fix(memory): never merge a Hindi memory into a fact about someone else

[itsskofficial]

Closes #339

Summary

Hindi memories were merged into, or skipped as copies of, facts about other people. The real mechanism on main
(engine log): covered_by reads only Latin words, so a Devanagari fact has "nothing new" next to any fact, and
fact_subjects finds no subject in Devanagari, which counts as "same person". The fix is a deterministic
different-subject check before the model decides, plus script-aware word checks.

 FactMemory._remember(fact)
   exact match -> SKIP
-  top neighbour >= duplicate_similarity -> SKIP
+  neighbour >= duplicate_similarity AND same subject AND no new Hindi words -> SKIP
+  others = close facts whose subject clearly differs ("प्रिया" vs "Riya", "करण" vs "Kabir")
-  ask the model with every close/related fact
+  ask the model with only the same-subject facts (others never offered)
-  UPDATE/SKIP/DELETE must target an offered id
+  ... and never an id in `others`
-  SKIP overruled when the fact adds a Latin name/place/number
+  ... or any Devanagari word the matched fact lacks
   covered_by(fact, existing)
+    False when the new fact has non-Latin words the existing one lacks

sentient/memory/
├── scripts.py   # NEW, small helper: Devanagari subject (first word that isn't a time word/pronoun,
│                #   "मेरी बहन" -> "maya's sister"), rough romanization, names_match across scripts
│                #   ("माया" = "Maya", "प्रिया" != "Riya", "करण" != "Kiran"), script_words
├── facts.py     # subject_aliases() + same_subject() use the helper; CUD guards above
└── vectors.py   # VecTable records the embedding model: a model switch re-embeds even at the same dimension

Coordination with #338: fact_subjects is untouched. Devanagari subject detection lives behind
scripts.devanagari_subjects, used only by same_subject (the add/update/skip path). If #338 lands, its
Unicode tokenizing can replace parts of scripts.py; the two don't conflict textually.

Multilingual embedding option. Researched the Ollama library: bge-m3 (567M, 1.2 GB, 1024 dims, 100+
languages, 8K context, no query prefix), paraphrase-multilingual (278M, 563 MB, 128-token context), granite-embedding
278m, snowflake-arctic-embed2 (1.2 GB), nomic-embed-text-v2-moe (958 MB, needs search_query: prefixes, 512 tokens)
and qwen3-embedding:0.6b (639 MB, needs a newer Ollama than the 0.9 here). bge-m3 is the safe pick and fits the 8 GB
tier. It is offered, not made the default:

• GET /api/system/hardware recommendation.multilingual_embedding (MULTILINGUAL_EMBEDDING in config/schema.py,
  null when a cloud model comes first).
• Onboarding > Memory search: "Writing in Hindi or another language besides English? bge-m3 (1.2 GB) keeps those
  memories apart and finds them better. Use bge-m3" (or Download bge-m3, which opens the download chips).
• bge-m3 added to the Ollama download suggestions; the embedding role description mentions it.
• Re-embed path: switching the embedding role rebuilds the facts, summaries and user-model indexes on next use. A
  dimension change already did that; now a model change at the same dimension does too (nomic-embed-text and
  paraphrase-multilingual are both 768, mxbai-embed-large and bge-m3 both 1024). Old installs without a recorded model
  keep their index.

Evidence

Real engine (fresh SENTIENT_HOME, port 8788, qwen3:8b + nomic-embed-text, under the Ollama lock), same nine facts
through POST /api/memories, user name Maya. Main was run from a git archive origin/main copy, the branch from this
worktree ([TRACE] sentient from ... confirmed both).

Before (main 0b4f017): every Hindi fact lost.

save 'प्रिया पुणे में रहती है।'          -> SKIP id=1 'Riya lives in Pune.'
save 'प्रिया अब दिल्ली में रहती है।'      -> SKIP id=2 'Kabir moved to Delhi last year.'
save 'करण गोवा गया।'                   -> SKIP id=1 'Riya lives in Pune.'
save 'करण विप्रो में काम करता है।'          -> SKIP id=2 'Kabir moved to Delhi last year.'
save 'पिछले साल कबीर दिल्ली चला गया।'      -> UPDATE id=2 (same person, content unchanged)
save 'माया को चाय बहुत पसंद है।'          -> SKIP id=1 'Riya lives in Pune.'
engine: CUD ADD skipped for 'करण गोवा गया।': fact 1 already says it      (covered_by hole)
after saving: 3 active facts

After (this branch): each person keeps their own memories.

save 'प्रिया पुणे में रहती है।'          -> ADD id=4 'Priya lives in Pune.'      (qwen3:8b wrote it in English)
save 'प्रिया अब दिल्ली में रहती है।'      -> UPDATE id=4 'Priya now lives in Delhi.'  (same person: allowed)
save 'करण गोवा गया।'                   -> ADD id=5
save 'करण विप्रो में काम करता है।'          -> ADD id=6
save 'पिछले साल कबीर दिल्ली चला गया।'      -> UPDATE id=2 (Kabir, same person: allowed)
save 'माया को चाय बहुत पसंद है।'          -> ADD id=7 'Maya likes tea very much.'
engine: CUD for 'करण गोवा गया।': facts [1, 2, 4] are about someone else, not offered
after saving: 7 active facts; Riya, Kabir and Maya's facts unchanged

Hindi search, nomic-embed-text vs bge-m3 (same 7 facts; switching the role re-embedded them:
rebuilding vector index facts_vec (dim 768->1024, ..., model ollama/nomic-embed-text->ollama/bge-m3), 8.3 s for the
first query including loading the model, then 0.6 to 0.7 s per search):

प्रिया कहाँ रहती है?         nomic: [6] करण विप्रो में काम करता है। 0.74     bge-m3: [4] Priya now lives in Delhi. 0.72
करण कहाँ गया?             nomic: [5] करण गोवा गया। 0.93              bge-m3: [5] करण गोवा गया। 0.75 (next: 0.61 Karan, 0.45 Kabir)
माया को क्या पीना पसंद है?    nomic: [5] करण गोवा गया। 0.82              bge-m3: [7] Maya likes tea very much. 0.73
Where does Riya live?       both: [1] Riya lives in Pune. (0.84)

Raw pair scores: nomic puts unrelated Hindi facts at 0.71 to 0.85 (Kabir/Delhi vs Maya/vegetarian 0.846); bge-m3 at
0.35 to 0.56, and the same fact across Hindi and English at 0.77 to 0.84.

Does it fit the 8 GB tier? RTX 4060 laptop, qwen3:8b at 8,192 tokens, alternating a chat reply and two embeddings
(coexist.sh, under the lock): qwen3:8b stays at 7%/93% processor/graphics and 29 to 31 tokens/s with either
embedding model. bge-m3 is 1.7 GB loaded (nomic 0.85 GB); Ollama swaps it in, so the first embedding after a chat
reply takes 5.4 s (nomic 2.3 s) and the next one 0.63 s (nomic 0.67 s). It fits, at a few seconds of extra latency
on a cold search, which is why it is offered rather than made the default.

Screenshot (built app, smoke.mjs onboarding/brain, fresh home): under Memory search with nomic-embed-text
picked, the hint "Writing in Hindi or another language besides English? bge-m3 (1.2 GB) keeps those memories apart
and finds them better. Use bge-m3" shows, with the accent-colored action; nothing else on the step moved.

Tests: tests/memory/test_cross_script.py (names across scripts, Devanagari subjects, same_subject,
covered_by, an embedder that scores everything 1.0 so the near-duplicate shortcut and the model's UPDATE/SKIP/DELETE
of Riya/Kabir are all refused, the same person across scripts can still be updated, a same-dimension model switch
re-embeds, an old index without a recorded model is kept) and the hardware recommendation test.
pytest -q: 1802 passed, 1 skipped. ruff check sentient tests packaging clean. npm run typecheck and
npm run build clean.

Merge Danger

Door: two-way. No schema change; the only new stored value is a <table>_model meta key.

Blast Radius: memory writes.

• Facts about clearly different people are no longer shown to the CUD model, so it can't merge them. English facts
  where fact_subjects finds two different names (e.g. "Priya ..." vs "Riya ...") also stop being merged or skipped,
  which is the intent.
• Decision for the owner: a Hindi fact is never skipped as a copy of an English one (there is no way to check its
  details), so "माया शाकाहारी है" next to "Maya is vegetarian" may be stored twice unless the model updates it. I chose
  a possible duplicate over a lost fact. The nightly tidy-up can merge such pairs.
• Someone who switches the embedding model now waits for one re-embed of memory on the next search (already true
  when the dimension changed). If that re-embed fails, the old model stays recorded so the next start tries again
  (CodeRabbit round 1).

Summary by CodeRabbit

• New Features
  • Local model recommendations now highlight bge-m3 for searching memories in Hindi and other languages, with a shortcut to select or download it.
  • Memory matching better recognizes Hindi names across scripts and distinguishes different people when updating or skipping similar facts.
• Bug Fixes
  • Changing the memory-search model now rebuilds stored memory indexes, including when the replacement model has the same vector size.
• Documentation
  • Updated API and model guidance to describe multilingual recommendations and memory matching behavior.

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📝 Walkthrough

The changes add Hindi and Devanagari-aware fact matching, rebuild vector indexes when the embedding model changes, and add a multilingual memory-search recommendation. Tests and API documentation cover these changes.

Changes: Multilingual Memory Search

Layer / File(s)	Summary
Cross-script fact matching sentient/memory/scripts.py, sentient/memory/facts.py, tests/memory/test_cross_script.py, docs/API.md, CHANGELOG.md	Devanagari subject extraction and cross-script name matching now inform fact coverage, duplicate checks, and candidate filtering. Regression tests cover separating facts about different people and updating facts about the same person.
Model-aware vector indexes sentient/memory/vectors.py, sentient/memory/episodic.py, sentient/memory/facts.py, sentient/memory/usermodel.py, tests/memory/test_cross_script.py, docs/API.md, CHANGELOG.md	Vector tables track embedding model identity and rebuild when a known model changes, including when vector dimensions match. Memory integrations pass the model identifier. Insight-vector lookup starts background re-embedding when stored vectors use another model.
Multilingual model recommendation sentient/config/schema.py, sentient/llm/hardware.py, desktop/src/features/onboarding/BrainStep.tsx, desktop/src/lib/models.ts, desktop/src/lib/types.ts, tests/test_hardware_context.py, docs/API.md	Hardware recommendations include bge-m3 for non-English memory search unless the tier is cloud-first. Onboarding offers to select the model if installed or reveal download controls otherwise.

Priority: ➖ Normal

Estimated code review effort: 3 (Moderate) | ~25 minutes

Change: Bug fix · Severity of issue fixed: Medium

Merge Risk: ⚪ Minimal · up to 1371e

No actionable issue remains in the reviewed change; it is mergeable after normal checks.

Security architecture risk: 🟡 Moderate · up to e0452

The change improves separation of memories about different people, but a failed or interrupted switch can leave saved information unavailable without automatic recovery.

Retained concerns

• Medium · reliability · inferred: A failed or interrupted embedding-model switch can leave an empty or partial index recorded as current, preventing automatic retry. Readiness is published before rebuilding finishes, and the rebuild runs outside the lock. The PR newly exposes this failure-containment and recovery gap to same-dimension model changes across shared memory components.

Security review details

Security Blast Radius

• inferred — The identified lifecycle failure affects derived fact, conversation-summary, and insight indexes in the application Store. A model switch and rebuild failure are sufficient; no privilege gain or cross-tenant exploit was established. Whether independent tenants can share that Store remains unknown.

Trust Boundaries and Controls

• observed — The changed internal decision path narrows the authority of model output by withholding clearly different-subject facts and validating returned IDs before destructive actions. The hardware recommendation remains behind the existing token dependency, and the onboarding hint does not automatically initiate downloads.

Resilience and Maintainability Implications

• inferred — The shared rebuild boundary lacks a durable incomplete-state marker. Failure or cancellation after publishing the target identity can strand partial contents; another same-model call can bypass rebuilding, including while the original callback is still running. This is a recovery concern, not evidence of unauthorized disclosure.

Hardening Proposals

• proposed — Track rebuild completeness durably and publish the target model only after successful repopulation. Gate readers and competing rebuilds on that transition, retain a recoverable previous generation where practical, and make interruption or failure retryable after restart.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 35.59% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 59 functions across 12 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Linked Issues check		Issue #339 requires separate storage for the reported Hindi facts in a real qwen3:8b run. The PR reports that run and separate storage for Priya, Karan, and Maya. scripts.py adds Devanagari subject …
Out of Scope Changes check		The changes remain within Issue #339. Subject matching, CUD filtering, script-aware duplicate checks, regression tests, bge-m3 guidance, and model-change re-embedding support the Hindi memory and mult…
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.
Title check		The title clearly identifies the primary fix: preventing Hindi memories from merging into facts about unrelated people. It is concise, specific, and directly related to the changes.

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_{A rabbit reads each name with care,
Through scripts and memories everywhere.
New vectors wake when models change,
While bge-m3 joins the range.
Hop, Hindi facts now find their place,
And people keep their own facts’ space.}

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Actionable comments posted: 2

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Inline comments:
Review comments at @sentient/memory/usermodel.py:
- Around line 888-900: Update UserModelService._insight_vectors() to check the
stored embedding model before returning cached vectors. When the stored model
differs from the current embedding model, clear _vectors, start _reembed(), and
return an empty mapping; preserve the existing vector-loading behavior
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Review comments at @sentient/memory/vectors.py:
- Around line 64-98: In VecTable.ensure, defer writing dimension, metric, and
model metadata and updating self.dim/self.model until after rebuild() succeeds.
If rebuild() fails, return without committing the new identity so a later
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