48 lines
1.9 KiB
Python
48 lines
1.9 KiB
Python
import os
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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 PineconeDBConfig(BaseVectorDbConfig):
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def __init__(
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self,
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index_name: Optional[str] = None,
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api_key: Optional[str] = None,
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vector_dimension: int = 1536,
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metric: Optional[str] = "cosine",
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pod_config: Optional[dict[str, any]] = None,
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serverless_config: Optional[dict[str, any]] = None,
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hybrid_search: bool = False,
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bm25_encoder: any = 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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self.metric = metric
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self.api_key = api_key
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self.index_name = index_name
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self.vector_dimension = vector_dimension
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self.extra_params = extra_params
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self.hybrid_search = hybrid_search
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self.bm25_encoder = bm25_encoder
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self.batch_size = batch_size
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if pod_config is None and serverless_config is None:
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# If no config is provided, use the default pod spec config
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pod_environment = os.environ.get("PINECONE_ENV", "gcp-starter")
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self.pod_config = {"environment": pod_environment, "metadata_config": {"indexed": ["*"]}}
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else:
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self.pod_config = pod_config
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self.serverless_config = serverless_config
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if self.pod_config and self.serverless_config:
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raise ValueError("Only one of pod_config or serverless_config can be provided.")
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if self.hybrid_search and self.metric != "dotproduct":
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raise ValueError(
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"Hybrid search is only supported with dotproduct metric in Pinecone. See full docs here: https://docs.pinecone.io/docs/hybrid-search#limitations"
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) # noqa:E501
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super().__init__(collection_name=self.index_name, dir=None)
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