Keep getting "Query call with protocol GRPC search failed with message Deadline Exceeded"

Description

I am using Azure Durable Functions that creates a new instance of WeaviateDB but does close it down when finished with it.

This is how I create the WeaviateDB:

def get_weaviate_db(kb_id: str):
    db = WeaviateVectorDB(kb_id=kb_id, http_host=os.getenv("WEAVIATE_HTTP_HOST"),
                          http_secure=True, grpc_host=os.getenv("WEAVIATE_GRPC_HOST"),
                          grpc_secure=True, weaviate_secret=os.getenv("WEAVIATE_API_KEY"))
    return db

class WeaviateVectorDB(VectorDB):
    """
    An implementation of the VectorDB interface for Weaviate using the Python v4 client.

    This class provides methods for adding, removing, and searching for vectorized data
    within a Weaviate instance.
    """

    def __init__(
        self,
        kb_id: str,
        http_host="localhost",
        http_port=8099,
        http_secure=False,
        grpc_host="localhost",
        grpc_port=50052,
        grpc_secure=False,
        weaviate_secret="secr3tk3y",
        init_timeout: int = 2,
        query_timeout: int = 45,
        insert_timeout: int = 120,
        use_embedded_weaviate: bool = False,
    ):
        """
        Initializes a WeaviateVectorDB instance.

        Args:
            http_host: The hostname of the Weaviate server.
            http_port: The HTTP port of the Weaviate server.
            http_secure: Whether to use HTTPS for the connection.
            grpc_host: The hostname of the Weaviate server for gRPC connections.
            grpc_port: The gRPC port of the Weaviate server.
            grpc_secure: Whether to use gRPCs for the connection.
            class_name: The name of the Weaviate class to use for storing data.
            kb_id: An optional identifier for the knowledge base.
        """

        # save all of these parameters as attributes so they're easily accessible for the to_dict method
        self.kb_id = kb_id
        self.http_host = http_host
        self.http_port = http_port
        self.http_secure = http_secure
        self.grpc_host = grpc_host
        self.grpc_port = grpc_port
        self.grpc_secure = grpc_secure
        self.weaviate_secret = weaviate_secret
        self.init_timeout = init_timeout
        self.query_timeout = query_timeout
        self.insert_timeout = insert_timeout
        self.use_embedded_weaviate = use_embedded_weaviate

        additional_headers = {}
        if use_embedded_weaviate:
            additional_headers["ENABLE_MODULES"] = (
                "backup-filesystem,text2vec-openai,text2vec-cohere,text2vec-huggingface,ref2vec-centroid,generative-openai,qna-openai"
            )
            additional_headers["BACKUP_FILESYSTEM_PATH"] = "./weaviate/backups"
            self.client = weaviate.WeaviateClient(
                embedded_options=weaviate.embedded.EmbeddedOptions(
                    persistence_data_path="./weaviate/data",
                ),
                additional_headers=additional_headers,
            )
        else:
            self.client = weaviate.connect_to_wcs(
                cluster_url=self.http_host,
                auth_credentials=weaviate.auth.AuthApiKey(self.weaviate_secret), skip_init_checks=True, additional_config=AdditionalConfig(
                connection=ConnectionConfig(
                    session_pool_connections=100,
                    session_pool_maxsize=500,
                    session_pool_max_retries=3,
                )
            ),)

        self.client.connect()
        self.collection_name = "dsrag_test"
        self.collection = self.client.collections.get(
            self.collection_name
        )

So when I create only 1 instance, is slowly but does work. When I have 10+ instances opened and running at the same time, I get this error:

Query call with protocol GRPC search failed with message Deadline Exceeded

Server Setup Information

• Weaviate Server Version: weaviate-client~=4.6.5
• Deployment Method: Using Python V4 SDK
• Multi Node? Number of Running Nodes: Not sure what does it mean
• Client Language and Version: Python V4
• Multitenancy?:

Any additional Information