445865e429
Some checks failed
- Integrate GPU scoring inline into reasoning/multi_path.py (auto-uses GPU when available) - Integrate GPU deduplication into multi_agent/consensus_engine.py - Add semantic_search() method to memory/semantic_graph.py with GPU acceleration - Integrate GPU training into self_improvement/training.py AutoTrainer - Fix all 758 ruff lint issues (whitespace, import sorting, unused imports, ambiguous vars, undefined names) - Fix all 40 mypy type errors across the codebase (no-any-return, union-attr, arg-type, etc.) - Fix deprecated ruff config keys (select/ignore -> [tool.ruff.lint]) - Add .dockerignore to exclude .venv/, tests/, docs/ from Docker builds - Add type hints and docstrings to verification/outcome.py - Fix E402 import ordering in witness_agent.py - Fix F821 undefined names in vector_pgvector.py and native.py - Fix E741 ambiguous variable names in reflective.py and recommender.py All 276 tests pass. 0 ruff errors. 0 mypy errors. Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
"""Rollouts: simulate plan before executing."""
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from typing import Any, Callable, Protocol
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from fusionagi._logger import logger
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from fusionagi.schemas.plan import Plan
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from fusionagi.schemas.world_model import StateTransition
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class WorldModelLike(Protocol):
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def predict(self, state: dict[str, Any], action: str, action_args: dict[str, Any]) -> StateTransition: ...
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def run_rollout(
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plan: Plan,
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initial_state: dict[str, Any],
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world_model: WorldModelLike,
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step_action_fn: Callable[[str, dict], str] | None = None,
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) -> tuple[bool, list[StateTransition], dict[str, Any]]:
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"""
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Simulate plan in world model. Returns (success, transitions, final_state).
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step_action_fn(step_id, step_dict) -> action name for prediction.
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"""
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state = dict(initial_state)
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transitions: list[StateTransition] = []
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for step in plan.steps:
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action = step.tool_name or "unknown"
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action_args = step.tool_args or {}
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if step_action_fn:
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action = step_action_fn(step.id, step.model_dump())
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trans = world_model.predict(state, action, action_args)
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transitions.append(trans)
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state = dict(trans.to_state)
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if trans.confidence < 0.3:
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logger.warning("Rollout low confidence", extra={"step_id": step.id, "confidence": trans.confidence})
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return False, transitions, state
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logger.info("Rollout completed", extra={"steps": len(transitions)})
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return True, transitions, state
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