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/rag/retrieval.py

@@ -0,0 +1,51 @@
+import asyncio
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.rulebook_chunk import RulebookChunk
+from app.rag.embeddings import embed_query
+
+DEFAULT_TOP_K = 5
+# Cosine distance cutoff (0 = identical, 2 = opposite) — drop weak matches rather than
+# stuffing the prompt with irrelevant rulebook text when nothing actually fits the query.
+# Calibrated against test queries: genuinely relevant chunks scored 0.30-0.45.
+MAX_DISTANCE = 0.6
+# Hard cap on injected rulebook text so one turn's context/cost doesn't balloon even when
+# several chunks all clear the relevance threshold.
+MAX_BLOCK_CHARS = 4000
+
+
+async def retrieve_relevant_chunks(
+    session: AsyncSession, query: str, top_k: int = DEFAULT_TOP_K
+) -> list[tuple[RulebookChunk, float]]:
+    # embed_query is a synchronous, CPU-bound sentence-transformers call — run it off the
+    # event loop so it doesn't stall other concurrent requests/WebSocket connections.
+    query_embedding = await asyncio.to_thread(embed_query, query)
+    distance = RulebookChunk.embedding.cosine_distance(query_embedding)
+
+    rows = (
+        await session.execute(
+            select(RulebookChunk, distance.label("distance")).order_by(distance).limit(top_k)
+        )
+    ).all()
+    return [(row[0], row[1]) for row in rows]
+
+
+async def build_rag_block(session: AsyncSession, query: str, top_k: int = DEFAULT_TOP_K) -> str:
+    results = await retrieve_relevant_chunks(session, query, top_k=top_k)
+    relevant = [chunk for chunk, distance in results if distance <= MAX_DISTANCE]
+    if not relevant:
+        return ""
+
+    parts = [
+        "Relevante Auszüge aus den Regelwerken — richte dich danach und nenne bei Bedarf die Quelle:"
+    ]
+    used_chars = 0
+    for chunk in relevant:
+        entry = f"\n[{chunk.source_document}]\n{chunk.content}"
+        if used_chars + len(entry) > MAX_BLOCK_CHARS and used_chars > 0:
+            break
+        parts.append(entry)
+        used_chars += len(entry)
+    return "\n".join(parts)