Zero-cost local embeddings against fixed-dimension vector indexes: zero-pad + per-deployment provider exclusivity + version stamping

CI needed real text embeddings (vector-dedup and semantic-search integration tests) but the repo had no embedding-provider API key — secrets.OPENAI_API_KEY had silently rendered empty forever, so every embedding-dependent test skipped or failed. Constraint: the production schema has FIXED 1536-dim cosine vector indexes (Neo4j per-label indexes + a pgvector(1536) column baked into migrations), sized for OpenAI text-embedding-3-small. Small local ONNX models emit 384-768 dims, and Matryoshka (MRL) is truncation-only — no model expands to 1536 — so the dimension gap looked like it forced either paid API keys in CI or invasive schema parameterization (divergent CI-vs-prod schemas).