fix(vfs): the VFS root never persists a zero-norm vector

A zero-norm vector is lawful inside brainy (cosine distance scores it at
maximum, never a false top hit) but a false attractor for a downstream
engine serving squared-euclidean distance, which cannot tell a real
all-zero vector apart from a legitimate origin point.

- The VFS root now persists with vector [] (the existing "unvectored"
  shape) instead of a real all-zero 384-dim placeholder, and is never
  routed into the deferred-embed pipeline.
- A one-time migration in the root-init path detects a pre-fix store's
  all-zero placeholder root (by norm, not length) and rewrites it to []
  through a new sanctioned Brainy method that keeps the canonical
  vectored-noun ledger honest and removes the row from the vector index.
- The vector-index write seam (AddToVectorIndexOperation,
  ReplaceInVectorIndexOperation, and the generation materializer's direct
  insert) now refuses any real all-zero vector before it reaches a
  provider, loudly naming the entity, while the canonical write still
  lands.
- add()'s dimension-pinning and HNSW-insert gates, and the add-params
  validator, now treat any empty vector as carrying no dimension
  information, closing a latent trap where an explicit `vector: []`
  would have pinned dimensions to 0.

src/utils/paramValidation.ts

@@ -588,8 +588,14 @@ export function validateAddParams(params: AddParams): void {
     )
   }
   
-  // Validate vector dimensions if provided
-  if (params.vector) {
+  // Validate vector dimensions if provided. A length-0 vector is the
+  // "unvectored" shape — an explicit `vector: []` (e.g. the VFS root's
+  // permanently-vectorless creation, see
+  // VirtualFileSystem.doInitializeRoot()'s zero-norm fix) carries no
+  // dimension information, exactly like an absent vector or a deferred
+  // embed's internal stub, so it is exempt from the dimension check rather
+  // than refused as a "0-dimensional vector".
+  if (params.vector && params.vector.length > 0) {
     const config = ValidationConfig.getInstance()
     if (params.vector.length !== config.maxVectorDimensions) {
       throw new Error(`vector must have exactly ${config.maxVectorDimensions} dimensions`)