Hacktoberfest 2026：メンテナが10月に向けて印を付けた、オープンで初心者向けの issue。 Hacktoberfest の issue を見る

epic: make the vector store a reconciled projection, then widen what enters it

オープン
  • 何か問題がありますか？
#228 コメント 0 件 リアクション 0 件 担当者 1 名 GitHub で見る

メンテナーはふだん 1 日以内に返信

@samarthsinh2660 がすでに取り組んでいます。

2026年10月12日 から。

評価

難易度
5/5
見積もり時間
1週間以上
初心者へのやさしさ
25/100
issue の種類
機能追加
明瞭さ
明確に書かれている
活発さ
活発
技術スタック
dart
領域
databases

調査の方向性

This is an umbrella issue and explicitly tracks no code of its own. Read docs/SHIVA/rag.md under “PDF documents” and the design at docs/superpowers/specs/2026-09-19-pdf-rag-design.md, then choose an open sub-issue (#231–#236) to work on. Done for this issue means its planned work is handled through those sub-issues; there is no standalone implementation or test target here.

索引モデルが issue の本文から書いたものです。

説明

enhancement P1-High

PDF content support landed in #226: a PDF attached to a note is now extracted, chunked, embedded and citable down to the page. Building it surfaced a set of gaps that are deliberately out of that issue's scope — some are pre-existing problems in the note side of the vector pipeline that only became visible once documents started retrieving reliably, others are capabilities the v1 design consciously deferred.

This issue is the umbrella for that follow-up work. It tracks no code of its own.

The idea

Retrieval is only as good as what made it into the vector store, and today the store is written once and never reconciled. A note embedded before the embedder existed stays unsearchable forever; a vector written by one tostore version can read back empty after a package bump; a chunk whose document was deleted leaves its vector behind with no way to reclaim it. None of these announce themselves — retrieval just quietly returns less.

The through-line for the next phases: make the vector store a reconciled, self-healing projection of what the user actually has, then widen what can enter it.

Ordered by what unblocks the most:

Phase A — make existing content searchable (correctness, the highest value work here)
Notes that never got a vector, and stores that silently read empty after an upgrade. Until this is fixed, every retrieval quality measurement is against an unknown fraction of the library.

Phase B — keep it healthy over time
Orphaned vectors, and the dimension the app declares versus the one the model emits.

Phase C — widen what documents contribute
A document's content reaches retrieval but not the knowledge graph, and a Manas-scoped chat cannot reach documents at all.

Phase D — reach and polish
Formats beyond PDF, and jumping to a cited page in-app.

Sub-issues

Phase A

  • #231 — Backfill: re-embed notes that have no vector
  • #232 — Version-stamp the vector store path so a package upgrade cannot read empty

Phase B

  • #233 — Rebuild path for orphaned chunk vectors
  • #234 — Reconcile the declared embedding dimension with what the model emits

Phase C

  • #235 — Documents contribute to the knowledge graph
  • #236 — A Manas-scoped chat should reach its notes' attached documents

Phase D

  • #237 — Jump to the cited page in-app
  • #238 — Support formats beyond PDF

Phase E — beyond text documents

  • #240 — Images: search the text inside attached images (on-device OCR)
  • #242 — Selective OCR for PDF and Word: scanned pages and pasted pictures only (measure on a device first)

Deliberately not planned

OCR for scanned documents. A PDF with no text layer stays attached, openable and marked not searchable. This is a product decision, not a backlog item — a scan's extracted text is unreliable enough that citing a page from it would be worse than not citing at all.

Reconsidered: OCR is now used for text inside images (#240), and is planned for scanned PDF pages and pictures pasted into Word files (#242) — page by page, never for a whole document, because a PDF's own text is exact and OCR's is not. #242 must be measured on a device before it is built.

Reference

  • Parent feature: #226
  • Design: docs/superpowers/specs/2026-09-19-pdf-rag-design.md
  • Current behaviour, with diagrams: docs/SHIVA/rag.md, section "PDF documents"
主要言語
Dart
スター
2
フォーク
2
平均マージ
1日 7時間
マージ済み PR（30日）
35

環境構築

はじめの一歩

  1. issue を最後まで読み、次にプロジェクトのコントリビューションガイドを読みます。
  2. 着手することを issue にコメントします — 二人が同じ作業をするのを防げます。
  3. リポジトリをフォークし、ブランチを切って変更します。
  4. issue 番号を参照したプルリクエストを送ります。

basictech01/uniun のほかの issue

basictech01/uniun の issue をすべて見る

似ている issue

Dart の issue をもっと見る

新しい issue をメールで受け取る

初心者向けの GitHub issue を短くまとめたダイジェスト。