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+/openai-0.27.4.tar.gz
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+%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
diff --git a/sources b/sources
new file mode 100644
index 0000000..28ac71c
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+d790233acb43c5c8cc9f34718cef12b4  openai-0.27.4.tar.gz