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#1	import os
#2	from typing import Optional
#3
#4	from embedchain.config.vector_db.base import BaseVectorDbConfig
#5	from embedchain.helpers.json_serializable import register_deserializable
#6
#7
#8	@register_deserializable
#9	class PineconeDBConfig(BaseVectorDbConfig):
#10	def __init__(
#11	self,
#12	index_name: Optional[str] = None,
#13	api_key: Optional[str] = None,
#14	vector_dimension: int = 1536,
#15	metric: Optional[str] = "cosine",
#16	pod_config: Optional[dict[str, any]] = None,
#17	serverless_config: Optional[dict[str, any]] = None,
#18	hybrid_search: bool = False,
#19	bm25_encoder: any = None,
#20	batch_size: Optional[int] = 100,
#21	**extra_params: dict[str, any],
#22	):
#23	self.metric = metric
#24	self.api_key = api_key
#25	self.index_name = index_name
#26	self.vector_dimension = vector_dimension
#27	self.extra_params = extra_params
#28	self.hybrid_search = hybrid_search
#29	self.bm25_encoder = bm25_encoder
#30	self.batch_size = batch_size
#31	if pod_config is None and serverless_config is None:
#32	# If no config is provided, use the default pod spec config
#33	pod_environment = os.environ.get("PINECONE_ENV", "gcp-starter")
#34	self.pod_config = {"environment": pod_environment, "metadata_config": {"indexed": ["*"]}}
#35	else:
#36	self.pod_config = pod_config
#37	self.serverless_config = serverless_config
#38
#39	if self.pod_config and self.serverless_config:
#40	raise ValueError("Only one of pod_config or serverless_config can be provided.")
#41
#42	if self.hybrid_search and self.metric != "dotproduct":
#43	raise ValueError(
#44	"Hybrid search is only supported with dotproduct metric in Pinecone. See full docs here: https://docs.pinecone.io/docs/hybrid-search#limitations"
#45	) # noqa:E501
#46
#47	super().__init__(collection_name=self.index_name, dir=None)
#48

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