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feat: add AI-assisted documentation guide to library search

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Évaluation

Difficulté
5/5
Temps estimé
Plus d'une semaine
Accessibilité débutants
30/100
Type d'issue
Fonctionnalité
Clarté
Plutôt claire
Activité
Active
Stack technique
javascript, json
Domaine
ai, backend, build-system, documentation, frontend, search, testing

Piste de recherche

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.

Rédigé par le modèle d'indexation à partir du texte de l'issue.

Description

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.

Langage dominant
JavaScript
Étoiles
0
Forks
0
Merge moyen
20 min
PR mergées (30 j)
8

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