fix(embeddings): survive numpy embeddings when restoring a reset lane (#3410)

When a lane reset fails to rewrite the recreated collection, the recovery path
re-adds the preserved rows. It read the embeddings with
`preserved.get("embeddings") or []` and gated the loop with
`if ids and docs and old_embeddings:`. chromadb returns embeddings as a numpy
ndarray, whose truth value is ambiguous, so both expressions raise ValueError
inside the except block — the restore is abandoned and every preserved row is
lost (the collection was already deleted), exactly when the code is trying to
avoid data loss.

Use an explicit `is None` check and `len(...)`, and convert ndarray batches to
lists before re-adding.

Adds tests/test_embedding_lane_ndarray_restore.py (preserved embeddings come
back as np.ndarray); existing test_embedding_lanes.py still passes.

src/embedding_lanes.py

@@ -196,13 +196,22 @@ def _get_or_reset_collection(chroma_client, name: str, metadata: Dict[str, Any],
         try:
             chroma_client.delete_collection(name)
             restored = chroma_client.get_or_create_collection(name=name, metadata=current)
-            old_embeddings = preserved.get("embeddings") or []
-            if ids and docs and old_embeddings:
+            # chromadb returns embeddings as a numpy ndarray, whose truth value
+            # is ambiguous — `preserved.get("embeddings") or []` and a bare
+            # `if ... and old_embeddings:` both raise ValueError, which aborts
+            # the restore and loses the rows the reset was supposed to keep.
+            # Use explicit None/len checks instead.
+            old_embeddings = preserved.get("embeddings")
+            if old_embeddings is None:
+                old_embeddings = []
+            if ids and docs and len(old_embeddings):
                 for start in range(0, len(ids), 100):
                     batch_ids = ids[start:start + 100]
                     batch_docs = docs[start:start + 100]
                     batch_metas = metas[start:start + 100]
                     batch_embeddings = old_embeddings[start:start + 100]
+                    if hasattr(batch_embeddings, "tolist"):
+                        batch_embeddings = batch_embeddings.tolist()
                     if len(batch_metas) < len(batch_ids):
                         batch_metas += [{}] * (len(batch_ids) - len(batch_metas))
                     restored.add(