feat(chat): integrate Langfuse tracing, prompt management & generation tracking

- shared/config.py: add LANGFUSE_SECRET_KEY, LANGFUSE_PUBLIC_KEY, LANGFUSE_HOST
- services/chat/app/tracing.py: new module — Langfuse client singleton,
  create_trace(), get_langfuse_callback(), get_prompt(), link_prompt_to_trace(),
  score_trace(), flush/shutdown helpers. Gracefully no-ops when keys are missing.
- services/chat/app/llm.py: add callbacks param to get_llm() for LangChain
  callback handler injection
- services/chat/app/deep_agent.py: accept langfuse_handler in all run_* and
  _run_single_agent* functions, pipe callbacks to LLM calls, fetch managed
  prompts from Langfuse with fallback to hardcoded system prompts
- services/chat/app/redis_consumer.py: create Langfuse trace per request
  (home_request/floating_request), pass callback handler to deep_agent,
  link prompt name to trace, attach output preview, flush after each request
- services/chat/app/main.py: shutdown Langfuse client in lifespan teardown
- services/chat/requirements.txt: add langfuse>=2.0.0

Langfuse prompt names: 'home_system', 'floating_system' — create these in
the Langfuse dashboard to manage prompts. Without them, hardcoded defaults
are used transparently.

services/chat/app/llm.py

@@ -42,6 +42,7 @@ def get_llm(
     *,
     model: str | None = None,
     temperature: float = 0,
+    callbacks: list | None = None,
 ) -> ChatOpenAI | ChatLiteLLM:
     model = model or settings.LLM_MODEL
 
@@ -49,22 +50,16 @@ def get_llm(
         os.environ.setdefault("GITHUB_COPILOT_TOKEN_DIR", settings.GITHUB_COPILOT_TOKEN_DIR)
 
     if "/" in model:
-        return ChatLiteLLM(model=model, temperature=temperature)
+        return ChatLiteLLM(model=model, temperature=temperature, callbacks=callbacks)
 
     return ChatOpenAI(
         model=model,
         temperature=temperature,
         api_key=_api_key_for_model(model),
+        callbacks=callbacks,
     )
 
 
-def get_router_llm(
-    *,
-    temperature: float = 0,
-) -> ChatOpenAI | ChatLiteLLM:
-    return get_llm(model=settings.LLM_ROUTER_MODEL, temperature=temperature)
-
-
 async def embed(text: str) -> list[float]:
     model = settings.LLM_EMBED_MODEL