Data Autocomplete

A fully local data autocomplete engine for Obsidian: it parses Excel/CSV files inside your vault and retrieves real values by report context, with forced source tracing. No external services, no LLM, no Python backend — your data never leaves the vault.

• Version: 0.1.8
• Category: Desktop plugin (isDesktopOnly: true)
• Install: copy this folder to <vault>/.obsidian/plugins/data-autocomplete/, then enable it in Settings → Community plugins (turn off restricted mode).

What it does

When writing reports or documents, you often need to fill "metric name + value" into the text. This plugin automates that:

1. Put your Excel/CSV files into the vault's data directory (default data-autocomplete-data/);
2. While writing, mark missing data with a placeholder (e.g. 本月试驾量为【待补充】), or just write the metric name without a value;
3. The plugin parses the report context (first-line title + current paragraph) to extract "metric / entity / date", then retrieves candidate values from real data;
4. Every candidate carries a source trace (file / sheet / row number). If nothing matches, it shows NO_RESULT — it never fabricates values.

It depends on no external service. Data stays inside the vault and works fully offline.

How the context recommendation works

When you type a placeholder (【待补充】) or a metric name without a value, the plugin runs a 5-stage retrieval state machine — it never invents numbers:

Retrieval priority: EXACT → ALIAS → PARTIAL → SEMANTIC_CANDIDATE → NO_RESULT. Structured hits always win over semantic (AD-21); the semantic fallback is a fully local hash-vector + cosine similarity (256-dim, no model, no network), accepting only strong approximations ≥ 0.5 to prevent weak substring overlaps from creating misleading candidates. All candidate facts come from the real record index, each with file / sheet / row source trace.

Demo

Watch a short demo of the plugin in action (placeholder completion, wavy-underline + dropdown, source tracing):

Or download it directly: demo.mp4

Install (3 steps)

1. Add data: create a data-autocomplete-data/ folder in your vault root and put Excel/CSV files inside (supported layouts:
   • Narrow table: dimension1/dimension2/month/metric/value/metric description
   • Hierarchical wide table / single-entity wide table: row 1 headers (…/specific metric/metric description/entity), row 2 date columns (累计值/2026-07月数据/ranking columns), data starts from row 3)
   • The directory name can be changed in settings.
2. Install the plugin: copy this folder to <vault>/.obsidian/plugins/data-autocomplete/, then enable it in Settings → Community plugins (turn off restricted mode).
3. Rebuild the index: run 「重建数据索引（解析 vault 内 Excel/CSV）」 from the command palette, or click "Rebuild index" on the settings page.

Usage

• Mark missing data with placeholders while writing, for example:

  华东区 2026年7月经营数据
  
  本月试驾量为【待补充】，成交量为【待补充】，客流量是【待补充】。
  客单价【待补充】，单车毛利为【待补充】，成交率【待补充】。
  

• Run 「分析当前笔记（数据自动补全候选）」 (Ctrl/Cmd+P), and a popup lists every data request:

  • Request type (placeholder / metric_no_value), confidence, metric word, original sentence
  • Parsed dimensions: metric (match mode) · entity (scope) · date (scope) · missing item
  • Retrieval status (EXACT / ALIAS / PARTIAL / NO_RESULT) with explanation
  • Each candidate fact: real value + entity + date + match mode + source trace (file/sheet/row)
  • "Copy candidate text" writes it into the note in one click.

• NO_RESULT is a normal path: it means there is no matching record in the real data. This plugin never invents numbers.

Commands

• 重建数据索引（解析 vault 内 Excel/CSV） — rebuild the data index
• 分析当前笔记（数据自动补全候选） — analyze the current note for autocomplete candidates
• 数据索引概览 — data index overview
• 手动搜索数据（查询理解 + 排序） — manual search with natural-language query understanding and ranking
• 数据预览：单主体单指标全部历史 — preview all history for one entity + one metric
• 浏览数据：全量明细分页 — browse all records with pagination
• 多表管理：切换活跃数据表 — switch the active data table (each subfolder = one named table)
• 创建数据备份（打包数据目录） — back up the data directory as a zip
• 恢复最新备份 — restore from the latest backup
• 确保数据目录存在（幂等） — ensure the data directory exists (idempotent)
• 一键补全全部占位符（auto/manual/nodata） — fill all placeholders in one pass (auto / manual / nodata)
• 展开省略号为【待补充】占位符 — expand ellipses (。。。/.../……) into placeholders

Settings

Data source & basics

• Data directory: default data-autocomplete-data (relative to vault root)
• Auto-rebuild index on startup: parse automatically when the vault opens (default on)
• Rebuild index now: rebuild manually after data files change (or use the command palette)

Retrieval & semantics

• Semantic fallback (approximate match, fully local): when structured retrieval misses, use local hash-vector approximation to find metrics, then query real data; turn off for strict structured matching only
• Semantic candidate count: how many candidate metrics the semantic fallback shows (default 3)
• Semantic hit threshold: below this similarity, "approximate metrics" don't count as hits (default 0.5)

Editor completion & detection

• Inline completion (placeholder + metric-name dropdown): typing 【 shows a real-data candidate dropdown in place (↑↓ to select, Enter/click to insert and auto-close 】); typing a metric-name prefix (e.g. 成交/本月成交) pops up a metric-name dropdown, Enter completes the name (default on)
• Type-B missing-value detection (wavy underline + Tab/dropdown completion): when a metric name has no value within 12 characters, candidates are generated: a red wavy underline under the metric name; when the cursor stops at the end of the metric name, a real-value candidate dropdown pops up (↑↓ to select, Enter/click to insert); Tab inserts the first candidate directly
• Record completion history: every popup copy / inline insert appends a line to 数据目录/history.jsonl

Local self-test

# Engine regression tests against real data (no Obsidian needed, plain node)
node test/engine.test.mjs

Current engine 25 assertions, wavy-mark 9 assertions, dropdown 29 assertions all pass: narrow table 1008 records, hierarchical wide table 498 records, single-entity wide table 10 entities × 20160 records, 21666 real records total / 13 entities / 263 metrics; all 7 placeholders in the sample note hit EXACT real values (试驾量 235.3, 成交量 35.2, 客流量 1086.5, 客单价 301569.4, 单车毛利 42552.6, 成交率 11.2, 总利润 8127), each with file/sheet/row source trace.

Development

npm install          # esbuild + xlsx
npm run build        # src/main.js → single-file main.js (2.1MB, includes xlsx)
npm run test:engine  # engine regression
node test/marks.test.mjs    # wavy-mark + Tab chain
node test/dropdown.test.mjs # dropdown decision

Module layout (mirrors the Python pipeline): parser.js (narrow/hierarchical wide tables) → index.js (in-memory index) → context.js (context/entity/date/metric extraction) → detector.js (placeholder/metric-missing detection) → retrieval.js (EXACT→ALIAS→PARTIAL→NO_RESULT) → engine.js (orchestration, output shaped like the Python /analyze-report).

M2: Semantic fallback (implemented in this version)

When structured retrieval (exact/alias/partial) all miss, semantic fallback kicks in:

1. Local hash-vector + cosine (semantic.js): embed metric "surface names" (canonical name + alias keys) as 256-dim unit vectors via character/bigram hashing; embed the query the same way, compute cosine, take top-3.
2. Threshold gating: cosine ≥ 0.5 to accept a candidate, preventing weak substring overlaps (e.g. 人数/数量) from creating misleading approximations (外星人数量 always stays NO_RESULT).
3. Structured always wins (AD-21): semantic only acts as SEMANTIC_CANDIDATE fallback after EXACT → ALIAS → PARTIAL all fail; candidate facts still come from the real record index (lookupMetric) — never generates values.
4. Can be disabled: the "semantic fallback" toggle in settings; when off, only strict structured matching.

New status SEMANTIC_CANDIDATE (shown in orange in the popup), retrieval order: EXACT → ALIAS → PARTIAL → SEMANTIC_CANDIDATE → NO_RESULT.

Example: note writes 本月到店人数为【待补充】 → semantic layer hits 进店人数 (0.71) → normalized to 客流量 → returns real value 1086.5 (2026-07, 经营数据-示例.xlsx row 76).

M3: Editor inline completion (implemented in this version)

When you type the placeholder 【, a candidate card pops up in place (src/editor/inlineSuggest.js, CodeMirror 6):

• Candidates: Engine.suggest(cursor-prefix) reuses the full pipeline — it automatically combines the note's first line (entity/date title) + the paragraph before the cursor into a query segment, then runs request detection → structured → semantic fallback;
• Interaction: ↓/↑ to switch candidates, Enter or click inserts the real value and auto-closes 】, Esc closes;
• Trace visible: each candidate shows value metric@date entity (file:row); status/explanation in the title line (EXACT/ALIAS/PARTIAL/SEMANTIC_CANDIDATE/NO_RESULT).
• Never fabricates: when nothing matches it shows NO_RESULT instead of forcing a number.

Toggle in settings (default on). CodeMirror packages are injected by the Obsidian runtime (build.mjs marks them external, not bundled).

Roadmap

• M4 Wide-table column recognition: auto-detect and fold arbitrary entity matrices (entity × metric × month).
• M5 Candidate history & frequent metrics: record every pick, weight future ranking.

License

MIT License — see LICENSE.