feat(settings): opt-in reasoning effort for utility model calls (#6572)

* feat(settings): opt-in reasoning effort for utility model calls

Add utility_reasoning_effort (empty = provider default) and let utility
calls inherit a chat session's effort only on that session's matching
endpoint+model route. Explicit caller effort wins; otherwise the utility
setting wins over session inheritance on utility-chain routes. Resolve
the chain and setting per user and validate levels against model evidence.
No-session work uses the setting only, with no chat-effort crossover.

- src/utility_effort.py owns effort_for_call and candidate_effort_factory
- remove global effort fill from llm_call/llm_call_async; utility call
  sites pass effort explicitly, so unrelated calls on the same route do
  not pick it up
- wire auto-title, memory/persona/skill extraction, memory suggest/import/
  audit, compaction, auto-sort, and per-owner background task candidates
- wire no-session email writing-style, summary, and translation calls,
  plus skills evaluation, necessity/retrieval audits, and improvement
- deliberately exclude email replies because thinking_mode='off'
- add the optional candidate_request_factory to llm_call_async_with_fallback
- add sync llm_call reasoning_effort and include it in the cache key
- show the Utility Model effort selector only for advertised levels

Fixes #6569

* fix(utility): exclude email replies and support inherited effort in settings

Pass reasoning_effort='' to task_llm_call_async for scheduled reply drafting to honor the email reply exclusion. In AI settings, resolve the effective Default Chat endpoint and model when Utility is set to 'Same as chat' so supported reasoning effort levels can be selected.

* feat(utility): support same-as-chat effort and session model fallback in compaction

In AI settings, show 'Same as chat' as the default effort option when Utility Model is inherited, and support an explicit 'Provider default' (off) override. Pass the active session fallback in context compaction callers so compaction shares the live chat session's model and reasoning effort for maximum prompt/KV cache affinity.

* fix(settings): clarify chat effort inheritance and keep option visible

Label inherited effort as 'Same as chat (when supported)' and mention
cache affinity and latency speedups in the help text. Ensure the effort
control remains visible when the utility model is set to 'Same as chat'
even if the default chat model lacks advertised reasoning levels, and
populate candidate levels across configured endpoints so the user can
configure effort or keep provider default.

* feat(settings): per-fallback reasoning effort for utility model

Each utility fallback row gets an Effort select, shown only when the
selected model advertises reasoning levels. The choice is stored as
reasoning_effort on the utility_model_fallbacks entry and sent only to
that candidate; an empty value means provider default.

* fix(utility): transmit reasoning effort in non-streaming calls

---------

Co-authored-by: Alexandre Teixeira <111787685+alteixeira20@users.noreply.github.com>

src/llm_core.py

@@ -2268,10 +2268,25 @@ def normalize_model_id(
             return a
     return None
 
+
+def _apply_explicit_nonstream_reasoning_effort(
+    payload: Dict, reasoning_effort: Optional[str]
+) -> None:
+    """Apply explicit effort to an OpenAI-compatible non-streaming request.
+
+    No explicit effort preserves existing provider-specific defaults.
+    Native Ollama, Anthropic and ChatGPT Responses use separate builders.
+    """
+    effort = str(reasoning_effort or "").strip().lower()
+    if effort and effort != "default":
+        payload["reasoning_effort"] = effort
+
+
 def llm_call(url: str, model: str, messages: List[Dict], temperature: float = LLMConfig.DEFAULT_TEMPERATURE,
              max_tokens: int = LLMConfig.DEFAULT_MAX_TOKENS, headers: Optional[Dict] = None,
              timeout: int = LLMConfig.DEFAULT_TIMEOUT, prompt_type: Optional[str] = None,
-             thinking_mode: Optional[str] = None) -> str:
+             thinking_mode: Optional[str] = None,
+             reasoning_effort: Optional[str] = None) -> str:
     """Synchronous LLM call with optional prompt type enhancement."""
     h = _provider_headers(_detect_provider(url))
     # Tolerate headers that arrive as a JSON string (some sessions stored them
@@ -2303,7 +2318,7 @@ def llm_call(url: str, model: str, messages: List[Dict], temperature: float = LL
     provider = _detect_provider(url)
     cache_key = _get_cache_key(
         url, model, messages_copy, temperature, max_tokens, headers=headers,
-        thinking_mode=thinking_mode,
+        thinking_mode=thinking_mode, reasoning_effort=reasoning_effort,
     )
     cached_response = _get_cached_response(cache_key)
     if cached_response:
@@ -2343,6 +2358,7 @@ def llm_call(url: str, model: str, messages: List[Dict], temperature: float = LL
         )
         if provider == "mistral" and _supports_thinking(model):
             payload["reasoning_effort"] = _MISTRAL_REASONING_EFFORT
+        _apply_explicit_nonstream_reasoning_effort(payload, reasoning_effort)
     try:
         note_model_activity(target_url, model)
         r = httpx_post_kimi_aware(target_url, h, json=payload, timeout=timeout)
@@ -2465,14 +2481,30 @@ def llm_call_with_fallback(candidates, messages, **kwargs) -> str:
 
 
 async def llm_call_async_with_fallback(candidates, messages, **kwargs) -> str:
-    """Async variant of `llm_call_with_fallback` — same semantics."""
+    """Async variant of `llm_call_with_fallback` — same semantics.
+
+    An optional ``candidate_request_factory(index, url, model, headers)`` may
+    return ``{"messages": ..., "kwargs": {...}}`` to adjust the request per
+    candidate (same contract as `llm_call_async_with_route_fallback`).
+    """
+    candidate_request_factory = kwargs.pop("candidate_request_factory", None)
     cands = dedupe_model_candidates(candidates)
     if not cands:
         raise HTTPException(503, "No model endpoint configured")
     last_err = None
     for i, (url, model, headers) in enumerate(cands):
         try:
-            return await llm_call_async(url, model, messages, headers=headers, **kwargs)
+            candidate_messages = messages
+            candidate_kwargs = kwargs
+            if candidate_request_factory is not None:
+                request = candidate_request_factory(i, url, model, headers) or {}
+                if hasattr(request, "__await__"):
+                    request = await request
+                candidate_messages = request.get("messages", messages)
+                candidate_kwargs = {**kwargs, **(request.get("kwargs") or {})}
+            return await llm_call_async(
+                url, model, candidate_messages, headers=headers, **candidate_kwargs
+            )
         except Exception as e:
             last_err = e
             tag = "primary" if i == 0 else "candidate"
@@ -2740,6 +2772,7 @@ async def llm_call_async(
         _apply_deepseek_v4_reasoning_defaults(
             payload, target_url, model, thinking_mode
         )
+        _apply_explicit_nonstream_reasoning_effort(payload, reasoning_effort)
 
     if _is_host_dead(target_url):
         raise HTTPException(503, f"Upstream {_host_key(target_url)} marked unreachable (cooldown active)")