Got this error along with the one above, even though the model cached in the UC Volume.

from fastembed import TextEmbedding, SparseTextEmbedding
from pyspark.sql.pandas.functions import pandas_udf, PandasUDFType
from pyspark.sql.types import StructType, StructField, StringType, ArrayType, FloatType, IntegerType
import pandas as pd

@pandas_udf(ArrayType(FloatType()))
def generate_dense_embeddings(contents: pd.Series) ->  pd.Series:
    small_embedding_model = TextEmbedding(model_name='BAAI/bge-base-en',cache_dir="/Volumes/qdrant_cache/default/model_cache/")
    dense_embeddings_list = small_embedding_model.embed(contents)
    return pd.Series(list(dense_embeddings_list))

from pyspark.sql.functions import col

df=df.limit(50)
df.show(10)
embeddings = df.withColumn("dense_vector", generate_dense_embeddings(col("content")))
embeddings.show(10)

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