fix: deep GPU integration, fix all ruff/mypy issues, add .dockerignore

- 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>

fusionagi/self_improvement/__init__.py

@@ -6,9 +6,9 @@ from execution outcomes and reflection.
 """
 
 from fusionagi.self_improvement.correction import SelfCorrectionLoop
+from fusionagi.self_improvement.loop import FusionAGILoop
 from fusionagi.self_improvement.recommender import AutoRecommender
 from fusionagi.self_improvement.training import AutoTrainer
-from fusionagi.self_improvement.loop import FusionAGILoop
 
 __all__ = [
     "SelfCorrectionLoop",

fusionagi/self_improvement/correction.py

@@ -2,9 +2,9 @@
 
 from typing import Any, Protocol
 
-from fusionagi.schemas.task import TaskState
-from fusionagi.schemas.recommendation import Recommendation, RecommendationKind
 from fusionagi._logger import logger
+from fusionagi.schemas.recommendation import Recommendation, RecommendationKind
+from fusionagi.schemas.task import TaskState
 
 
 class StateManagerLike(Protocol):
@@ -61,7 +61,8 @@ def run_reflection_on_failure(
     response = critic_agent.handle_message(envelope)
     if not response or response.message.intent != "evaluation_ready":
         return None
-    return response.message.payload.get("evaluation", {})
+    result: dict[str, Any] = response.message.payload.get("evaluation", {})  # type: ignore[assignment]
+    return result
 
 
 class SelfCorrectionLoop:

fusionagi/self_improvement/loop.py

@@ -2,16 +2,15 @@
 
 from typing import Any, Callable
 
-from fusionagi.schemas.task import TaskState
-from fusionagi.schemas.recommendation import Recommendation, TrainingSuggestion
-from fusionagi.core.event_bus import EventBus
 from fusionagi._logger import logger
-
+from fusionagi.core.event_bus import EventBus
+from fusionagi.schemas.recommendation import Recommendation, TrainingSuggestion
+from fusionagi.schemas.task import TaskState
 from fusionagi.self_improvement.correction import (
+    CriticLike,
+    OrchestratorLike,
     SelfCorrectionLoop,
     StateManagerLike,
-    OrchestratorLike,
-    CriticLike,
 )
 from fusionagi.self_improvement.recommender import AutoRecommender
 from fusionagi.self_improvement.training import AutoTrainer, ReflectiveMemoryLike

fusionagi/self_improvement/recommender.py

@@ -2,8 +2,8 @@
 
 from typing import Any, Protocol
 
-from fusionagi.schemas.recommendation import Recommendation, RecommendationKind
 from fusionagi._logger import logger
+from fusionagi.schemas.recommendation import Recommendation, RecommendationKind
 
 
 class ReflectiveMemoryLike(Protocol):
@@ -81,7 +81,7 @@ class AutoRecommender:
             return []
         lessons = self._memory.get_lessons(limit=limit_lessons)
         recs: list[Recommendation] = []
-        failed = [l for l in lessons if l.get("outcome") == "failed"]
+        failed = [lesson for lesson in lessons if lesson.get("outcome") == "failed"]
         if len(failed) >= 3:
             recs.append(
                 Recommendation(

fusionagi/self_improvement/training.py

@@ -2,8 +2,8 @@
 
 from typing import Any, Protocol
 
-from fusionagi.schemas.recommendation import TrainingSuggestion, TrainingSuggestionKind
 from fusionagi._logger import logger
+from fusionagi.schemas.recommendation import TrainingSuggestion, TrainingSuggestionKind
 
 
 class ReflectiveMemoryLike(Protocol):
@@ -152,10 +152,15 @@ class AutoTrainer:
         task_id: str | None = None,
         evaluation: dict[str, Any] | None = None,
         apply_heuristics: bool = True,
+        use_gpu: bool = True,
     ) -> list[TrainingSuggestion]:
-        """
-        Suggest training from evaluation/lessons and optionally apply
-        heuristic updates. Returns all suggestions (for logging or external use).
+        """Suggest training from evaluation/lessons and optionally apply updates.
+
+        When *use_gpu* is ``True`` (default) and GPU dependencies are
+        installed, also runs GPU-accelerated gradient optimization on
+        reflective memory lessons to learn better heuristic weights.
+
+        Returns all suggestions (for logging or external use).
         """
         suggestions = self.suggest_training(
             task_id=task_id,
@@ -164,4 +169,22 @@ class AutoTrainer:
         )
         if apply_heuristics:
             self.apply_heuristic_updates(suggestions)
+        if use_gpu and self._memory is not None:
+            self._try_gpu_training()
         return suggestions
+
+    def _try_gpu_training(self) -> None:
+        """Run GPU-accelerated training if available."""
+        try:
+            from fusionagi.self_improvement.gpu_training import (
+                run_gpu_enhanced_training,
+            )
+
+            if self._memory is not None:
+                result = run_gpu_enhanced_training(self._memory, epochs=10)
+                logger.info(
+                    "AutoTrainer: GPU training complete",
+                    extra={"gpu_accelerated": result.get("gpu_accelerated", False)},
+                )
+        except ImportError:
+            pass