build(agent): molt-y#23e5c8 iteration

interplanetary_edge_orchestrator_privacy/adapters/__init__.py

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+"""Adapters package for canonical/interoperable surface.
+
+This module is intentionally lightweight and serves as a namespace holder
+for toy adapters used in MVP experiments.
+"""

interplanetary_edge_orchestrator_privacy/adapters/canonical.py

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+"""Canonical adapter surface.
+
+This module provides a tiny, test-friendly bridge that demonstrates how a
+real adapter could map its internal representation to the DSL types defined
+in interplanetary_edge_orchestrator_privacy.dsl.
+"""
+from __future__ import annotations
+
+from typing import Any, Dict, List
+
+from ..dsl import LocalProblem, SharedVariables, PlanDelta
+
+
+class CanonicalAdapter:
+    """A minimal adapter conforming to the canonical interface.
+
+    It exposes two simple methods:
+    - map_local_problem: from an internal problem to a LocalProblem DSL object
+    - map_plan_delta: from internal delta to a PlanDelta DSL object
+    """
+
+    def map_local_problem(self, problem_id: str, features: List[float] | Dict[str, Any]) -> LocalProblem:
+        return LocalProblem(problem_id=problem_id, features=features, objective="minimize_cost")
+
+    def map_plan_delta(self, version: int, delta: Dict[str, Any], insight: str | None = None) -> PlanDelta:
+        return PlanDelta(version=version, delta=delta, insight=insight)

interplanetary_edge_orchestrator_privacy/adapters/toy_adapter.py

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+"""Toy adapter example: rover Habitat pair maps to the DSL.
+
+This adapter is intentionally tiny and designed to be a drop-in example for
+demonstrating interoperability (CatOpt-like bridge) without pulling in heavy
+dependencies.
+"""
+from __future__ import annotations
+
+from typing import Any, Dict, List
+
+from .canonical import CanonicalAdapter
+from ..dsl import LocalProblem, SharedVariables, PlanDelta
+
+
+class RoverAdapter:
+    def __init__(self):
+        self._canon = CanonicalAdapter()
+
+    def to_local_problem(self, problem_id: str, features: List[float]) -> LocalProblem:
+        return self._canon.map_local_problem(problem_id, features)
+
+    def delta_to_plan(self, version: int, delta: Dict[str, Any]) -> PlanDelta:
+        return self._canon.map_plan_delta(version, delta, insight="rover_delta")
+
+
+class HabitatAdapter(RoverAdapter):
+    # Reuse the same mapping for demonstration; in a real system this would vary.
+    pass

interplanetary_edge_orchestrator_privacy/dsl.py

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+"""Minimal DSL sketch for LocalProblem / SharedVariables / PlanDelta.
+
+This is intentionally lightweight and dependency-free. It provides a
+canonical, vendor-agnostic contract surface that adapters can map to/from
+their internal representations.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+
+@dataclass
+class LocalProblem:
+    """Represents a per-agent optimization task.
+
+    - problem_id: unique identifier for the local problem
+    - features: simple feature vector or dictionary describing the task
+    - objective: optional objective descriptor (string or structured)
+    - metadata: extensible metadata for compatibility checks
+    """
+    problem_id: str
+    features: List[float] | Dict[str, Any] = field(default_factory=list)
+    objective: Optional[str] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class SharedVariables:
+    """Represents shared summaries, priors, or signals exchanged between agents."""
+    version: int
+    data: Dict[str, Any] = field(default_factory=dict)
+    timestamp: Optional[float] = None
+
+
+@dataclass
+class PlanDelta:
+    """Represents an incremental plan change derived from optimization."""
+    version: int
+    delta: Dict[str, Any] = field(default_factory=dict)
+    insight: Optional[str] = None