superduper_openai
Superduper allows users to work with openai API models.
Installation
pip install superduper_openai
API
| Class | Description |
|---|---|
superduper_openai.model.OpenAIEmbedding |
OpenAI embedding predictor. |
superduper_openai.model.OpenAIChatCompletion |
OpenAI chat completion predictor. |
superduper_openai.model.OpenAIImageCreation |
OpenAI image creation predictor. |
superduper_openai.model.OpenAIImageEdit |
OpenAI image edit predictor. |
superduper_openai.model.OpenAIAudioTranscription |
OpenAI audio transcription predictor. |
superduper_openai.model.OpenAIAudioTranslation |
OpenAI audio translation predictor. |
Examples
OpenAIEmbedding
from superduper_openai.model import OpenAIEmbedding
model = OpenAIEmbedding(identifier='text-embedding-ada-002')
model.predict('Hello, world!')
OpenAIChatCompletion
from superduper_openai.model import OpenAIChatCompletion
model = OpenAIChatCompletion(model='gpt-3.5-turbo', prompt='Hello, {context}')
model.predict('Hello, world!')
OpenAIImageCreation
from superduper_openai.model import OpenAIImageCreation
model = OpenAIImageCreation(
model="dall-e",
prompt="a close up, studio photographic portrait of a {context}",
response_format="url",
)
model.predict("cat")
OpenAIImageEdit
import io
from superduper_openai.model import OpenAIImageEdit
model = OpenAIImageEdit(
model="dall-e",
prompt="A celebration party at the launch of {context}",
response_format="url",
)
with open("test/material/data/rickroll.png", "rb") as f:
buffer = io.BytesIO(f.read())
model.predict(buffer, context=["superduper"])
OpenAIAudioTranscription
import io
from superduper_openai.model import OpenAIAudioTranscription
with open('test/material/data/test.wav', 'rb') as f:
buffer = io.BytesIO(f.read())
buffer.name = 'test.wav'
prompt = (
'i have some advice for you. write all text in lower-case.'
'only make an exception for the following words: {context}'
)
model = OpenAIAudioTranscription(identifier='whisper-1', prompt=prompt)
model.predict(buffer, context=['United States'])
OpenAIAudioTranslation
import io
from superduper_openai.model import OpenAIAudioTranslation
with open('test/material/data/german.wav', 'rb') as f:
buffer = io.BytesIO(f.read())
buffer.name = 'test.wav'
prompt = (
'i have some advice for you. write all text in lower-case.'
'only make an exception for the following words: {context}'
)
e = OpenAIAudioTranslation(identifier='whisper-1', prompt=prompt)
resp = e.predict(buffer, context=['Emmerich'])
buffer.close()
Metadata
Release files for superduper-openai 0.10.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| superduper_openai-0.10.0.tar.gz | 14.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| superduper_openai-0.10.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 26.8 kB
Release files / superduper_openai-0.10.0.tar.gz
| Download URL | superduper_openai-0.10.0.tar.gz |
|---|---|
| Size | 14.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 26, 2025.
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