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/services/generation_task.py

@@ -25,7 +25,6 @@ from fabledassistant.services.generation_buffer import (
 from fabledassistant.services.llm import ChatChunk, build_context, generate_completion, stream_chat, stream_chat_with_tools, summarize_history_for_context, wait_for_model_loaded
 from fabledassistant.services.chat import update_conversation_title
 from fabledassistant.services.logging import log_generation
-from fabledassistant.services.settings import get_setting
 from fabledassistant.services.tools import get_tools_for_user, execute_tool
 from fabledassistant.services.research import run_research_pipeline
 
@@ -156,16 +155,12 @@ async def run_generation(
 
     buf.append_event("status", {"status": "Building context..."})
 
-    # Phase 1: Quick DB calls — resolve tools list and intent model in parallel.
-    tools, intent_model_setting = await asyncio.gather(
-        get_tools_for_user(user_id),
-        get_setting(user_id, "intent_model", ""),
-    )
-    intent_model = intent_model_setting or Config.OLLAMA_INTENT_MODEL or model
+    # Phase 1: Resolve the tools list for this user.
+    tools = await get_tools_for_user(user_id)
 
     logger.info(
-        "Starting generation for conv %d: model=%s, intent_model=%s, tools=%d",
-        conv_id, model, intent_model, len(tools),
+        "Starting generation for conv %d: model=%s, tools=%d",
+        conv_id, model, len(tools),
     )
 
     # Phase 2: Summarize long conversation history if needed.
@@ -173,7 +168,7 @@ async def run_generation(
     history_summary: str | None = None
     if len(history) > 20:  # matches _HISTORY_SUMMARY_THRESHOLD in llm.py
         buf.append_event("status", {"status": "Summarizing conversation history..."})
-        history_to_use, history_summary = await summarize_history_for_context(history, intent_model)
+        history_to_use, history_summary = await summarize_history_for_context(history, model)
 
     # Phase 3: Build context and wait for model in parallel.
     model_load_task = asyncio.create_task(wait_for_model_loaded(model, timeout=90.0))
@@ -259,7 +254,7 @@ async def run_generation(
                         if tool_name == "research_topic":
                             topic = arguments.get("topic", "")
                             try:
-                                note = await run_research_pipeline(topic, user_id, model, intent_model, buf)
+                                note = await run_research_pipeline(topic, user_id, model, buf)
                                 result = {
                                     "success": True,
                                     "type": "research_note",