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@@ -397,9 +397,10 @@ CHROMA_DATA_PATH = f"{DATA_DIR}/vector_db"
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# this uses the model defined in the Dockerfile ENV variable. If you dont use docker or docker based deployments such as k8s, the default embedding model will be used (all-MiniLM-L6-v2)
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RAG_EMBEDDING_MODEL = os.environ.get("RAG_EMBEDDING_MODEL", "all-MiniLM-L6-v2")
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log.info(f"Embedding model set: {RAG_EMBEDDING_MODEL}"),
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-RAG_EMBEDDING_MODEL_AUTO_UPDATE = False
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-if os.environ.get("RAG_EMBEDDING_MODEL_AUTO_UPDATE", "").lower() == "true":
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- RAG_EMBEDDING_MODEL_AUTO_UPDATE = True
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+
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+RAG_EMBEDDING_MODEL_AUTO_UPDATE = (
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+ os.environ.get("RAG_EMBEDDING_MODEL_AUTO_UPDATE", "").lower() == "true"
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+)
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# device type ebbeding models - "cpu" (default), "cuda" (nvidia gpu required) or "mps" (apple silicon) - choosing this right can lead to better performance
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