Merge pull request #257846 from MicrosoftDocs/main

11/7 11:00 AM IST Publishing

Author: Saisang

.openpublishing.redirection.json

@@ -783,6 +783,76 @@
     {
       "source_path": "articles/cognitive-services/bing-visual-search/quickstarts/ruby.md",
       "redirect_url": "/previous-versions/azure/cognitive-services/bing-visual-search/quickstarts/ruby",
+      "redirect_document_id": false,
+    },
+    {
+      "source_path": "articles/api-management/api-management-api-templates.md",
+      "redirect_url": "/previous-versions/azure/api-management/api-management-api-templates",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/api-management/api-management-application-templates.md",
+      "redirect_url": "/previous-versions/azure/api-management/api-management-application-templates",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/api-management/api-management-customize-styles.md",
+      "redirect_url": "/previous-versions/azure/api-management/api-management-customize-styles",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/api-management/api-management-developer-portal-templates-reference.md",
+      "redirect_url": "/previous-versions/azure/api-management/api-management-developer-portal-templates-reference",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/api-management/api-management-developer-portal-templates.md",
+      "redirect_url": "/previous-versions/azure/api-management/api-management-developer-portal-templates",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/api-management/api-management-issue-templates.md",
+      "redirect_url": "/previous-versions/azure/api-management/api-management-issue-templates",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/api-management/api-management-modify-content-layout.md",
+      "redirect_url": "/previous-versions/azure/api-management/api-management-modify-content-layout",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/api-management/api-management-page-controls.md",
+      "redirect_url": "/previous-versions/azure/api-management/api-management-page-controls",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/api-management/api-management-page-templates.md",
+      "redirect_url": "/previous-versions/azure/api-management/api-management-page-templates",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/api-management/api-management-product-templates.md",
+      "redirect_url": "/previous-versions/azure/api-management/api-management-product-templates",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/api-management/api-management-template-data-model-reference.md",
+      "redirect_url": "/previous-versions/azure/api-management/api-management-template-data-model-reference",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/api-management/api-management-template-resources.md",
+      "redirect_url": "/previous-versions/azure/api-management/api-management-template-resources",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/api-management/api-management-user-profile-templates.md",
+      "redirect_url": "/previous-versions/azure/api-management/api-management-user-profile-templates",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/api-management/developer-portal-deprecated-migration.md",
+      "redirect_url": "/previous-versions/azure/api-management/developer-portal-deprecated-migration",
       "redirect_document_id": false
     },
     {

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

@@ -6,7 +6,7 @@ author: mrbullwinkle
 ms.author: mbullwin
 ms.service: azure-ai-openai
 ms.topic: conceptual 
-ms.date: 09/15/2023
+ms.date: 11/06/2023
 ms.custom: template-concept
 manager: nitinme
 keywords: 
@@ -294,11 +294,10 @@ When annotations are enabled as shown in the code snippet below, the following i
 
 Annotations are currently in preview for Completions and Chat Completions (GPT models); the following code snippet shows how to use annotations in preview:
 
-# [Python](#tab/python)
+# [OpenAI Python 0.28.1](#tab/python)
 
 
 ```python
-# Note: The openai-python library support for Azure OpenAI is in preview.
 # os.getenv() for the endpoint and key assumes that you are using environment variables.
 
 import os
@@ -387,7 +386,6 @@ print(response)
 The following code snippet shows how to retrieve annotations when content was filtered:
 
 ```python
-# Note: The openai-python library support for Azure OpenAI is in preview.
 # os.getenv() for the endpoint and key assumes that you are using environment variables.
 
 import os
@@ -416,6 +414,29 @@ 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_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/how-to/function-calling.md

@@ -6,7 +6,7 @@ author: mrbullwinkle #dereklegenzoff
 ms.author: mbullwin #delegenz
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 07/20/2023
+ms.date: 11/06/2023
 manager: nitinme
 ---
 
@@ -29,8 +29,10 @@ To use function calling with the Chat Completions API, you need to include two n
 
 When functions are provided, by default the `function_call` will be set to `"auto"` and the model will decide whether or not a function should be called. Alternatively, you can set the `function_call` parameter to `{"name": "<insert-function-name>"}` to force the API to call a specific function or you can set the parameter to `"none"` to prevent the model from calling any functions.
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 ```python
-# Note: The openai-python library support for Azure OpenAI is in preview.
+
 import os
 import openai
 
@@ -69,7 +71,7 @@ functions= [
 ]  
 
 response = openai.ChatCompletion.create(
-    engine="gpt-35-turbo-0613",
+    engine="gpt-35-turbo-0613", # engine = "deployment_name"
     messages=messages,
     functions=functions,
     function_call="auto", 
@@ -92,6 +94,74 @@ The response from the API includes a `function_call` property if the model deter
 
 In some cases, the model may generate both `content` and a `function_call`. For example, for the prompt above the content could say something like "Sure, I can help you find some hotels in San Diego that match your criteria" along with the function_call.
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+import os
+from openai import AzureOpenAI
+
+client = AzureOpenAI(
+  api_key=os.getenv("AZURE_OPENAI_KEY"),  
+  api_version="2023-10-01-preview",
+  azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"
+)
+
+messages= [
+    {"role": "user", "content": "Find beachfront hotels in San Diego for less than $300 a month with free breakfast."}
+]
+
+functions= [  
+    {
+        "name": "search_hotels",
+        "description": "Retrieves hotels from the search index based on the parameters provided",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description": "The location of the hotel (i.e. Seattle, WA)"
+                },
+                "max_price": {
+                    "type": "number",
+                    "description": "The maximum price for the hotel"
+                },
+                "features": {
+                    "type": "string",
+                    "description": "A comma separated list of features (i.e. beachfront, free wifi, etc.)"
+                }
+            },
+            "required": ["location"]
+        }
+    }
+]  
+
+response = client.chat.completions.create(
+    model="gpt-35-turbo-0613", # model = "deployment_name"
+    messages= messages,
+    functions = functions,
+    function_call="auto",
+)
+
+print(response.choices[0].message.model_dump_json(indent=2))
+```
+
+The response from the API includes a `function_call` property if the model determines that a function should be called. The `function_call` property includes the name of the function to call and the arguments to pass to the function. The arguments are a JSON string that you can parse and use to call your function.
+
+```json
+{
+  "content": null,
+  "role": "assistant",
+  "function_call": {
+    "arguments": "{\n  \"location\": \"San Diego\",\n  \"max_price\": 300,\n  \"features\": \"beachfront, free breakfast\"\n}",
+    "name": "search_hotels"
+  }
+}
+```
+
+In some cases, the model may generate both `content` and a `function_call`. For example, for the prompt above the content could say something like "Sure, I can help you find some hotels in San Diego that match your criteria" along with the function_call.
+
+---
+
 ## Working with function calling
 
 The following section goes into additional detail on how to effectively use functions with the Chat Completions API.
@@ -107,6 +177,7 @@ If you want to describe a function that doesn't accept any parameters, use `{"ty
 ### Managing the flow with functions
 
 ```python
+
 response = openai.ChatCompletion.create(
     deployment_id="gpt-35-turbo-0613",
     messages=messages,

articles/ai-services/openai/tutorials/embeddings.md

@@ -6,7 +6,7 @@ services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: tutorial
-ms.date: 09/12/2023
+ms.date: 11/06/2023
 author: mrbullwinkle #noabenefraim
 ms.author: mbullwin
 recommendations: false
@@ -46,10 +46,20 @@ In this tutorial, you learn how to:
 
 If you haven't already, you need to install the following libraries:
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 ```cmd
 pip install "openai==0.28.1" num2words matplotlib plotly scipy scikit-learn pandas tiktoken
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```console
+pip install openai num2words matplotlib plotly scipy scikit-learn pandas tiktoken
+```
+
+---
+
 <!--Alternatively, you can use our [requirements.txt file](https://github.com/Azure-Samples/Azure-OpenAI-Docs-Samples/blob/main/Samples/Tutorials/Embeddings/requirements.txt).-->
 
 ### Download the BillSum dataset
@@ -105,7 +115,9 @@ Run the following code in your preferred Python IDE:
 
 <!--If you wish to view the Jupyter notebook that corresponds to this tutorial you can download the tutorial from our [samples repo](https://github.com/Azure-Samples/Azure-OpenAI-Docs-Samples/blob/main/Samples/Tutorials/Embeddings/embedding_billsum.ipynb).-->
 
-## Import libraries and list models
+## Import libraries
+
+# [OpenAI Python 0.28.1](#tab/python)
 
 ```python
 import openai
@@ -193,6 +205,23 @@ print(r.text)
 
 The output of this command will vary based on the number and type of models you've deployed. In this case, we need to confirm that we have an entry for **text-embedding-ada-002**. If you find that you're missing this model, you'll need to [deploy the model](../how-to/create-resource.md#deploy-a-model) to your resource before proceeding.
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+import os
+import re
+import requests
+import sys
+from num2words import num2words
+import os
+import pandas as pd
+import numpy as np
+import tiktoken
+from openai import AzureOpenAI
+```
+
+---
+
 Now we need to read our csv file and create a pandas DataFrame. After the initial DataFrame is created, we can view the contents of the table by running `df`.
 
 ```python
@@ -334,10 +363,29 @@ len(decode)
 
 Now that we understand more about how tokenization works we can move on to embedding. It is important to note, that we haven't actually tokenized the documents yet. The `n_tokens` column is simply a way of making sure none of the data we pass to the model for tokenization and embedding exceeds the input token limit of 8,192. When we pass the documents to the embeddings model, it will break the documents into tokens similar (though not necessarily identical) to the examples above and then convert the tokens to a series of floating point numbers that will be accessible via vector search. These embeddings can be stored locally or in an [Azure Database to support Vector Search](../../../cosmos-db/mongodb/vcore/vector-search.md). As a result, each bill will have its own corresponding embedding vector in the new `ada_v2` column on the right side of the DataFrame.
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 ```python
 df_bills['ada_v2'] = df_bills["text"].apply(lambda x : get_embedding(x, engine = 'text-embedding-ada-002')) # engine should be set to the deployment name you chose when you deployed the text-embedding-ada-002 (Version 2) model
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+client = AzureOpenAI(
+  api_key = os.getenv("AZURE_OPENAI_API_KEY"),  
+  api_version = "2023-05-15",
+  azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
+)
+
+def generate_embeddings(text, model="text-embedding-ada-002"): # model = "deployment_name"
+    return client.embeddings.create(input = [text], model=model).data[0].embedding
+
+df_bills['ada_v2'] = df_bills["text"].apply(lambda x : generate_embeddings (x, model = 'text-embedding-ada-002')) # model should be set to the deployment name you chose when you deployed the text-embedding-ada-002 (Version 2) model
+```
+
+---
+
 ```python
 df_bills
 ```
@@ -348,6 +396,8 @@ df_bills
 
 As we run the search code block below, we'll embed the search query *"Can I get information on cable company tax revenue?"* with the same **text-embedding-ada-002 (Version 2)** model. Next we'll find the closest bill embedding to the newly embedded text from our query ranked by [cosine similarity](../concepts/understand-embeddings.md).
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 ```python
 # search through the reviews for a specific product
 def search_docs(df, user_query, top_n=3, to_print=True):
@@ -369,6 +419,36 @@ def search_docs(df, user_query, top_n=3, to_print=True):
 res = search_docs(df_bills, "Can I get information on cable company tax revenue?", top_n=4)
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+def cosine_similarity(a, b):
+    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
+
+def get_embedding(text, model="text-embedding-ada-002"): # model = "deployment_name"
+    return client.embeddings.create(input = [text], model=model).data[0].embedding
+
+def search_docs(df, user_query, top_n=4, to_print=True):
+    embedding = get_embedding(
+        user_query,
+        model="text-embedding-ada-002" # model should be set to the deployment name you chose when you deployed the text-embedding-ada-002 (Version 2) model
+    )
+    df["similarities"] = df.ada_v2.apply(lambda x: cosine_similarity(x, embedding))
+
+    res = (
+        df.sort_values("similarities", ascending=False)
+        .head(top_n)
+    )
+    if to_print:
+        display(res)
+    return res
+
+
+res = search_docs(df_bills, "Can I get information on cable company tax revenue?", top_n=4)
+```
+
+---
+
 **Output**:
 
 :::image type="content" source="../media/tutorials/query-result.png" alt-text="Screenshot of the formatted results of res once the search query has been run." lightbox="../media/tutorials/query-result.png":::

articles/ai-services/security-controls-policy.md

@@ -1,7 +1,7 @@
 ---
 title: Azure Policy Regulatory Compliance controls for Azure AI services
 description: Lists Azure Policy Regulatory Compliance controls available for Azure AI services. These built-in policy definitions provide common approaches to managing the compliance of your Azure resources.
-ms.date: 10/23/2023
+ms.date: 11/06/2023
 ms.topic: sample
 author: PatrickFarley
 ms.author: pafarley