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>
104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
"""pgvector-backed VectorMemory adapter. Requires: pip install fusionagi[vector]."""
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from typing import Any
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from fusionagi._logger import logger
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def create_vector_memory_pgvector(
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connection_string: str,
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table_name: str = "embeddings",
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dimension: int = 1536,
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) -> Any:
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"""
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Create pgvector-backed VectorMemory when pgvector is installed.
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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 # 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 # 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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return VectorMemoryPgvector(connection_string, table_name, dimension)
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class VectorMemoryPgvector:
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"""VectorMemory implementation using pgvector."""
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def __init__(
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self,
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connection_string: str,
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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 psycopg
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from pgvector.psycopg import register_vector
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self._conn_str = connection_string
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self._table = table_name
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self._dim = dimension
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with psycopg.connect(connection_string) as conn:
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register_vector(conn)
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with conn.cursor() as cur:
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cur.execute("CREATE EXTENSION IF NOT EXISTS vector")
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cur.execute(
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f"""
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CREATE TABLE IF NOT EXISTS {table_name} (
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id TEXT PRIMARY KEY,
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embedding vector({dimension}),
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metadata JSONB,
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created_at TIMESTAMPTZ DEFAULT NOW()
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)
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"""
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)
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conn.commit()
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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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with psycopg.connect(self._conn_str) as conn:
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register_vector(conn)
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with conn.cursor() as cur:
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cur.execute(
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f"""
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INSERT INTO {self._table} (id, embedding, metadata)
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VALUES (%s, %s, %s)
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ON CONFLICT (id) DO UPDATE SET embedding = EXCLUDED.embedding, metadata = EXCLUDED.metadata
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""",
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(id, embedding, json.dumps(metadata or {})),
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)
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conn.commit()
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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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with psycopg.connect(self._conn_str) as conn:
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register_vector(conn)
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with conn.cursor() as cur:
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cur.execute(
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f"""
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SELECT id, metadata
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FROM {self._table}
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ORDER BY embedding <-> %s
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LIMIT %s
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""",
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(query_embedding, top_k),
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)
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rows = cur.fetchall()
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return [{"id": r[0], "metadata": json.loads(r[1]) if r[1] else {}} for r in rows]
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