feat(neural): wire the LSTM engine into candidates (opt-in)

[mkpoli]

The final deployment step — puts the proven neural model on the on-device path.

Adds suggestions.engine = "ngram" (default) | "neural". With neural selected and the model bundled, the candidate window is driven by the LSTM with full recent-sentence context; otherwise — and as a fallback when the model is absent — the n-gram engine runs unchanged. Opt-in default ⇒ the shipped experience can't regress.

• config: Engine enum + suggestions.engine.
• text_service: committed_words — a sliding window (≤40) of recent committed words (the neural context); cleared on deactivation.
• composition: commit() appends to it; refresh_candidates() streams the LSTM over BOS + window, ranks prefix completions by full-context logits, builds the list via CandidateList::from_words; falls back to n-gram if neural::global() is None.
• candidates: from_words(...) constructor (+ test). neural: bos().

Clippy -D warnings clean (Windows target); candidate/from_words tests green via host harness.

⚠️ Needs a Windows run to verify in-IME behaviour + per-keystroke latency. The +7 KSR win over n-gram is proven offline; this realizes it on device.

Stack: feat/neural-export (int8 lstm.bin) → feat/neural-rs (module) → this. Merge in that order. Note: this adds suggestions.engine; when it lands alongside the self-documenting-config PR (#32), to_documented_toml + its test must be updated to include the new field (trivial follow-up).

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