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my-project-public — gitlawb

#1	from typing import Optional
#2
#3	from embedchain.config.vector_db.base import BaseVectorDbConfig
#4	from embedchain.helpers.json_serializable import register_deserializable
#5
#6
#7	@register_deserializable
#8	class OpenSearchDBConfig(BaseVectorDbConfig):
#9	def __init__(
#10	self,
#11	opensearch_url: str,
#12	http_auth: tuple[str, str],
#13	vector_dimension: int = 1536,
#14	collection_name: Optional[str] = None,
#15	dir: Optional[str] = None,
#16	batch_size: Optional[int] = 100,
#17	**extra_params: dict[str, any],
#18	):
#19	"""
#20	Initializes a configuration class instance for an OpenSearch client.
#21
#22	:param collection_name: Default name for the collection, defaults to None
#23	:type collection_name: Optional[str], optional
#24	:param opensearch_url: URL of the OpenSearch domain
#25	:type opensearch_url: str, Eg, "http://localhost:9200"
#26	:param http_auth: Tuple of username and password
#27	:type http_auth: tuple[str, str], Eg, ("username", "password")
#28	:param vector_dimension: Dimension of the vector, defaults to 1536 (openai embedding model)
#29	:type vector_dimension: int, optional
#30	:param dir: Path to the database directory, where the database is stored, defaults to None
#31	:type dir: Optional[str], optional
#32	:param batch_size: Number of items to insert in one batch, defaults to 100
#33	:type batch_size: Optional[int], optional
#34	"""
#35	self.opensearch_url = opensearch_url
#36	self.http_auth = http_auth
#37	self.vector_dimension = vector_dimension
#38	self.extra_params = extra_params
#39	self.batch_size = batch_size
#40
#41	super().__init__(collection_name=collection_name, dir=dir)
#42

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