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		|  | @ -397,9 +397,10 @@ CHROMA_DATA_PATH = f"{DATA_DIR}/vector_db" | |||
| # 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) | ||||
| RAG_EMBEDDING_MODEL = os.environ.get("RAG_EMBEDDING_MODEL", "all-MiniLM-L6-v2") | ||||
| log.info(f"Embedding model set: {RAG_EMBEDDING_MODEL}"), | ||||
| RAG_EMBEDDING_MODEL_AUTO_UPDATE = False | ||||
| if os.environ.get("RAG_EMBEDDING_MODEL_AUTO_UPDATE", "").lower() == "true": | ||||
|     RAG_EMBEDDING_MODEL_AUTO_UPDATE = True | ||||
| 
 | ||||
| RAG_EMBEDDING_MODEL_AUTO_UPDATE = ( | ||||
|     os.environ.get("RAG_EMBEDDING_MODEL_AUTO_UPDATE", "").lower() == "true" | ||||
| ) | ||||
| 
 | ||||
| 
 | ||||
| # 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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