Embedder was instantiated at module import (~30-60s, ~200MB) regardless
of whether new conversations existed. On nights with no new content
(most nights per the logs), the script paid the load cost and exited
immediately. ingest.py:134 already uses lazy loading; this brings the
two ingest scripts into a consistent shape.
Writers now enforce type and created_at:
- encoding.py: ValueError raised at write_embeddings_batch if row dict lacks
'type'. created_at remains SQL-supplied (NOW() server-side). ON CONFLICT
DO UPDATE now also rewrites type=EXCLUDED.type and preserves the original
created_at via COALESCE(embeddings.created_at, EXCLUDED.created_at) — a
re-ingest re-classifies type but does not overwrite a backfilled mtime.
- ingest_conversations.py: same assertion. ON CONFLICT intentionally keeps
EXCLUDED.created_at semantics (Aaron-AI conversation created_at tracks
convo.updated_at; re-runs should refresh).
- Column-level NOT NULL is not added; application-layer raise gives a
faster, more debuggable failure than a Postgres constraint error.
Retrieval propagates type into chunks:
- retrieve() SELECT now includes type; chunk dicts carry "type": etype.
- WHERE clause built dynamically from excluded_sources and the new
--type-filter CLI arg (experimental, default None, pgvector retrieval
only — Graphiti chunks have no embeddings.type to filter on).
- retrieve_graphiti unchanged; its chunks lack the type field.
Manifests carry type_distribution per stage:
- dream_pipeline writes stage_data[<stage>]["type_distribution"] for nrem,
early_rem, late_rem — a Counter over chunk types, filtering None so
Graphiti chunks (when DREAMER_SUBSTRATE=graphiti) don't pollute the
distribution. Pgvector chunks always carry type post-backfill; if None
appears, the backfill or writer enforcement has regressed.
Verification:
B1 force re-ingest of "Finite and infinite games -- James Carse.pdf":
all 84 chunks preserved created_at=2026-04-27T06:11:55Z
B2 missing-type assertion raises ValueError, no row leaked to embeddings
B3 ast.parse(*) clean; EXPLAIN renders for {no excl/no filter,
type_filter only, excl 2 elems, excl 1 elem edge case, both};
all five plans use HNSW index scan with correct Filter clauses
C1 retrieve("nrem") returns 8 chunks each carrying "type" key
C2 type_distribution = {'document': 5, 'chatgpt_conversation': 3} —
2 distinct types, 62.5/37.5 split (looser bar: >=2 types,
no single type >=90%)
The type and created_at fields are now load-bearing: every dream manifest
emits type_distribution per stage. Reverting the backfill makes the
distribution show NULLs at every dream run.
aaron