Cameron Cordes
f707353807
Implement end-to-end nightly pre-generation of memory reels with agentic
scripting that grounds narration in calendar, location, messages, and RAG.
Sections A-E from the plan:
A. Extract produce_reel pipeline core from run_reel_job with
ScripterMode::Fast/Agentic and progress callbacks.
B. Agentic scripter: factor run_readonly_tool_loop from the insight
generator, build read-only tool gate, prompt builder with GPS, and
generate_script_agentic with fallback to fast path.
C. Precomputed reels ledger (SQLite table + DAO), GET /reels/precomputed
handler with validity gate, GET /reels/by-key/{key}/video streaming,
and normalize_library_key helper.
D. Nightly scheduler: spawn_pregen_scheduler with configurable hour,
run_pregen_batch (day/week/month spans), pregen_one with dedup and
disk-check, secs_until_next_run_hour time math.
E. user_ai_prefs passive mirror table + DAO for param capture in
create_reel_handler and replay in the scheduler.
Also fixes resolve_library_param signature to take &[Library] and adds
resolve_library_param_state wrapper for AppState callers.
New files: migrations/2026-06-13-000000_add_precomputed_reels/,
migrations/2026-06-13-000010_add_user_ai_prefs/,
src/database/precomputed_reel_dao.rs,
src/database/user_ai_prefs_dao.rs
2 lines
36 B
SQL
2 lines
36 B
SQL
DROP TABLE IF EXISTS user_ai_prefs;
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