aaron 7c7b649775 embeddings: enforce type/created_at on writers; manifests carry type_distribution (Improvement #2 part B+C)
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.
2026-05-04 00:15:43 +00:00
2026-04-25 02:05:42 +00:00
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