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/reasoning/tot.py

@@ -17,9 +17,9 @@ import uuid
 from dataclasses import dataclass, field
 from typing import Any
 
-from fusionagi.adapters.base import LLMAdapter
-from fusionagi.reasoning.cot import run_chain_of_thought, build_cot_messages
 from fusionagi._logger import logger
+from fusionagi.adapters.base import LLMAdapter
+from fusionagi.reasoning.cot import run_chain_of_thought
 
 
 @dataclass
@@ -132,9 +132,9 @@ def _generate_branch(
             f"Approach {b.branch_id}: {b.thought[:100]}..."
             for b in previous_branches
         ]
-        diversity_hint = f"\n\nPrevious approaches tried:\n" + "\n".join(prev_summaries)
+        diversity_hint = "\n\nPrevious approaches tried:\n" + "\n".join(prev_summaries)
         diversity_hint += "\n\nGenerate a DIFFERENT approach."
-    
+
     messages = [
         {"role": "system", "content": TOT_GENERATION_SYSTEM},
         {
@@ -142,9 +142,9 @@ def _generate_branch(
             "content": f"Query: {query}{diversity_hint}" + (f"\n\nContext: {context}" if context else ""),
         },
     ]
-    
+
     response = adapter.complete(messages, **kwargs)
-    
+
     return ThoughtBranch(
         branch_id=branch_num,
         thought=response,
@@ -166,9 +166,9 @@ def _evaluate_branch(
             "content": f"Query: {query}\n\nReasoning approach:\n{branch.thought}\n\nScore this approach.",
         },
     ]
-    
+
     response = adapter.complete(messages, **kwargs)
-    
+
     # Parse score from response
     try:
         # Try to extract JSON
@@ -182,7 +182,7 @@ def _evaluate_branch(
             return max(0.0, min(1.0, score))  # Clamp to [0, 1]
     except (json.JSONDecodeError, ValueError, KeyError):
         pass
-    
+
     # Fallback: try to extract a number
     import re
     numbers = re.findall(r"0?\.\d+|1\.0|[01]", response)
@@ -191,7 +191,7 @@ def _evaluate_branch(
             return max(0.0, min(1.0, float(numbers[0])))
         except ValueError:
             pass
-    
+
     return 0.5  # Default score if parsing fails
 
 
@@ -199,14 +199,14 @@ def _select_best_branch(branches: list[ThoughtBranch]) -> tuple[ThoughtBranch, s
     """Select the best branch based on scores."""
     if not branches:
         raise ValueError("No branches to select from")
-    
+
     if len(branches) == 1:
         return branches[0], "Only one branch available"
-    
+
     # Sort by score descending
     sorted_branches = sorted(branches, key=lambda b: b.score, reverse=True)
     best = sorted_branches[0]
-    
+
     # Check if there's a clear winner
     if len(sorted_branches) > 1:
         score_diff = best.score - sorted_branches[1].score
@@ -216,7 +216,7 @@ def _select_best_branch(branches: list[ThoughtBranch]) -> tuple[ThoughtBranch, s
             reason = f"Selected highest score {best.score:.2f} among close alternatives"
     else:
         reason = f"Single branch with score {best.score:.2f}"
-    
+
     return best, reason
 
 
@@ -231,7 +231,7 @@ def run_tree_of_thought(
 ) -> tuple[str, list[str]]:
     """
     Run Tree-of-Thought reasoning with multiple branches.
-    
+
     Args:
         adapter: LLM adapter for generation and evaluation.
         query: The question or problem to reason about.
@@ -240,44 +240,44 @@ def run_tree_of_thought(
         depth: Number of refinement iterations (1 = single pass, 2+ = iterative refinement).
         prune_threshold: Minimum score to keep a branch (branches below are pruned).
         **kwargs: Additional arguments passed to adapter.complete().
-    
+
     Returns:
         Tuple of (best_response, trace_list).
     """
     if max_branches < 1:
         max_branches = 1
-    
+
     if max_branches == 1:
         # Fall back to simple CoT for single branch
         return run_chain_of_thought(adapter, query, context=context, **kwargs)
-    
+
     logger.info(
         "Starting Tree-of-Thought",
         extra={"query_length": len(query), "max_branches": max_branches, "depth": depth},
     )
-    
+
     total_llm_calls = 0
     branches: list[ThoughtBranch] = []
-    
+
     # Generate initial branches
     for i in range(max_branches):
         branch = _generate_branch(adapter, query, context, i, branches, **kwargs)
         total_llm_calls += 1
         branches.append(branch)
-    
+
     # Evaluate all branches
     for branch in branches:
         branch.score = _evaluate_branch(adapter, branch, query, **kwargs)
         total_llm_calls += 1
-    
+
     # Prune low-quality branches
     branches = [b for b in branches if b.score >= prune_threshold]
-    
+
     if not branches:
         # All branches pruned - fall back to CoT
         logger.warning("All ToT branches pruned, falling back to CoT")
         return run_chain_of_thought(adapter, query, context=context, **kwargs)
-    
+
     # Iterative refinement for depth > 1
     for d in range(1, depth):
         refined_branches = []
@@ -290,35 +290,35 @@ Score: {branch.score:.2f}
 Feedback: {branch.metadata.get('evaluation_reason', 'N/A')}
 
 Improve this approach based on the feedback. Make it more complete and rigorous."""
-            
+
             messages = [
                 {"role": "system", "content": TOT_GENERATION_SYSTEM},
                 {"role": "user", "content": f"Query: {query}\n\n{refinement_prompt}"},
             ]
-            
+
             refined_thought = adapter.complete(messages, **kwargs)
             total_llm_calls += 1
-            
+
             refined_branch = ThoughtBranch(
                 branch_id=branch.branch_id,
                 thought=refined_thought,
                 trace=branch.trace + [f"[Refinement {d}] {refined_thought}"],
             )
-            
+
             refined_branch.score = _evaluate_branch(adapter, refined_branch, query, **kwargs)
             total_llm_calls += 1
-            
+
             # Keep the better version
             if refined_branch.score > branch.score:
                 refined_branches.append(refined_branch)
             else:
                 refined_branches.append(branch)
-        
+
         branches = refined_branches
-    
+
     # Select the best branch
     best_branch, selection_reason = _select_best_branch(branches)
-    
+
     logger.info(
         "Tree-of-Thought completed",
         extra={
@@ -327,7 +327,7 @@ Improve this approach based on the feedback. Make it more complete and rigorous.
             "total_llm_calls": total_llm_calls,
         },
     )
-    
+
     # Build comprehensive trace
     trace = [
         f"[ToT Branch {best_branch.branch_id}] Score: {best_branch.score:.2f}",
@@ -336,7 +336,7 @@ Improve this approach based on the feedback. Make it more complete and rigorous.
     if best_branch.metadata.get("evaluation_reason"):
         trace.append(f"[Evaluation] {best_branch.metadata['evaluation_reason']}")
     trace.append(f"[Selection] {selection_reason}")
-    
+
     return best_branch.thought, trace
 
 
@@ -351,12 +351,12 @@ def run_tree_of_thought_detailed(
 ) -> ToTResult:
     """
     Run Tree-of-Thought and return detailed results including all branches.
-    
+
     Same as run_tree_of_thought but returns a ToTResult with full information.
     """
     if max_branches < 1:
         max_branches = 1
-    
+
     if max_branches == 1:
         response, trace = run_chain_of_thought(adapter, query, context=context, **kwargs)
         single_branch = ThoughtBranch(branch_id=0, thought=response, trace=trace, score=0.5)
@@ -368,10 +368,10 @@ def run_tree_of_thought_detailed(
             total_llm_calls=1,
             selection_reason="Single branch (CoT mode)",
         )
-    
+
     total_llm_calls = 0
     branches: list[ThoughtBranch] = []
-    
+
     # Generate and evaluate branches
     for i in range(max_branches):
         branch = _generate_branch(adapter, query, context, i, branches, **kwargs)
@@ -379,19 +379,19 @@ def run_tree_of_thought_detailed(
         branch.score = _evaluate_branch(adapter, branch, query, **kwargs)
         total_llm_calls += 1
         branches.append(branch)
-    
+
     all_branches = list(branches)  # Keep all for result
-    
+
     # Prune
     branches = [b for b in branches if b.score >= prune_threshold]
-    
+
     if not branches:
         # Use best of all branches even if below threshold
         branches = sorted(all_branches, key=lambda b: b.score, reverse=True)[:1]
-    
+
     # Select best
     best_branch, selection_reason = _select_best_branch(branches)
-    
+
     return ToTResult(
         best_response=best_branch.thought,
         best_trace=best_branch.trace,