…rtial index

Three fixes for embed --stale on large brains (300K+ chunks):

## 1. Cursor-paginated listStaleChunks (embed timeout fix)

The previous implementation pulled ALL stale rows (up to 100K) in one
query. On a 373K-row content_chunks table with 48K stale rows, this
query took >2 min and hit Supabase's 2-min statement_timeout, causing
embed --stale to silently fail with zero progress.

Fix: keyset pagination on (page_id, chunk_index) with a default batch
size of 2000 rows. Each query finishes in <1s. The embedAllStale loop
pages through batches, embeds each batch, then advances the cursor.

## 2. Rate-limit-aware retry (429 backoff)

The OpenAI SDK's built-in retry has a ~4s max backoff window, which is
too short for TPM (tokens-per-minute) limits on large pages (~90K
tokens). The embed loop would fail after 3 SDK retries and skip the
page entirely.

Fix: embedBatchWithBackoff wrapper parses the retry delay from the
429 error message (e.g. 'try again in 248ms') and sleeps for that
duration + 500ms padding. Up to 5 retries with parsed delays (60s
fallback when unparseable).

## 3. Migration v58: partial index for NULL embeddings

`CREATE INDEX idx_chunks_embedding_null ON content_chunks (page_id,
chunk_index) WHERE embedding IS NULL` — makes countStaleChunks() and
the paginated listStaleChunks() instant instead of full-table-scanning
373K rows.

## Testing

Verified on a 99K-page / 373K-chunk brain with 48K stale chunks.
Before: embed --stale hung for 2+ min then timed out (0 progress).
After: loads 2K rows in <1s, embeds concurrently, pages through all
stale chunks without timeout.