Add rulebook RAG pipeline and LLM-driven game setup wizard

RAG: switch Postgres to pgvector, chunk and embed the three D&D
rulebooks locally via sentence-transformers, and retrieve relevant
excerpts per DM turn (query = latest player message) to ground the
system prompt. Retrieval runs off the event loop and is capped by a
relevance threshold and a max character budget so it can't blow up
context size or cost.

Game setup wizard: creating a game now opens a short chat where the
DM asks about genre, length, and the player's experience level, then
proposes a name and description via a tool call. The player can edit
both before creating the game. Stateless endpoint — the frontend
carries the conversation, no DB needed since the game doesn't exist
yet.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

backend/app/llm/client.py

@@ -13,6 +13,11 @@ def get_dm_system_prompt() -> str:
     return (PROMPTS_DIR / "dm_system_prompt.txt").read_text(encoding="utf-8")
 
 
+@lru_cache
+def get_game_setup_prompt() -> str:
+    return (PROMPTS_DIR / "game_setup_prompt.txt").read_text(encoding="utf-8")
+
+
 @lru_cache
 def get_llm_client() -> AsyncOpenAI:
     return AsyncOpenAI(api_key=settings.xai_api_key, base_url=settings.xai_base_url)