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>

data/_catalog/manifests/ad393eee-ba0c-4338-9a8b-236bba3816ac.json

@@ -0,0 +1,15 @@
+{
+  "id": "ad393eee-ba0c-4338-9a8b-236bba3816ac",
+  "name": "placements",
+  "schema_fingerprint": "auto",
+  "objects": [
+    {
+      "bucket": "data",
+      "key": "datasets/placements.parquet",
+      "size_bytes": 217395,
+      "created_at": "2026-03-27T13:11:41.433628136Z"
+    }
+  ],
+  "created_at": "2026-03-27T13:11:41.433633927Z",
+  "updated_at": "2026-03-27T13:11:41.433633927Z"
+}