# Copyright 2023 Google LLC

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# https://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# [START aiplatform_sdk_embedding]

from typing import List, Optional

from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel

def embed_text(

texts: List[str] = ["banana muffins? ", "banana bread? banana muffins?"],

task: str = "RETRIEVAL_DOCUMENT",

model_name: str = "text-embedding-004",

dimensionality: Optional[int] = 256,

) -> List[List[float]]:

"""Embeds texts with a pre-trained, foundational model."""

model = TextEmbeddingModel.from_pretrained(model_name)

inputs = [TextEmbeddingInput(text, task) for text in texts]

kwargs = dict(output_dimensionality=dimensionality) if dimensionality else {}

embeddings = model.get_embeddings(inputs, **kwargs)

return [embedding.values for embedding in embeddings]

# [END aiplatform_sdk_embedding]

if __name__ == "__main__":

embed_text()