Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into aca/config-server

Author: craigshoemaker

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

@@ -316,6 +316,29 @@ When displaying code in your application, we strongly recommend that the applica
 
 Annotations are currently available in the GA API version `2024-02-01` and in all preview versions starting from `2023-06-01-preview` for Completions and Chat Completions (GPT models). The following code snippet shows how to use annotations:
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+# os.getenv() for the endpoint and key assumes that you are using environment variables.
+
+import os
+from openai import AzureOpenAI
+client = AzureOpenAI(
+    api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
+    api_version="2023-10-01-preview",
+    azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") 
+    )
+
+response = client.completions.create(
+    model="gpt-35-turbo-instruct", # model = "deployment_name".
+    prompt="{Example prompt where a severity level of low is detected}" 
+    # Content that is detected at severity level medium or high is filtered, 
+    # while content detected at severity level low isn't filtered by the content filters.
+)
+
+print(response.model_dump_json(indent=2))
+```
+
 # [OpenAI Python 0.28.1](#tab/python)
 
 ```python
@@ -456,29 +479,6 @@ except openai.error.InvalidRequestError as e:
 
 ```
 
-# [OpenAI Python 1.x](#tab/python-new)
-
-```python
-# os.getenv() for the endpoint and key assumes that you are using environment variables.
-
-import os
-from openai import AzureOpenAI
-client = AzureOpenAI(
-    api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
-    api_version="2023-10-01-preview",
-    azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") 
-    )
-
-response = client.completions.create(
-    model="gpt-35-turbo-instruct", # model = "deployment_name".
-    prompt="{Example prompt where a severity level of low is detected}" 
-    # Content that is detected at severity level medium or high is filtered, 
-    # while content detected at severity level low isn't filtered by the content filters.
-)
-
-print(response.model_dump_json(indent=2))
-```
-
 # [JavaScript](#tab/javascrit)
 
 [Azure OpenAI JavaScript SDK source code & samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai)

articles/ai-services/openai/faq.yml

@@ -173,7 +173,7 @@ sections:
       - question: |
           How do I deploy a model with the REST API? 
         answer:
-          There are currently two different REST APIs that allow model deployment. For the latest model deployment features such as the ability to specify a model version during deployment for models like text-embedding-ada-002 Version 2, use the [Deployments - Create Or Update](/rest/api/cognitiveservices/accountmanagement/deployments/create-or-update?tabs=HTTP) REST API call. 
+          There are currently two different REST APIs that allow model deployment. For the latest model deployment features such as the ability to specify a model version during deployment for models like text-embedding-ada-002 Version 2, use the [Deployments - Create Or Update](/rest/api/aiservices/accountmanagement/deployments/create-or-update?tabs=HTTP) REST API call. 
       - question: |
           Can I use quota to increase the max token limit of a model?
         answer:

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

@@ -29,24 +29,6 @@ curl https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYM
   -d '{"input": "Sample Document goes here"}'
 ```
 
-# [OpenAI Python 0.28.1](#tab/python)
-
-```python
-import openai
-
-openai.api_type = "azure"
-openai.api_key = YOUR_API_KEY
-openai.api_base = "https://YOUR_RESOURCE_NAME.openai.azure.com"
-openai.api_version = "2023-05-15"
-
-response = openai.Embedding.create(
-    input="Your text string goes here",
-    engine="YOUR_DEPLOYMENT_NAME"
-)
-embeddings = response['data'][0]['embedding']
-print(embeddings)
-```
-
 # [OpenAI Python 1.x](#tab/python-new)
 
 ```python
@@ -67,6 +49,24 @@ response = client.embeddings.create(
 print(response.model_dump_json(indent=2))
 ```
 
+# [OpenAI Python 0.28.1](#tab/python)
+
+```python
+import openai
+
+openai.api_type = "azure"
+openai.api_key = YOUR_API_KEY
+openai.api_base = "https://YOUR_RESOURCE_NAME.openai.azure.com"
+openai.api_version = "2023-05-15"
+
+response = openai.Embedding.create(
+    input="Your text string goes here",
+    engine="YOUR_DEPLOYMENT_NAME"
+)
+embeddings = response['data'][0]['embedding']
+print(embeddings)
+```
+
 # [C#](#tab/csharp)
 ```csharp
 using Azure;

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

@@ -41,16 +41,16 @@ As this is a new version of the library with breaking changes, you should test y
 
 To make the migration process easier, we're updating existing code examples in our docs for Python to a tabbed experience:
 
-# [OpenAI Python 0.28.1](#tab/python)
+# [OpenAI Python 1.x](#tab/python-new)
 
 ```console
-pip install openai==0.28.1
+pip install openai --upgrade
 ```
 
-# [OpenAI Python 1.x](#tab/python-new)
+# [OpenAI Python 0.28.1](#tab/python)
 
 ```console
-pip install openai --upgrade
+pip install openai==0.28.1
 ```
 
 ---
@@ -59,32 +59,6 @@ This provides context for what has changed and allows you to test the new librar
 
 ## Chat completions
 
-# [OpenAI Python 0.28.1](#tab/python)
-
-You need to set the `engine` variable to the deployment name you chose when you deployed the GPT-3.5-Turbo or GPT-4 models. Entering the model name will result in an error unless you chose a deployment name that is identical to the underlying model name.
-
-```python
-import os
-import openai
-openai.api_type = "azure"
-openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT") 
-openai.api_key = os.getenv("AZURE_OPENAI_API_KEY")
-openai.api_version = "2023-05-15"
-
-response = openai.ChatCompletion.create(
-    engine="gpt-35-turbo", # engine = "deployment_name".
-    messages=[
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": "Does Azure OpenAI support customer managed keys?"},
-        {"role": "assistant", "content": "Yes, customer managed keys are supported by Azure OpenAI."},
-        {"role": "user", "content": "Do other Azure AI services support this too?"}
-    ]
-)
-
-print(response)
-print(response['choices'][0]['message']['content'])
-```
-
 # [OpenAI Python 1.x](#tab/python-new)
 
 You need to set the `model` variable to the deployment name you chose when you deployed the GPT-3.5-Turbo or GPT-4 models. Entering the model name results in an error unless you chose a deployment name that is identical to the underlying model name.
@@ -114,31 +88,36 @@ print(response.choices[0].message.content)
 
 Additional examples can be found in our [in-depth Chat Completion article](chatgpt.md).
 
----
-
-## Completions
-
 # [OpenAI Python 0.28.1](#tab/python)
 
+You need to set the `engine` variable to the deployment name you chose when you deployed the GPT-3.5-Turbo or GPT-4 models. Entering the model name will result in an error unless you chose a deployment name that is identical to the underlying model name.
+
 ```python
 import os
 import openai
-
+openai.api_type = "azure"
+openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT") 
 openai.api_key = os.getenv("AZURE_OPENAI_API_KEY")
-openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT") # your endpoint should look like the following https://YOUR_RESOURCE_NAME.openai.azure.com/
-openai.api_type = 'azure'
-openai.api_version = '2023-05-15' # this might change in the future
+openai.api_version = "2023-05-15"
 
-deployment_name='REPLACE_WITH_YOUR_DEPLOYMENT_NAME' #This will correspond to the custom name you chose for your deployment when you deployed a model. 
+response = openai.ChatCompletion.create(
+    engine="gpt-35-turbo", # engine = "deployment_name".
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Does Azure OpenAI support customer managed keys?"},
+        {"role": "assistant", "content": "Yes, customer managed keys are supported by Azure OpenAI."},
+        {"role": "user", "content": "Do other Azure AI services support this too?"}
+    ]
+)
 
-# Send a completion call to generate an answer
-print('Sending a test completion job')
-start_phrase = 'Write a tagline for an ice cream shop. '
-response = openai.Completion.create(engine=deployment_name, prompt=start_phrase, max_tokens=10)
-text = response['choices'][0]['text'].replace('\n', '').replace(' .', '.').strip()
-print(start_phrase+text)
+print(response)
+print(response['choices'][0]['message']['content'])
 ```
 
+---
+
+## Completions
+
 # [OpenAI Python 1.x](#tab/python-new)
 
 ```python
@@ -160,28 +139,31 @@ response = client.completions.create(model=deployment_name, prompt=start_phrase,
 print(response.choices[0].text)
 ```
 
----
-
-## Embeddings
-
 # [OpenAI Python 0.28.1](#tab/python)
 
 ```python
+import os
 import openai
 
-openai.api_type = "azure"
-openai.api_key = YOUR_API_KEY
-openai.api_base = "https://YOUR_RESOURCE_NAME.openai.azure.com"
-openai.api_version = "2023-05-15"
+openai.api_key = os.getenv("AZURE_OPENAI_API_KEY")
+openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT") # your endpoint should look like the following https://YOUR_RESOURCE_NAME.openai.azure.com/
+openai.api_type = 'azure'
+openai.api_version = '2023-05-15' # this might change in the future
 
-response = openai.Embedding.create(
-    input="Your text string goes here",
-    engine="YOUR_DEPLOYMENT_NAME"
-)
-embeddings = response['data'][0]['embedding']
-print(embeddings)
+deployment_name='REPLACE_WITH_YOUR_DEPLOYMENT_NAME' #This will correspond to the custom name you chose for your deployment when you deployed a model. 
+
+# Send a completion call to generate an answer
+print('Sending a test completion job')
+start_phrase = 'Write a tagline for an ice cream shop. '
+response = openai.Completion.create(engine=deployment_name, prompt=start_phrase, max_tokens=10)
+text = response['choices'][0]['text'].replace('\n', '').replace(' .', '.').strip()
+print(start_phrase+text)
 ```
 
+---
+
+## Embeddings
+
 # [OpenAI Python 1.x](#tab/python-new)
 
 ```python
@@ -204,6 +186,24 @@ print(response.model_dump_json(indent=2))
 
 Additional examples including how to handle semantic text search without `embeddings_utils.py` can be found in our [embeddings tutorial](../tutorials/embeddings.md).
 
+# [OpenAI Python 0.28.1](#tab/python)
+
+```python
+import openai
+
+openai.api_type = "azure"
+openai.api_key = YOUR_API_KEY
+openai.api_base = "https://YOUR_RESOURCE_NAME.openai.azure.com"
+openai.api_version = "2023-05-15"
+
+response = openai.Embedding.create(
+    input="Your text string goes here",
+    engine="YOUR_DEPLOYMENT_NAME"
+)
+embeddings = response['data'][0]['embedding']
+print(embeddings)
+```
+
 ---
 
 ## Async
@@ -260,6 +260,52 @@ print(completion.model_dump_json(indent=2))
 ## Use your data
 
 For the full configuration steps that are required to make these code examples work, consult the [use your data quickstart](../use-your-data-quickstart.md).
+
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+import os
+import openai
+import dotenv
+
+dotenv.load_dotenv()
+
+endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT")
+api_key = os.environ.get("AZURE_OPENAI_API_KEY")
+deployment = os.environ.get("AZURE_OPEN_AI_DEPLOYMENT_ID")
+
+client = openai.AzureOpenAI(
+    base_url=f"{endpoint}/openai/deployments/{deployment}/extensions",
+    api_key=api_key,
+    api_version="2023-08-01-preview",
+)
+
+completion = client.chat.completions.create(
+    model=deployment,
+    messages=[
+        {
+            "role": "user",
+            "content": "How is Azure machine learning different than Azure OpenAI?",
+        },
+    ],
+    extra_body={
+        "dataSources": [
+            {
+                "type": "AzureCognitiveSearch",
+                "parameters": {
+                    "endpoint": os.environ["AZURE_AI_SEARCH_ENDPOINT"],
+                    "key": os.environ["AZURE_AI_SEARCH_API_KEY"],
+                    "indexName": os.environ["AZURE_AI_SEARCH_INDEX"]
+                }
+            }
+        ]
+    }
+)
+
+print(completion.model_dump_json(indent=2))
+```
+
+
 # [OpenAI Python 0.28.1](#tab/python)
 
 ```python
@@ -319,50 +365,6 @@ completion = openai.ChatCompletion.create(
 print(completion)
 ```
 
-# [OpenAI Python 1.x](#tab/python-new)
-
-```python
-import os
-import openai
-import dotenv
-
-dotenv.load_dotenv()
-
-endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT")
-api_key = os.environ.get("AZURE_OPENAI_API_KEY")
-deployment = os.environ.get("AZURE_OPEN_AI_DEPLOYMENT_ID")
-
-client = openai.AzureOpenAI(
-    base_url=f"{endpoint}/openai/deployments/{deployment}/extensions",
-    api_key=api_key,
-    api_version="2023-08-01-preview",
-)
-
-completion = client.chat.completions.create(
-    model=deployment,
-    messages=[
-        {
-            "role": "user",
-            "content": "How is Azure machine learning different than Azure OpenAI?",
-        },
-    ],
-    extra_body={
-        "dataSources": [
-            {
-                "type": "AzureCognitiveSearch",
-                "parameters": {
-                    "endpoint": os.environ["AZURE_AI_SEARCH_ENDPOINT"],
-                    "key": os.environ["AZURE_AI_SEARCH_API_KEY"],
-                    "indexName": os.environ["AZURE_AI_SEARCH_INDEX"]
-                }
-            }
-        ]
-    }
-)
-
-print(completion.model_dump_json(indent=2))
-```
-
 ---
 
 ## DALL-E fix