Upgrade intent model to qwen2.5:7b, simplify intent prompt rules

config.py:
- Default OLLAMA_INTENT_MODEL: qwen2.5:1.5b → qwen2.5:7b
- Startup will auto-pull and warm the new model on next container restart

intent.py:
- Replaced phrase-matching examples in search_web and research_topic rules
  with semantic descriptions. The 7B model doesn't need example phrases to
  understand intent — it can reason from the tool's purpose. Removes implied
  usage patterns that caused misclassifications on conversational phrasing
  (e.g. "I've been thinking about buying shirts, can you research this?").
- research_topic rule now explicitly covers any subject regardless of phrasing,
  including shopping decisions, comparisons, how-things-work questions, etc.
- search_web rule clarified as "short summary, no note" vs research_topic's
  "comprehensive written reference"

The 1.5B model required prescriptive phrase examples to route correctly; the
7B model has sufficient language understanding to classify from semantic intent.
Expected improvement: ~1-2s intent calls (vs 0.4-9s for the 1.5B model which
sometimes timed out or misclassified longer/conversational messages).

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

src/fabledassistant/config.py

@@ -24,9 +24,9 @@ class Config:
     )
     OLLAMA_URL: str = os.environ.get("OLLAMA_URL", "http://localhost:11434")
     OLLAMA_MODEL: str = os.environ.get("OLLAMA_MODEL", "qwen3:latest")
-    # Optional dedicated model for intent classification (should be small/fast).
+    # Optional dedicated model for intent classification.
     # Falls back to OLLAMA_MODEL if not set. Can also be overridden per-user via settings.
-    OLLAMA_INTENT_MODEL: str = os.environ.get("OLLAMA_INTENT_MODEL", "qwen2.5:1.5b")
+    OLLAMA_INTENT_MODEL: str = os.environ.get("OLLAMA_INTENT_MODEL", "qwen2.5:7b")
     # KV cache context window for generation. Lower = less VRAM, less throughput impact.
     # 8192 is sufficient for most conversations; raise if you paste large documents.
     OLLAMA_NUM_CTX: int = int(os.environ.get("OLLAMA_NUM_CTX", "8192"))