feat: cap narrative response length + tune sampling

Bot replies were running long (4 paragraphs of action+dialogue beats
per turn) because we never set max_tokens on the narrative call. Three
tunable knobs now in Settings (set in data/config.toml to override):

- narrative_max_tokens: int = 400
  Hard cap on each generated response. ~400 tokens ≈ 1–2 short
  paragraphs. Drop to 200 for terse banter, bump to 800+ for longer
  scenes.

- narrative_temperature: float = 0.85
  Sampling temperature. 0.7 = grounded/consistent (slightly stiff),
  0.85 = creative-but-in-character (default), 1.0 = wide variety,
  >1.0 = often off-the-rails.

- prompt closing instruction now nudges: "Keep your response to a
  single beat — one or two short paragraphs at most. Don't monologue;
  leave room for the other person to react."

Both turns.py (post_turn) and regenerate.py forward the params to
client.stream(). FeatherlessClient already passes **params through to
the OpenAI-compat endpoint.

Note: temperature doesn't control length — that was a common
misconception. max_tokens is the actual length cap. Lower temperature
makes word choice more predictable (slightly stiffer voice), not
shorter. Both knobs are useful for different goals.

chat/services/prompt.py

@@ -211,7 +211,9 @@ def _closing_instruction(speaker_name: str, addressee_name: str) -> str:
         f"Continue the scene as {speaker_name}, in their voice, responding "
         "naturally. Use *asterisks* for actions and quotes for dialogue. "
         f"Stay in character. Do not narrate {addressee_name}'s actions or "
-        "thoughts."
+        "thoughts. "
+        "Keep your response to a single beat — one or two short paragraphs "
+        "at most. Don't monologue; leave room for the other person to react."
     )
 
 

chat/services/regenerate.py

@@ -156,7 +156,10 @@ async def regenerate_assistant_turn(
     # 5. Stream the new narrative.
     accumulated: list[str] = []
     async for chunk in client.stream(
-        messages, model=settings.narrative_model
+        messages,
+        model=settings.narrative_model,
+        max_tokens=settings.narrative_max_tokens,
+        temperature=settings.narrative_temperature,
     ):
         accumulated.append(chunk)
         await publish(