intent
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qlm.record_operator_intent(
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intent="Fix database connection bug",
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intent_node_id="intent-bugfix-001",
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)
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# Agent hits error
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print("🤖 Agent encounters error...")
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qlm.record_agent_error(
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agent_id="agent-001",
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task_id="task-bugfix-001",
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error="Database connection refused",
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intent_node_id="intent-bugfix-001",
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)
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# Agent self-corrects (QI emergence!)
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print("🤖 Agent self-corrects (trying alternative approach)...")
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qlm.record_agent_execution(
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agent_id="agent-001",
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task_description="Fix database connection bug (retry with connection pool)",
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task_id="task-bugfix-001",
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intent_node_id="intent-bugfix-001",
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)
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qlm.record_agent_completion(
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agent_id="agent-001",
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task_id="task-bugfix-001",
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success=True,
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result={"approach": "connection_pool", "self_corrected": True},
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intent_node_id="intent-bugfix-001",
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)
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print()
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print("✨ QI Emergence Detection:")
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emergences = qlm.get_recent_emergences()
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if emergences:
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for em in emergences:
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print(f" Pattern: {em.pattern_name}")
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print(f" Explanation: {em.explanation}")
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print(f" Confidence: {em.confidence:.0%}")
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print(f" Impact: {em.impact_score:.1f}/1.0")
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else:
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print(" (No emergence detected - pattern matching may need tuning)")
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print()
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def demo_operator_queries():
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"""Demonstrate natural language queries"""
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print("=" * 60)
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print("QLM Demo: Operator Queries")
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print("=" * 60)
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print()
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qlm = QLMInterface()
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# Setup some activity
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qlm.register_agent("agent-001", "Agent1", ActorRole.EXECUTOR)
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qlm.record_operator_intent("Test operator queries")
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qlm.record_agent_execution("agent-001", "Do something", task_id="task-001")
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qlm.record_agent_completion("agent-001", "task-001", success=True)
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qlm.record_operator_approval("Agent work", task_id="task-001")
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# Ask questions
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questions = [
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"What did agents do today?",
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"Are we aligned with my intent?",
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"What's the status?",
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]
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for question in questions:
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print(f"👤 Operator: '{question}'")
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print("-" * 60)
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answer = qlm.ask(question)
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print(answer)
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print()
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def demo_full_scenario():
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"""Demonstrate a complete multi-agent workflow"""
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print("=" * 60)
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print("QLM Demo: Full Multi-Agent Scenario")
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print("=" * 60)
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print()
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qlm = QLMInterface()
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# Register agents
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coder = qlm.register_agent("coder", "CodeWriter", ActorRole.CODER)
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reviewer = qlm.register_agent("reviewer", "CodeReviewer", ActorRole.REVIEWER)
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tester = qlm.register_agent("tester", "TestRunner", ActorRole.TESTER)
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print(f"✓ Registered 3 agents: {coder.name}, {reviewer.name}, {tester.name}")
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print()
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# Operator intent
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print("👤 Operator: 'Ship payment integration'")
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intent = qlm.record_operator_intent(
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intent="Ship payment integration",
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description="Integrate Stripe, add tests, deploy",
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intent_node_id="intent-payment-001",
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)
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print()
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# Coder implements
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print("🤖 CodeWriter: Implementing...")
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qlm.record_agent_execution(
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"coder", "Implement Stripe integration", "task-001", "intent-payment-001"
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)
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qlm.record_agent_completion("coder", "task-001", True)
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print(" ✓ Completed")
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print()
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# Handoff to reviewer
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print("🤖 CodeWriter → CodeReviewer")
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qlm.record_agent_handoff(
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"coder", "reviewer", "task-001", "Ready for review"
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)
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print()
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# Reviewer reviews
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print("🤖 CodeReviewer: Reviewing code...")
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qlm.record_agent_execution("reviewer", "Review Stripe code", "task-002")
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qlm.record_agent_completion("reviewer", "task-002", True)
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print(" ✓ Approved")
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print()
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# Handoff to tester
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print("🤖 CodeReviewer → TestRunner")
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qlm.record_agent_handoff("reviewer", "tester", "task-002", "Ready for testing")
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print()
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# Tester runs tests
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print("🤖 TestRunner: Running tests...")
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qlm.record_system_event(
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EventType.SYSTEM_TEST,
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"Payment integration tests",
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task_id="task-003",
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metadata={"passed": True, "test_count": 15},
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)
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print(" ✓ All tests passed")
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print()
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# Operator approves
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print("👤 Operator: 'Ship it!'")
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qlm.record_operator_approval(
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"Payment integration", intent_node_id="intent-payment-001"
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)
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print()
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# Deploy
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print("🚀 Deploying...")
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qlm.record_system_event(
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EventType.SYSTEM_DEPLOY,
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"Deployed payment feature to production",
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metadata={"environment": "production", "version": "v1.2.0"},
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)
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print(" ✓ Deployed")
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print()
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# Show results
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print("=" * 60)
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print("📊 Final State")
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print("=" * 60)
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print()
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print("Metrics:")
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print(f" Total events: {len(qlm.state.events)}")
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print(f" HI events: {qlm.state.metrics.hi_events}")
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print(f" AI events: {qlm.state.metrics.ai_events}")
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print(f" System events: {qlm.state.metrics.system_events}")
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print(f" Alignment: {qlm.get_alignment_score():.1%}")
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print()
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print("Event Timeline:")
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for event in qlm.state.events:
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print(f" [{event.timestamp.strftime('%H:%M:%S')}] {event.event_type.value}: {event.data}")
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print()
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def main():
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"""Run all demos"""
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print("\n🌟 QLM (Quantum Language Model) Demo Suite 🌟\n")
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try:
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demo_basic_workflow()
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input("Press Enter to continue to next demo...")
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print("\n")
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demo_qi_emergence()
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input("Press Enter to continue to next demo...")
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print("\n")
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demo_operator_queries()
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input("Press Enter to continue to next demo...")
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print("\n")
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demo_full_scenario()
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except KeyboardInterrupt:
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print("\n\nDemo interrupted by user.")
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except Exception as e:
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print(f"\n\n❌ Demo error: {e}")
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import traceback
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traceback.print_exc()
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print("\n✨ Demo complete! ✨\n")
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print("Next steps:")
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print(" 1. Run experiments: python -m qlm_lab.experiments.run_all")
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print(" 2. Read docs: docs/QLM.md")
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print(" 3. Integrate with your system!")
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print()
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if __name__ == "__main__":
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main()
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