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>

Cargo.lock

@@ -2392,6 +2392,7 @@ dependencies = [
  "tracing",
  "tracing-opentelemetry",
  "tracing-subscriber",
+ "vectord",
 ]
 
 [[package]]
@@ -5407,6 +5408,24 @@ version = "0.1.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "ba73ea9cf16a25df0c8caa16c51acb937d5712a8429db78a3ee29d5dcacd3a65"
 
+[[package]]
+name = "vectord"
+version = "0.1.0"
+dependencies = [
+ "aibridge",
+ "arrow",
+ "axum",
+ "bytes",
+ "object_store",
+ "parquet",
+ "serde",
+ "serde_json",
+ "shared",
+ "storaged",
+ "tokio",
+ "tracing",
+]
+
 [[package]]
 name = "version_check"
 version = "0.9.5"