docs/architecture.md

Architecture — Plane Mode Scholar

Agent pipeline (Best Agent)

User message
  → classify_intent
  → retrieve_chunks (vector search on pack)
  → retrieve_memories (hybrid filter + rerank)
  → pack_prompt (budget: chunks + memories + working memory)
  → generate (Qwen3.5-9B, streaming)
  → extract_candidates (regex + LLM JSON)
  → persist / consolidate / index_memory

Memory types

Type	Written when
misconception	Quiz miss, teach-back gap, user states confusion
review_item	SRS scheduler after miss or gap
session_summary	End session
study_goal / deadline	Pack create or chat extraction
user_preference	Chat patterns

Spaced repetition

SM-2 lite in memory/scheduler.py. First review is due immediately after a quiz miss so the Home dashboard and recall banner work within the same demo session.

Storage

• SQLite: packs, sessions, turns, memories, logs
• Vector stores: pack_{id} for chunks, memory_{pack_id} for memories
• Persistent disk on HF Space: /data/plane_mode_scholar

Judge automation

• docs/readiness_cases.jsonl — 5 scripted scenarios
• scripts/readiness_check.py — automated SRS + trace shape checks
• tests/test_scheduler.py — unit tests for due-now scheduling