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92 lines
3.3 KiB
92 lines
3.3 KiB
3 years ago
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import base64
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import time
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from openai import util
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from openai.api_resources.abstract.engine_api_resource import EngineAPIResource
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from openai.datalib import numpy as np, assert_has_numpy
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from openai.error import TryAgain
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class Embedding(EngineAPIResource):
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OBJECT_NAME = "embeddings"
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@classmethod
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def create(cls, *args, **kwargs):
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"""
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Creates a new embedding for the provided input and parameters.
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See https://platform.openai.com/docs/api-reference/embeddings for a list
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of valid parameters.
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"""
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start = time.time()
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timeout = kwargs.pop("timeout", None)
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user_provided_encoding_format = kwargs.get("encoding_format", None)
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# If encoding format was not explicitly specified, we opaquely use base64 for performance
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if not user_provided_encoding_format:
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kwargs["encoding_format"] = "base64"
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while True:
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try:
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response = super().create(*args, **kwargs)
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# If a user specifies base64, we'll just return the encoded string.
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# This is only for the default case.
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if not user_provided_encoding_format:
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for data in response.data:
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# If an engine isn't using this optimization, don't do anything
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if type(data["embedding"]) == str:
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assert_has_numpy()
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data["embedding"] = np.frombuffer(
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base64.b64decode(data["embedding"]), dtype="float32"
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).tolist()
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return response
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except TryAgain as e:
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if timeout is not None and time.time() > start + timeout:
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raise
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util.log_info("Waiting for model to warm up", error=e)
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@classmethod
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async def acreate(cls, *args, **kwargs):
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"""
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Creates a new embedding for the provided input and parameters.
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See https://platform.openai.com/docs/api-reference/embeddings for a list
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of valid parameters.
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"""
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start = time.time()
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timeout = kwargs.pop("timeout", None)
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user_provided_encoding_format = kwargs.get("encoding_format", None)
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# If encoding format was not explicitly specified, we opaquely use base64 for performance
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if not user_provided_encoding_format:
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kwargs["encoding_format"] = "base64"
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while True:
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try:
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response = await super().acreate(*args, **kwargs)
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# If a user specifies base64, we'll just return the encoded string.
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# This is only for the default case.
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if not user_provided_encoding_format:
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for data in response.data:
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# If an engine isn't using this optimization, don't do anything
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if type(data["embedding"]) == str:
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data["embedding"] = np.frombuffer(
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base64.b64decode(data["embedding"]), dtype="float32"
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).tolist()
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return response
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except TryAgain as e:
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if timeout is not None and time.time() > start + timeout:
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raise
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util.log_info("Waiting for model to warm up", error=e)
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