#
# Licensed to the Apache Software Foundation (ASF) under one or more
# 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
#
# http://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.
# 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.
import functools
import logging
from collections.abc import Callable
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
from typing import Optional
from typing import cast
from google.api_core.exceptions import ServerError
from google.api_core.exceptions import TooManyRequests
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 RemoteModelHandler
from apache_beam.ml.inference.base import RunInference
from apache_beam.ml.rag.types import Chunk
from apache_beam.ml.rag.types import Embedding
from apache_beam.ml.transforms.base import EmbeddingsManager
from apache_beam.ml.transforms.base import EmbeddingTypeAdapter
from apache_beam.ml.transforms.base import _ImageEmbeddingHandler
from apache_beam.ml.transforms.base import _MultiModalEmbeddingHandler
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
from vertexai.vision_models import MultiModalEmbeddingResponse
from vertexai.vision_models import Video
from vertexai.vision_models import VideoEmbedding
from vertexai.vision_models import VideoSegmentConfig
__all__ = [
"VertexAITextEmbeddings",
"VertexAIImageEmbeddings",
"VertexAIMultiModalEmbeddings",
"VertexAIMultiModalInput",
]
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.
LOGGER = logging.getLogger("VertexAIEmbeddings")
def _retry_on_appropriate_gcp_error(exception):
"""
Retry filter that returns True if a returned HTTP error code is 5xx or 429.
This is used to retry remote requests that fail, most notably 429
(TooManyRequests.)
Args:
exception: the returned exception encountered during the request/response
loop.
Returns:
boolean indication whether or not the exception is a Server Error (5xx) or
a TooManyRequests (429) error.
"""
return isinstance(exception, (TooManyRequests, ServerError))
class _VertexAITextEmbeddingHandler(RemoteModelHandler):
"""
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,
**kwargs):
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
super().__init__(
namespace='VertexAITextEmbeddingHandler',
retry_filter=_retry_on_appropriate_gcp_error,
**kwargs)
def request(
self,
batch: Sequence[str],
model: TextEmbeddingModel,
inference_args: Optional[dict[str, Any]] = None):
embeddings = []
batch_size = _BATCH_SIZE
for i in range(0, len(batch), batch_size):
text_batch_strs = batch[i:i + batch_size]
text_batch = [
TextEmbeddingInput(
text=text, title=self.title, task_type=self.task_type)
for text in text_batch_strs
]
embeddings_batch = model.get_embeddings(list(text_batch))
embeddings.extend([el.values for el in embeddings_batch])
return embeddings
def create_client(self) -> TextEmbeddingModel:
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
self.kwargs = kwargs
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,
**self.kwargs)
[docs]
def get_ptransform_for_processing(self, **kwargs) -> beam.PTransform:
return RunInference(
model_handler=_TextEmbeddingHandler(self),
inference_args=self.inference_args)
class _VertexAIImageEmbeddingHandler(RemoteModelHandler):
def __init__(
self,
model_name: str,
dimension: Optional[int] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[Credentials] = None,
**kwargs):
vertexai.init(project=project, location=location, credentials=credentials)
self.model_name = model_name
self.dimension = dimension
super().__init__(
namespace='VertexAIImageEmbeddingHandler',
retry_filter=_retry_on_appropriate_gcp_error,
**kwargs)
def request(
self,
imgs: Sequence[Image],
model: MultiModalEmbeddingModel,
inference_args: Optional[dict[str, Any]] = None):
embeddings = []
# Max request size for multi-modal embedding models is 1
for img in imgs:
prediction = model.get_embeddings(image=img, dimension=self.dimension)
embeddings.append(prediction.image_embedding)
return embeddings
def create_client(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 image 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
self.kwargs = kwargs
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,
**self.kwargs)
@dataclass
class VertexImage:
image_content: Image
embedding: Optional[list[float]] = None
@dataclass
class VertexVideo:
video_content: Video
config: VideoSegmentConfig
embeddings: Optional[list[VideoEmbedding]] = None
class _VertexAIMultiModalEmbeddingHandler(RemoteModelHandler):
def __init__(
self,
model_name: str,
dimension: Optional[int] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[Credentials] = None,
**kwargs):
vertexai.init(project=project, location=location, credentials=credentials)
self.model_name = model_name
self.dimension = dimension
super().__init__(
namespace='VertexAIMultiModelEmbeddingHandler',
retry_filter=_retry_on_appropriate_gcp_error,
**kwargs)
def request(
self,
batch: Sequence[VertexAIMultiModalInput],
model: MultiModalEmbeddingModel,
inference_args: Optional[dict[str, Any]] = None):
embeddings = []
# Max request size for multi-modal embedding models is 1
for input in batch:
image_content: Optional[Image] = None
video_content: Optional[Video] = None
text_content: Optional[str] = None
video_config: Optional[VideoSegmentConfig] = None
if input.image:
image_content = input.image.image_content
if input.video:
video_content = input.video.video_content
video_config = input.video.config
if input.contextual_text:
text_content = input.contextual_text.content.text
prediction = model.get_embeddings(
image=image_content,
video=video_content,
contextual_text=text_content,
dimension=self.dimension,
video_segment_config=video_config)
embeddings.append(prediction)
return embeddings
def create_client(self) -> MultiModalEmbeddingModel:
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 'VertexAIMultiModalEmbeddings'
def _multimodal_dict_input_fn(
image_column: Optional[str],
video_column: Optional[str],
text_column: Optional[str],
batch: Sequence[dict[str, Any]]) -> list[VertexAIMultiModalInput]:
multimodal_inputs: list[VertexAIMultiModalInput] = []
for item in batch:
img: Optional[VertexImage] = None
vid: Optional[VertexVideo] = None
text: Optional[Chunk] = None
if image_column:
img = item[image_column]
if video_column:
vid = item[video_column]
if text_column:
text = item[text_column]
multimodal_inputs.append(
VertexAIMultiModalInput(image=img, video=vid, contextual_text=text))
return multimodal_inputs
def _multimodal_dict_output_fn(
image_column: Optional[str],
video_column: Optional[str],
text_column: Optional[str],
batch: Sequence[dict[str, Any]],
embeddings: Sequence[MultiModalEmbeddingResponse]) -> list[dict[str, Any]]:
results = []
for batch_idx, item in enumerate(batch):
mm_embedding = embeddings[batch_idx]
if image_column:
item[image_column].embedding = mm_embedding.image_embedding
if video_column:
item[video_column].embeddings = mm_embedding.video_embeddings
if text_column:
item[text_column].embedding = Embedding(
dense_embedding=mm_embedding.text_embedding)
results.append(item)
return results
def _create_multimodal_dict_adapter(
image_column: Optional[str],
video_column: Optional[str],
text_column: Optional[str]
) -> EmbeddingTypeAdapter[dict[str, Any], dict[str, Any]]:
return EmbeddingTypeAdapter[dict[str, Any], dict[str, Any]](
input_fn=cast(
Callable[[Sequence[dict[str, Any]]], list[str]],
functools.partial(
_multimodal_dict_input_fn,
image_column,
video_column,
text_column)),
output_fn=cast(
Callable[[Sequence[dict[str, Any]], Sequence[Any]],
list[dict[str, Any]]],
functools.partial(
_multimodal_dict_output_fn,
image_column,
video_column,
text_column)))
[docs]
class VertexAIMultiModalEmbeddings(EmbeddingsManager):
def __init__(
self,
model_name: str,
image_column: Optional[str] = None,
video_column: Optional[str] = None,
text_column: Optional[str] = None,
dimension: Optional[int] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[Credentials] = None,
**kwargs):
"""
Embedding Config for Vertex AI Multi-Modal Embedding models following
https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-multimodal-embeddings # pylint: disable=line-too-long
Multi-Modal Embeddings are generated for a batch of image, video, and
string groupings using the Vertex AI API. Embeddings are returned in a list
for each image in the batch as MultiModalEmbeddingResponses. 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.
image_column: The column containing image data to be embedded. This data
is expected to be formatted as VertexImage objects, containing a Vertex
Image object.
video_column: The column containing video data to be embedded. This data
is expected to be formatted as VertexVideo objects, containing a Vertex
Video object an a VideoSegmentConfig object.
text_column: The column containing text data to be embedded. This data is
expected to be formatted as Chunk objects, containing the string to be
embedded in the Chunk's content field.
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.
If submitting video content, dimension *musst* be 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
self.kwargs = kwargs
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
if not image_column and not video_column and not text_column:
raise ValueError("at least one input column must be specified")
if video_column is not None and dimension != 1408:
raise ValueError(
"Vertex AI does not support custom dimensions for video input, want dimension = 1408, got ",
dimension)
self.type_adapter = _create_multimodal_dict_adapter(
image_column=image_column,
video_column=video_column,
text_column=text_column)
super().__init__(type_adapter=self.type_adapter, **kwargs)
[docs]
def get_model_handler(self) -> ModelHandler:
return _VertexAIMultiModalEmbeddingHandler(
model_name=self.model_name,
dimension=self.dimension,
project=self.project,
location=self.location,
credentials=self.credentials,
**self.kwargs)