Phase 7: Vector index + RAG pipeline

- vectord crate: chunk → embed → store → search → RAG
- chunker: configurable chunk size + overlap, sentence-boundary aware splitting
- store: embeddings as Parquet (binary blob f32 vectors), portable format
- search: brute-force cosine similarity (works up to ~100K vectors)
- rag: full pipeline — embed question → search index → retrieve context → LLM answer
- Endpoints: POST /vectors/index, /vectors/search, /vectors/rag
- Gateway wired with vectord service
- Tested: 200 candidate resumes indexed in 5.4s, semantic search + RAG working
- 20 unit tests passing (chunker, search, ingestd, shared)
- AI gives honest "no match found" when context doesn't support an answer

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

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+  "name": "timesheets",
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+  "objects": [
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+      "key": "datasets/timesheets.parquet",
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+    }
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+  "created_at": "2026-03-27T13:11:42.084217486Z",
+  "updated_at": "2026-03-27T13:11:42.084217486Z"
+}