research-document
Search Architecture
Search Architecture
Generated: 2026-07-22T15:10:59+00:00
Recommendation
Use hybrid retrieval:
- Metadata filters for precision
- BM25 or equivalent lexical search for exact terminology
- Embedding search for semantic recall
- Graph traversal for concept-aware expansion and citation paths
Retrieval Stack
- Normalize metadata and chunk canonical summaries plus section-level document chunks.
- Build a lexical index over full text, titles, headings, IDs, and concept labels.
- Build a vector index over canonical summaries and semantically chunked sections.
- Build a graph index over concepts, documents, evidence, and lineage edges.
- Fuse results with confidence, authority, and recency weighting.
Query Modes
- Concept search
- Document search
- Evidence search
- Timeline search
- Relationship search
- Project-scoped search
Ranking Factors
- Title and heading match
- Canonical status
- Authority score
- Confidence score
- Shared concept count
- Citation and backlink count
- Version freshness without hiding superseded history
Why Not Single-Mode Search
Lexical search alone misses conceptual paraphrases. Vector search alone hides exact IDs, terminology, and structured filters. Graph traversal alone cannot serve as the first-pass retriever. The repository needs all three because it is a research corpus rather than a simple doc site.
Current Product Research
- Quartz documents built-in full-text search, graph view, wikilinks, transclusions, and backlinks: https://quartz.jzhao.xyz/
- VitePress documents local full-text search with an in-browser index and Algolia support: https://vitepress.dev/reference/default-theme-search
- Docusaurus documents official Algolia DocSearch support and contextual search across versions: https://docusaurus.io/docs/search