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/multi_agent/consensus_engine.py

@@ -1,13 +1,17 @@
-"""Consensus engine: claim collection, deduplication, conflict detection, scoring."""
+"""Consensus engine: claim collection, deduplication, conflict detection, scoring.
+
+Supports GPU-accelerated deduplication when ``fusionagi[gpu]`` is installed;
+falls back to word-overlap heuristics otherwise.
+"""
 
 from __future__ import annotations
 
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import Any
 
-from fusionagi.schemas.head import HeadId, HeadOutput, HeadClaim
-from fusionagi.schemas.witness import AgreementMap
 from fusionagi._logger import logger
+from fusionagi.schemas.head import HeadId, HeadOutput
+from fusionagi.schemas.witness import AgreementMap
 
 
 @dataclass
@@ -57,6 +61,16 @@ def _looks_contradictory(a: str, b: str) -> bool:
     return False
 
 
+def _try_gpu_dedup(claims: list[str]) -> list[list[int]] | None:
+    """Attempt GPU-accelerated claim deduplication; return ``None`` if unavailable."""
+    try:
+        from fusionagi.gpu.tensor_similarity import deduplicate_claims
+
+        return deduplicate_claims(claims, threshold=0.85)
+    except ImportError:
+        return None
+
+
 def collect_claims(outputs: list[HeadOutput]) -> list[CollectedClaim]:
     """Flatten all head claims with source metadata."""
     collected: list[CollectedClaim] = []
@@ -107,25 +121,48 @@ def run_consensus(
     collected = collect_claims(outputs)
 
     # Group by similarity (merge near-duplicates)
-    merged: list[CollectedClaim] = []
+    # Try GPU-accelerated deduplication first; fall back to word-overlap
+    gpu_groups = _try_gpu_dedup([c.claim_text for c in collected])
+
+    claim_groups: list[list[CollectedClaim]] = []
     used: set[int] = set()
-    for i, ca in enumerate(collected):
-        if i in used:
-            continue
-        group = [ca]
-        used.add(i)
-        for j, cb in enumerate(collected):
-            if j in used:
+
+    if gpu_groups is not None:
+        for group_indices in gpu_groups:
+            filtered = [
+                idx for idx in group_indices
+                if idx not in used
+                and not any(
+                    _looks_contradictory(collected[idx].claim_text, collected[other].claim_text)
+                    for other in group_indices if other != idx
+                )
+            ]
+            if not filtered:
                 continue
-            if _are_similar(ca.claim_text, cb.claim_text) and not _looks_contradictory(ca.claim_text, cb.claim_text):
-                group.append(cb)
-                used.add(j)
-        # Aggregate: weighted avg confidence, combine heads
+            claim_groups.append([collected[idx] for idx in filtered])
+            used.update(filtered)
+    else:
+        for i, ca in enumerate(collected):
+            if i in used:
+                continue
+            group = [ca]
+            used.add(i)
+            for j, cb in enumerate(collected):
+                if j in used:
+                    continue
+                if _are_similar(ca.claim_text, cb.claim_text) and not _looks_contradictory(ca.claim_text, cb.claim_text):
+                    group.append(cb)
+                    used.add(j)
+            claim_groups.append(group)
+
+    # Aggregate: weighted avg confidence, combine heads
+    merged: list[CollectedClaim] = []
+    for group in claim_groups:
         if len(group) == 1:
             c = group[0]
             score = c.confidence * weights.get(c.head_id, 1.0)
             if c.evidence_count > 0:
-                score *= 1.1  # boost for citations
+                score *= 1.1
             merged.append(
                 CollectedClaim(
                     claim_text=c.claim_text,