fix: deep GPU integration, fix all ruff/mypy issues, add .dockerignore
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- 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>
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@@ -15,14 +15,14 @@ def create_vector_memory_pgvector(
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Returns None if pgvector/database unavailable.
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"""
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try:
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import pgvector
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from pgvector.psycopg import register_vector
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import pgvector # noqa: F401
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from pgvector.psycopg import register_vector # noqa: F401
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except ImportError:
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logger.debug("pgvector not installed; use pip install fusionagi[vector]")
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return None
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try:
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import psycopg
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import psycopg # noqa: F401
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except ImportError:
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logger.debug("psycopg not installed; use pip install fusionagi[memory]")
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return None
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@@ -39,7 +39,7 @@ class VectorMemoryPgvector:
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table_name: str = "embeddings",
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dimension: int = 1536,
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) -> None:
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import pgvector
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import psycopg
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from pgvector.psycopg import register_vector
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self._conn_str = connection_string
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@@ -64,6 +64,7 @@ class VectorMemoryPgvector:
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def add(self, id: str, embedding: list[float], metadata: dict[str, Any] | None = None) -> None:
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import json
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import psycopg
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from pgvector.psycopg import register_vector
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@@ -82,6 +83,7 @@ class VectorMemoryPgvector:
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def search(self, query_embedding: list[float], top_k: int = 10) -> list[dict[str, Any]]:
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import json
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import psycopg
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from pgvector.psycopg import register_vector
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