Source code for apache_beam.ml.transforms.embeddings.vertex_ai

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# 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
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#    http://www.apache.org/licenses/LICENSE-2.0
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# Vertex AI Python SDK is required for this module.
# Follow https://cloud.google.com/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk # 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 apache_beam.ml.inference.base import ModelHandler
from apache_beam.ml.inference.base import RunInference
from apache_beam.ml.transforms.base import EmbeddingsManager
from apache_beam.ml.transforms.base import _ImageEmbeddingHandler
from apache_beam.ml.transforms.base import _TextEmbeddingHandler
from vertexai.language_models import TextEmbeddingInput
from vertexai.language_models import TextEmbeddingModel
from vertexai.vision_models import Image
from vertexai.vision_models import MultiModalEmbeddingModel

__all__ = ["VertexAITextEmbeddings", "VertexAIImageEmbeddings"]

DEFAULT_TASK_TYPE = "RETRIEVAL_DOCUMENT"
# TODO: https://github.com/apache/beam/issues/29356
# Can this list be automatically pulled from Vertex SDK?
TASK_TYPE_INPUTS = [
    "RETRIEVAL_DOCUMENT",
    "RETRIEVAL_QUERY",
    "SEMANTIC_SIMILARITY",
    "CLASSIFICATION",
    "CLUSTERING"
]
_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__(
      self,
      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(
      self,
      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 = [
          TextEmbeddingInput(
              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 https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings # 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 https://cloud.google.com/vertex-ai/docs/generative-ai/learn/model-versioning#stable-versions-available.md # 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 https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings # 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)
class _VertexAIImageEmbeddingHandler(ModelHandler): def __init__( self, model_name: str, dimension: Optional[int] = None, 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 self.dimension = dimension def run_inference( self, batch: Sequence[Image], model: MultiModalEmbeddingModel, inference_args: Optional[Dict[str, Any]] = None, ) -> Iterable: embeddings = [] # Maximum request size for muli-model embedding models is 1. for img in batch: embedding_response = model.get_embeddings( image=img, dimension=self.dimension) embeddings.append(embedding_response.image_embedding) return embeddings def load_model(self): model = MultiModalEmbeddingModel.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 'VertexAIImageEmbeddings'
[docs]class VertexAIImageEmbeddings(EmbeddingsManager): def __init__( self, model_name: str, columns: List[str], dimension: Optional[int], project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[Credentials] = None, **kwargs): """ Embedding Config for Vertex AI Image Embedding models following https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-multimodal-embeddings # pylint: disable=line-too-long Image Embeddings are generated for a batch of images using the Vertex AI API. Embeddings are returned in a list for each image in the batch. This transform makes remote calls to the Vertex AI service and may incur costs for use. Args: model_name: The name of the Vertex AI Multi-Modal Embedding model. columns: The columns containing the text to be embedded. dimension: The length of the embedding vector to generate. Must be one of 128, 256, 512, or 1408. If not set, Vertex AI's default value is 1408. 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 if dimension is not None and dimension not in (128, 256, 512, 1408): raise ValueError( "dimension argument must be one of 128, 256, 512, or 1408") self.dimension = dimension super().__init__(columns=columns, **kwargs)
[docs] def get_model_handler(self) -> ModelHandler: return _VertexAIImageEmbeddingHandler( model_name=self.model_name, dimension=self.dimension, project=self.project, location=self.location, credentials=self.credentials, )
[docs] def get_ptransform_for_processing(self, **kwargs) -> beam.PTransform: return RunInference( model_handler=_ImageEmbeddingHandler(self), inference_args=self.inference_args)