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‎.openpublishing.redirection.azure-kubernetes-service.json

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},
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{
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"source_path_from_root": "/articles/aks/operator-best-practices-run-at-scale.md",
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"redirect_url": "/azure/aks/best-practices-performance-scale-large.md",
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"redirect_url": "/azure/aks/best-practices-performance-scale-large",
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"redirect_document_id": false
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}
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]

‎articles/ai-services/computer-vision/includes/how-to-guides/analyze-image-40-rest.md

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@@ -74,7 +74,7 @@ You can also do image analysis with a custom trained model. To create and train
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To use a custom model, don't use the features query parameter. Instead, set the `model-name` parameter to the name of your model as shown here. Replace `MyCustomModelName` with your custom model name.
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`https://<endpoint>/computervision/imageanalysis:analyze?api-version=2024-02-01&model-name=MyCustomModelName`
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`https://<endpoint>/computervision/imageanalysis:analyze?api-version=2023-02-01&model-name=MyCustomModelName`
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### Specify languages

‎articles/ai-services/openai/concepts/content-filter.md

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from openai import AzureOpenAI
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client = AzureOpenAI(
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api_key=os.getenv("AZURE_OPENAI_API_KEY"),
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api_version="2023-10-01-preview",
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api_version="2024-03-01-preview",
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azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
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)
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print(response.model_dump_json(indent=2))
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```
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### Output
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```json
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{
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"choices": [
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{
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"content_filter_results": {
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"hate": {
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"filtered": false,
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"severity": "safe"
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},
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"protected_material_code": {
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"citation": {
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"URL": " https://github.com/username/repository-name/path/to/file-example.txt",
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"license": "EXAMPLE-LICENSE"
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},
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"detected": true,
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"filtered": false
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},
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"protected_material_text": {
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"detected": false,
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"filtered": false
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},
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"self_harm": {
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"filtered": false,
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"severity": "safe"
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},
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"sexual": {
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"filtered": false,
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"severity": "safe"
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},
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"violence": {
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"filtered": false,
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"severity": "safe"
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}
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},
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"finish_reason": "stop",
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"index": 0,
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"message": {
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"content": "Example model response will be returned ",
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"role": "assistant"
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}
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}
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],
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"created": 1699386280,
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"id": "chatcmpl-8IMI4HzcmcK6I77vpOJCPt0Vcf8zJ",
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"model": "gpt-35-turbo-instruct",
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"object": "text.completion",
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"usage": {
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"completion_tokens": 40,
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"prompt_tokens": 11,
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"total_tokens": 417
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},
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"prompt_filter_results": [
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{
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"content_filter_results": {
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"hate": {
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"filtered": false,
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"severity": "safe"
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},
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"jailbreak": {
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"detected": false,
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"filtered": false
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},
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"profanity": {
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"detected": false,
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"filtered": false
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},
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"self_harm": {
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"filtered": false,
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"severity": "safe"
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},
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"sexual": {
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"filtered": false,
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"severity": "safe"
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},
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"violence": {
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"filtered": false,
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"severity": "safe"
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}
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},
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"prompt_index": 0
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}
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]
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}
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```
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# [OpenAI Python 0.28.1](#tab/python)
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[!INCLUDE [Deprecation](../includes/deprecation.md)]
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```python
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# os.getenv() for the endpoint and key assumes that you are using environment variables.
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import os
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import openai
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openai.api_type = "azure"
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openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT")
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openai.api_version = "2023-06-01-preview" # API version required to use Annotations
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openai.api_version = "2024-03-01-preview" # API version required to use Annotations
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openai.api_key = os.getenv("AZURE_OPENAI_API_KEY")
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response = openai.Completion.create(
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engine="gpt-35-turbo", # engine = "deployment_name".
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engine="gpt-35-turbo-instruct", # engine = "deployment_name".
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messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Example prompt that leads to a protected code completion that was detected, but not filtered"}] # Content that is detected at severity level medium or high is filtered,
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# while content detected at severity level low isn't filtered by the content filters.
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)
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],
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"created": 1699386280,
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"id": "chatcmpl-8IMI4HzcmcK6I77vpOJCPt0Vcf8zJ",
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"model": "gpt-35-turbo",
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"object": "chat.completion",
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"model": "gpt-35-turbo-instruct",
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"object": "text.completion",
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"usage": {
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"completion_tokens": 40,
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"prompt_tokens": 11,
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"total_tokens": 417
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},
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"prompt_filter_results": [
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{
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"content_filter_results": {
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},
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"prompt_index": 0
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}
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],
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"usage": {
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"completion_tokens": 40,
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"prompt_tokens": 11,
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"total_tokens": 417
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}
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]
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}
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```
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import openai
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openai.api_type = "azure"
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openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT")
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openai.api_version = "2023-06-01-preview" # API version required to use Annotations
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openai.api_version = "2024-03-01-preview" # API version required to use Annotations
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openai.api_key = os.getenv("AZURE_OPENAI_API_KEY")
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try:

‎articles/ai-services/openai/how-to/embeddings.md

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# [OpenAI Python 0.28.1](#tab/python)
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[!INCLUDE [Deprecation](../includes/deprecation.md)]
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```python
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import openai
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‎articles/ai-services/openai/how-to/function-calling.md

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# [OpenAI Python 0.28.1](#tab/python)
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[!INCLUDE [Deprecation](../includes/deprecation.md)]
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```python
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import os

‎articles/ai-services/openai/includes/chat-completion.md

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# [OpenAI Python 0.28.1](#tab/python)
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[!INCLUDE [Deprecation](../includes/deprecation.md)]
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```python
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import os
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import openai

‎articles/ai-services/openai/includes/chatgpt-python.md

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# [OpenAI Python 0.28.1](#tab/python)
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[!INCLUDE [Deprecation](../includes/deprecation.md)]
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```console
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pip install openai==0.28.1
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```

‎articles/ai-services/openai/includes/deprecation.md

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---
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title: Python 0.28.1 deprecated
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titleSuffix: Azure OpenAI Service
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description: Python library deprecation
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manager: nitinme
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ms.service: azure-ai-openai
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ms.topic: include
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ms.date: 03/13/2024
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---
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> [!NOTE]
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> The OpenAI Python library version `0.28.1` is deprecated. We recommend using `1.x`. Consult our [migration guide](../how-to/migration.md) for information on moving from `0.28.1` to `1.x`.

‎articles/ai-services/openai/includes/embeddings-python.md

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# [OpenAI Python 0.28.1](#tab/python)
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[!INCLUDE [Deprecation](../includes/deprecation.md)]
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```cmd
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pip install "openai==0.28.1" num2words matplotlib plotly scipy scikit-learn pandas tiktoken
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```

‎articles/ai-services/openai/includes/fine-tuning-python.md

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# [OpenAI Python 0.28.1](#tab/python)
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[!INCLUDE [Deprecation](../includes/deprecation.md)]
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```python
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# Upload fine-tuning files
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