Source code for

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# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

# Vertex AI Python SDK is required for this module.
# Follow # pylint: disable=line-too-long
# to install Vertex AI Python SDK.

from typing import Any
from typing import Dict
from typing import Iterable
from typing import List
from typing import Optional
from typing import Sequence

from google.auth.credentials import Credentials

import apache_beam as beam
import vertexai
from import ModelHandler
from import RunInference
from import EmbeddingsManager
from import _TextEmbeddingHandler
from vertexai.language_models import TextEmbeddingInput
from vertexai.language_models import TextEmbeddingModel

__all__ = ["VertexAITextEmbeddings"]

# Can this list be automatically pulled from Vertex SDK?
_BATCH_SIZE = 5  # Vertex AI limits requests to 5 at a time.

class _VertexAITextEmbeddingHandler(ModelHandler):
  Note: Intended for internal use and guarantees no backwards compatibility.
  def __init__(
      model_name: str,
      title: Optional[str] = None,
      task_type: str = DEFAULT_TASK_TYPE,
      project: Optional[str] = None,
      location: Optional[str] = None,
      credentials: Optional[Credentials] = None,
    vertexai.init(project=project, location=location, credentials=credentials)
    self.model_name = model_name
    if task_type not in TASK_TYPE_INPUTS:
      raise ValueError(
          f"task_type must be one of {TASK_TYPE_INPUTS}, got {task_type}")
    self.task_type = task_type
    self.title = title

  def run_inference(
      batch: Sequence[str],
      model: Any,
      inference_args: Optional[Dict[str, Any]] = None,
  ) -> Iterable:
    embeddings = []
    batch_size = _BATCH_SIZE
    for i in range(0, len(batch), batch_size):
      text_batch = batch[i:i + batch_size]
      text_batch = [
              text=text, title=self.title, task_type=self.task_type)
          for text in text_batch
      embeddings_batch = model.get_embeddings(text_batch)
      embeddings.extend([el.values for el in embeddings_batch])
    return embeddings

  def load_model(self):
    model = TextEmbeddingModel.from_pretrained(self.model_name)
    return model

  def __repr__(self):
    # ModelHandler is internal to the user and is not exposed.
    # Hence we need to override the __repr__ method to expose
    # the name of the class.
    return 'VertexAITextEmbeddings'

[docs]class VertexAITextEmbeddings(EmbeddingsManager): def __init__( self, model_name: str, columns: List[str], title: Optional[str] = None, task_type: str = DEFAULT_TASK_TYPE, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[Credentials] = None, **kwargs): """ Embedding Config for Vertex AI Text Embedding models following # pylint: disable=line-too-long Text Embeddings are generated for a batch of text using the Vertex AI SDK. Embeddings are returned in a list for each text in the batch. Look at # pylint: disable=line-too-long for more information on model versions and lifecycle. Args: model_name: The name of the Vertex AI Text Embedding model. columns: The columns containing the text to be embedded. task_type: The downstream task for the embeddings. Valid values are RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY, CLASSIFICATION, CLUSTERING. For more information on the task type, look at # pylint: disable=line-too-long title: Identifier of the text content. project: The default GCP project for API calls. location: The default location for API calls. credentials: Custom credentials for API calls. Defaults to environment credentials. """ self.model_name = model_name self.project = project self.location = location self.credentials = credentials self.title = title self.task_type = task_type super().__init__(columns=columns, **kwargs)
[docs] def get_model_handler(self) -> ModelHandler: return _VertexAITextEmbeddingHandler( model_name=self.model_name, project=self.project, location=self.location, credentials=self.credentials, title=self.title, task_type=self.task_type, )
[docs] def get_ptransform_for_processing(self, **kwargs) -> beam.PTransform: return RunInference( model_handler=_TextEmbeddingHandler(self), inference_args=self.inference_args)