fix: add content_extractor to smart-create endpoint

The POST /wiki/pages/smart-create endpoint was failing with 500
Internal Server Error because HybridRAGService.__init__() was
missing the required content_extractor parameter.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

CHANGELOG.md

@@ -5,6 +5,12 @@ All notable changes to Library Desk will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [1.3.1] - 2025-12-16
+
+### Fixed
+
+- Smart create endpoint missing `content_extractor` dependency causing 500 errors on `POST /wiki/pages/smart-create`
+
 ## [1.3.0] - 2025-12-15
 
 ### Changed

docs/HYBRIDRAG.md

@@ -0,0 +1,58 @@
+# 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.

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "library-desk"
-version = "1.3.0"
+version = "1.3.1"
 description = "Coordination service for The Library system - HybridRAG queries, document ingestion, entity extraction, and knowledge consolidation"
 readme = "README.md"
 requires-python = ">=3.12"

src/routers/wiki.py

@@ -24,7 +24,7 @@ from src.clients.neo4j_client import Neo4jClient
 from src.clients.qdrant_client import QdrantClientWrapper
 from src.clients.ollama_client import OllamaClient
 from src.core.dependencies import (
-    WikiJSDep, Neo4jDep, QdrantDep, OllamaDep, SearXNGDep,
+    WikiJSDep, Neo4jDep, QdrantDep, OllamaDep, SearXNGDep, ContentExtractorDep,
     verify_api_key, get_settings, get_hybrid_rag_service, get_ingestion_service
 )
 from src.core.multi_tenancy import DEFAULT_USER
@@ -180,6 +180,7 @@ async def smart_create_page(
     qdrant_client: QdrantDep,
     ollama_client: OllamaDep,
     searxng_client: SearXNGDep,
+    content_extractor: ContentExtractorDep,
     settings: Settings = Depends(get_settings),
     api_key: str = Depends(verify_api_key)
 ):
@@ -225,6 +226,7 @@ async def smart_create_page(
             graph_service=graph_service,
             searxng_client=searxng_client,
             ollama_client=ollama_client,
+            content_extractor=content_extractor,
             settings=settings
         )
         wiki_page_writer = WikiPageWriter(ollama_client=ollama_client)