docs(checkpoint-postgres): point stale delta query comments at the builders

The module comment still described one column pair per channel, no
subqueries and a stage 2 on channel = ANY(%s), and the row-shape comment
named hs_i keys that no longer exist. Point both at the builder docstrings
instead of keeping a second description of each query.

libs/checkpoint-postgres/langgraph/checkpoint/postgres/base.py

@@ -172,30 +172,8 @@ class _DeltaStage2Row(TypedDict, total=False):
     version: str | None  # "b" rows only
 
 
-# Multi-channel two-stage DeltaChannel reconstruction.
-#
-# Stage 1 scans checkpoint metadata (no blob bytes) and emits one row per
-# checkpoint with K parallel JSONB key lookups (one column pair per
-# requested delta channel: ver_i / hs_i).  No subqueries, no aggregation.
-# Python walks the parent chain once across all channels.
-#
-# Stage 2 fetches all writes and the seed blobs for ALL channels in a
-# single roundtrip via `channel = ANY(%s)` and chain/seed-version
-# filtering.
-#
-# Empirical comparison vs an alternative "ship full channel_versions /
-# channel_values JSONB and let Python pick" form (1000 checkpoints,
-# 8 total channels in graph, 3 delta channels requested):
-#
-#   Postgres execution:    A=0.24ms vs B=0.38ms   (both negligible)
-#   End-to-end latency:    A=6.83ms vs B=2.28ms   (B is 3.0x faster)
-#   Wire payload:          A=836KB  vs B=330KB    (61% smaller)
-#   Buffer hits:           identical (167 blocks)
-#
-# B (this dynamic-columns design) wins because it avoids JSONB
-# serialization on the wire and JSONB-to-dict deserialization in
-# psycopg.  Even at K=8 (8 delta channels = 16 dynamic columns), B
-# still beats A end-to-end (4.2ms vs 6.8ms).
+# Delta history is rebuilt in two queries; `_build_delta_stage1_sql` and
+# `_build_delta_stage2_sql` document their shapes.
 
 
 def _build_delta_stage1_sql(channels: Sequence[str], *, paged: bool) -> str:
@@ -335,10 +313,8 @@ def _build_delta_stage2_sql(
     return " UNION ALL ".join(branches)
 
 
-# Stage 1 rows are dynamic-shape dicts: {checkpoint_id, parent_checkpoint_id,
-# ver_0, hs_0, ver_1, hs_1, ...}.  Walking is parameterized by the channel
-# list to map indices back to channel names — no static TypedDict here.
-# `dict[str, Any]` is the practical signature.
+# Stage 1 rows are dicts keyed by the per-channel aliases
+# `_build_delta_stage1_sql` emits, so there is no static TypedDict.
 
 
 class BasePostgresSaver(BaseCheckpointSaver[str]):