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/models/__init__.py

@@ -1,6 +1,7 @@
 from app.models.character import Character
 from app.models.game import Game, GameParticipant
 from app.models.message import Message
+from app.models.rulebook_chunk import RulebookChunk
 from app.models.user import User
 
-__all__ = ["User", "Game", "GameParticipant", "Character", "Message"]
+__all__ = ["User", "Game", "GameParticipant", "Character", "Message", "RulebookChunk"]