Remove intent model entirely; quick-capture uses primary model

The separate intent model (OLLAMA_INTENT_MODEL / qwen2.5:7b) is removed
from every part of the system. All classification now uses the primary model.

Changes:
- config.py: remove OLLAMA_INTENT_MODEL
- intent.py: remove classify_intent() and all supporting infrastructure
  (_SYSTEM_PROMPT_TEMPLATE, _RESEARCH_PREFIX, _PRIOR_WORK_REFS); file now
  only contains the quick-capture classifier
- quick_capture.py: classify_capture_intent() now called with Config.OLLAMA_MODEL
- generation_task.py: remove intent_model_setting DB lookup and get_setting import;
  history summarization and research pipeline use the primary model directly
- research.py: remove intent_model parameter from run_research_pipeline() and
  _generate_sub_queries(); both use the model param throughout
- routes/settings.py: remove intent_model from model-key validation and response
- app.py: remove intent model pre-warming at startup
- SettingsView.vue: remove Intent Model selector and related refs/state

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/fabledassistant/routes/quick_capture.py

@@ -78,7 +78,7 @@ async def quick_capture_route():
     if not text:
         return jsonify({"error": "text is required"}), 400
 
-    intent_model = Config.OLLAMA_INTENT_MODEL or Config.OLLAMA_MODEL
+    model = Config.OLLAMA_MODEL
 
     # Build tool list for this user, then restrict to capture-only operations.
     all_tools = await get_tools_for_user(uid)
@@ -86,7 +86,7 @@ async def quick_capture_route():
         t for t in all_tools if t.get("function", {}).get("name") in _CAPTURE_TOOL_NAMES
     ]
 
-    intent = await classify_capture_intent(text, capture_tools, intent_model)
+    intent = await classify_capture_intent(text, capture_tools, model)
 
     if intent.should_execute:
         # research_topic bypasses execute_tool — run the pipeline directly
@@ -94,9 +94,8 @@ async def quick_capture_route():
             from fabledassistant.services.research import run_research_pipeline
 
             topic = intent.arguments.get("topic", text)
-            model = Config.OLLAMA_MODEL
             try:
-                note = await run_research_pipeline(topic, uid, model, intent_model)
+                note = await run_research_pipeline(topic, uid, model)
                 logger.info(
                     "Quick-capture uid=%d: research note id=%d '%s'",
                     uid, note.id, note.title,
@@ -114,7 +113,6 @@ async def quick_capture_route():
         # For notes, run a second LLM pass to generate a proper title and
         # well-formed body rather than using the raw capture text verbatim.
         if intent.tool_name == "create_note":
-            model = Config.OLLAMA_MODEL
             title, body = await _process_note(text, model)
             intent.arguments["title"] = title
             intent.arguments["body"] = body
@@ -141,7 +139,6 @@ async def quick_capture_route():
 
     # Fallback: classify_capture_intent returned no-tool (e.g. LLM parse failure).
     # Still process the text through the note enrichment pass.
-    model = Config.OLLAMA_MODEL
     fallback_title, fallback_body = await _process_note(text, model)
 
     result = await execute_tool(