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>
23 lines
693 B
Python
23 lines
693 B
Python
from functools import lru_cache
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from sentence_transformers import SentenceTransformer
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# Multilingual so German player messages retrieve relevant chunks from the
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# English-language rulebooks. 384-dim output, matching RulebookChunk.embedding.
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MODEL_NAME = "paraphrase-multilingual-MiniLM-L12-v2"
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@lru_cache
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def get_embedding_model() -> SentenceTransformer:
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return SentenceTransformer(MODEL_NAME)
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def embed_texts(texts: list[str]) -> list[list[float]]:
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model = get_embedding_model()
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embeddings = model.encode(texts, normalize_embeddings=True, show_progress_bar=False)
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return embeddings.tolist()
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def embed_query(text: str) -> list[float]:
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return embed_texts([text])[0]
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