Jake
d1eb56fac9
kon-llm now owns a real LlamaBackend + LlamaModel, with three Qwen3 tiers (1.7B Q4, 4B-Instruct-2507 Q4, 14B Q5) selectable per hardware. Downloads are resumable with SHA-256 verification and stored under ~/.kon/models/llm. Engine exposes three high-level surfaces — all greedy/temp-0, GBNF-constrained where output shape matters: - cleanup_text (prompt-injection-hardened system prompt; profile terms appended as "preserve these spellings" suffix) - decompose_task (3–7 micro-steps, constrained JSON array) - extract_tasks (optional-array; empty when no explicit commitments) post_process_segments now takes an Option<&LlmEngine> and, when loaded and format_mode != Raw, joins segments → cleanup → replaces segments with the cleaned text (first segment span). Rule-based path still runs first; LLM errors log and keep rule-based output. Tauri commands: recommend_llm_tier, check_llm_model, download_llm_model, load_llm_model, unload_llm_model, delete_llm_model, get_llm_status, cleanup_transcript_text_cmd, extract_tasks_from_transcript_cmd, decompose_and_store (LLM-backed subtasks). Settings: AI tier toggle (off / cleanup / tasks), model picker with downloaded/loaded status, download progress events via kon:llm-download-progress. Dictation: ensureLlmModelLoaded on mount, cleanupTranscriptIfEnabled after stop when tier != off and format_mode != Raw, LLM task extraction when tier=tasks (regex fallback on failure). Interim: both llama-cpp-sys-2 and whisper-rs-sys statically link their own ggml, so src-tauri/build.rs emits -Wl,--allow-multiple-definition on Linux. Replace with a system-ggml shared-lib setup as a follow-up. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
13 lines
667 B
Rust
13 lines
667 B
Rust
pub const DECOMPOSE_TASK_SYSTEM: &str = "\
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You are a task-decomposition assistant. Given a task description, produce \
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between 3 and 7 concrete, physical micro-steps. Each step must be a short \
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imperative sentence, actionable today, with no commentary. Output ONLY a \
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JSON array of strings.";
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pub const EXTRACT_TASKS_SYSTEM: &str = "\
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You are a task-extraction assistant. Given a transcript of spoken notes, \
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output a JSON array of action items the speaker committed to. Each item must \
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be a short imperative sentence. Omit observations, wishes, and background \
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context that are not explicit commitments. Output an empty array if there are \
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no action items.";
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