jrmccluskey commented on code in PR #34700: URL: https://github.com/apache/beam/pull/34700#discussion_r2056184655
########## sdks/python/apache_beam/ml/transforms/embeddings/open_ai.py: ########## @@ -0,0 +1,223 @@ +# 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. + +# OpenAI Python SDK is required for this module. +# Install using: pip install openai + +import logging +import time +from collections.abc import Iterable +from collections.abc import Sequence +from typing import Any, Optional, TypeVar, Union, Tuple, cast Review Comment: Prefer to use the built-in `tuple` over the typing types - see https://peps.python.org/pep-0585/ ########## sdks/python/apache_beam/ml/transforms/embeddings/open_ai.py: ########## @@ -0,0 +1,223 @@ +# 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. + +# OpenAI Python SDK is required for this module. +# Install using: pip install openai + +import logging +import time +from collections.abc import Iterable +from collections.abc import Sequence +from typing import Any, Optional, TypeVar, Union, Tuple, cast + +import apache_beam as beam +from apache_beam.pvalue import PCollection, Row +from apache_beam.io.components.adaptive_throttler import AdaptiveThrottler +from apache_beam.metrics.metric import Metrics +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 _TextEmbeddingHandler +from apache_beam.utils import retry + +__all__ = ["OpenAITextEmbeddings"] + +# Define a type variable for the output +MLTransformOutputT = TypeVar('MLTransformOutputT') + +# Default batch size for OpenAI calls +_BATCH_SIZE = 20 # OpenAI can handle larger batches than Vertex +_MSEC_TO_SEC = 1000 + +LOGGER = logging.getLogger("OpenAIEmbeddings") + + +def _retry_on_appropriate_openai_error(exception): + """ + Retry filter that returns True if a returned error is rate limit (429) or server error (5xx). + + 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 RateLimitError (429) error. + """ + return isinstance(exception, (RateLimitError, APIError)) + + +class _OpenAITextEmbeddingHandler(ModelHandler): + """ + Note: Intended for internal use and guarantees no backwards compatibility. + """ + + def __init__( + self, + model_name: str, + api_key: Optional[str] = None, + organization: Optional[str] = None, + dimensions: Optional[int] = None, + user: Optional[str] = None, + ): + """Initialize OpenAI embedding handler. + + Args: + model_name: Name of the OpenAI embedding model (e.g., "text-embedding-3-small") + api_key: OpenAI API key + organization: OpenAI organization ID + dimensions: Specific embedding dimensions to use (if model supports it) + user: End-user identifier for tracking and rate limit calculations + """ + # Store only configuration parameters, not the client + self.model_name = model_name + self.api_key = api_key + self.organization = organization + self.dimensions = dimensions + self.user = user + + # Configure AdaptiveThrottler and throttling metrics for client-side + # throttling behavior. + self.throttled_secs = Metrics.counter( + OpenAITextEmbeddings, "cumulativeThrottlingSeconds") + self.throttler = AdaptiveThrottler( + window_ms=1, bucket_ms=1, overload_ratio=2) + + @retry.with_exponential_backoff( + num_retries=5, retry_filter=_retry_on_appropriate_openai_error) + def get_request( + self, text_batch: Sequence[str], model: Any, throttle_delay_secs: int): + while self.throttler.throttle_request(time.time() * _MSEC_TO_SEC): + LOGGER.info( + "Delaying request for %d seconds due to previous failures", + throttle_delay_secs) + time.sleep(throttle_delay_secs) + self.throttled_secs.inc(throttle_delay_secs) + + try: + req_time = time.time() + kwargs = { + "model": self.model_name, + "input": text_batch, + } + if self.dimensions: + kwargs["dimensions"] = self.dimensions + if self.user: + kwargs["user"] = self.user + + response = model.embeddings.create(**kwargs) + self.throttler.successful_request(req_time * _MSEC_TO_SEC) + return [item.embedding for item in response.data] + except RateLimitError as e: + LOGGER.warning("Request was rate limited by OpenAI API") + raise + except Exception as e: + LOGGER.error("Unexpected exception raised as part of request: %s", e) + raise + + def run_inference( + self, + batch: Sequence[str], + model: Any, + inference_args: Optional[dict[str, Any]] = None, + ) -> Iterable: + # Validate that all elements in the batch are strings + for item in batch: + if not isinstance(item, str): + raise TypeError( + f"Embeddings can only be generated on strings. Got {type(item)} instead." + ) Review Comment: No need to explicitly check your input type here, the type checking infrastructure will handle it with the `Sequence[str]` hint ########## sdks/python/apache_beam/ml/transforms/embeddings/open_ai_test.py: ########## @@ -0,0 +1,291 @@ +# +# 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. + +import os +import shutil +import tempfile +import unittest +import uuid + +import apache_beam as beam +from apache_beam.ml.inference.base import RunInference +from apache_beam.ml.transforms import base +from apache_beam.ml.transforms.base import MLTransform + +try: + from sdks.python.apache_beam.ml.transforms.embeddings.open_ai import OpenAITextEmbeddings +except ImportError: + OpenAITextEmbeddings = None +# pylint: disable=ungrouped-imports +try: + import tensorflow_transform as tft + from apache_beam.ml.transforms.tft import ScaleTo01 +except ImportError: + tft = None Review Comment: A tests_requirements.txt file similar to what we do for other frameworks (like the vertex AI one [here](https://github.com/apache/beam/blob/5c4b42f81c67fefec42e28cc6faae52f21ba8f56/sdks/python/apache_beam/ml/inference/vertex_ai_tests_requirements.txt)) would be a good inclusion for test setup. ########## sdks/python/apache_beam/ml/transforms/embeddings/open_ai_test.py: ########## @@ -0,0 +1,291 @@ +# +# 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. + +import os +import shutil +import tempfile +import unittest +import uuid + +import apache_beam as beam +from apache_beam.ml.inference.base import RunInference +from apache_beam.ml.transforms import base +from apache_beam.ml.transforms.base import MLTransform + +try: + from sdks.python.apache_beam.ml.transforms.embeddings.open_ai import OpenAITextEmbeddings +except ImportError: + OpenAITextEmbeddings = None +# pylint: disable=ungrouped-imports +try: + import tensorflow_transform as tft + from apache_beam.ml.transforms.tft import ScaleTo01 +except ImportError: + tft = None + +test_query = "This is a test" +test_query_column = "embedding" +model_name: str = "text-embedding-3-small" + + +@unittest.skipIf( + OpenAITextEmbeddings is None, 'OpenAI Python SDK is not installed.') +class OpenAIEmbeddingsTest(unittest.TestCase): + print("check1", OpenAITextEmbeddings) Review Comment: holdover print statement? ########## sdks/python/apache_beam/ml/transforms/embeddings/open_ai.py: ########## @@ -0,0 +1,223 @@ +# 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. + +# OpenAI Python SDK is required for this module. +# Install using: pip install openai + +import logging +import time +from collections.abc import Iterable +from collections.abc import Sequence +from typing import Any, Optional, TypeVar, Union, Tuple, cast + +import apache_beam as beam +from apache_beam.pvalue import PCollection, Row +from apache_beam.io.components.adaptive_throttler import AdaptiveThrottler +from apache_beam.metrics.metric import Metrics +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 _TextEmbeddingHandler +from apache_beam.utils import retry + +__all__ = ["OpenAITextEmbeddings"] + +# Define a type variable for the output +MLTransformOutputT = TypeVar('MLTransformOutputT') + +# Default batch size for OpenAI calls +_BATCH_SIZE = 20 # OpenAI can handle larger batches than Vertex +_MSEC_TO_SEC = 1000 + +LOGGER = logging.getLogger("OpenAIEmbeddings") + + +def _retry_on_appropriate_openai_error(exception): + """ + Retry filter that returns True if a returned error is rate limit (429) or server error (5xx). + + 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 RateLimitError (429) error. + """ + return isinstance(exception, (RateLimitError, APIError)) + + +class _OpenAITextEmbeddingHandler(ModelHandler): + """ + Note: Intended for internal use and guarantees no backwards compatibility. + """ + + def __init__( + self, + model_name: str, + api_key: Optional[str] = None, + organization: Optional[str] = None, + dimensions: Optional[int] = None, + user: Optional[str] = None, + ): + """Initialize OpenAI embedding handler. + + Args: + model_name: Name of the OpenAI embedding model (e.g., "text-embedding-3-small") + api_key: OpenAI API key + organization: OpenAI organization ID + dimensions: Specific embedding dimensions to use (if model supports it) + user: End-user identifier for tracking and rate limit calculations + """ + # Store only configuration parameters, not the client + self.model_name = model_name + self.api_key = api_key + self.organization = organization + self.dimensions = dimensions + self.user = user + + # Configure AdaptiveThrottler and throttling metrics for client-side + # throttling behavior. + self.throttled_secs = Metrics.counter( + OpenAITextEmbeddings, "cumulativeThrottlingSeconds") + self.throttler = AdaptiveThrottler( + window_ms=1, bucket_ms=1, overload_ratio=2) + + @retry.with_exponential_backoff( + num_retries=5, retry_filter=_retry_on_appropriate_openai_error) + def get_request( + self, text_batch: Sequence[str], model: Any, throttle_delay_secs: int): + while self.throttler.throttle_request(time.time() * _MSEC_TO_SEC): + LOGGER.info( + "Delaying request for %d seconds due to previous failures", + throttle_delay_secs) + time.sleep(throttle_delay_secs) + self.throttled_secs.inc(throttle_delay_secs) + + try: + req_time = time.time() + kwargs = { + "model": self.model_name, + "input": text_batch, + } + if self.dimensions: + kwargs["dimensions"] = self.dimensions + if self.user: + kwargs["user"] = self.user + + response = model.embeddings.create(**kwargs) + self.throttler.successful_request(req_time * _MSEC_TO_SEC) + return [item.embedding for item in response.data] + except RateLimitError as e: + LOGGER.warning("Request was rate limited by OpenAI API") + raise + except Exception as e: + LOGGER.error("Unexpected exception raised as part of request: %s", e) + raise + + def run_inference( + self, + batch: Sequence[str], + model: Any, + inference_args: Optional[dict[str, Any]] = None, + ) -> Iterable: + # Validate that all elements in the batch are strings + for item in batch: + if not isinstance(item, str): + raise TypeError( + f"Embeddings can only be generated on strings. Got {type(item)} instead." + ) + + embeddings = [] + batch_size = _BATCH_SIZE + for i in range(0, len(batch), batch_size): + text_batch = batch[i:i + batch_size] + embeddings_batch = self.get_request( + text_batch=text_batch, model=model, throttle_delay_secs=5) + embeddings.extend(embeddings_batch) + return embeddings Review Comment: You should be able to avoid the `batch` being larger than the `_BATCH_SIZE` by implementing `batch_elements_kwargs()` [from the parent class](https://github.com/apache/beam/blob/5c4b42f81c67fefec42e28cc6faae52f21ba8f56/sdks/python/apache_beam/ml/inference/base.py#L213) and using the _BATCH_SIZE value as the `max_batch_size` kwarg ########## sdks/python/apache_beam/ml/transforms/embeddings/open_ai.py: ########## @@ -0,0 +1,223 @@ +# 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. + +# OpenAI Python SDK is required for this module. +# Install using: pip install openai + +import logging +import time +from collections.abc import Iterable +from collections.abc import Sequence +from typing import Any, Optional, TypeVar, Union, Tuple, cast + +import apache_beam as beam +from apache_beam.pvalue import PCollection, Row +from apache_beam.io.components.adaptive_throttler import AdaptiveThrottler +from apache_beam.metrics.metric import Metrics +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 _TextEmbeddingHandler +from apache_beam.utils import retry + +__all__ = ["OpenAITextEmbeddings"] + +# Define a type variable for the output +MLTransformOutputT = TypeVar('MLTransformOutputT') + +# Default batch size for OpenAI calls +_BATCH_SIZE = 20 # OpenAI can handle larger batches than Vertex +_MSEC_TO_SEC = 1000 + +LOGGER = logging.getLogger("OpenAIEmbeddings") + + +def _retry_on_appropriate_openai_error(exception): + """ + Retry filter that returns True if a returned error is rate limit (429) or server error (5xx). + + 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 RateLimitError (429) error. + """ + return isinstance(exception, (RateLimitError, APIError)) + + +class _OpenAITextEmbeddingHandler(ModelHandler): + """ + Note: Intended for internal use and guarantees no backwards compatibility. + """ + + def __init__( + self, + model_name: str, + api_key: Optional[str] = None, + organization: Optional[str] = None, + dimensions: Optional[int] = None, + user: Optional[str] = None, + ): + """Initialize OpenAI embedding handler. + + Args: + model_name: Name of the OpenAI embedding model (e.g., "text-embedding-3-small") + api_key: OpenAI API key + organization: OpenAI organization ID + dimensions: Specific embedding dimensions to use (if model supports it) + user: End-user identifier for tracking and rate limit calculations Review Comment: Check the formatting/linting guide for how to automate fixing formatting - https://cwiki.apache.org/confluence/display/BEAM/Python+Tips#PythonTips-LintandFormattingChecks ########## sdks/python/apache_beam/ml/transforms/embeddings/open_ai.py: ########## @@ -0,0 +1,223 @@ +# 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. + +# OpenAI Python SDK is required for this module. +# Install using: pip install openai + +import logging +import time +from collections.abc import Iterable +from collections.abc import Sequence +from typing import Any, Optional, TypeVar, Union, Tuple, cast + +import apache_beam as beam +from apache_beam.pvalue import PCollection, Row +from apache_beam.io.components.adaptive_throttler import AdaptiveThrottler +from apache_beam.metrics.metric import Metrics +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 _TextEmbeddingHandler +from apache_beam.utils import retry + +__all__ = ["OpenAITextEmbeddings"] + +# Define a type variable for the output +MLTransformOutputT = TypeVar('MLTransformOutputT') + +# Default batch size for OpenAI calls +_BATCH_SIZE = 20 # OpenAI can handle larger batches than Vertex +_MSEC_TO_SEC = 1000 + +LOGGER = logging.getLogger("OpenAIEmbeddings") + + +def _retry_on_appropriate_openai_error(exception): + """ + Retry filter that returns True if a returned error is rate limit (429) or server error (5xx). + + 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 RateLimitError (429) error. + """ + return isinstance(exception, (RateLimitError, APIError)) Review Comment: Need an import for these exceptions ########## sdks/python/apache_beam/ml/transforms/embeddings/open_ai.py: ########## @@ -0,0 +1,223 @@ +# 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. + +# OpenAI Python SDK is required for this module. +# Install using: pip install openai + +import logging +import time +from collections.abc import Iterable +from collections.abc import Sequence +from typing import Any, Optional, TypeVar, Union, Tuple, cast + +import apache_beam as beam +from apache_beam.pvalue import PCollection, Row +from apache_beam.io.components.adaptive_throttler import AdaptiveThrottler +from apache_beam.metrics.metric import Metrics +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 _TextEmbeddingHandler +from apache_beam.utils import retry + +__all__ = ["OpenAITextEmbeddings"] + +# Define a type variable for the output +MLTransformOutputT = TypeVar('MLTransformOutputT') + +# Default batch size for OpenAI calls +_BATCH_SIZE = 20 # OpenAI can handle larger batches than Vertex +_MSEC_TO_SEC = 1000 + +LOGGER = logging.getLogger("OpenAIEmbeddings") + + +def _retry_on_appropriate_openai_error(exception): + """ + Retry filter that returns True if a returned error is rate limit (429) or server error (5xx). + + 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 RateLimitError (429) error. + """ + return isinstance(exception, (RateLimitError, APIError)) + + +class _OpenAITextEmbeddingHandler(ModelHandler): + """ + Note: Intended for internal use and guarantees no backwards compatibility. + """ + + def __init__( + self, + model_name: str, + api_key: Optional[str] = None, + organization: Optional[str] = None, + dimensions: Optional[int] = None, + user: Optional[str] = None, + ): + """Initialize OpenAI embedding handler. + + Args: + model_name: Name of the OpenAI embedding model (e.g., "text-embedding-3-small") + api_key: OpenAI API key + organization: OpenAI organization ID + dimensions: Specific embedding dimensions to use (if model supports it) + user: End-user identifier for tracking and rate limit calculations + """ + # Store only configuration parameters, not the client + self.model_name = model_name + self.api_key = api_key + self.organization = organization + self.dimensions = dimensions + self.user = user + + # Configure AdaptiveThrottler and throttling metrics for client-side + # throttling behavior. + self.throttled_secs = Metrics.counter( + OpenAITextEmbeddings, "cumulativeThrottlingSeconds") + self.throttler = AdaptiveThrottler( + window_ms=1, bucket_ms=1, overload_ratio=2) + + @retry.with_exponential_backoff( + num_retries=5, retry_filter=_retry_on_appropriate_openai_error) + def get_request( + self, text_batch: Sequence[str], model: Any, throttle_delay_secs: int): + while self.throttler.throttle_request(time.time() * _MSEC_TO_SEC): + LOGGER.info( + "Delaying request for %d seconds due to previous failures", + throttle_delay_secs) + time.sleep(throttle_delay_secs) + self.throttled_secs.inc(throttle_delay_secs) + + try: + req_time = time.time() + kwargs = { + "model": self.model_name, + "input": text_batch, + } + if self.dimensions: + kwargs["dimensions"] = self.dimensions + if self.user: + kwargs["user"] = self.user + + response = model.embeddings.create(**kwargs) + self.throttler.successful_request(req_time * _MSEC_TO_SEC) + return [item.embedding for item in response.data] + except RateLimitError as e: + LOGGER.warning("Request was rate limited by OpenAI API") + raise + except Exception as e: + LOGGER.error("Unexpected exception raised as part of request: %s", e) + raise + + def run_inference( + self, + batch: Sequence[str], + model: Any, + inference_args: Optional[dict[str, Any]] = None, + ) -> Iterable: + # Validate that all elements in the batch are strings + for item in batch: + if not isinstance(item, str): + raise TypeError( + f"Embeddings can only be generated on strings. Got {type(item)} instead." + ) + + embeddings = [] + batch_size = _BATCH_SIZE + for i in range(0, len(batch), batch_size): + text_batch = batch[i:i + batch_size] + embeddings_batch = self.get_request( + text_batch=text_batch, model=model, throttle_delay_secs=5) + embeddings.extend(embeddings_batch) + return embeddings + + def load_model(self): + # Create the client just before it's needed during pipeline execution + if self.api_key: + client = open_ai.OpenAI( Review Comment: This import is missing. the package is also `openai` based on the official library docs - https://platform.openai.com/docs/libraries?language=python ########## sdks/python/apache_beam/ml/transforms/embeddings/open_ai.py: ########## @@ -0,0 +1,223 @@ +# 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. + +# OpenAI Python SDK is required for this module. +# Install using: pip install openai + +import logging +import time +from collections.abc import Iterable +from collections.abc import Sequence +from typing import Any, Optional, TypeVar, Union, Tuple, cast + +import apache_beam as beam +from apache_beam.pvalue import PCollection, Row +from apache_beam.io.components.adaptive_throttler import AdaptiveThrottler +from apache_beam.metrics.metric import Metrics +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 _TextEmbeddingHandler +from apache_beam.utils import retry + +__all__ = ["OpenAITextEmbeddings"] + +# Define a type variable for the output +MLTransformOutputT = TypeVar('MLTransformOutputT') + +# Default batch size for OpenAI calls +_BATCH_SIZE = 20 # OpenAI can handle larger batches than Vertex Review Comment: This could likely be handled in the model handler instead of being hard-coded here ########## sdks/python/apache_beam/ml/transforms/embeddings/open_ai_test.py: ########## Review Comment: I think this would be better suited as open_ai_it_test.py since it's composed of pipeline examples that depend on calls to external services. The unit test side of things would be pretty light like the [vertex AI tests](https://github.com/apache/beam/blob/5c4b42f81c67fefec42e28cc6faae52f21ba8f56/sdks/python/apache_beam/ml/inference/vertex_ai_inference_test.py) -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: github-unsubscr...@beam.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
