docs: annotate known limitations and Frigate API improvement hooks

Adds inline LIMITATION / TODO(frigate-api) comments at each specific
code site rather than a separate doc that would drift from the code.

frigate_api.py — recognize_face:
  Mean-embedding limitation: score reflects the arithmetic mean of all
  training embeddings. A bimodal set (frontals + profiles) has a mean
  between clusters, making both ends look more novel than they are.
  Fixable if Frigate exposes per-file embeddings for nearest-neighbour
  comparison.

frigate_api.py — get_all_frigate_person_files:
  "train" key exclusion is a hardcoded string. If Frigate adds other
  special top-level keys in /api/faces they'll be silently treated as
  person names. Needs a typed schema when Frigate documents the contract.

executor.py — recognize_face call site:
  Async rebuild: each deletion triggers a background model rebuild in
  Frigate. Subsequent recognize calls in the same run return None
  (rebuild in progress), degrading quality replacement for later
  candidates. Fixable with a rebuild-complete signal from Frigate.

executor.py — effective_count / manual file handling:
  Manually-added files are invisible to diversity decisions. Winnow
  observes their effect only indirectly via the Frigate score, not by
  measuring their embedding distribution. Per-file embeddings from
  Frigate would allow direct diversity measurement against the full set.

executor.py — Frigate version assumption:
  All face training endpoints are v0.16+. No version check at startup;
  failures on older versions are opaque 404s.

cache.py — MODEL_VERSIONS:
  Version string is a hardcoded constant. Manual model file replacement
  (custom weights, InsightFace update) won't invalidate cached embeddings.
  Needs file-checksum-derived versioning or a CLEAR_EMBEDDING_CACHE flag.

diversity.py — thumbnail-resolution embeddings:
  Diversity selection runs InsightFace on preview thumbnails; the actual
  training crop comes from full-resolution originals. Negligible in
  practice but degrades if Immich preview quality is low.

winnow/frigate_api.py

@@ -33,6 +33,10 @@ def get_all_frigate_person_files() -> dict[str, list[str]] | None:
         return None
     # Response: {person_name: [file, ...], "train": [...], ...}
     # "train" is a flat pending list, not a person — skip it.
+    # TODO(frigate-api): "train" is the only known special key as of Frigate v0.16.
+    # If Frigate adds other top-level non-person keys, they'll be silently treated
+    # as person names here. Switch to an allowlist or a typed schema when Frigate
+    # documents its response contract.
     return {
         name: files
         for name, files in data.items()
@@ -75,6 +79,16 @@ def recognize_face(file_path: str) -> tuple[str | None, float] | None:
 
     Returns None if FRIGATE_URL is unset, the API is unreachable, no face is
     detected, or face recognition is not enabled in Frigate.
+
+    LIMITATION — mean embedding comparison: the score reflects similarity to
+    the arithmetic mean of all training embeddings, not to individual ones.
+    A bimodal training set (e.g. frontals + profiles) has a mean that sits
+    between both clusters, making candidates from either cluster look more
+    novel than they are. Winnow could add redundant frontals while the score
+    suggests novelty, because the mean is pulled toward profiles.
+    TODO(frigate-api): if Frigate exposes per-file embeddings via the API,
+    replace mean-comparison with nearest-neighbour distance across individual
+    training embeddings for accurate coverage detection.
     """
     frigate_url = os.environ.get("FRIGATE_URL", "").rstrip("/")
     if not frigate_url: