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>

crates/gateway/Cargo.toml

@@ -10,6 +10,7 @@ catalogd = { path = "../catalogd" }
 queryd = { path = "../queryd" }
 aibridge = { path = "../aibridge" }
 ingestd = { path = "../ingestd" }
+vectord = { path = "../vectord" }
 tokio = { workspace = true }
 axum = { workspace = true }
 serde = { workspace = true }