feat: workspace-level semantic search across all notebooks #6

Problem

The v2 API only exposes notebook-scoped search (POST /api/v2/workspaces/{workspaceId}/notebooks/{notebookId}/search). There is no way to search across all notebooks in a workspace in a single request.

Why it matters

AEGIS uses MindSpring in its recall pipeline during every dispatch — recall.ts fires a semantic query in parallel with the Memory Worker to inject relevant context before generating a response. Currently this only hits the legacy global search endpoint (GET /api/search), which covers the original conversation archive.

We've now built a write pipeline that pushes extracted facts to per-topic notebooks (e.g. aegis, infrastructure, project) and session transcripts to a session_history notebook. These are dark at query time — ingested but never retrievable — because querying them would require:

1. Listing all notebooks in the workspace
2. Firing N parallel search requests (one per notebook)
3. Merging and re-ranking results

That's too expensive per-dispatch (adds N subrequests + latency on the hot path).

Proposed endpoint

POST /api/v2/workspaces/{workspaceId}/search

Body: same shape as notebook search — { query, limit?, threshold? }

Response: results with a notebook_id + notebook_title field on each hit so callers can attribute the source.

This would be a one-line change in recall.ts:

/api/search?q=...  →  /api/v2/workspaces/aegis-daemon/search  (POST, body: { query, limit, threshold })

And would make all notebook content — session history, extracted facts, topic archives — retrievable on every AEGIS dispatch with zero added latency.

Alternative considered

Hardcoding specific notebook IDs in recall and firing parallel per-notebook searches. Rejected: IDs change across environments, and N parallel subrequests on the hot path is worse than one workspace search.