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Parse Markdown chunks, preserve path and heading provenance, index lexical matches with SQLite FTS5 or BM25, add local embeddings, fuse rankings with RRF, and show the exact reasons a note matched.
A Markdown knowledge vault usually starts as a personal memory system: meeting follow-through, decision logs, stakeholder maps, project notes, prompt playbooks, and workflow observations. Search becomes risky when a result cannot explain which file, heading, chunk, and score path produced it.
The workbench pattern keeps retrieval local and reviewable. It materializes chunks, lexical candidates, local embeddings, RRF fused results, reason snippets, and fallback state before the dashboard opens.
Each step writes a bounded artifact that the next step can inspect. The UI reads those artifacts instead of reparsing notes, rebuilding indexes, or hiding retrieval reasons behind a model response.
A source manifest with note_path, source_hash, modified_at, byte size, heading count, and parse status for each Markdown note.
The workbench can explain that a result came from a specific file snapshot instead of an unknown live crawl.
Chunk rows with chunk_id, note_path, heading_path, heading_level, line_start, line_end, source_hash, and chunk_text.
Every match can show path provenance and heading provenance before the user opens the note.
A lexical candidate table with query_id, chunk_id, lexical_rank, bm25_score, matched_terms, and snippet offsets.
The result can say which exact terms matched and whether BM25, heading text, or note path carried the ranking.
An embedding candidate table with query_id, chunk_id, embedding_rank, embedding_score, vector_version, and embedding_status.
The workbench can identify semantic matches while still showing when an embedding job was skipped, stale, or failed.
A fused candidate table with lexical_rank, embedding_rank, rrf_score, source_methods, and rank_explanation.
Users can see if a chunk ranked because both systems agreed, because lexical recall found an exact term, or because embeddings found nearby language.
A search_result table with rank, note_path, heading_path, snippet, reason_codes, bm25_score, embedding_score, rrf_score, and freshness fields.
The result card can show the terms, heading, score mix, stale flags, and source path without recomputing retrieval live.
A local workbench view with query history, result reasons, provenance, stale index warnings, and lexical-only fallback banners.
The UI stays useful on a normal office laptop because search reads small SQLite tables instead of rebuilding embeddings or chunks on every query.
SQLite is enough for the first version: source notes, chunks, FTS5, local embedding metadata, and materialized search results. DuckDB can join larger note-derived facts later if the workflow grows.
The dashboard should make retrieval state visible. A user should know when the index is fresh, why a match appeared, and which fallback mode is active.
The user can tell if embeddings are active before trusting a semantic result.
Match reasons are visible next to the score mix, not hidden behind a generic relevance label.
The user can open the exact Markdown source location and verify the context before acting on the result.
The workbench names degraded retrieval states instead of silently pretending hybrid search is available.
Local embeddings are useful, but they are not a reason to make search fragile. The workbench should keep returning exact Markdown matches when vectors are missing, stale, or failed.
embedding_failed for one or more chunks, no local model available, vector store missing, or vector_version mismatch.
Use lexical-only SQLite FTS5 search with BM25 rank, matched terms, note_path, heading_path, and reason codes.
Semantic ranking is unavailable for this index version. Results are lexical-only and sorted by BM25 plus heading and path signals.
The embedding index is older than the latest source_hash manifest.
Keep returning FTS5 or BM25 results and mark embedding_score as unavailable until the local embeddings refresh finishes.
Some notes changed after embeddings were built. The workbench is using fallback to lexical search until vectors are refreshed.
RRF receives only lexical candidates because the embedding candidate table is empty.
Set fallback_mode to lexical-only, keep rrf_score derived from lexical rank, and show reason codes for exact term matches.
No semantic candidates were produced. Exact Markdown matches are still available with path and heading provenance.
Run these checks before publishing the workbench as a reusable pattern, starter repo, or internal local dashboard.
People use knowledge vault search to act on notes, so every result needs enough provenance to open the exact source.
A result card shows a relevant passage but cannot name the file path, heading, line range, or source snapshot.
Lexical recall catches exact names, stakeholder terms, systems, decision ids, and acronyms that embeddings can smooth over.
The workbench finds vague semantic matches but misses the exact operating phrase typed into the query console.
Reviewers need to know whether a match came from exact language, local embeddings, or agreement between both systems.
The result list only shows a single relevance score with no match reasons or score components.
A normal office laptop may pause, sleep, or fail a local embedding batch, but Markdown note search should remain usable.
Search returns no results because embedding_failed rows prevent FTS5 or BM25 fallback from running.
Use this before turning a Markdown vault into an AI-assisted work system, decision log, meeting follow-through search, or personal leverage dashboard.
Parse Markdown notes into stable chunks with note_path, heading_path, line ranges, source_hash, and chunk_id.
Index lexical matches with SQLite FTS5 or BM25 before building local embeddings.
Store local embeddings with vector_version and embedding_status so failures are visible.
Fuse lexical and semantic candidates with reciprocal rank fusion instead of mixing raw scores.
Materialize match reasons, reason codes, snippets, score components, provenance, and freshness before the dashboard opens.
Fallback to lexical search when embeddings fail and label the UI as lexical-only.
Pair the workbench with materialized retrieval outputs and hot marts so repeated questions read accepted chunks, score components, snippets, and freshness state.
It is a local search and review surface for Markdown notes. It parses chunks, preserves path and heading provenance, indexes lexical matches, adds local embeddings when available, and shows why each result matched.
Lexical search catches exact names, systems, acronyms, dates, and phrases. Embeddings help with nearby meaning, but exact workplace language still needs BM25-style recall.
Use reciprocal rank fusion. RRF merges ranked lists without requiring BM25 scores and embedding scores to share the same scale.
The workbench should fall back to lexical search, label the result set as lexical-only, and keep showing note path, heading path, matched terms, snippets, and freshness status.
A useful vault workbench does not just retrieve a passage. It names the file, heading, chunk, score mix, freshness boundary, and fallback state so the user can decide whether the match is worth acting on.
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