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>
90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
"""Scheduler: think vs act, tool selection, retry logic, fallback modes for AGI."""
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from enum import Enum
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from typing import Any
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from fusionagi._logger import logger
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class SchedulerMode(str, Enum):
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"""Whether to think (reason) or act (tool) next."""
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THINK = "think"
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ACT = "act"
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class FallbackMode(str, Enum):
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"""Fallback when primary path fails."""
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RETRY = "retry"
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SIMPLIFY_PLAN = "simplify_plan"
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HUMAN_HANDOFF = "human_handoff"
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ABORT = "abort"
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class Scheduler:
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"""
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Decides think vs act, tool selection policy, retry/backoff, fallback.
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Callers (e.g. Supervisor) query next_action() and record outcomes.
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"""
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def __init__(
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self,
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default_mode: SchedulerMode = SchedulerMode.ACT,
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max_retries_per_step: int = 2,
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fallback_sequence: list[FallbackMode] | None = None,
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) -> None:
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self._default_mode = default_mode
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self._max_retries = max_retries_per_step
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self._fallback_sequence = fallback_sequence or [
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FallbackMode.RETRY,
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FallbackMode.SIMPLIFY_PLAN,
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FallbackMode.HUMAN_HANDOFF,
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FallbackMode.ABORT,
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]
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self._retry_counts: dict[str, int] = {} # step_key -> count
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self._fallback_index: dict[str, int] = {} # task_id -> index into fallback_sequence
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def next_mode(
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self,
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task_id: str,
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step_id: str,
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context: dict[str, Any] | None = None,
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) -> SchedulerMode:
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"""
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Return whether to think (reason more) or act (execute step).
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Override via context["force_think"] or context["force_act"].
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"""
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if context:
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if context.get("force_think"):
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return SchedulerMode.THINK
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if context.get("force_act"):
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return SchedulerMode.ACT
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return self._default_mode
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def should_retry(self, task_id: str, step_id: str) -> bool:
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"""Return True if step should be retried (under max_retries)."""
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key = f"{task_id}:{step_id}"
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count = self._retry_counts.get(key, 0)
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return count < self._max_retries
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def record_retry(self, task_id: str, step_id: str) -> None:
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"""Increment retry count for step."""
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key = f"{task_id}:{step_id}"
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self._retry_counts[key] = self._retry_counts.get(key, 0) + 1
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logger.debug("Scheduler recorded retry", extra={"task_id": task_id, "step_id": step_id})
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def next_fallback(self, task_id: str) -> FallbackMode | None:
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"""Return next fallback mode for task, or None if exhausted."""
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idx = self._fallback_index.get(task_id, 0)
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if idx >= len(self._fallback_sequence):
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return None
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mode = self._fallback_sequence[idx]
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self._fallback_index[task_id] = idx + 1
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logger.info("Scheduler fallback", extra={"task_id": task_id, "fallback": mode.value})
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return mode
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def reset_fallback(self, task_id: str) -> None:
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"""Reset fallback index for task (e.g. after success)."""
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self._fallback_index.pop(task_id, None)
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