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* feat(memory): phase 1 memory tree - multi-source ingestion & canonical chunks (#707) Adds an isolated memory tree layer under src/openhuman/memory/tree/ implementing Phase 1 of the new memory architecture (umbrella #711). Zero edits to existing memory/*.rs files - the new layer coexists with the legacy TinyHumans-backed client. - Source adapters: chat / email / document -> canonical Markdown - Token-bounded chunker with deterministic SHA-256 chunk IDs - SQLite persistence at <workspace>/memory_tree/chunks.db with full provenance metadata (source_kind, source_id, owner, timestamps, tags, time_range) and back-pointer to raw source - Unified JSON-RPC ingest (dispatches on source_kind + JSON payload): openhuman.memory_tree_ingest, _list_chunks, _get_chunk - DataSource enum covering the 8 providers from m.excalidraw step 1 (Discord/Telegram/Whatsapp/Gmail/OtherEmail/Notion/MeetingNotes/DriveDocs) - ~40 unit tests (chunk ID stability, UTF-8-safe splitting, canonicalisation idempotence, store round-trip, filter behavior) Additive only: new tables in a new DB file, new JSON-RPC namespace, no existing behavior changes. Feeds #708 (scoring), #709 (summary trees), #710 (query tools). Closes #707. Parent: #711. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(memory): phase 2 memory tree - preprocessing, scoring, admission gate (#708) Adds the scoring / admission layer between Phase 1's chunker and store. Stacked on feat/707-memory-ingestion (PR #732) - depends on Phase 1's chunk substrate. - Pluggable EntityExtractor trait + CompositeExtractor chain - RegexEntityExtractor: mechanical entities (emails, URLs, @handles, #hashtags) - Always on, deterministic, zero deps, UTF-8-safe char spans - Five weighted signals: token count, unique-word ratio, metadata weight, source weight (per-DataSource), interaction (reply/sent/mention/dm tags), entity density - Exact-match entity canonicalisation (email lowercased, @ and # stripped) - Admission gate drops chunks below configurable threshold (default 0.3) - Score rationale persists for EVERY chunk (kept or dropped) for debugging - Entities indexed for KEPT chunks only - Two new SQLite tables added to the memory_tree DB: - mem_tree_score: per-chunk score rationale with all signal values - mem_tree_entity_index: inverted index entity_id -> node_id - Idempotent ALTER TABLE migration adds embedding BLOB column to mem_tree_chunks (used in Phase 3 retrieval, wired but not populated here) - Ingest pipeline converted to async to accommodate the extractor trait; blocking SQLite work isolated on spawn_blocking; JSON-RPC surface unchanged (same memory_tree_ingest / list / get methods) - Phase 2 deliberately ships without GLiNER/semantic NER - per-chunk semantic entities land later behind a cargo feature flag; the composite extractor interface keeps that drop-in trivial Additive only: new tables, new columns, new module. Existing Phase 1 behavior unchanged except that low-signal chunks are now dropped before reaching mem_tree_chunks. Raise score_drop_threshold to 0 to disable the gate and restore Phase-1-identical behavior. Closes #708. Parent: #711. Depends on: #707 (#732). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * Fix memory tree scoring persistence issues * Fix memory tree scoring robustness issues from PR review - ingest: fail fast if scorer returns fewer/more results than chunks (silent zip truncation would drop chunks or their score rationale) - score::persist_score{,_tx}: clear stale entity-index rows before re-indexing a re-scored chunk, since INSERT OR REPLACE never deletes rows whose entity_id is no longer in the new extraction - score::store::lookup_entity: clamp limit to i64::MAX before casting to prevent a large usize wrapping into a negative LIMIT Adds clear_entity_index_drops_stale_rows regression test. --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> Co-authored-by: Steven Enamakel <enamakel@tinyhumans.ai>