Refactoring vectordb naming convention in embedchain.config (#1469)

embedchain/config/vector_db/opensearch.py

@@ -0,0 +1,41 @@
+from typing import Optional
+
+from embedchain.config.vector_db.base import BaseVectorDbConfig
+from embedchain.helpers.json_serializable import register_deserializable
+
+
+@register_deserializable
+class OpenSearchDBConfig(BaseVectorDbConfig):
+    def __init__(
+        self,
+        opensearch_url: str,
+        http_auth: tuple[str, str],
+        vector_dimension: int = 1536,
+        collection_name: Optional[str] = None,
+        dir: Optional[str] = None,
+        batch_size: Optional[int] = 100,
+        **extra_params: dict[str, any],
+    ):
+        """
+        Initializes a configuration class instance for an OpenSearch client.
+
+        :param collection_name: Default name for the collection, defaults to None
+        :type collection_name: Optional[str], optional
+        :param opensearch_url: URL of the OpenSearch domain
+        :type opensearch_url: str, Eg, "http://localhost:9200"
+        :param http_auth: Tuple of username and password
+        :type http_auth: tuple[str, str], Eg, ("username", "password")
+        :param vector_dimension: Dimension of  the vector, defaults to 1536 (openai embedding model)
+        :type vector_dimension: int, optional
+        :param dir: Path to the database directory, where the database is stored, defaults to None
+        :type dir: Optional[str], optional
+        :param batch_size: Number of items to insert in one batch, defaults to 100
+        :type batch_size: Optional[int], optional
+        """
+        self.opensearch_url = opensearch_url
+        self.http_auth = http_auth
+        self.vector_dimension = vector_dimension
+        self.extra_params = extra_params
+        self.batch_size = batch_size
+
+        super().__init__(collection_name=collection_name, dir=dir)