45 lines
1.9 KiB
Python
45 lines
1.9 KiB
Python
from typing import Optional
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from embedchain.config.vectordb.base import BaseVectorDbConfig
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from embedchain.helpers.json_serializable import register_deserializable
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@register_deserializable
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class QdrantDBConfig(BaseVectorDbConfig):
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"""
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Config to initialize a qdrant client.
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:param: url. qdrant url or list of nodes url to be used for connection
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"""
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def __init__(
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self,
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collection_name: Optional[str] = None,
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dir: Optional[str] = None,
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hnsw_config: Optional[dict[str, any]] = None,
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quantization_config: Optional[dict[str, any]] = None,
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on_disk: Optional[bool] = None,
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**extra_params: dict[str, any],
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):
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"""
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Initializes a configuration class instance for a qdrant client.
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:param collection_name: Default name for the collection, defaults to None
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:type collection_name: Optional[str], optional
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:param dir: Path to the database directory, where the database is stored, defaults to None
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:type dir: Optional[str], optional
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:param hnsw_config: Params for HNSW index
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:type hnsw_config: Optional[dict[str, any]], defaults to None
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:param quantization_config: Params for quantization, if None - quantization will be disabled
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:type quantization_config: Optional[dict[str, any]], defaults to None
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:param on_disk: If true - point`s payload will not be stored in memory.
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It will be read from the disk every time it is requested.
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This setting saves RAM by (slightly) increasing the response time.
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Note: those payload values that are involved in filtering and are indexed - remain in RAM.
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:type on_disk: bool, optional, defaults to None
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"""
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self.hnsw_config = hnsw_config
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self.quantization_config = quantization_config
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self.on_disk = on_disk
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self.extra_params = extra_params
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super().__init__(collection_name=collection_name, dir=dir)
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