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B-VEC: Migration 0118 — HNSW-Indizes mit optimierten Parametern (ef_construction=128, m=16) - 5 Tabellen: contacts, mails, files, calendar_entries, tags - config.py: hnsw_ef_construction, hnsw_m, hnsw_ef_search, vector_index_type Settings - base_provider.py + search_engine.py: SET LOCAL hnsw.ef_search vor Vector-Queries B-VEC-IVF: IVFFlat als Alternative dokumentiert (vector_index_type Setting) B-VEC-BATCH: Batch-Embedding verifiziert (generate_embeddings_batch nutzt llm_embed()) B-VEC-TEST: Performance-Tests auf Coolify-Instanz verschoben (benötigt 10k+ Datensätze)
89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
"""Optimize HNSW index parameters for better vector search recall.
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Recreates existing HNSW indices with tuned parameters:
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- ef_construction=128 (default 64, higher = better index quality, slower build)
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- m=16 (default 16, higher = more memory, better recall)
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IVFFlat Alternative (B-VEC-IVF):
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-----------------------------
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comm_messages uses IVFFlat with lists=100 (migration 0035).
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Rule of thumb for IVFFlat: lists = sqrt(rows)
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~10k rows → lists ≈ 100
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~50k rows → lists ≈ 224
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~100k rows → lists ≈ 316
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IVFFlat builds faster but HNSW has better recall.
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To switch: DROP INDEX + CREATE INDEX ... USING hnsw (embedding vector_cosine_ops)
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WITH (ef_construction=128, m=16)
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Config: vector_index_type setting in app/config.py (default 'hnsw', alternative 'ivfflat').
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Revision ID: 0118
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Revises: 0117
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"""
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from alembic import op
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import sqlalchemy as sa
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revision = "0118"
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down_revision = "0117"
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branch_labels = None
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depends_on = None
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# Optimized HNSW parameters
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EF_CONSTRUCTION = 128
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M = 16
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# Tables with HNSW indices (from migration 0104)
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# Format: (table_name, index_name)
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HNSW_TABLES = [
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("contacts", "ix_contacts_embedding"),
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("mails", "ix_mails_embedding"),
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("files", "ix_files_embedding"),
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("calendar_entries", "ix_calendar_entries_embedding"),
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("tags", "ix_tags_embedding"),
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]
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def upgrade() -> None:
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conn = op.get_bind()
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for table_name, index_name in HNSW_TABLES:
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# Check if table exists
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table_exists = conn.execute(sa.text(
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"SELECT EXISTS (SELECT 1 FROM information_schema.tables WHERE table_name = :t)"
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), {"t": table_name}).scalar()
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if not table_exists:
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continue
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# Drop existing HNSW index (regardless of parameters)
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op.execute(f"DROP INDEX IF EXISTS {index_name}")
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# Recreate with optimized parameters
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op.execute(
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f"CREATE INDEX IF NOT EXISTS {index_name} "
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f"ON {table_name} USING hnsw (embedding vector_cosine_ops) "
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f"WITH (ef_construction={EF_CONSTRUCTION}, m={M})"
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)
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def downgrade() -> None:
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"""Recreate HNSW indices with default parameters (no WITH clause)."""
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conn = op.get_bind()
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for table_name, index_name in HNSW_TABLES:
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table_exists = conn.execute(sa.text(
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"SELECT EXISTS (SELECT 1 FROM information_schema.tables WHERE table_name = :t)"
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), {"t": table_name}).scalar()
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if not table_exists:
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continue
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# Drop optimized index
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op.execute(f"DROP INDEX IF EXISTS {index_name}")
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# Recreate with default parameters (no WITH clause = pgvector defaults)
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op.execute(
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f"CREATE INDEX IF NOT EXISTS {index_name} "
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f"ON {table_name} USING hnsw (embedding vector_cosine_ops)"
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)
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