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40 lines
1.5 KiB
Python
40 lines
1.5 KiB
Python
# This file is purely for development use, not included in any builds
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from danswer.configs.model_configs import DOC_EMBEDDING_DIM
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from danswer.datastores.typesense.store import create_typesense_collection
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from danswer.utils.clients import get_qdrant_client
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from danswer.utils.clients import get_typesense_client
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from danswer.utils.logger import setup_logger
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from qdrant_client.http.models import Distance
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from qdrant_client.http.models import VectorParams
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from typesense.exceptions import ObjectNotFound # type: ignore
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logger = setup_logger()
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def recreate_qdrant_collection(
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collection_name: str, embedding_dim: int = DOC_EMBEDDING_DIM
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) -> None:
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logger.info(f"Attempting to recreate Qdrant collection {collection_name}")
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result = get_qdrant_client().recreate_collection(
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collection_name=collection_name,
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vectors_config=VectorParams(size=embedding_dim, distance=Distance.COSINE),
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)
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if not result:
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raise RuntimeError("Could not create Qdrant collection")
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def recreate_typesense_collection(collection_name: str) -> None:
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logger.info(f"Attempting to recreate Typesense collection {collection_name}")
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ts_client = get_typesense_client()
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try:
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ts_client.collections[collection_name].delete()
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except ObjectNotFound:
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logger.debug(f"Collection {collection_name} does not already exist")
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create_typesense_collection(collection_name)
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if __name__ == "__main__":
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recreate_qdrant_collection("danswer_index")
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recreate_typesense_collection("danswer_index")
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