feat(llm): adaptive num_ctx tiers + fix KV cache priming num_ctx mismatch

Adds pick_num_ctx() which selects the smallest context window tier
(8192, 16384, 32768) that fits the current messages with 25% headroom,
capped at OLLAMA_NUM_CTX. Threads num_ctx through generation_task.py so
every chat request uses the computed tier rather than a fixed 16384.

Fixes a critical cache miss bug: KV cache priming in app.py and
settings.py was sending requests without num_ctx, so Ollama sized the
cache at its model default (different from the 16384 real requests used),
forcing a full model reload on the first real user message. Both priming
sites now call pick_num_ctx() and pass the matching value.

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

src/fabledassistant/services/generation_task.py

@@ -22,7 +22,7 @@ from fabledassistant.services.generation_buffer import (
     GenerationBuffer,
     GenerationState,
 )
-from fabledassistant.services.llm import ChatChunk, build_context, generate_completion, stream_chat, stream_chat_with_tools, summarize_history_for_context
+from fabledassistant.services.llm import ChatChunk, build_context, generate_completion, pick_num_ctx, stream_chat, stream_chat_with_tools, summarize_history_for_context
 from fabledassistant.services.chat import update_conversation_title
 from fabledassistant.services.settings import get_setting
 from fabledassistant.services.logging import log_generation
@@ -168,6 +168,7 @@ async def _stream_with_retry(
     model: str,
     tools: list[dict],
     think: bool,
+    num_ctx: int | None = None,
 ) -> AsyncGenerator[ChatChunk, None]:
     """stream_chat_with_tools with automatic retry on Ollama 500 errors.
 
@@ -184,7 +185,7 @@ async def _stream_with_retry(
             )
             await asyncio.sleep(delay)
         try:
-            async for chunk in stream_chat_with_tools(messages, model, tools=tools, think=think):
+            async for chunk in stream_chat_with_tools(messages, model, tools=tools, think=think, num_ctx=num_ctx):
                 yield chunk
             return
         except httpx.HTTPStatusError as exc:
@@ -260,6 +261,11 @@ async def run_generation(
         voice_speech_style=voice_speech_style,
     )
 
+    # Pick the smallest context tier that fits the current messages.
+    # Using the minimum needed tier reduces KV cache size and speeds up prefill.
+    num_ctx = pick_num_ctx(messages)
+    logger.debug("Adaptive num_ctx=%d for conv %d", num_ctx, conv_id)
+
     # Emit context event
     buf.append_event("context", {"context": context_meta})
 
@@ -269,6 +275,7 @@ async def run_generation(
     t_start = time.monotonic()
     timing: dict = {
         "think": think,
+        "num_ctx": num_ctx,
         "tools": [],
         "rounds": 0,
         "prompt_tokens": None,
@@ -298,7 +305,7 @@ async def run_generation(
             buf.append_event("status", {"status": "Generating response..." if _round == 0 else "Composing response..."})
             t_stream = time.monotonic()
 
-            async for chunk in _stream_with_retry(messages, model, tools, think):
+            async for chunk in _stream_with_retry(messages, model, tools, think, num_ctx=num_ctx):
                 if buf.cancel_event.is_set():
                     cancelled = True
                     break
@@ -535,7 +542,7 @@ async def run_assist_generation(
             await asyncio.sleep(delay)
         try:
             buf.content_so_far = ""
-            async for chunk in stream_chat(messages, model, options={"num_predict": Config.OLLAMA_NUM_CTX}):
+            async for chunk in stream_chat(messages, model, options={"num_predict": num_ctx}, num_ctx=num_ctx):
                 buf.content_so_far += chunk
                 buf.append_event("chunk", {"chunk": chunk})