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/src/main.rs

@@ -42,10 +42,14 @@ async fn main() {
         .nest("/storage", storaged::service::router(store.clone()))
         .nest("/catalog", catalogd::service::router(registry.clone()))
         .nest("/query", queryd::service::router(engine))
-        .nest("/ai", aibridge::service::router(ai_client))
+        .nest("/ai", aibridge::service::router(ai_client.clone()))
         .nest("/ingest", ingestd::service::router(ingestd::service::IngestState {
             store: store.clone(),
             registry: registry.clone(),
+        }))
+        .nest("/vectors", vectord::service::router(vectord::service::VectorState {
+            store: store.clone(),
+            ai_client: ai_client.clone(),
         }));
 
     // Auth middleware (if enabled)