42 lines
1.7 KiB
Python
42 lines
1.7 KiB
Python
from typing import Optional
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from embedchain.config.vector_db.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 OpenSearchDBConfig(BaseVectorDbConfig):
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def __init__(
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self,
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opensearch_url: str,
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http_auth: tuple[str, str],
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vector_dimension: int = 1536,
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collection_name: Optional[str] = None,
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dir: Optional[str] = None,
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batch_size: Optional[int] = 100,
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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 an OpenSearch 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 opensearch_url: URL of the OpenSearch domain
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:type opensearch_url: str, Eg, "http://localhost:9200"
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:param http_auth: Tuple of username and password
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:type http_auth: tuple[str, str], Eg, ("username", "password")
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:param vector_dimension: Dimension of the vector, defaults to 1536 (openai embedding model)
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:type vector_dimension: int, 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 batch_size: Number of items to insert in one batch, defaults to 100
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:type batch_size: Optional[int], optional
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"""
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self.opensearch_url = opensearch_url
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self.http_auth = http_auth
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self.vector_dimension = vector_dimension
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self.extra_params = extra_params
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self.batch_size = batch_size
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super().__init__(collection_name=collection_name, dir=dir)
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