docs: add HybridRAG architecture documentation

Documents two-stage RRF, configuration options, and notes potential vector search noise improvements for future reference. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2025-12-15 17:54:38 +01:00
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+# HybridRAG Architecture
+
+## Overview
+
+HybridRAG combines three search sources to provide comprehensive results:
+- **Vector search** (Qdrant) - Semantic similarity via embeddings
+- **Graph search** (Neo4j) - Entity relationships in knowledge graph
+- **Web search** (SearXNG) - External web results via Trafilatura extraction
+
+## Two-Stage RRF Fusion (v1.3.0+)
+
+To ensure fair ranking between wiki and web results, we use a two-stage Reciprocal Rank Fusion:
+
+```
+Stage 1: Wiki Merge
+  vector results ─┬─→ Mini-RRF ─→ Unified wiki ranking
+  graph results  ─┘
+
+Stage 2: Final RRF
+  wiki (merged)  ─┬─→ Final RRF ─→ Combined results
+  web results    ─┘
+```
+
+**Why two stages?**
+
+Previously, wiki pages found by BOTH vector and graph received double RRF contribution, giving them an unfair 2x advantage over web results. The two-stage approach:
+1. Merges vector+graph into a single "wiki" source
+2. Wiki's internal ranking still benefits from multi-source confirmation
+3. Wiki and web compete as equals in final ranking
+
+## Configuration
+
+| Setting | Default | Description |
+|---------|---------|-------------|
+| `VECTOR_SIMILARITY_THRESHOLD` | 0.7 | Minimum similarity score for vector results |
+| `HYBRID_RAG_VECTOR_LIMIT` | 10 | Max vector results |
+| `HYBRID_RAG_GRAPH_LIMIT` | 10 | Max graph results |
+| `HYBRID_RAG_WEB_LIMIT` | 5 | Max web results |
+
+## Known Limitations & Future Improvements
+
+### Vector Search Noise
+
+**Status:** Open for improvement if needed after observation period.
+
+Vector search may return generic category/index pages (e.g., "Reference", "Projects", "Places") with high similarity scores (~0.86). These pages often have similar boilerplate content leading to uniform scores.
+
+**Potential solutions if this becomes problematic:**
+1. **Raise threshold** - Increase `VECTOR_SIMILARITY_THRESHOLD` to 0.85+
+2. **Page-type filtering** - Exclude pages tagged as category/index/stub
+3. **Content length signal** - Penalize pages with minimal content
+4. **Duplicate score detection** - Flag results with suspiciously identical scores
+
+The LLM re-ranking phase typically demotes these low-quality results, so this may not require immediate action.
+
+### Graph Search
+
+Graph search uses only core keywords (no LLM-generated synonyms) to avoid false matches like "author" → "author2000". This is intentional - vector search handles semantic similarity via embeddings.