release: v1.3.3 - LLM prompt improvements and dead code cleanup

- Improved LLM prompts with temperature control and negative constraints
- Removed dead code and unused imports
- Wired /query/semantic and /query/graph to real implementations
- Updated test fixtures for external service access

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

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

AGENTS.md

@@ -23,6 +23,32 @@
 * **Update `CHANGELOG.md`** with every user-facing change.
 * Format: `## [Unreleased] - YYYY-MM-DD` followed by `### Added`, `### Changed`, or `### Fixed`.
 
+### 🚀 Release Flow
+When changes are ready for deployment:
+
+1. **Ask user if deploy cycle is desired **
+
+2. **Update version** in `pyproject.toml`:
+   - Bug fixes: bump patch version (1.8.3 → 1.8.4)
+   - New features: bump minor version (1.8.4 → 1.9.0)
+
+3. **Update CHANGELOG.md**:
+   - Move items from `[Unreleased]` to new version section
+   - Add release date: `## [1.8.4] - 2025-12-16`
+
+4. **Commit and tag**:
+   ```bash
+   git add -A
+   git commit -m "fix: description of changes"
+   git tag v1.8.4
+   git push origin main --tags
+   ```
+
+5. **CI/CD triggers automatically**:
+   - Gitea CI builds Docker image on new tag
+   - Watchtower pulls and deploys to production
+   - Verify deployment: `curl http://192.168.86.149:8000/health`
+
 ---
 
 ## 2. FastAPI Architecture & Best Practices

CHANGELOG.md

@@ -5,6 +5,78 @@ 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).
 
+## [1.3.3] - 2025-12-23
+
+### Added
+
+- Temperature parameter to `OllamaClient.generate_text()` for controlling output determinism
+- `TODO.md` tracking remaining stub endpoints to implement
+- Wired `/query/semantic` endpoint to VectorService
+- Wired `/query/graph` endpoint to GraphService
+
+### Changed
+
+- **Improved LLM prompts** based on llm-findings.md recommendations:
+  - Keyword extraction: temperature 0.0, negative constraints
+  - LLM re-ranking: temperature 0.0, explicit rules
+  - Conflict detection: temperature 0.0, analysis steps (CoT)
+  - Wiki page creation: temperature 0.3, anti-hallucination constraints
+  - Page reconstruction: temperature 0.2, preservation constraints
+  - Web results analysis: temperature 0.0, conservative approach
+- Test fixtures now use configurable host (TEST_HOST) instead of Docker hostnames
+
+### Removed
+
+- Dead code: unused `get_default_user()` function
+- Unused imports from routers (wiki.py, graph.py, hybrid_rag.py)
+- Stub endpoints shadowed by real implementations (/stats, /ingest/document, /ingest/batch)
+
+## [1.3.2] - 2025-12-22
+
+### Changed
+
+- **Consolidated Ollama model configuration** - All LLM operations now use single `OLLAMA_MODEL` environment variable
+  - Removed separate `reranker_model` setting
+  - HybridRAG re-ranking, consolidation analysis, and wiki page writing all use the same model
+  - Improves VRAM efficiency by keeping one model hot
+- Added `OLLAMA_EMBEDDING_MODEL` environment variable for embedding model (previously overloaded `OLLAMA_MODEL`)
+- Updated WikiPageWriter to accept settings instead of hardcoded model name
+
+## [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
+
+- **Two-Stage RRF Architecture** - Major refactor to level the playing field between wiki and web results
+  - Stage 1: Vector and graph results merged into single "wiki" ranking using mini-RRF
+  - Stage 2: Final RRF between wiki (single source) and web (single source)
+  - Wiki pages no longer get 2x advantage from appearing in both vector and graph searches
+  - Multi-source confirmation still determines wiki internal ranking
+
+- **Skip synonyms in graph search** - LLM-generated synonyms (e.g., "author") no longer match unrelated graph entities (e.g., "author2000")
+  - Vector search still uses synonyms for semantic similarity
+  - Graph search uses only core keywords for exact entity matching
+
+### Added
+
+- `VECTOR_SIMILARITY_THRESHOLD` config setting (default: 0.7) to filter weak vector matches
+- Deduplication in graph search to prevent same document appearing multiple times
+
+### Fixed
+
+- Graph search duplicate entity bug where same document could appear twice if entity linked multiple times
+
+## [1.2.1] - 2025-12-15
+
+### Fixed
+
+- HybridRAG router missing `content_extractor` dependency causing 500 errors on `/query/hybrid` endpoint
+
 ## [1.2.0] - 2025-12-15
 
 ### Added

README.md

@@ -49,7 +49,8 @@ QDRANT_PORT=6333
 WIKIJS_URL=http://wiki:3000
 SEARXNG_URL=http://searxng:8080
 OLLAMA_URL=http://ollama:11434
-OLLAMA_MODEL=nomic-embed-text
+OLLAMA_MODEL=mistral-nemo-large:latest
+OLLAMA_EMBEDDING_MODEL=nomic-embed-text
 REDIS_HOST=redis-shared
 REDIS_PORT=6379
 REDIS_DB=2

TODO.md

@@ -0,0 +1,52 @@
+# TODO
+
+Outstanding work items for Library Desk.
+
+## Stub Endpoints to Implement
+
+The following endpoints in `src/main.py` return stub responses and need real implementations:
+
+### Ingestion Status Endpoints
+
+#### `POST /ingest/check-updates`
+Check which documents need updating based on content hashes. Used by Scheduler to determine what changed since last sync.
+
+**Implementation needed:**
+1. Query existing documents by path
+2. Compare content hashes
+3. Return list of updates needed
+
+#### `GET /ingest/status/{document_id}`
+Get processing status for a document.
+
+**Implementation needed:**
+- Status tracking system (Redis or database)
+- Track ingestion progress per document
+
+#### `GET /ingest/repo-status/{repository}`
+Get indexing status for an entire repository.
+
+**Implementation needed:**
+- Repository-level statistics
+- Track which documents from a repo are indexed
+
+### Deduplication
+
+#### `POST /deduplicate/check`
+Check for duplicate or highly similar documents using vector similarity and graph analysis.
+
+**Implementation needed:**
+1. Get document embedding from Qdrant
+2. Find similar vectors above threshold
+3. Check graph relationships
+4. Return candidates with similarity scores
+
+## System Statistics
+
+#### `GET /stats`
+Get system statistics (wiki pages, neo4j nodes, qdrant vectors).
+
+**Implementation needed:**
+- Query Neo4j for node count
+- Query Qdrant for vector count
+- Query Wiki.js for page count

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 = "1.2.0"
+version = "1.3.3"
 description = "Coordination service for The Library system - HybridRAG queries, document ingestion, entity extraction, and knowledge consolidation"
 readme = "README.md"
 requires-python = ">=3.12"

src/clients/ollama_client.py

@@ -249,7 +249,8 @@ class OllamaClient:
         self,
         prompt: str,
         model: Optional[str] = None,
-        stream: bool = False
+        stream: bool = False,
+        temperature: Optional[float] = None
     ) -> Optional[str]:
         """
         Generate text completion (for non-embedding use cases).
@@ -258,12 +259,15 @@ class OllamaClient:
             prompt: Input prompt
             model: Model name (defaults to self.model)
             stream: Enable streaming response
+            temperature: Sampling temperature (0.0 = deterministic, higher = more creative)
+                        None uses model default (~0.7 for mistral-nemo)
 
         Returns:
             Generated text or None on failure
 
-        Note: This is primarily for debugging/testing. Use specialized
-        LLM services for production text generation.
+        Note: Use temperature=0.0 for deterministic outputs like JSON parsing,
+        ranking, and factual extraction. Use higher values (0.3-0.7) for
+        creative content generation.
         """
         try:
             payload = {
@@ -272,6 +276,10 @@ class OllamaClient:
                 "stream": stream
             }
 
+            # Add temperature to options if specified
+            if temperature is not None:
+                payload["options"] = {"temperature": temperature}
+
             response = await self.client.post(
                 self.generate_url,
                 json=payload

src/config.py

@@ -62,16 +62,17 @@ class Settings(BaseSettings):
     # SearXNG Configuration
     searxng_url: str = Field(default="http://searxng:8080", description="SearXNG URL")
 
-    # Ollama Configuration (for embeddings)
+    # Ollama Configuration
     ollama_url: str = Field(default="http://ollama:11434", description="Ollama URL")
-    ollama_model: str = Field(default="nomic-embed-text", description="Ollama embedding model")
+    ollama_model: str = Field(default="mistral-nemo-large:latest", description="Ollama LLM model")
+    ollama_embedding_model: str = Field(default="nomic-embed-text", description="Ollama embedding model")
 
     # HybridRAG Configuration
-    reranker_model: str = Field(default="mistral-nemo", description="Model for LLM re-ranking")
     reranker_enabled: bool = Field(default=True, description="Enable LLM re-ranking")
     hybrid_rag_vector_limit: int = Field(default=10, ge=1, le=50, description="Vector search limit")
     hybrid_rag_graph_limit: int = Field(default=10, ge=1, le=50, description="Graph search limit")
     hybrid_rag_web_limit: int = Field(default=5, ge=1, le=20, description="Web search limit")
+    vector_similarity_threshold: float = Field(default=0.7, ge=0.0, le=1.0, description="Minimum similarity score for vector results")
 
     # Entity Linking Fuzzy Matching Configuration
     entity_linking_min_confidence: float = Field(default=0.70, ge=0.0, le=1.0, description="Minimum confidence for entity-document matching")

src/core/dependencies.py

@@ -113,7 +113,7 @@ def get_ollama_client() -> OllamaClient:
     settings = get_settings()
     client = OllamaClient(
         base_url=settings.ollama_url,
-        model=settings.ollama_model
+        model=settings.ollama_embedding_model
     )
     logger.debug("Created Ollama client instance")
     return client
@@ -395,18 +395,6 @@ def get_rag_search_service() -> "RAGSearchService":
     )
 
 
-# Utility: Get default user from settings or multi_tenancy
-def get_default_user() -> str:
-    """
-    Get default user for operations.
-
-    Returns:
-        Default user identifier
-    """
-    from src.core.multi_tenancy import DEFAULT_USER
-    return DEFAULT_USER
-
-
 # Authentication
 from fastapi import Security, HTTPException
 from fastapi.security import HTTPBearer

src/main.py

@@ -8,7 +8,7 @@ Following best practices:
 - OpenAPI documentation
 """
 
-from fastapi import FastAPI, HTTPException, Depends
+from fastapi import FastAPI, HTTPException, Depends, Query
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.staticfiles import StaticFiles
 from pydantic import BaseModel
@@ -17,7 +17,10 @@ import logging
 from pathlib import Path
 
 from src.config import Settings, get_settings, __version__
-from src.core.dependencies import verify_api_key
+from src.core.dependencies import (
+    verify_api_key, QdrantDep, WikiJSDep, OllamaDep, Neo4jDep
+)
+from src.core.multi_tenancy import DEFAULT_USER
 
 # Configure logging
 logging.basicConfig(
@@ -78,13 +81,6 @@ class HealthResponse(BaseModel):
     services: Dict[str, Any]
 
 
-class StatsResponse(BaseModel):
-    """Statistics response model."""
-    wiki_pages: int
-    neo4j_nodes: int
-    qdrant_vectors: int
-
-
 # Routes
 @app.get("/", tags=["Root"])
 async def root() -> Dict[str, str]:
@@ -141,77 +137,6 @@ async def health(settings: Settings = Depends(get_settings)) -> HealthResponse:
     )
 
 
-@app.get("/stats", response_model=StatsResponse, tags=["System"])
-async def stats(
-    api_key: str = Depends(verify_api_key)
-) -> StatsResponse:
-    """
-    Get system statistics.
-    Protected endpoint - requires API key.
-
-    TODO: Implement actual stats gathering from:
-    - Neo4j (node count)
-    - Qdrant (vector count)
-    - Wiki.js (page count)
-    """
-    return StatsResponse(
-        wiki_pages=0,
-        neo4j_nodes=0,
-        qdrant_vectors=0
-    )
-
-
-# Ingestion endpoints (for Scheduler integration)
-@app.post("/ingest/document", tags=["Ingestion"])
-async def ingest_document(
-    document: Dict[str, Any],
-    api_key: str = Depends(verify_api_key)
-) -> Dict[str, Any]:
-    """
-    Ingest a single document for indexing.
-    Used by The Scheduler to add mirrored documentation to the knowledge base.
-
-    Expected fields:
-    - source: str (e.g., "github", "gitea")
-    - repository: str (e.g., "anthropic-cookbook")
-    - path: str (file path)
-    - content: str (document content)
-    - metadata: dict (commit, author, tags, etc.)
-
-    TODO: Implement document ingestion pipeline:
-    1. Chunk content
-    2. Generate embeddings (Ollama)
-    3. Extract entities (NLP)
-    4. Index in Qdrant
-    5. Create graph nodes/relationships in Neo4j
-    """
-    return {
-        "message": "Document ingestion not yet implemented",
-        "document_id": f"doc_{document.get('path', 'unknown')}",
-        "status": "stub"
-    }
-
-
-@app.post("/ingest/batch", tags=["Ingestion"])
-async def batch_ingest(
-    batch: Dict[str, Any],
-    api_key: str = Depends(verify_api_key)
-) -> Dict[str, Any]:
-    """
-    Ingest multiple documents in a batch.
-    More efficient than individual ingestion for large syncs.
-
-    TODO: Implement batch processing with task queue
-    """
-    document_count = len(batch.get("documents", []))
-    return {
-        "message": "Batch ingestion not yet implemented",
-        "batch_id": "batch_stub",
-        "total_documents": document_count,
-        "status": "stub"
-    }
-
-
 @app.post("/ingest/check-updates", tags=["Ingestion"])
 async def check_updates(
     documents: Dict[str, Any],
@@ -269,41 +194,85 @@ async def get_repo_status(
     }
 
 
-# Query endpoints (stubs for future implementation)
-# NOTE: /query/hybrid is now implemented in routers/hybrid_rag.py
+# Query endpoints
+# NOTE: /query/hybrid is implemented in routers/hybrid_rag.py
 
 @app.post("/query/semantic", tags=["Query"])
 async def semantic_query(
-    query: Dict[str, Any],
+    query: str = Query(..., min_length=1, description="Search query text"),
+    user: str = Query(default=DEFAULT_USER, description="User identifier"),
+    limit: int = Query(default=10, ge=1, le=100, description="Maximum results"),
+    score_threshold: float = Query(default=0.5, ge=0.0, le=1.0, description="Minimum similarity score"),
+    qdrant_client: QdrantDep = None,
+    wiki_client: WikiJSDep = None,
+    ollama_client: OllamaDep = None,
     api_key: str = Depends(verify_api_key)
-) -> Dict[str, Any]:
+):
     """
-    Semantic search via Qdrant.
-    Pure vector similarity search.
+    Semantic search via Qdrant vector similarity.
 
-    TODO: Implement semantic search
+    Searches document chunks using embedding similarity. Returns matching
+    chunks with relevance scores, page titles, and paths.
+
+    **Example:**
+    ```
+    POST /query/semantic?query=docker%20configuration&user=jpmschweitzer&limit=10
+    ```
+
+    **Returns:** List of matching chunks with similarity scores (0-1)
     """
-    return {
-        "message": "Semantic search not yet implemented",
-        "query": query
-    }
+    from src.services.vector_service import VectorService
+
+    vector_service = VectorService(qdrant_client, wiki_client, ollama_client)
+    try:
+        return await vector_service.search(
+            query=query,
+            user=user,
+            limit=limit,
+            score_threshold=score_threshold
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        logger.error(f"Semantic search failed: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail="Search failed")
 
 
 @app.post("/query/graph", tags=["Query"])
 async def graph_query(
-    query: Dict[str, Any],
+    query: str = Query(..., description="Cypher query to execute"),
+    user: str = Query(default=DEFAULT_USER, description="User for scoping (auto-filters results)"),
+    neo4j_client: Neo4jDep = None,
+    wiki_client: WikiJSDep = None,
     api_key: str = Depends(verify_api_key)
-) -> Dict[str, Any]:
+):
     """
-    Graph traversal via Neo4j.
-    Execute Cypher queries.
+    Execute a Cypher query against the Neo4j knowledge graph.
 
-    TODO: Implement graph queries
+    Queries are automatically scoped to the user's data for security.
+    Use this for custom graph traversals beyond what /graph/nodes provides.
+
+    **Example:**
+    ```
+    POST /query/graph?query=MATCH%20(d:Document)-[:MENTIONS]->(p:Person)%20RETURN%20d,p&user=jpmschweitzer
+    ```
+
+    **Security:** All queries are user-scoped to prevent cross-user data access.
     """
-    return {
-        "message": "Graph query not yet implemented",
-        "query": query
-    }
+    from src.services.graph_service import GraphService
+
+    graph_service = GraphService(neo4j_client, wiki_client)
+    try:
+        return await graph_service.execute_query(
+            query=query,
+            parameters={},
+            user=user
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        logger.error(f"Graph query failed: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail="Query execution failed")
 
 
 # Deduplication endpoints

src/routers/graph.py

@@ -15,8 +15,6 @@ from src.models.graph import (
     MindMapResponse
 )
 from src.services.graph_service import GraphService
-from src.clients.neo4j_client import Neo4jClient
-from src.clients.wikijs_client import WikiJSClient
 from src.core.dependencies import Neo4jDep, WikiJSDep, verify_api_key
 from src.core.multi_tenancy import DEFAULT_USER
 

src/routers/hybrid_rag.py

@@ -10,13 +10,9 @@ import logging
 
 from src.models.hybrid_rag import HybridRAGRequest, HybridRAGResponse
 from src.services.hybrid_rag_service import HybridRAGService
-from src.services.vector_service import VectorService
-from src.services.graph_service import GraphService
-from src.clients.searxng_client import SearXNGClient
-from src.clients.ollama_client import OllamaClient
 from src.core.dependencies import (
     Neo4jDep, WikiJSDep, QdrantDep, OllamaDep,
-    SearXNGDep, verify_api_key, get_settings
+    SearXNGDep, ContentExtractorDep, verify_api_key, get_settings
 )
 from src.config import Settings
 
@@ -32,6 +28,7 @@ def get_hybrid_rag_service(
     qdrant_client: QdrantDep,
     ollama_client: OllamaDep,
     searxng_client: SearXNGDep,
+    content_extractor: ContentExtractorDep,
     settings: Settings = Depends(get_settings)
 ) -> HybridRAGService:
     """Get HybridRAG service instance with all dependencies."""
@@ -48,6 +45,7 @@ def get_hybrid_rag_service(
         graph_service=graph_service,
         searxng_client=searxng_client,
         ollama_client=ollama_client,
+        content_extractor=content_extractor,
         settings=settings
     )
 

src/routers/wiki.py

@@ -5,8 +5,7 @@ Endpoints for wiki page and dossier management.
 All operations are scoped to user namespaces for multi-tenancy.
 """
 
-from fastapi import APIRouter, HTTPException, Depends, Query, Security, BackgroundTasks
-from fastapi.security import HTTPAuthorizationCredentials
+from fastapi import APIRouter, HTTPException, Depends, Query, BackgroundTasks
 from typing import Optional
 import logging
 
@@ -24,7 +23,7 @@ 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 +179,7 @@ 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)
 ):
@@ -225,9 +225,10 @@ 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)
+        wiki_page_writer = WikiPageWriter(ollama_client=ollama_client, settings=settings)
 
         # Step 1-5: Research + Generate + Create page
         page, research_data = await wiki_service.smart_create_page(

src/services/consolidation_service.py

@@ -47,7 +47,7 @@ class ConsolidationService:
         self.ollama = ollama
         self.wiki = wiki
         self.settings = settings
-        self.wiki_page_writer = WikiPageWriter(ollama_client=ollama)
+        self.wiki_page_writer = WikiPageWriter(ollama_client=ollama, settings=settings)
         self.ingestion_service = ingestion_service  # Optional to avoid circular dependency
 
     async def consolidate_knowledge(
@@ -410,13 +410,19 @@ This is a PERSONAL knowledge base using Schema.org-aligned taxonomy that capture
 - Projects: Work projects, personal projects (Schema.org: Project)
 - Reference: General knowledge, how-tos (Custom extension)
 
-Identify information worth documenting:
-1. New topics/people/things that deserve their own wiki page
-2. Facts that could enhance existing pages
-3. Entities (people, places, things, concepts) for the knowledge graph
+ANALYSIS STEPS:
+1. Read each web result carefully for substantive, factual content
+2. Identify genuinely novel information not likely already known
+3. Match topics to appropriate taxonomy categories
+4. Generate valid paths following the exact format below
 
-Be INCLUSIVE - if someone searched for it, it's likely worth documenting.
-Personal information is just as valuable as technical information.
+RULES:
+- Do NOT suggest pages for topics with insufficient information in results
+- Do NOT invent entities not explicitly mentioned in results
+- Do NOT suggest paths that don't match the taxonomy exactly
+- Do NOT suggest generic or vague page topics
+- Be CONSERVATIVE - fewer high-quality suggestions is better than many low-quality ones
+- ONLY suggest documentation for substantive, specific information
 
 **CRITICAL: Use ONLY these Schema.org-aligned path prefixes (case-sensitive):**
 
@@ -462,11 +468,12 @@ Return ONLY valid JSON:
 JSON:"""
 
         try:
-            # Call Ollama for analysis
+            # Call Ollama for analysis (temperature=0.0 for consistent classification)
             response = await self.ollama.generate_text(
                 prompt=prompt,
-                model=self.settings.reranker_model,  # Use mistral-nemo
-                stream=False
+                model=self.settings.ollama_model,
+                stream=False,
+                temperature=0.0
             )
 
             if not response:

src/services/graph_service.py

@@ -1077,8 +1077,18 @@ Feel free to expand it with more details!
                 search_query,
                 {"terms": all_terms, "limit": limit}
             )
-            logger.info(f"Graph search found {len(results)} documents")
-            return results
+
+            # Deduplicate by page_id (safety net for any edge cases)
+            seen_page_ids = set()
+            unique_results = []
+            for r in results:
+                page_id = r.get("page_id")
+                if page_id and page_id not in seen_page_ids:
+                    seen_page_ids.add(page_id)
+                    unique_results.append(r)
+
+            logger.info(f"Graph search found {len(unique_results)} unique documents (raw: {len(results)})")
+            return unique_results
         except Exception as e:
             logger.error(f"Graph document search failed: {e}", exc_info=True)
             return []

src/services/hybrid_rag_service.py

@@ -6,7 +6,7 @@ HybridRAG service combining vector, graph, and web search.
 1. Parallel Retrieval - Vector + Graph + Web search
 2. RRF Fusion - Merge results with Reciprocal Rank Fusion
 3. Enrichment - Add related dossiers via graph
-4. LLM Re-ranking - Re-rank with mistral-nemo
+4. LLM Re-ranking - Re-rank with configured Ollama model
 5. Context Formatting - Format for LLM consumption
 6. Persistence - Store for Librarian processing
 """
@@ -65,7 +65,7 @@ class HybridRAGService:
         self.ollama = ollama_client
         self.content_extractor = content_extractor
         self.settings = settings
-        self.reranker_model = settings.reranker_model
+        self.reranker_model = settings.ollama_model
 
     async def search(
         self,
@@ -105,14 +105,20 @@ class HybridRAGService:
         timing["graph_ms"] = raw_results.get("timing", {}).get("graph_ms", 0)
         timing["web_ms"] = raw_results.get("timing", {}).get("web_ms", 0)
 
-        # Phase 2: RRF Fusion
+        # Phase 2: Two-Stage RRF Fusion
         phase2_start = time.time()
+
+        # Stage 1: Merge wiki sources (vector + graph) into single ranking
+        wiki_merged = self._merge_wiki_sources(
+            vector_results=raw_results.get("vector", []),
+            graph_results=raw_results.get("graph", []),
+            k=config.rrf_k
+        )
+
+        # Stage 2: Final RRF between wiki and web (equal footing)
         fused_results = self._reciprocal_rank_fusion(
-            results_by_source={
-                "vector": raw_results.get("vector", []),
-                "graph": raw_results.get("graph", []),
-                "web": raw_results.get("web", [])
-            },
+            wiki_results=wiki_merged,
+            web_results=raw_results.get("web", []),
             k=config.rrf_k
         )
         timing["fusion_ms"] = (time.time() - phase2_start) * 1000
@@ -189,25 +195,21 @@ class HybridRAGService:
         Returns:
             Dictionary with keywords, entities, synonyms, expansions
         """
-        prompt = f"""Extract search terms from this query. For each important word, provide synonyms and expansions.
+        prompt = f"""Extract search terms from this query.
 
 Query: "{query}"
 
-Return ONLY valid JSON:
-{{
-  "core_keywords": ["key", "words", "from", "query"],
-  "synonyms": {{
-    "word": ["alternative", "terms"]
-  }}
-}}
+RULES:
+- Extract ONLY keywords explicitly present or directly implied in the query
+- Do NOT invent terms, concepts, or synonyms not clearly related
+- Do NOT add general knowledge or associations
+- Provide synonyms ONLY for technical terms with well-known alternatives
+- Return valid JSON only, no commentary
 
-Example for "Docker container hosting":
+Return format:
 {{
-  "core_keywords": ["docker", "container", "hosting"],
-  "synonyms": {{
-    "docker": ["containerization", "container runtime"],
-    "hosting": ["server", "infrastructure"]
-  }}
+  "core_keywords": ["words", "from", "query"],
+  "synonyms": {{"term": ["direct", "alternatives"]}}
 }}
 
 JSON:"""
@@ -215,7 +217,8 @@ JSON:"""
         try:
             response = await self.ollama.generate_text(
                 prompt=prompt,
-                model=self.reranker_model
+                model=self.reranker_model,
+                temperature=0.0  # Deterministic for consistent extraction
             )
 
             # Parse JSON response (handle potential extra text)
@@ -288,7 +291,8 @@ JSON:"""
                     response = await self.vector.search(
                         query=query,
                         user=user,
-                        limit=config.vector_limit
+                        limit=config.vector_limit,
+                        score_threshold=self.settings.vector_similarity_threshold
                     )
                     results = [
                         {
@@ -313,11 +317,19 @@ JSON:"""
             async def graph_search():
                 start = time.time()
                 try:
+                    # Skip synonyms for graph search - only use core keywords
+                    # Synonyms like "author" can match unrelated entities like "author2000"
+                    graph_keywords = {
+                        "core_keywords": keywords_data.get("core_keywords", []),
+                        "entities": keywords_data.get("entities", []),
+                        "synonyms": {},  # No synonyms for exact entity matching
+                        "expansions": {}
+                    }
                     results = await self.graph.search_documents(
                         query=query,
                         user=user,
                         limit=config.graph_limit,
-                        keywords_data=keywords_data
+                        keywords_data=graph_keywords
                     )
                     formatted = [
                         {
@@ -394,52 +406,134 @@ JSON:"""
 
         return output
 
-    def _reciprocal_rank_fusion(
+    def _merge_wiki_sources(
         self,
-        results_by_source: Dict[str, List],
+        vector_results: List[Dict],
+        graph_results: List[Dict],
         k: int = 60
     ) -> List[Dict[str, Any]]:
         """
-        Phase 2: Merge results using Reciprocal Rank Fusion.
+        Stage 1: Merge vector and graph into single wiki ranking using RRF.
 
-        RRF formula: score = sum(1 / (k + rank)) for each source
+        Both sources search the same wiki pool, so we combine them before
+        final RRF with web to avoid double-counting wiki pages.
 
         Args:
-            results_by_source: Results from each source
+            vector_results: Results from vector search
+            graph_results: Results from graph search
             k: RRF constant (default 60)
 
         Returns:
-            Merged and sorted results
+            Merged wiki results sorted by wiki RRF score
+        """
+        wiki_scores = {}
+
+        # Process vector results
+        for rank, result in enumerate(vector_results, start=1):
+            page_id = result.get("page_id")
+            if not page_id:
+                continue
+            result_id = f"page_{page_id}"
+
+            if result_id not in wiki_scores:
+                wiki_scores[result_id] = {
+                    "result": dict(result),  # Copy to avoid mutation
+                    "wiki_rrf_score": 0.0,
+                    "found_by": []
+                }
+
+            wiki_scores[result_id]["wiki_rrf_score"] += 1 / (k + rank)
+            wiki_scores[result_id]["found_by"].append("vector")
+
+        # Process graph results
+        for rank, result in enumerate(graph_results, start=1):
+            page_id = result.get("page_id")
+            if not page_id:
+                continue
+            result_id = f"page_{page_id}"
+
+            if result_id not in wiki_scores:
+                wiki_scores[result_id] = {
+                    "result": dict(result),
+                    "wiki_rrf_score": 0.0,
+                    "found_by": []
+                }
+
+            wiki_scores[result_id]["wiki_rrf_score"] += 1 / (k + rank)
+            wiki_scores[result_id]["found_by"].append("graph")
+
+            # Add graph metadata to existing result
+            wiki_scores[result_id]["result"]["entity_matches"] = result.get("entity_matches")
+            wiki_scores[result_id]["result"]["matched_entities"] = result.get("matched_entities")
+
+        # Sort by wiki RRF score
+        sorted_wiki = sorted(
+            wiki_scores.values(),
+            key=lambda x: x["wiki_rrf_score"],
+            reverse=True
+        )
+
+        # Return merged results with wiki ranking
+        merged = []
+        for wiki_rank, item in enumerate(sorted_wiki, start=1):
+            merged.append({
+                **item["result"],
+                "wiki_rank": wiki_rank,
+                "wiki_rrf_score": item["wiki_rrf_score"],
+                "found_by": item["found_by"],
+                "source": "wiki"
+            })
+
+        logger.info(f"Wiki merge: {len(merged)} unique pages from vector+graph")
+        return merged
+
+    def _reciprocal_rank_fusion(
+        self,
+        wiki_results: List[Dict],
+        web_results: List[Dict],
+        k: int = 60
+    ) -> List[Dict[str, Any]]:
+        """
+        Stage 2: Final RRF between wiki (single source) and web.
+
+        Wiki results are pre-merged from vector+graph, so wiki and web
+        now compete on equal footing.
+
+        Args:
+            wiki_results: Pre-merged wiki results from _merge_wiki_sources()
+            web_results: Results from web search
+            k: RRF constant (default 60)
+
+        Returns:
+            Final merged and sorted results
         """
         rrf_scores = {}
 
-        for source, results in results_by_source.items():
-            for rank, result in enumerate(results, start=1):
-                # Use page_id for wiki results, url hash for web results
-                if result.get("page_id"):
-                    result_id = f"page_{result['page_id']}"
-                elif result.get("url"):
-                    result_id = f"url_{hash(result['url'])}"
-                else:
-                    continue  # Skip results without ID
+        # Wiki results (single source, already merged)
+        for rank, result in enumerate(wiki_results, start=1):
+            page_id = result.get("page_id")
+            if not page_id:
+                continue
+            result_id = f"page_{page_id}"
+            rrf_scores[result_id] = {
+                "result": result,
+                "rrf_score": 1 / (k + rank),
+                "sources": result.get("found_by", ["wiki"]),
+                "source_type": "wiki"
+            }
 
-                if result_id not in rrf_scores:
-                    rrf_scores[result_id] = {
-                        "result": result,
-                        "rrf_score": 0.0,
-                        "sources": [],
-                        "source_type": source
-                    }
-
-                # RRF formula: sum of 1/(k + rank) across sources
-                rrf_scores[result_id]["rrf_score"] += 1 / (k + rank)
-                rrf_scores[result_id]["sources"].append(source)
-
-                # If result appears in multiple sources, update source_type
-                if len(rrf_scores[result_id]["sources"]) > 1:
-                    rrf_scores[result_id]["source_type"] = "+".join(
-                        sorted(set(rrf_scores[result_id]["sources"]))
-                    )
+        # Web results (single source)
+        for rank, result in enumerate(web_results, start=1):
+            url = result.get("url")
+            if not url:
+                continue
+            result_id = f"url_{hash(url)}"
+            rrf_scores[result_id] = {
+                "result": result,
+                "rrf_score": 1 / (k + rank),
+                "sources": ["web"],
+                "source_type": "web"
+            }
 
         # Sort by RRF score descending
         sorted_results = sorted(
@@ -448,7 +542,7 @@ JSON:"""
             reverse=True
         )
 
-        logger.info(f"RRF fusion: {len(sorted_results)} unique results from {len(results_by_source)} sources")
+        logger.info(f"Final RRF: {len(sorted_results)} results (wiki + web)")
 
         return sorted_results
 
@@ -526,21 +620,27 @@ JSON:"""
                 for i, r in enumerate(results)
             ])
 
-            prompt = f"""Given this search query and documents, rank them by relevance.
+            prompt = f"""Rank these documents by relevance to the query.
 
 Query: {query}
 
 Documents:
 {docs_text}
 
-Return only the numbers in order of relevance (most relevant first).
-Example: 3,1,5,2,4
+RULES:
+- Rank ONLY by how well content answers the query
+- Do NOT consider document length, formatting, or style
+- Do NOT add explanation or commentary
+- Return ONLY comma-separated numbers, most relevant first
+
+Example output: 3,1,5,2,4
 
 Ranking:"""
 
             response = await self.ollama.generate_text(
                 prompt=prompt,
-                model=self.reranker_model
+                model=self.reranker_model,
+                temperature=0.0  # Deterministic for consistent rankings
             )
 
             # Parse response: "3,1,5,2,4" → [2, 0, 4, 1, 3] (0-indexed)

src/services/wiki_page_writer.py

@@ -1,7 +1,7 @@
 """
 Intelligent Wiki Page Writer Service
 
-Uses LLM (mistral-nemo) to create and reconstruct wiki pages with:
+Uses LLM to create and reconstruct wiki pages with:
 - Holistic content restructuring
 - Zero fact loss (unless superseded)
 - Conflict detection and flagging
@@ -25,15 +25,16 @@ class WikiPageWriter:
     Intelligent wiki page writer using LLM for content generation and restructuring.
     """
 
-    def __init__(self, ollama_client):
+    def __init__(self, ollama_client, settings):
         """
         Initialize wiki page writer.
 
         Args:
             ollama_client: OllamaClient for LLM operations
+            settings: Application settings
         """
         self.ollama = ollama_client
-        self.model = "mistral-nemo"  # Default model for writing
+        self.model = settings.ollama_model
 
     async def create_page(
         self,
@@ -130,8 +131,8 @@ class WikiPageWriter:
             conflicts=conflicts
         )
 
-        # Reconstruct with LLM
-        reconstructed = await self._call_llm(prompt)
+        # Reconstruct with LLM (lower temperature for precise merging)
+        reconstructed = await self._call_llm(prompt, temperature=0.2)
 
         # Ensure standard sections are present
         reconstructed = self._ensure_standard_sections(
@@ -154,7 +155,7 @@ class WikiPageWriter:
         Returns:
             List of conflicts with: {fact_a, fact_b, confidence, context}
         """
-        prompt = f"""Analyze these two pieces of content for factual conflicts.
+        prompt = f"""Analyze these contents for direct factual conflicts.
 
 EXISTING CONTENT:
 {existing_content[:2000]}
@@ -162,25 +163,26 @@ EXISTING CONTENT:
 NEW INFORMATION:
 {new_information[:2000]}
 
-Identify any facts that contradict each other. For each conflict, provide:
-1. The fact from existing content
-2. The contradicting fact from new information
-3. Confidence level (low/medium/high)
-4. Context/explanation
+ANALYSIS STEPS:
+1. Identify specific factual claims in existing content (dates, numbers, names, states)
+2. Identify specific factual claims in new content
+3. Compare ONLY for direct contradictions (X says A, Y says not-A)
 
-Return ONLY valid JSON:
+RULES:
+- Do NOT flag differences in wording or phrasing as conflicts
+- Do NOT flag new/additional information as conflicts
+- Do NOT flag opinion differences as conflicts
+- ONLY flag direct factual contradictions
+- Return valid JSON only, no commentary
+
+Return format:
 {{
   "conflicts": [
-    {{
-      "existing_fact": "fact from old content",
-      "new_fact": "contradicting fact",
-      "confidence": "medium",
-      "context": "explanation of why these conflict"
-    }}
+    {{"existing_fact": "...", "new_fact": "...", "confidence": "low/medium/high", "context": "..."}}
   ]
 }}
 
-If no conflicts, return: {{"conflicts": []}}
+If no conflicts: {{"conflicts": []}}
 
 JSON:"""
 
@@ -188,7 +190,8 @@ JSON:"""
             response = await self.ollama.generate_text(
                 prompt=prompt,
                 model=self.model,
-                stream=False
+                stream=False,
+                temperature=0.0  # Deterministic for consistent conflict detection
             )
 
             # Extract JSON
@@ -347,6 +350,13 @@ FORMATTING RULES:
 - Keep sections focused and scannable
 - Adapt structure to content - not all sections apply to all topics
 
+CRITICAL CONSTRAINTS:
+- Do NOT invent facts not present in the source information above
+- Do NOT add speculative information or assumptions
+- Do NOT fill sections with placeholder text or generic statements
+- If information for a section is not available, OMIT the section entirely
+- Base ALL content strictly on provided source information
+
 Generate ONLY the markdown content (do not include Sources, Knowledge Graph, or Mind Map sections - those are added automatically).
 
 MARKDOWN:"""
@@ -402,6 +412,13 @@ FORMATTING RULES:
 - Bold important terms
 - Add subsections (###) where it improves clarity
 
+CRITICAL CONSTRAINTS:
+- Do NOT rephrase facts in ways that change their meaning
+- Do NOT remove ANY information unless explicitly superseded by newer facts
+- Do NOT add information not present in existing content or new information
+- Preserve exact quotes, dates, numbers, and names verbatim
+- Do NOT fill gaps with assumptions or general knowledge
+
 OUTPUT INSTRUCTIONS:
 - Return complete page content (do not include Sources, Knowledge Graph, Mind Map - those are added automatically)
 - Include updated "Changes & Updates" section noting what was changed today
@@ -409,13 +426,21 @@ OUTPUT INSTRUCTIONS:
 
 RECONSTRUCTED MARKDOWN:"""
 
-    async def _call_llm(self, prompt: str) -> str:
-        """Call LLM with prompt and return response."""
+    async def _call_llm(self, prompt: str, temperature: float = 0.3) -> str:
+        """
+        Call LLM with prompt and return response.
+
+        Args:
+            prompt: The prompt text
+            temperature: Sampling temperature (0.0=deterministic, higher=creative)
+                        Default 0.3 for controlled but natural content generation
+        """
         try:
             response = await self.ollama.generate_text(
                 prompt=prompt,
                 model=self.model,
-                stream=False
+                stream=False,
+                temperature=temperature
             )
 
             if not response:

tests/conftest.py

@@ -1,5 +1,6 @@
 """Pytest configuration and shared fixtures for Library Desk tests."""
 
+import os
 import pytest
 import pytest_asyncio
 from typing import AsyncGenerator
@@ -7,6 +8,9 @@ from typing import AsyncGenerator
 # Test configuration
 pytest_plugins = ("pytest_asyncio",)
 
+# Use real host for tests (services available at this IP)
+TEST_HOST = os.environ.get("TEST_HOST", "192.168.86.149")
+
 
 @pytest.fixture
 def test_user() -> str:
@@ -17,49 +21,56 @@ def test_user() -> str:
 @pytest.fixture
 def neo4j_test_uri() -> str:
     """Test Neo4j URI."""
-    return "bolt://neo4j:7687"
+    return f"bolt://{TEST_HOST}:7687"
 
 
 @pytest.fixture
 def neo4j_test_auth() -> tuple:
     """Test Neo4j authentication."""
-    return ("neo4j", "test_password")
+    from src.config import get_settings
+    settings = get_settings()
+    return ("neo4j", settings.neo4j_password)
 
 
 @pytest.fixture
 def qdrant_test_url() -> str:
     """Test Qdrant URL."""
-    return "http://qdrant:6333"
+    return f"http://{TEST_HOST}:6333"
 
 
 @pytest.fixture
 def wikijs_test_config() -> dict:
     """Test Wiki.js configuration."""
+    from src.config import get_settings
+    settings = get_settings()
     return {
-        "base_url": "http://wiki:3000",
-        "api_key": "test_api_key"
+        "base_url": f"http://{TEST_HOST}:3000",
+        "username": settings.wikijs_username,
+        "password": settings.wikijs_password
     }
 
 
 @pytest.fixture
 def searxng_test_url() -> str:
     """Test SearXNG URL."""
-    return "http://searxng:8080"
+    return f"http://{TEST_HOST}:8080"
 
 
 @pytest.fixture
 def ollama_test_config() -> dict:
     """Test Ollama configuration."""
+    from src.config import get_settings
+    settings = get_settings()
     return {
-        "base_url": "http://ollama:11434",
-        "model": "nomic-embed-text"
+        "base_url": f"http://{TEST_HOST}:11434",
+        "model": settings.ollama_embedding_model
     }
 
 
 @pytest.fixture
 def redis_test_url() -> str:
     """Test Redis URL."""
-    return "redis://redis-shared:6379/4"
+    return f"redis://{TEST_HOST}:6379/4"
 
 
 @pytest.fixture

tests/test_entity_linking.py

@@ -38,10 +38,10 @@ def settings():
 
 
 @pytest_asyncio.fixture
-async def neo4j_client(settings) -> AsyncGenerator[Neo4jClient, None]:
+async def neo4j_client(settings, neo4j_test_uri) -> AsyncGenerator[Neo4jClient, None]:
     """Get connected Neo4j client."""
     client = Neo4jClient(
-        uri=settings.neo4j_uri,
+        uri=neo4j_test_uri,
         user=settings.neo4j_user,
         password=settings.neo4j_password
     )
@@ -51,12 +51,12 @@ async def neo4j_client(settings) -> AsyncGenerator[Neo4jClient, None]:
 
 
 @pytest_asyncio.fixture
-async def wiki_client(settings) -> AsyncGenerator[WikiJSClient, None]:
+async def wiki_client(wikijs_test_config) -> AsyncGenerator[WikiJSClient, None]:
     """Get Wiki.js client."""
     client = WikiJSClient(
-        base_url=settings.wikijs_url,
-        username=settings.wikijs_username,
-        password=settings.wikijs_password
+        base_url=wikijs_test_config["base_url"],
+        username=wikijs_test_config["username"],
+        password=wikijs_test_config["password"]
     )
     yield client
 

tests/test_hybrid_rag.py

@@ -43,10 +43,10 @@ def settings():
 
 
 @pytest_asyncio.fixture
-async def neo4j_client(settings) -> AsyncGenerator[Neo4jClient, None]:
+async def neo4j_client(settings, neo4j_test_uri) -> AsyncGenerator[Neo4jClient, None]:
     """Get connected Neo4j client."""
     client = Neo4jClient(
-        uri=settings.neo4j_uri,
+        uri=neo4j_test_uri,
         user=settings.neo4j_user,
         password=settings.neo4j_password
     )
@@ -56,32 +56,35 @@ async def neo4j_client(settings) -> AsyncGenerator[Neo4jClient, None]:
 
 
 @pytest.fixture
-def qdrant_client(settings) -> QdrantClientWrapper:
+def qdrant_client(qdrant_test_url) -> QdrantClientWrapper:
     """Get Qdrant client."""
-    return QdrantClientWrapper(url=settings.qdrant_url)
+    return QdrantClientWrapper(url=qdrant_test_url)
 
 
 @pytest_asyncio.fixture
-async def wiki_client(settings) -> AsyncGenerator[WikiJSClient, None]:
+async def wiki_client(wikijs_test_config) -> AsyncGenerator[WikiJSClient, None]:
     """Get Wiki.js client."""
     client = WikiJSClient(
-        base_url=settings.wikijs_url,
-        username=settings.wikijs_username,
-        password=settings.wikijs_password
+        base_url=wikijs_test_config["base_url"],
+        username=wikijs_test_config["username"],
+        password=wikijs_test_config["password"]
     )
     yield client
 
 
 @pytest.fixture
-def searxng_client(settings) -> SearXNGClient:
+def searxng_client(searxng_test_url) -> SearXNGClient:
     """Get SearXNG client."""
-    return SearXNGClient(base_url=settings.searxng_url)
+    return SearXNGClient(base_url=searxng_test_url)
 
 
 @pytest.fixture
-def ollama_client(settings) -> OllamaClient:
+def ollama_client(ollama_test_config) -> OllamaClient:
     """Get Ollama client."""
-    return OllamaClient(base_url=settings.ollama_url)
+    return OllamaClient(
+        base_url=ollama_test_config["base_url"],
+        model=ollama_test_config["model"]
+    )
 
 
 @pytest.fixture
@@ -219,53 +222,54 @@ async def test_vector_data(vector_service, test_wiki_page):
 # ============================================================================
 
 class TestRRFFusion:
-    """Test Reciprocal Rank Fusion algorithm."""
+    """Test two-stage Reciprocal Rank Fusion algorithm."""
 
-    def test_rrf_single_source(self, hybrid_rag_service):
-        """Test RRF with single source."""
-        results_by_source = {
-            "vector": [
-                {"page_id": 1, "title": "Doc 1", "content": "test"},
-                {"page_id": 2, "title": "Doc 2", "content": "test"}
-            ]
-        }
+    def test_wiki_merge_single_source(self, hybrid_rag_service):
+        """Test wiki merge with single source (vector only)."""
+        vector_results = [
+            {"page_id": 1, "title": "Doc 1", "content": "test"},
+            {"page_id": 2, "title": "Doc 2", "content": "test"}
+        ]
 
-        fused = hybrid_rag_service._reciprocal_rank_fusion(results_by_source, k=60)
+        merged = hybrid_rag_service._merge_wiki_sources(vector_results, [], k=60)
 
-        assert len(fused) == 2
-        assert fused[0]["rrf_score"] > fused[1]["rrf_score"]  # Rank 1 > Rank 2
-        assert fused[0]["sources"] == ["vector"]
+        assert len(merged) == 2
+        assert merged[0]["wiki_rrf_score"] > merged[1]["wiki_rrf_score"]  # Rank 1 > Rank 2
+        assert merged[0]["found_by"] == ["vector"]
 
-    def test_rrf_multiple_sources_same_doc(self, hybrid_rag_service):
-        """Test RRF with same document from multiple sources."""
-        results_by_source = {
-            "vector": [{"page_id": 1, "title": "Doc 1", "content": "test"}],
-            "graph": [{"page_id": 1, "title": "Doc 1", "content": ""}],
-        }
+    def test_wiki_merge_multiple_sources_same_doc(self, hybrid_rag_service):
+        """Test wiki merge with same document from vector and graph."""
+        vector_results = [{"page_id": 1, "title": "Doc 1", "content": "test"}]
+        graph_results = [{"page_id": 1, "title": "Doc 1", "content": ""}]
 
-        fused = hybrid_rag_service._reciprocal_rank_fusion(results_by_source, k=60)
+        merged = hybrid_rag_service._merge_wiki_sources(vector_results, graph_results, k=60)
 
-        assert len(fused) == 1  # Deduplicated
-        assert len(fused[0]["sources"]) == 2  # Both sources
-        assert "vector" in fused[0]["sources"]
-        assert "graph" in fused[0]["sources"]
-        # RRF score should be sum: 1/(60+1) + 1/(60+1)
+        assert len(merged) == 1  # Deduplicated
+        assert len(merged[0]["found_by"]) == 2  # Both sources
+        assert "vector" in merged[0]["found_by"]
+        assert "graph" in merged[0]["found_by"]
+        # Wiki RRF score should be sum: 1/(60+1) + 1/(60+1)
         expected_score = 1/61 + 1/61
-        assert abs(fused[0]["rrf_score"] - expected_score) < 0.001
+        assert abs(merged[0]["wiki_rrf_score"] - expected_score) < 0.001
 
-    def test_rrf_web_results(self, hybrid_rag_service):
-        """Test RRF with web results (URL-based)."""
-        results_by_source = {
-            "web": [
-                {"url": "https://example.com/1", "title": "Web 1", "content": "test"},
-                {"url": "https://example.com/2", "title": "Web 2", "content": "test"}
-            ]
-        }
+    def test_final_rrf_wiki_and_web(self, hybrid_rag_service):
+        """Test final RRF between wiki and web results."""
+        # Pre-merged wiki results
+        wiki_results = [
+            {"page_id": 1, "title": "Wiki 1", "content": "test", "found_by": ["vector"]}
+        ]
+        web_results = [
+            {"url": "https://example.com/1", "title": "Web 1", "content": "test"},
+            {"url": "https://example.com/2", "title": "Web 2", "content": "test"}
+        ]
 
-        fused = hybrid_rag_service._reciprocal_rank_fusion(results_by_source, k=60)
+        fused = hybrid_rag_service._reciprocal_rank_fusion(wiki_results, web_results, k=60)
 
-        assert len(fused) == 2
-        assert fused[0]["result"]["url"] == "https://example.com/1"
+        assert len(fused) == 3
+        # Wiki rank 1 and web rank 1 should have same RRF score
+        wiki_score = next(r["rrf_score"] for r in fused if r["source_type"] == "wiki")
+        web_score = next(r["rrf_score"] for r in fused if r["source_type"] == "web")
+        assert abs(wiki_score - web_score) < 0.001  # Equal footing
 
 
 class TestContextFormatting:

tests/test_integration.py

@@ -30,10 +30,10 @@ def settings():
 
 
 @pytest_asyncio.fixture
-async def neo4j_client(settings) -> AsyncGenerator[Neo4jClient, None]:
+async def neo4j_client(settings, neo4j_test_uri) -> AsyncGenerator[Neo4jClient, None]:
     """Get connected Neo4j client."""
     client = Neo4jClient(
-        uri=settings.neo4j_uri,
+        uri=neo4j_test_uri,
         user=settings.neo4j_user,
         password=settings.neo4j_password
     )
@@ -43,45 +43,46 @@ async def neo4j_client(settings) -> AsyncGenerator[Neo4jClient, None]:
 
 
 @pytest.fixture
-def qdrant_client(settings) -> QdrantClientWrapper:
+def qdrant_client(settings, qdrant_test_url) -> QdrantClientWrapper:
     """Get Qdrant client."""
-    return QdrantClientWrapper(url=settings.qdrant_url)
+    return QdrantClientWrapper(url=qdrant_test_url)
 
 
 @pytest_asyncio.fixture
-async def wikijs_client(settings) -> AsyncGenerator[WikiJSClient, None]:
+async def wikijs_client(wikijs_test_config) -> AsyncGenerator[WikiJSClient, None]:
     """Get Wiki.js client."""
     client = WikiJSClient(
-        base_url=settings.wikijs_url,
-        api_key=settings.wikijs_api_key
+        base_url=wikijs_test_config["base_url"],
+        username=wikijs_test_config["username"],
+        password=wikijs_test_config["password"]
     )
     yield client
     await client.close()
 
 
 @pytest_asyncio.fixture
-async def searxng_client(settings) -> AsyncGenerator[SearXNGClient, None]:
+async def searxng_client(searxng_test_url) -> AsyncGenerator[SearXNGClient, None]:
     """Get SearXNG client."""
-    client = SearXNGClient(base_url=settings.searxng_url)
+    client = SearXNGClient(base_url=searxng_test_url)
     yield client
     await client.close()
 
 
 @pytest_asyncio.fixture
-async def ollama_client(settings) -> AsyncGenerator[OllamaClient, None]:
-    """Get Ollama client."""
+async def ollama_client(ollama_test_config) -> AsyncGenerator[OllamaClient, None]:
+    """Get Ollama client for embeddings."""
     client = OllamaClient(
-        base_url=settings.ollama_url,
-        model=settings.ollama_model
+        base_url=ollama_test_config["base_url"],
+        model=ollama_test_config["model"]
     )
     yield client
     await client.close()
 
 
 @pytest_asyncio.fixture
-async def job_manager(settings) -> AsyncGenerator[JobManager, None]:
+async def job_manager(redis_test_url) -> AsyncGenerator[JobManager, None]:
     """Get job manager."""
-    manager = JobManager(redis_url=settings.redis_url)
+    manager = JobManager(redis_url=redis_test_url)
     await manager.connect()
     yield manager
     await manager.close()