- index each compression-dropped turn once: messages and context share
  objects, and earlier drops stay in the display history
- label raw excerpts against facts even when recall asks for scope=raw
- top up short vector results from the lexical index (NULL embeddings)
- count whole content words only and cap query terms at 12
- carry source_session_id on fact hits
- cap injected_chunk_ids at 100 entries
- keep a session delete successful when chunk cleanup fails
- stop logging query text in the memory paths
- stop fabricating similarity scores for recent chunks
- consult session_messages_archive in latest_summary_at

Compression removes messages from the model's window, not from the
conversation: every compression path now writes the turns it demotes into
`memory_chunks` — verbatim, append-only, with the turn range, timestamps and
breadcrumb keys (the summarizer's `KEYS:` line plus a deterministic extractor,
which also covers the hard-truncate fallback that never sees a summarizer).

`memory_search` is the single read path over the three stores: curated facts
first, then raw excerpts by relevance, then the current session's sketches.
Every hit carries provenance (source, session, turn range, timestamp), and a
raw excerpt that a newer curated fact overrides is labelled outdated rather
than hidden — "we changed our mind, the old value was X" is often the
question. The agent also gets up to two relevant excerpts injected per turn
once the context holds a summary, deduped across turns.

Fixes found by running the pilot against a real database:
- `search_facts` returns ISO-string timestamps while chunks return datetimes;
  the freshness comparison raised TypeError and took the whole search down.
- an existing `memory_chunks` table created without pgvector never gained the
  `embedding` column (CREATE TABLE IF NOT EXISTS does not alter), and the
  INSERT failed on it; the DDL now adds the column, and a database without
  pgvector writes text-only rows found through FTS.