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@@ -0,0 +1 @@ +/openai-0.27.4.tar.gz diff --git a/python-openai.spec b/python-openai.spec new file mode 100644 index 0000000..cc2c290 --- /dev/null +++ b/python-openai.spec @@ -0,0 +1,980 @@ +%global _empty_manifest_terminate_build 0 +Name: python-openai +Version: 0.27.4 +Release: 1 +Summary: Python client library for the OpenAI API +License: MIT License +URL: https://github.com/openai/openai-python +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d6/f0/e80cef4ff77f100ebb70b51a9a43a3dcd989a938a7c6ba299d987fd867e9/openai-0.27.4.tar.gz +BuildArch: noarch + +Requires: python3-requests +Requires: python3-tqdm +Requires: python3-aiohttp +Requires: python3-typing-extensions +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-pandas-stubs +Requires: python3-openpyxl +Requires: python3-black +Requires: python3-pytest +Requires: python3-pytest-asyncio +Requires: python3-pytest-mock +Requires: python3-scikit-learn +Requires: python3-tenacity +Requires: python3-matplotlib +Requires: python3-plotly +Requires: python3-numpy +Requires: python3-scipy +Requires: python3-pandas +Requires: python3-pandas-stubs +Requires: python3-openpyxl +Requires: python3-wandb +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-pandas-stubs +Requires: python3-openpyxl + +%description +# OpenAI Python Library + +The OpenAI Python library provides convenient access to the OpenAI API +from applications written in the Python language. It includes a +pre-defined set of classes for API resources that initialize +themselves dynamically from API responses which makes it compatible +with a wide range of versions of the OpenAI API. + +You can find usage examples for the OpenAI Python library in our [API reference](https://beta.openai.com/docs/api-reference?lang=python) and the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/). + +## Installation + +You don't need this source code unless you want to modify the package. If you just +want to use the package, just run: + +```sh +pip install --upgrade openai +``` + +Install from source with: + +```sh +python setup.py install +``` + +### Optional dependencies + +Install dependencies for [`openai.embeddings_utils`](openai/embeddings_utils.py): + +```sh +pip install openai[embeddings] +``` + +Install support for [Weights & Biases](https://wandb.me/openai-docs): + +``` +pip install openai[wandb] +``` + +Data libraries like `numpy` and `pandas` are not installed by default due to their size. They’re needed for some functionality of this library, but generally not for talking to the API. If you encounter a `MissingDependencyError`, install them with: + +```sh +pip install openai[datalib] +```` + +## Usage + +The library needs to be configured with your account's secret key which is available on the [website](https://platform.openai.com/account/api-keys). Either set it as the `OPENAI_API_KEY` environment variable before using the library: + +```bash +export OPENAI_API_KEY='sk-...' +``` + +Or set `openai.api_key` to its value: + +```python +import openai +openai.api_key = "sk-..." + +# list models +models = openai.Model.list() + +# print the first model's id +print(models.data[0].id) + +# create a completion +completion = openai.Completion.create(model="ada", prompt="Hello world") + +# print the completion +print(completion.choices[0].text) +``` + + +### Params +All endpoints have a `.create` method that supports a `request_timeout` param. This param takes a `Union[float, Tuple[float, float]]` and will raise an `openai.error.Timeout` error if the request exceeds that time in seconds (See: https://requests.readthedocs.io/en/latest/user/quickstart/#timeouts). + +### Microsoft Azure Endpoints + +In order to use the library with Microsoft Azure endpoints, you need to set the `api_type`, `api_base` and `api_version` in addition to the `api_key`. The `api_type` must be set to 'azure' and the others correspond to the properties of your endpoint. +In addition, the deployment name must be passed as the engine parameter. + +```python +import openai +openai.api_type = "azure" +openai.api_key = "..." +openai.api_base = "https://example-endpoint.openai.azure.com" +openai.api_version = "2023-03-15-preview" + +# create a completion +completion = openai.Completion.create(deployment_id="deployment-name", prompt="Hello world") + +# print the completion +print(completion.choices[0].text) +``` + +Please note that for the moment, the Microsoft Azure endpoints can only be used for completion, embedding, and fine-tuning operations. +For a detailed example of how to use fine-tuning and other operations using Azure endpoints, please check out the following Jupyter notebooks: +* [Using Azure completions](https://github.com/openai/openai-cookbook/tree/main/examples/azure/completions.ipynb) +* [Using Azure fine-tuning](https://github.com/openai/openai-cookbook/tree/main/examples/azure/finetuning.ipynb) +* [Using Azure embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/azure/embeddings.ipynb) + +### Microsoft Azure Active Directory Authentication + +In order to use Microsoft Active Directory to authenticate to your Azure endpoint, you need to set the `api_type` to "azure_ad" and pass the acquired credential token to `api_key`. The rest of the parameters need to be set as specified in the previous section. + + +```python +from azure.identity import DefaultAzureCredential +import openai + +# Request credential +default_credential = DefaultAzureCredential() +token = default_credential.get_token("https://cognitiveservices.azure.com/.default") + +# Setup parameters +openai.api_type = "azure_ad" +openai.api_key = token.token +openai.api_base = "https://example-endpoint.openai.azure.com/" +openai.api_version = "2023-03-15-preview" + +# ... +``` +### Command-line interface + +This library additionally provides an `openai` command-line utility +which makes it easy to interact with the API from your terminal. Run +`openai api -h` for usage. + +```sh +# list models +openai api models.list + +# create a completion +openai api completions.create -m ada -p "Hello world" + +# create a chat completion +openai api chat_completions.create -m gpt-3.5-turbo -g user "Hello world" + +# generate images via DALL·E API +openai api image.create -p "two dogs playing chess, cartoon" -n 1 +``` + +## Example code + +Examples of how to use this Python library to accomplish various tasks can be found in the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/). It contains code examples for: + +* Classification using fine-tuning +* Clustering +* Code search +* Customizing embeddings +* Question answering from a corpus of documents +* Recommendations +* Visualization of embeddings +* And more + +Prior to July 2022, this OpenAI Python library hosted code examples in its examples folder, but since then all examples have been migrated to the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/). + +### Chat + +Conversational models such as `gpt-3.5-turbo` can be called using the chat completions endpoint. + +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose + +completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello world!"}]) +print(completion.choices[0].message.content) +``` + +### Embeddings + +In the OpenAI Python library, an embedding represents a text string as a fixed-length vector of floating point numbers. Embeddings are designed to measure the similarity or relevance between text strings. + +To get an embedding for a text string, you can use the embeddings method as follows in Python: + +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose + +# choose text to embed +text_string = "sample text" + +# choose an embedding +model_id = "text-similarity-davinci-001" + +# compute the embedding of the text +embedding = openai.Embedding.create(input=text_string, model=model_id)['data'][0]['embedding'] +``` + +An example of how to call the embeddings method is shown in this [get embeddings notebook](https://github.com/openai/openai-cookbook/blob/main/examples/Get_embeddings.ipynb). + +Examples of how to use embeddings are shared in the following Jupyter notebooks: + +- [Classification using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Classification_using_embeddings.ipynb) +- [Clustering using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Clustering.ipynb) +- [Code search using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Code_search.ipynb) +- [Semantic text search using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Semantic_text_search_using_embeddings.ipynb) +- [User and product embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/User_and_product_embeddings.ipynb) +- [Zero-shot classification using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Zero-shot_classification_with_embeddings.ipynb) +- [Recommendation using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Recommendation_using_embeddings.ipynb) + +For more information on embeddings and the types of embeddings OpenAI offers, read the [embeddings guide](https://beta.openai.com/docs/guides/embeddings) in the OpenAI documentation. + +### Fine-tuning + +Fine-tuning a model on training data can both improve the results (by giving the model more examples to learn from) and reduce the cost/latency of API calls (chiefly through reducing the need to include training examples in prompts). + +Examples of fine-tuning are shared in the following Jupyter notebooks: + +- [Classification with fine-tuning](https://github.com/openai/openai-cookbook/blob/main/examples/Fine-tuned_classification.ipynb) (a simple notebook that shows the steps required for fine-tuning) +- Fine-tuning a model that answers questions about the 2020 Olympics + - [Step 1: Collecting data](https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-1-collect-data.ipynb) + - [Step 2: Creating a synthetic Q&A dataset](https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-2-create-qa.ipynb) + - [Step 3: Train a fine-tuning model specialized for Q&A](https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-3-train-qa.ipynb) + +Sync your fine-tunes to [Weights & Biases](https://wandb.me/openai-docs) to track experiments, models, and datasets in your central dashboard with: + +```bash +openai wandb sync +``` + +For more information on fine-tuning, read the [fine-tuning guide](https://beta.openai.com/docs/guides/fine-tuning) in the OpenAI documentation. + +### Moderation + +OpenAI provides a Moderation endpoint that can be used to check whether content complies with the OpenAI [content policy](https://platform.openai.com/docs/usage-policies) + +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose + +moderation_resp = openai.Moderation.create(input="Here is some perfectly innocuous text that follows all OpenAI content policies.") +``` + +See the [moderation guide](https://platform.openai.com/docs/guides/moderation) for more details. + +## Image generation (DALL·E) + +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose + +image_resp = openai.Image.create(prompt="two dogs playing chess, oil painting", n=4, size="512x512") + +``` + +## Audio transcription (Whisper) +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose +f = open("path/to/file.mp3", "rb") +transcript = openai.Audio.transcribe("whisper-1", f) + +``` + +## Async API + +Async support is available in the API by prepending `a` to a network-bound method: + +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose + +async def create_completion(): + completion_resp = await openai.Completion.acreate(prompt="This is a test", model="davinci") + +``` + +To make async requests more efficient, you can pass in your own +``aiohttp.ClientSession``, but you must manually close the client session at the end +of your program/event loop: + +```python +import openai +from aiohttp import ClientSession + +openai.aiosession.set(ClientSession()) +# At the end of your program, close the http session +await openai.aiosession.get().close() +``` + +See the [usage guide](https://platform.openai.com/docs/guides/images) for more details. + +## Requirements + +- Python 3.7.1+ + +In general, we want to support the versions of Python that our +customers are using. If you run into problems with any version +issues, please let us know on our [support page](https://help.openai.com/en/). + +## Credit + +This library is forked from the [Stripe Python Library](https://github.com/stripe/stripe-python). + + +%package -n python3-openai +Summary: Python client library for the OpenAI API +Provides: python-openai +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-openai +# OpenAI Python Library + +The OpenAI Python library provides convenient access to the OpenAI API +from applications written in the Python language. It includes a +pre-defined set of classes for API resources that initialize +themselves dynamically from API responses which makes it compatible +with a wide range of versions of the OpenAI API. + +You can find usage examples for the OpenAI Python library in our [API reference](https://beta.openai.com/docs/api-reference?lang=python) and the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/). + +## Installation + +You don't need this source code unless you want to modify the package. If you just +want to use the package, just run: + +```sh +pip install --upgrade openai +``` + +Install from source with: + +```sh +python setup.py install +``` + +### Optional dependencies + +Install dependencies for [`openai.embeddings_utils`](openai/embeddings_utils.py): + +```sh +pip install openai[embeddings] +``` + +Install support for [Weights & Biases](https://wandb.me/openai-docs): + +``` +pip install openai[wandb] +``` + +Data libraries like `numpy` and `pandas` are not installed by default due to their size. They’re needed for some functionality of this library, but generally not for talking to the API. If you encounter a `MissingDependencyError`, install them with: + +```sh +pip install openai[datalib] +```` + +## Usage + +The library needs to be configured with your account's secret key which is available on the [website](https://platform.openai.com/account/api-keys). Either set it as the `OPENAI_API_KEY` environment variable before using the library: + +```bash +export OPENAI_API_KEY='sk-...' +``` + +Or set `openai.api_key` to its value: + +```python +import openai +openai.api_key = "sk-..." + +# list models +models = openai.Model.list() + +# print the first model's id +print(models.data[0].id) + +# create a completion +completion = openai.Completion.create(model="ada", prompt="Hello world") + +# print the completion +print(completion.choices[0].text) +``` + + +### Params +All endpoints have a `.create` method that supports a `request_timeout` param. This param takes a `Union[float, Tuple[float, float]]` and will raise an `openai.error.Timeout` error if the request exceeds that time in seconds (See: https://requests.readthedocs.io/en/latest/user/quickstart/#timeouts). + +### Microsoft Azure Endpoints + +In order to use the library with Microsoft Azure endpoints, you need to set the `api_type`, `api_base` and `api_version` in addition to the `api_key`. The `api_type` must be set to 'azure' and the others correspond to the properties of your endpoint. +In addition, the deployment name must be passed as the engine parameter. + +```python +import openai +openai.api_type = "azure" +openai.api_key = "..." +openai.api_base = "https://example-endpoint.openai.azure.com" +openai.api_version = "2023-03-15-preview" + +# create a completion +completion = openai.Completion.create(deployment_id="deployment-name", prompt="Hello world") + +# print the completion +print(completion.choices[0].text) +``` + +Please note that for the moment, the Microsoft Azure endpoints can only be used for completion, embedding, and fine-tuning operations. +For a detailed example of how to use fine-tuning and other operations using Azure endpoints, please check out the following Jupyter notebooks: +* [Using Azure completions](https://github.com/openai/openai-cookbook/tree/main/examples/azure/completions.ipynb) +* [Using Azure fine-tuning](https://github.com/openai/openai-cookbook/tree/main/examples/azure/finetuning.ipynb) +* [Using Azure embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/azure/embeddings.ipynb) + +### Microsoft Azure Active Directory Authentication + +In order to use Microsoft Active Directory to authenticate to your Azure endpoint, you need to set the `api_type` to "azure_ad" and pass the acquired credential token to `api_key`. The rest of the parameters need to be set as specified in the previous section. + + +```python +from azure.identity import DefaultAzureCredential +import openai + +# Request credential +default_credential = DefaultAzureCredential() +token = default_credential.get_token("https://cognitiveservices.azure.com/.default") + +# Setup parameters +openai.api_type = "azure_ad" +openai.api_key = token.token +openai.api_base = "https://example-endpoint.openai.azure.com/" +openai.api_version = "2023-03-15-preview" + +# ... +``` +### Command-line interface + +This library additionally provides an `openai` command-line utility +which makes it easy to interact with the API from your terminal. Run +`openai api -h` for usage. + +```sh +# list models +openai api models.list + +# create a completion +openai api completions.create -m ada -p "Hello world" + +# create a chat completion +openai api chat_completions.create -m gpt-3.5-turbo -g user "Hello world" + +# generate images via DALL·E API +openai api image.create -p "two dogs playing chess, cartoon" -n 1 +``` + +## Example code + +Examples of how to use this Python library to accomplish various tasks can be found in the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/). It contains code examples for: + +* Classification using fine-tuning +* Clustering +* Code search +* Customizing embeddings +* Question answering from a corpus of documents +* Recommendations +* Visualization of embeddings +* And more + +Prior to July 2022, this OpenAI Python library hosted code examples in its examples folder, but since then all examples have been migrated to the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/). + +### Chat + +Conversational models such as `gpt-3.5-turbo` can be called using the chat completions endpoint. + +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose + +completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello world!"}]) +print(completion.choices[0].message.content) +``` + +### Embeddings + +In the OpenAI Python library, an embedding represents a text string as a fixed-length vector of floating point numbers. Embeddings are designed to measure the similarity or relevance between text strings. + +To get an embedding for a text string, you can use the embeddings method as follows in Python: + +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose + +# choose text to embed +text_string = "sample text" + +# choose an embedding +model_id = "text-similarity-davinci-001" + +# compute the embedding of the text +embedding = openai.Embedding.create(input=text_string, model=model_id)['data'][0]['embedding'] +``` + +An example of how to call the embeddings method is shown in this [get embeddings notebook](https://github.com/openai/openai-cookbook/blob/main/examples/Get_embeddings.ipynb). + +Examples of how to use embeddings are shared in the following Jupyter notebooks: + +- [Classification using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Classification_using_embeddings.ipynb) +- [Clustering using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Clustering.ipynb) +- [Code search using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Code_search.ipynb) +- [Semantic text search using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Semantic_text_search_using_embeddings.ipynb) +- [User and product embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/User_and_product_embeddings.ipynb) +- [Zero-shot classification using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Zero-shot_classification_with_embeddings.ipynb) +- [Recommendation using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Recommendation_using_embeddings.ipynb) + +For more information on embeddings and the types of embeddings OpenAI offers, read the [embeddings guide](https://beta.openai.com/docs/guides/embeddings) in the OpenAI documentation. + +### Fine-tuning + +Fine-tuning a model on training data can both improve the results (by giving the model more examples to learn from) and reduce the cost/latency of API calls (chiefly through reducing the need to include training examples in prompts). + +Examples of fine-tuning are shared in the following Jupyter notebooks: + +- [Classification with fine-tuning](https://github.com/openai/openai-cookbook/blob/main/examples/Fine-tuned_classification.ipynb) (a simple notebook that shows the steps required for fine-tuning) +- Fine-tuning a model that answers questions about the 2020 Olympics + - [Step 1: Collecting data](https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-1-collect-data.ipynb) + - [Step 2: Creating a synthetic Q&A dataset](https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-2-create-qa.ipynb) + - [Step 3: Train a fine-tuning model specialized for Q&A](https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-3-train-qa.ipynb) + +Sync your fine-tunes to [Weights & Biases](https://wandb.me/openai-docs) to track experiments, models, and datasets in your central dashboard with: + +```bash +openai wandb sync +``` + +For more information on fine-tuning, read the [fine-tuning guide](https://beta.openai.com/docs/guides/fine-tuning) in the OpenAI documentation. + +### Moderation + +OpenAI provides a Moderation endpoint that can be used to check whether content complies with the OpenAI [content policy](https://platform.openai.com/docs/usage-policies) + +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose + +moderation_resp = openai.Moderation.create(input="Here is some perfectly innocuous text that follows all OpenAI content policies.") +``` + +See the [moderation guide](https://platform.openai.com/docs/guides/moderation) for more details. + +## Image generation (DALL·E) + +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose + +image_resp = openai.Image.create(prompt="two dogs playing chess, oil painting", n=4, size="512x512") + +``` + +## Audio transcription (Whisper) +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose +f = open("path/to/file.mp3", "rb") +transcript = openai.Audio.transcribe("whisper-1", f) + +``` + +## Async API + +Async support is available in the API by prepending `a` to a network-bound method: + +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose + +async def create_completion(): + completion_resp = await openai.Completion.acreate(prompt="This is a test", model="davinci") + +``` + +To make async requests more efficient, you can pass in your own +``aiohttp.ClientSession``, but you must manually close the client session at the end +of your program/event loop: + +```python +import openai +from aiohttp import ClientSession + +openai.aiosession.set(ClientSession()) +# At the end of your program, close the http session +await openai.aiosession.get().close() +``` + +See the [usage guide](https://platform.openai.com/docs/guides/images) for more details. + +## Requirements + +- Python 3.7.1+ + +In general, we want to support the versions of Python that our +customers are using. If you run into problems with any version +issues, please let us know on our [support page](https://help.openai.com/en/). + +## Credit + +This library is forked from the [Stripe Python Library](https://github.com/stripe/stripe-python). + + +%package help +Summary: Development documents and examples for openai +Provides: python3-openai-doc +%description help +# OpenAI Python Library + +The OpenAI Python library provides convenient access to the OpenAI API +from applications written in the Python language. It includes a +pre-defined set of classes for API resources that initialize +themselves dynamically from API responses which makes it compatible +with a wide range of versions of the OpenAI API. + +You can find usage examples for the OpenAI Python library in our [API reference](https://beta.openai.com/docs/api-reference?lang=python) and the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/). + +## Installation + +You don't need this source code unless you want to modify the package. If you just +want to use the package, just run: + +```sh +pip install --upgrade openai +``` + +Install from source with: + +```sh +python setup.py install +``` + +### Optional dependencies + +Install dependencies for [`openai.embeddings_utils`](openai/embeddings_utils.py): + +```sh +pip install openai[embeddings] +``` + +Install support for [Weights & Biases](https://wandb.me/openai-docs): + +``` +pip install openai[wandb] +``` + +Data libraries like `numpy` and `pandas` are not installed by default due to their size. They’re needed for some functionality of this library, but generally not for talking to the API. If you encounter a `MissingDependencyError`, install them with: + +```sh +pip install openai[datalib] +```` + +## Usage + +The library needs to be configured with your account's secret key which is available on the [website](https://platform.openai.com/account/api-keys). Either set it as the `OPENAI_API_KEY` environment variable before using the library: + +```bash +export OPENAI_API_KEY='sk-...' +``` + +Or set `openai.api_key` to its value: + +```python +import openai +openai.api_key = "sk-..." + +# list models +models = openai.Model.list() + +# print the first model's id +print(models.data[0].id) + +# create a completion +completion = openai.Completion.create(model="ada", prompt="Hello world") + +# print the completion +print(completion.choices[0].text) +``` + + +### Params +All endpoints have a `.create` method that supports a `request_timeout` param. This param takes a `Union[float, Tuple[float, float]]` and will raise an `openai.error.Timeout` error if the request exceeds that time in seconds (See: https://requests.readthedocs.io/en/latest/user/quickstart/#timeouts). + +### Microsoft Azure Endpoints + +In order to use the library with Microsoft Azure endpoints, you need to set the `api_type`, `api_base` and `api_version` in addition to the `api_key`. The `api_type` must be set to 'azure' and the others correspond to the properties of your endpoint. +In addition, the deployment name must be passed as the engine parameter. + +```python +import openai +openai.api_type = "azure" +openai.api_key = "..." +openai.api_base = "https://example-endpoint.openai.azure.com" +openai.api_version = "2023-03-15-preview" + +# create a completion +completion = openai.Completion.create(deployment_id="deployment-name", prompt="Hello world") + +# print the completion +print(completion.choices[0].text) +``` + +Please note that for the moment, the Microsoft Azure endpoints can only be used for completion, embedding, and fine-tuning operations. +For a detailed example of how to use fine-tuning and other operations using Azure endpoints, please check out the following Jupyter notebooks: +* [Using Azure completions](https://github.com/openai/openai-cookbook/tree/main/examples/azure/completions.ipynb) +* [Using Azure fine-tuning](https://github.com/openai/openai-cookbook/tree/main/examples/azure/finetuning.ipynb) +* [Using Azure embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/azure/embeddings.ipynb) + +### Microsoft Azure Active Directory Authentication + +In order to use Microsoft Active Directory to authenticate to your Azure endpoint, you need to set the `api_type` to "azure_ad" and pass the acquired credential token to `api_key`. The rest of the parameters need to be set as specified in the previous section. + + +```python +from azure.identity import DefaultAzureCredential +import openai + +# Request credential +default_credential = DefaultAzureCredential() +token = default_credential.get_token("https://cognitiveservices.azure.com/.default") + +# Setup parameters +openai.api_type = "azure_ad" +openai.api_key = token.token +openai.api_base = "https://example-endpoint.openai.azure.com/" +openai.api_version = "2023-03-15-preview" + +# ... +``` +### Command-line interface + +This library additionally provides an `openai` command-line utility +which makes it easy to interact with the API from your terminal. Run +`openai api -h` for usage. + +```sh +# list models +openai api models.list + +# create a completion +openai api completions.create -m ada -p "Hello world" + +# create a chat completion +openai api chat_completions.create -m gpt-3.5-turbo -g user "Hello world" + +# generate images via DALL·E API +openai api image.create -p "two dogs playing chess, cartoon" -n 1 +``` + +## Example code + +Examples of how to use this Python library to accomplish various tasks can be found in the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/). It contains code examples for: + +* Classification using fine-tuning +* Clustering +* Code search +* Customizing embeddings +* Question answering from a corpus of documents +* Recommendations +* Visualization of embeddings +* And more + +Prior to July 2022, this OpenAI Python library hosted code examples in its examples folder, but since then all examples have been migrated to the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/). + +### Chat + +Conversational models such as `gpt-3.5-turbo` can be called using the chat completions endpoint. + +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose + +completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello world!"}]) +print(completion.choices[0].message.content) +``` + +### Embeddings + +In the OpenAI Python library, an embedding represents a text string as a fixed-length vector of floating point numbers. Embeddings are designed to measure the similarity or relevance between text strings. + +To get an embedding for a text string, you can use the embeddings method as follows in Python: + +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose + +# choose text to embed +text_string = "sample text" + +# choose an embedding +model_id = "text-similarity-davinci-001" + +# compute the embedding of the text +embedding = openai.Embedding.create(input=text_string, model=model_id)['data'][0]['embedding'] +``` + +An example of how to call the embeddings method is shown in this [get embeddings notebook](https://github.com/openai/openai-cookbook/blob/main/examples/Get_embeddings.ipynb). + +Examples of how to use embeddings are shared in the following Jupyter notebooks: + +- [Classification using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Classification_using_embeddings.ipynb) +- [Clustering using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Clustering.ipynb) +- [Code search using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Code_search.ipynb) +- [Semantic text search using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Semantic_text_search_using_embeddings.ipynb) +- [User and product embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/User_and_product_embeddings.ipynb) +- [Zero-shot classification using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Zero-shot_classification_with_embeddings.ipynb) +- [Recommendation using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Recommendation_using_embeddings.ipynb) + +For more information on embeddings and the types of embeddings OpenAI offers, read the [embeddings guide](https://beta.openai.com/docs/guides/embeddings) in the OpenAI documentation. + +### Fine-tuning + +Fine-tuning a model on training data can both improve the results (by giving the model more examples to learn from) and reduce the cost/latency of API calls (chiefly through reducing the need to include training examples in prompts). + +Examples of fine-tuning are shared in the following Jupyter notebooks: + +- [Classification with fine-tuning](https://github.com/openai/openai-cookbook/blob/main/examples/Fine-tuned_classification.ipynb) (a simple notebook that shows the steps required for fine-tuning) +- Fine-tuning a model that answers questions about the 2020 Olympics + - [Step 1: Collecting data](https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-1-collect-data.ipynb) + - [Step 2: Creating a synthetic Q&A dataset](https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-2-create-qa.ipynb) + - [Step 3: Train a fine-tuning model specialized for Q&A](https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-3-train-qa.ipynb) + +Sync your fine-tunes to [Weights & Biases](https://wandb.me/openai-docs) to track experiments, models, and datasets in your central dashboard with: + +```bash +openai wandb sync +``` + +For more information on fine-tuning, read the [fine-tuning guide](https://beta.openai.com/docs/guides/fine-tuning) in the OpenAI documentation. + +### Moderation + +OpenAI provides a Moderation endpoint that can be used to check whether content complies with the OpenAI [content policy](https://platform.openai.com/docs/usage-policies) + +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose + +moderation_resp = openai.Moderation.create(input="Here is some perfectly innocuous text that follows all OpenAI content policies.") +``` + +See the [moderation guide](https://platform.openai.com/docs/guides/moderation) for more details. + +## Image generation (DALL·E) + +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose + +image_resp = openai.Image.create(prompt="two dogs playing chess, oil painting", n=4, size="512x512") + +``` + +## Audio transcription (Whisper) +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose +f = open("path/to/file.mp3", "rb") +transcript = openai.Audio.transcribe("whisper-1", f) + +``` + +## Async API + +Async support is available in the API by prepending `a` to a network-bound method: + +```python +import openai +openai.api_key = "sk-..." # supply your API key however you choose + +async def create_completion(): + completion_resp = await openai.Completion.acreate(prompt="This is a test", model="davinci") + +``` + +To make async requests more efficient, you can pass in your own +``aiohttp.ClientSession``, but you must manually close the client session at the end +of your program/event loop: + +```python +import openai +from aiohttp import ClientSession + +openai.aiosession.set(ClientSession()) +# At the end of your program, close the http session +await openai.aiosession.get().close() +``` + +See the [usage guide](https://platform.openai.com/docs/guides/images) for more details. + +## Requirements + +- Python 3.7.1+ + +In general, we want to support the versions of Python that our +customers are using. If you run into problems with any version +issues, please let us know on our [support page](https://help.openai.com/en/). + +## Credit + +This library is forked from the [Stripe Python Library](https://github.com/stripe/stripe-python). + + +%prep +%autosetup -n openai-0.27.4 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-openai -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.27.4-1 +- Package Spec generated @@ -0,0 +1 @@ +d790233acb43c5c8cc9f34718cef12b4 openai-0.27.4.tar.gz |