fix(celery+lmstudio): PYTHONPATH for forked workers + qwen3.5-9b thinking model support

Three root causes fixed:

1. docker-compose.yml — celery-worker and celery-beat missing PYTHONPATH=/app
   ForkPoolWorker processes inherit sys.path with '' (CWD), but fork can change
   CWD so lazy imports like `from db.session import ...` raised ModuleNotFoundError.
   Adding PYTHONPATH=/app ensures /app is always explicit in the path.

2. provider_config.py — lmstudio SUPPORTS_JSON_MODE set to False
   LM Studio only accepts response_format type 'json_schema' or 'text', not
   'json_object'. GenericOpenAIProvider now omits response_format for lmstudio
   and relies on parse_classification() fallback (same path as Gemini).

3. generic_openai_provider.py — max_tokens raised 1024→4096 (classify) / 256→1024 (suggest)
   Qwen3 thinking models (like qwen/qwen3.5-9b) consume reasoning tokens from the
   same budget. With max_tokens=1024 the model exhausts the budget in the thinking
   trace, leaving content=''. 4096 gives room for both thinking and the JSON output.

Also updates lmstudio PROVIDER_DEFAULTS model to qwen/qwen3.5-9b (confirmed loaded
in LM Studio) and context_chars to 32_000 (Qwen3 context window).

Tested: classify(invoice_text, ['Finance','Legal','HR']) → topics=['Finance'] ✓
Backend tests: 376 passed ✓

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

backend/ai/generic_openai_provider.py

@@ -60,7 +60,7 @@ class GenericOpenAIProvider(OpenAIProvider):
         )
         create_kwargs = dict(
             model=self._model,
-            max_tokens=1024,
+            max_tokens=4096,
             messages=[
                 {"role": "system", "content": system_prompt},
                 {"role": "user", "content": user_msg},
@@ -87,7 +87,7 @@ class GenericOpenAIProvider(OpenAIProvider):
         )
         create_kwargs = dict(
             model=self._model,
-            max_tokens=256,
+            max_tokens=1024,
             messages=[
                 {"role": "system", "content": system_prompt},
                 {"role": "user", "content": user_msg},