mirror of
https://github.com/blackboxprogramming/BlackRoad-Operating-System.git
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e478add607
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.
77 lines
2.0 KiB
Python
77 lines
2.0 KiB
Python
"""
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QLM Lab - Quantum Language Model Implementation
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This module implements the QLM (Quantum Language Model) system for BlackRoad OS.
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QLM is a stateful semantic layer that:
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- Tracks HI (Human Intelligence), AI (Agent Intelligence), and QI (Quantum/Emergent Intelligence)
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- Connects Operator intent to system execution
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- Detects emergent behaviors in HI+AI feedback loops
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- Provides introspection and control tools for the Operator
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Key Components:
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- models: Core data structures (IntelligenceLayer, Actor, QLMEvent, QIEmergence)
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- state: QLM state management and transitions
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- events: Event ingestion and processing
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- api: Public API for QLM operations
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- ingestion: Connectors to real system data (git, CI, agents)
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- experiments: Validation experiments and metrics
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- visualization: Tools for visualizing QLM state
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Integration Points:
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- cognitive.intent_graph: Foundation for intent tracking
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- cognitive.agent_coordination: Multi-agent collaboration
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- operator_engine.scheduler: Background QLM analysis
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- agents: Event source for AI actions
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Usage:
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from qlm_lab import QLMState, QLMEvent
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from qlm_lab.api import QLMInterface
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# Initialize QLM
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qlm = QLMInterface()
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# Record Operator intent
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qlm.record_operator_intent("Deploy authentication feature")
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# Record agent execution
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qlm.record_agent_execution(agent_id="coder-001", task="implement login")
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# Query state
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state = qlm.get_current_state()
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summary = qlm.summarize_for_operator(days=7)
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"""
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__version__ = "0.1.0"
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from qlm_lab.models import (
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IntelligenceType,
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ActorType,
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ActorRole,
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IntelligenceLayer,
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Actor,
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QLMEvent,
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EventType,
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QIEmergence,
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QLMMetrics,
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)
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from qlm_lab.state import QLMState, StateTransition
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from qlm_lab.api import QLMInterface
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__all__ = [
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"IntelligenceType",
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"ActorType",
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"ActorRole",
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"IntelligenceLayer",
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"Actor",
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"QLMEvent",
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"EventType",
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"QIEmergence",
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"QLMMetrics",
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"QLMState",
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"StateTransition",
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"QLMInterface",
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]
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