INPUT (markdown files, git repo)
  ↓
FILE RESOLUTION (local → .redirect → .supabase → error)
  ↓
MARKDOWN PARSER (gray-matter frontmatter + body)
  → compiled_truth + timeline separation
  ↓
CONTENT HASH (SHA-256 idempotency check — skip if unchanged)
  ↓
CHUNKING (3 strategies, configurable)
  ├── Recursive: 300-word chunks, 50-word overlap, 5-level delimiter hierarchy
  ├── Semantic: embed sentences, cosine similarity, Savitzky-Golay smoothing
  └── LLM-guided: Claude Haiku identifies topic shifts in 128-word candidates
  ↓
EMBEDDING (OpenAI text-embedding-3-large, 1536 dimensions)
  → batch 100, exponential backoff, non-fatal if fails
  ↓
DATABASE TRANSACTION (atomic: page + chunks + tags + version)
  ↓
SEARCH (hybrid, available immediately)

User Query
  ↓
EXPANSION (optional: Claude Haiku generates 2 alternative phrasings)
  ↓
  ├── VECTOR SEARCH (pgvector HNSW, cosine distance)
  │     → 2x limit results per query variant
  │
  └── KEYWORD SEARCH (PostgreSQL tsvector, ts_rank)
        → 2x limit results
  ↓
RRF MERGE (score = Σ(1/(60 + rank)), balances both fairly)
  ↓
4-LAYER DEDUP
  ├── Best 3 chunks per page (source dedup)
  ├── Jaccard similarity > 0.85 (text dedup)
  ├── No type exceeds 60% (diversity)
  └── Max 2 chunks per page (page cap)
  ↓
TOP N RESULTS (default 20)