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feat: add AI-assisted documentation guide to library search

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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
30/100
issue の種類
機能追加
明瞭さ
おおむね明確
活発さ
活発
技術スタック
javascript, json
領域
ai, backend, build-system, documentation, frontend, search, testing

調査の方向性

Start by reviewing the build-time library search index in #114 and the MCP documentation exposure in #117, then trace how kb-docs artifacts feed the search index and MCP document discovery. Done means a versioned source manifest, validation failures for missing or malformed data, grounded guide answers with citations, compatible identifiers, and automated tests for validation and failure cases.

索引モデルが issue の本文から書いたものです。

説明

enhancement

Feature Description

Turn Library landing-page search into an AI-assisted documentation guide. Users should ask natural-language questions and receive grounded answers with relevant documentation links and cited sources, rather than only keyword-matched results.

Documentation publishers must provide supplemental machine-consumable source metadata/content so search indexing and MCP clients can retrieve reliable source material without scraping rendered HTML.

Problem / Opportunity

Keyword search helps users find known titles, but does not guide users who describe an outcome, problem, or concept in natural language. Current rendered documentation is also unsuitable as a stable source interface for AI and MCP consumers.

A shared structured source contract lets Library UI and MCP server use same canonical documentation data.

Acceptance Criteria

  • Library landing page provides AI-assisted guide mode alongside existing search experience.
  • User can submit natural-language documentation questions.
  • Guide returns answer grounded only in indexed documentation sources.
  • Every answer shows links or citations to supporting documentation pages.
  • Documentation artifacts support supplemental machine-consumable source data.
  • Source contract defines stable document identifiers, content/source locations, title, route, description, tags, MIME type, and modification metadata.
  • Contract format and validation are documented; JSON is preferred unless YAML provides a clear contract advantage.
  • Build validates structured source data and incorporates it into generated search/MCP indexes.
  • Missing, malformed, or stale source data produces actionable build validation failures.
  • Search index, AI guide, and MCP server consume compatible stable identifiers and metadata.
  • Automated tests cover source-contract validation, grounded guide responses/citations, and failure cases.

Proposed Solution

Define versioned structured documentation-source manifest shipped with kb-docs artifacts. Generate catalog/search data from manifest during build. Use this shared data source for landing-page AI guide and MCP document discovery/retrieval.

Keep AI answer generation behind an interface so deployment can select model/provider without coupling artifact contract to one vendor.

Dependencies / Related

Depends on #114 — build-time library search index.
Depends on #117 — expose knowledge-base documentation through an MCP server.

Additional Context

This issue delivers guided natural-language discovery above deterministic search. It should not require scraping rendered HTML. Structured source data must remain safe for machine consumption by MCP-compatible clients and future AI integrations.

主要言語
JavaScript
スター
0
フォーク
0
平均マージ
20分
マージ済み PR（30日）
8

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。

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