Thorsten
419f5e3a89
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>
24 lines
833 B
Python
24 lines
833 B
Python
import uuid
|
|
|
|
from pgvector.sqlalchemy import Vector
|
|
from sqlalchemy import Integer, String, Text
|
|
from sqlalchemy.dialects.postgresql import JSONB, UUID
|
|
from sqlalchemy.orm import Mapped, mapped_column
|
|
|
|
from app.db import Base
|
|
|
|
EMBEDDING_DIM = 384
|
|
|
|
|
|
class RulebookChunk(Base):
|
|
__tablename__ = "rulebook_chunks"
|
|
|
|
id: Mapped[uuid.UUID] = mapped_column(
|
|
UUID(as_uuid=True), primary_key=True, default=uuid.uuid4
|
|
)
|
|
source_document: Mapped[str] = mapped_column(String(length=200), nullable=False, index=True)
|
|
chunk_index: Mapped[int] = mapped_column(Integer, nullable=False)
|
|
content: Mapped[str] = mapped_column(Text, nullable=False)
|
|
embedding: Mapped[list[float]] = mapped_column(Vector(EMBEDDING_DIM), nullable=False)
|
|
doc_metadata: Mapped[dict] = mapped_column(JSONB, nullable=False, default=dict)
|