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/schemas/game_setup.py

@@ -0,0 +1,16 @@
+from pydantic import BaseModel
+
+
+class GameSetupChatRequest(BaseModel):
+    messages: list[dict] = []
+
+
+class GameSetupProposal(BaseModel):
+    name: str
+    description: str
+
+
+class GameSetupChatResponse(BaseModel):
+    messages: list[dict]
+    assistant_text: str | None
+    proposal: GameSetupProposal | None