step B.6 complete: POST /api/v1/storage/vectors/embed endpoint

AI_REFACTOR_PLAN.md

@@ -205,7 +205,7 @@ Tools must use **camelCase** field names (Drizzle maps them to snake_case intern
 - **Outcome:** Clean final frame. No more action descriptors in the protocol.
 
 ### Step B.6 — Add `/vectors/embed` endpoint
-- [ ] Add to `app/api/routes/vectors.py`:
+- [x] Add to `app/api/routes/vectors.py`:
   - `POST /api/v1/storage/vectors/embed`:
     - Request: `{ text: str }`
     - Response: `{ vector: list[float] }` (1536-dim from `text-embedding-3-small`)

app/api/routes/vectors.py

@@ -1,4 +1,4 @@
-"""Vectors routes: upsert, search, and delete cloud vector store entries."""
+"""Vectors routes: upsert, search, delete cloud vector store entries, and embed text."""
 
 from __future__ import annotations
 
@@ -6,6 +6,7 @@ from fastapi import APIRouter, Depends
 from pydantic import BaseModel
 
 from app.api.deps import get_current_user
+from app.core.llm import embed
 from app.schemas import (
     UserProfile,
     VectorSearchRequest,
@@ -24,6 +25,14 @@ class _VectorDeleteRequest(BaseModel):
     ids: list[str]
 
 
+class _EmbedRequest(BaseModel):
+    text: str
+
+
+class _EmbedResponse(BaseModel):
+    vector: list[float]
+
+
 @router.post("/vectors/upsert", response_model=dict)
 async def upsert_vectors(
     body: VectorUpsertRequest,
@@ -54,3 +63,17 @@ async def delete_vectors(
     """Delete vectors by ID, scoped to the authenticated user."""
     await _vector_store.delete(current_user.id, body.ids)
     return {"ok": True}
+
+
+@router.post("/vectors/embed", response_model=_EmbedResponse)
+async def embed_text(
+    body: _EmbedRequest,
+    current_user: UserProfile = Depends(get_current_user),
+) -> _EmbedResponse:
+    """Generate a 1536-dim embedding vector for the given text.
+
+    Uses ``text-embedding-3-small`` via OpenAI.  Auth required (JWT).
+    Used by backend tools (note_agent) and Electron (vectordb.ts) alike.
+    """
+    vector = await embed(body.text)
+    return _EmbedResponse(vector=vector)