feat: integrate Paperless documents and volatile cache into Librarian

- Add Paperless document search to HybridRAG pipeline
- Add volatile cache (weather, forecast, news, stocks) to HybridRAG
- Add include_documents and include_volatile params to hybrid_search
- Add 📑 and ⚡ icons for document/volatile sources
- Update Librarian prompt with new data source awareness
- Fix Biographer routing: personal memory queries now route correctly
- Add location keywords to Steward pre-fetch logic

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

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

src/agents/librarian/agent.py

@@ -39,7 +39,14 @@ Your role is to help users find, understand, synthesize, and manage information
 - The personal wiki (Wiki.js) containing documentation and notes
 - The knowledge graph (Neo4j) with entities and relationships
 - Vector embeddings (Qdrant) for semantic search
-- Web search (SearXNG) for current information
+- Paperless documents (📑) - indexed PDFs, scanned documents, invoices, receipts from the user's document archive
+- Volatile cache (⚡) - pre-fetched real-time data for user-relevant locations and items:
+  - weather/forecast: conditions and forecasts for user's configured cities
+  - news: headlines from user's preferred sources
+  - stock/crypto: quotes for user's watched symbols
+  - sun/air_quality: data for user's locations
+  - Note: volatile data may not exist for arbitrary queries - falls back to web search
+- Web search (SearXNG) for current information not available in cache
 
 ## Your Personality
 - Scholarly and thorough in your research
@@ -60,7 +67,13 @@ Your role is to help users find, understand, synthesize, and manage information
   - Use for: comparing multiple sources, gathering info from several pages
 
 ### Internal Research Tools
-- **hybrid_search**: Your primary research tool - searches wiki, graph, and web at once
+- **hybrid_search**: Your primary research tool - searches ALL sources at once:
+  - Wiki pages (vector similarity)
+  - Knowledge graph (entity relationships)
+  - Paperless documents (📑 indexed PDFs, scans)
+  - Volatile cache (⚡ weather, news, stocks - when available)
+  - Web search (current information)
+  Results are fused and re-ranked by relevance. Volatile data gets priority when fresh.
 - **search_wiki**: Find specific wiki pages by keyword
 - **semantic_search**: Find conceptually similar content
 - **explore_knowledge_graph** / **find_related_entities**: Discover connections

src/agents/librarian/client.py

@@ -225,18 +225,22 @@ class LibraryDeskClient:
         vector_limit: int = 10,
         graph_limit: int = 10,
         web_limit: int = 5,
+        document_limit: int = 5,
+        volatile_limit: int = 3,
         enable_reranking: bool = True,
         final_result_count: int = 10,
     ) -> HybridRAGResponse:
         """
-        Execute HybridRAG search combining vector, graph, and web results.
+        Execute HybridRAG search combining vector, graph, documents, volatile, and web.
 
         Args:
             query: Search query
             user: User identifier for multi-tenancy (defaults to request context)
-            vector_limit: Max results from vector search
+            vector_limit: Max results from vector search (wiki pages)
             graph_limit: Max results from graph search
-            web_limit: Max results from web search
+            web_limit: Max results from web search (0 to disable)
+            document_limit: Max results from Paperless documents (0 to disable)
+            volatile_limit: Max results from volatile cache (0 to disable)
             enable_reranking: Whether to rerank with LLM
             final_result_count: Number of final results after fusion
 
@@ -252,6 +256,11 @@ class LibraryDeskClient:
                 "vector_limit": vector_limit,
                 "graph_limit": graph_limit,
                 "web_limit": web_limit,
+                "document_limit": document_limit,
+                "volatile_limit": volatile_limit,
+                "enable_documents": document_limit > 0,
+                "enable_volatile": volatile_limit > 0,
+                "enable_web": web_limit > 0,
                 "enable_reranking": enable_reranking,
                 "final_result_count": final_result_count,
             },

src/agents/librarian/tools.py

@@ -17,20 +17,26 @@ logger = get_logger(__name__)
 async def hybrid_search(
     query: str,
     include_web: bool = True,
+    include_documents: bool = True,
+    include_volatile: bool = True,
 ) -> str:
     """
     Search across all knowledge sources using HybridRAG.
 
     This is the primary research tool, combining:
-    - Vector search (semantic similarity over documents)
+    - Vector search (semantic similarity over wiki pages)
     - Knowledge graph (entities and relationships)
+    - Paperless documents (📑 indexed PDFs, scans, invoices)
+    - Volatile cache (⚡ weather, news, stocks - for user's configured items)
     - Web search (current information from SearXNG)
 
-    Results are fused and re-ranked by relevance.
+    Results are fused and re-ranked by relevance. Volatile data gets priority when fresh.
 
     Args:
         query: Natural language research query
         include_web: Whether to include web results (default: True)
+        include_documents: Whether to include Paperless documents (default: True)
+        include_volatile: Whether to include volatile cache data (default: True)
 
     Returns:
         Formatted search results with sources and context
@@ -38,12 +44,16 @@ async def hybrid_search(
     Examples:
         hybrid_search("How does Docker orchestration work with Kubernetes?")
         hybrid_search("What projects use Neo4j?", include_web=False)
+        hybrid_search("Find my electricity invoices", include_web=False, include_volatile=False)
+        hybrid_search("What's the weather in Rotterdam?")  # May hit volatile cache
     """
     try:
         async with LibraryDeskClient() as client:
             response = await client.hybrid_search(
                 query=query,
                 web_limit=5 if include_web else 0,
+                document_limit=5 if include_documents else 0,
+                volatile_limit=3 if include_volatile else 0,
             )
 
             if not response.results:
@@ -70,6 +80,8 @@ async def hybrid_search(
                     "vector": "📄",
                     "graph": "🔗",
                     "web": "🌐",
+                    "document": "📑",
+                    "volatile": "⚡",
                 }.get(result.source, "•")
 
                 output_parts.append(