Merge pull request #5130 from MicrosoftDocs/main

Merged by Learn.Build PR Management system

Author: learn-build-service-prod[bot]

articles/ai-foundry/how-to/connections-add.md

@@ -53,7 +53,7 @@ Here's a table of some of the available connection types in Azure AI Foundry por
 |-------------------------------|:-------:|:--------------------------------------:|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | Azure AI Search               |         | ✓                                      | Azure AI Search is an Azure resource that supports information retrieval over your vector and textual data stored in search indexes.                                                   |
 | Azure Storage                 |         | ✓                                      | Azure Storage is a cloud storage solution for storing unstructured data like documents, images, videos, and application installers.                                                   |
-| Azure Cosmos DB                |         | ✓                                      | Azure Cosmos DB is a globally distributed, multi-model database service that offers low latency, high availability, and scalability across multiple geographical regions.                |
+| Azure Cosmos DB               | ✓       | ✓                                      | Azure Cosmos DB is a globally distributed, multi-model database service that offers low latency, high availability, and scalability across multiple geographical regions.                |
 | Azure OpenAI                  |         |                                        | Azure OpenAI is a service that provides access to OpenAI's models including the GPT-4o, GPT-4o mini, GPT-4, GPT-4 Turbo with Vision, GPT-3.5-Turbo, DALLE-3, and Embeddings model series with the security and enterprise capabilities of Azure. |
 | Application Insights          |         |                                        | Azure Application Insights is a service within Azure Monitor that enables developers and DevOps teams to automatically detect performance anomalies, diagnose issues, and gain deep insights into application usage and behavior through powerful telemetry and analytics tools. |
 | API key                       |         |                                        | API Key connections handle authentication to your specified target on an individual basis. |

articles/ai-foundry/how-to/data-add.md

@@ -9,7 +9,7 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: how-to
-ms.date: 02/11/2025
+ms.date: 05/21/2025
 ms.author: franksolomon
 author: fbsolo-ms1
 ---
@@ -29,12 +29,10 @@ Data can help when you need these capabilities:
 > - **Lineage:** For any given data, you can view which jobs or prompt flow pipelines consume the data.
 > - **Ease-of-use:** An Azure AI Foundry data resembles web browser bookmarks (favorites). Instead of remembering long storage paths that *reference* your frequently-used data on Azure Storage, you can create a data *version* and then access that version of the asset with a friendly name.
 
-## Prerequisites
 
-To create and work with data, you need:
+## Prerequisites
 
-- An Azure subscription. If you don't have one, create a [free account](https://azure.microsoft.com/free/).
-- An [Azure AI Foundry project](../how-to/create-projects.md).
+[!INCLUDE [hub-only-prereq](../includes/hub-only-prereq.md)]
 
 ## Create data
 

articles/ai-foundry/model-inference/includes/how-to-prerequisites-openai-python.md

@@ -0,0 +1,22 @@
+---
+manager: nitinme
+ms.service: azure-ai-model-inference
+ms.topic: include
+ms.date: 1/21/2025
+ms.author: fasantia
+author: santiagxf
+---
+
+* Install the SDK with the following command:
+
+    # [OpenAI API](#tab/openai)
+    
+    ```bash
+    pip install -U openai
+    ```
+    
+    # [Model Inference API (preview)](#tab/inference)
+    
+    ```bash
+    pip install -U azure-ai-inference
+    ```

articles/ai-foundry/model-inference/includes/use-chat-reasoning/python.md

@@ -19,7 +19,7 @@ To complete this tutorial, you need:
 
 [!INCLUDE [how-to-prerequisites](../how-to-prerequisites.md)]
 
-[!INCLUDE [how-to-prerequisites-python](../how-to-prerequisites-python.md)]
+[!INCLUDE [how-to-prerequisites-openai-python](../how-to-prerequisites-openai-python.md)]
 
 * A model with reasoning capabilities model deployment. If you don't have one read [Add and configure Foundry Models](../../how-to/create-model-deployments.md) to add a reasoning model. 
 
@@ -29,6 +29,21 @@ To complete this tutorial, you need:
 
 First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables.
 
+# [OpenAI API](#tab/openai)
+
+```python
+import os
+from openai import AzureOpenAI
+    
+client = AzureOpenAI(
+    azure_endpoint = "https://<resource>.services.ai.azure.com"
+    api_key=os.getenv("AZURE_INFERENCE_CREDENTIAL"),  
+    api_version="2024-10-21",
+)
+```
+
+# [Model Inference API (preview)](#tab/inference)
+
 ```python
 import os
 from azure.ai.inference import ChatCompletionsClient
@@ -40,12 +55,30 @@ client = ChatCompletionsClient(
     model="deepseek-r1"
 )
 ```
-
-> [!TIP]
-> Verify that you have deployed the model to Azure AI Services resource with The Azure AI Model Inference API. `Deepseek-R1` is also available as standard deployments. However, those endpoints don't take the parameter `model` as explained in this tutorial. You can verify that by going to [Azure AI Foundry portal]() > Models + endpoints, and verify that the model is listed under the section **Azure AI Services**.
+---
 
 If you have configured the resource to with **Microsoft Entra ID** support, you can use the following code snippet to create a client.
 
+# [OpenAI API](#tab/openai)
+
+```python
+import os
+from openai import AzureOpenAI
+from azure.identity import DefaultAzureCredential, get_bearer_token_provider
+
+token_provider = get_bearer_token_provider(
+    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
+)
+
+client = AzureOpenAI(
+    azure_endpoint = "https://<resource>.services.ai.azure.com"
+    azure_ad_token_provider=token_provider,
+    api_version="2024-10-21",
+)
+```
+
+# [Model Inference API (preview)](#tab/inference)
+
 ```python
 import os
 from azure.ai.inference import ChatCompletionsClient
@@ -58,13 +91,29 @@ client = ChatCompletionsClient(
     model="deepseek-r1"
 )
 ```
+---
 
 [!INCLUDE [best-practices](best-practices.md)]
 
 ### Create a chat completion request
 
 The following example shows how you can create a basic chat request to the model.
 
+# [OpenAI API](#tab/openai)
+
+```python
+response = client.chat.completions.create(
+    model="deepseek-r1",
+    messages=[
+        {"role": "user", "content": "How many languages are in the world?"}
+    ]
+)
+
+print(response.model_dump_json(indent=2)
+```
+
+# [Model Inference API (preview)](#tab/inference)
+
 ```python
 from azure.ai.inference.models import SystemMessage, UserMessage
 
@@ -74,9 +123,12 @@ response = client.complete(
     ],
 )
 ```
+---
 
 The response is as follows, where you can see the model's usage statistics:
 
+# [OpenAI API](#tab/openai)
+
 ```python
 print("Response:", response.choices[0].message.content)
 print("Model:", response.model)
@@ -87,47 +139,70 @@ print("\tCompletion tokens:", response.usage.completion_tokens)
 ```
 
 ```console
-Response: <think>Okay, the user is asking how many languages exist in the world. I need to provide a clear and accurate answer...</think>As of now, it's estimated that there are about 7,000 languages spoken around the world. However, this number can vary as some languages become extinct and new ones develop. It's also important to note that the number of speakers can greatly vary between languages, with some having millions of speakers and others only a few hundred.
+Response: As of now, it's estimated that there are about 7,000 languages spoken around the world. However, this number can vary as some languages become extinct and new ones develop. It's also important to note that the number of speakers can greatly vary between languages, with some having millions of speakers and others only a few hundred.
 Model: deepseek-r1
 Usage: 
   Prompt tokens: 11
   Total tokens: 897
   Completion tokens: 886
 ```
 
+# [Model Inference API (preview)](#tab/inference)
+
+```python
+print("Response:", response.choices[0].message.content)
+print("Model:", response.model)
+print("Usage:")
+print("\tPrompt tokens:", response.usage.prompt_tokens)
+print("\tTotal tokens:", response.usage.total_tokens)
+print("\tCompletion tokens:", response.usage.completion_tokens)
+```
+
+```console
+Response: <think>Okay, the user is asking how many languages exist in the world. I need to provide a clear and accurate answer...</think>As of now, it's estimated that there are about 7,000 languages spoken around the world. However, this number can vary as some languages become extinct and new ones develop. It's also important to note that the number of speakers can greatly vary between languages, with some having millions of speakers and others only a few hundred.
+Model: deepseek-r1
+Usage: 
+  Prompt tokens: 11
+  Total tokens: 897
+  Completion tokens: 886
+```
+---
 
 ### Reasoning content
 
-Some reasoning models, like DeepSeek-R1, generate completions and include the reasoning behind it. The reasoning associated with the completion is included in the response's content within the tags `<think>` and `</think>`. The model may select on which scenarios to generate reasoning content. You can extract the reasoning content from the response to understand the model's thought process as follows:
+Some reasoning models, like DeepSeek-R1, generate completions and include the reasoning behind it. 
+
+# [OpenAI API](#tab/openai)
+
+The reasoning associated with the completion is included in the field `reasoning_content`. The model may select on which scenearios to generate reasoning content. 
+
+```python
+print("Thinking:", response.choices[0].message.reasoning_content)
+```
+
+```console
+Thinking: Okay, the user is asking how many languages exist in the world. I need to provide a clear and accurate answer...
+```
+
+# [Model Inference API (preview)](#tab/inference)
+
+The reasoning associated with the completion is included in the response's content within the tags `<think>` and `</think>`. The model may select on which scenarios to generate reasoning content. You can extract the reasoning content from the response to understand the model's thought process as follows:
 
 ```python
 import re
 
 match = re.match(r"<think>(.*?)</think>(.*)", response.choices[0].message.content, re.DOTALL)
 
-print("Response:", )
 if match:
     print("\tThinking:", match.group(1))
-    print("\tAnswer:", match.group(2))
 else:
     print("\tAnswer:", response.choices[0].message.content)
-print("Model:", response.model)
-print("Usage:")
-print("\tPrompt tokens:", response.usage.prompt_tokens)
-print("\tTotal tokens:", response.usage.total_tokens)
-print("\tCompletion tokens:", response.usage.completion_tokens)
 ```
 
 ```console
-Thinking: Okay, the user is asking how many languages exist in the world. I need to provide a clear and accurate answer. Let's start by recalling the general consensus from linguistic sources. I remember that the number often cited is around 7,000, but maybe I should check some reputable organizations.\n\nEthnologue is a well-known resource for language data, and I think they list about 7,000 languages. But wait, do they update their numbers? It might be around 7,100 or so. Also, the exact count can vary because some sources might categorize dialects differently or have more recent data. \n\nAnother thing to consider is language endangerment. Many languages are endangered, with some having only a few speakers left. Organizations like UNESCO track endangered languages, so mentioning that adds context. Also, the distribution isn't even. Some countries have hundreds of languages, like Papua New Guinea with over 800, while others have just a few. \n\nA user might also wonder why the exact number is hard to pin down. It's because the distinction between a language and a dialect can be political or cultural. For example, Mandarin and Cantonese are considered dialects of Chinese by some, but they're mutually unintelligible, so others classify them as separate languages. Also, some regions are under-researched, making it hard to document all languages. \n\nI should also touch on language families. The 7,000 languages are grouped into families like Indo-European, Sino-Tibetan, Niger-Congo, etc. Maybe mention a few of the largest families. But wait, the question is just about the count, not the families. Still, it's good to provide a bit more context. \n\nI need to make sure the information is up-to-date. Let me think – recent estimates still hover around 7,000. However, languages are dying out rapidly, so the number decreases over time. Including that note about endangerment and language extinction rates could be helpful. For instance, it's often stated that a language dies every few weeks. \n\nAnother point is sign languages. Does the count include them? Ethnologue includes some, but not all sources might. If the user is including sign languages, that adds more to the count, but I think the 7,000 figure typically refers to spoken languages. For thoroughness, maybe mention that there are also over 300 sign languages. \n\nSummarizing, the answer should state around 7,000, mention Ethnologue's figure, explain why the exact number varies, touch on endangerment, and possibly note sign languages as a separate category. Also, a brief mention of Papua New Guinea as the most linguistically diverse country. \n\nWait, let me verify Ethnologue's current number. As of their latest edition (25th, 2022), they list 7,168 living languages. But I should check if that's the case. Some sources might round to 7,000. Also, SIL International publishes Ethnologue, so citing them as reference makes sense. \n\nOther sources, like Glottolog, might have a different count because they use different criteria. Glottolog might list around 7,000 as well, but exact numbers vary. It's important to highlight that the count isn't exact because of differing definitions and ongoing research. \n\nIn conclusion, the approximate number is 7,000, with Ethnologue being a key source, considerations of endangerment, and the challenges in counting due to dialect vs. language distinctions. I should make sure the answer is clear, acknowledges the variability, and provides key points succinctly.
-
-Answer: The exact number of languages in the world is challenging to determine due to differences in definitions (e.g., distinguishing languages from dialects) and ongoing documentation efforts. However, widely cited estimates suggest there are approximately **7,000 languages** globally.
-Model: DeepSeek-R1
-Usage: 
-  Prompt tokens: 11
-  Total tokens: 897
-  Completion tokens: 886
+Thinking: Okay, the user is asking how many languages exist in the world. I need to provide a clear and accurate answer. Let's start...
 ```
+---
 
 When making multi-turn conversations, it's useful to avoid sending the reasoning content in the chat history as reasoning tends to generate long explanations.
 
@@ -139,6 +214,19 @@ You can _stream_ the content to get it as it's being generated. Streaming conten
 
 To stream completions, set `stream=True` when you call the model.
 
+# [OpenAI API](#tab/openai)
+
+```python
+response = client.chat.completions.create(
+    model="deepseek-r1",
+    messages=[
+        {"role": "user", "content": "How many languages are in the world?"}
+    ],
+    stream=True
+)
+```
+
+# [Model Inference API (preview)](#tab/inference)
 
 ```python
 response = client.complete(
@@ -150,9 +238,38 @@ response = client.complete(
     stream=True,
 )
 ```
+---
 
 To visualize the output, define a helper function to print the stream. The following example implements a routing that stream only the answer without the reasoning content:
 
+# [OpenAI API](#tab/openai)
+
+Reasoning content is also included inside of the delta pieces of the response, in the key `reasoning_content`.
+
+```python
+def print_stream(completion):
+    """
+    Prints the chat completion with streaming.
+    """
+    is_thinking = False
+    for event in completion:
+        if event.choices:
+            content = event.choices[0].delta.content
+            reasoning_content = event.choices[0].delta.reasoning_content
+            if reasoning_content:
+                is_thinking = True
+                print("🧠 Thinking...", end="", flush=True)
+            elif content:
+                if is_thinking:
+                    is_thinking = False
+                    print("🛑\n\n")
+                print(content, end="", flush=True)
+```
+
+# [Model Inference API (preview)](#tab/inference)
+
+When streaming, pay closer attention to the `<think>` tag that may be included inside of the `content` field.
+
 ```python
 def print_stream(completion):
     """
@@ -171,6 +288,7 @@ def print_stream(completion):
             elif content:
                 print(content, end="", flush=True)
 ```
+---
 
 You can visualize how streaming generates content:
 
@@ -196,6 +314,31 @@ The Azure AI Model Inference API supports [Azure AI Content Safety](https://aka.
 
 The following example shows how to handle events when the model detects harmful content in the input prompt.
 
+# [OpenAI API](#tab/openai)
+
+```python
+try:
+    response = client.chat.completions.create(
+        model="deepseek-r1",
+        messages=[
+            {"role": "user", "content": "Chopping tomatoes and cutting them into cubes or wedges are great ways to practice your knife skills."}
+        ],
+    )
+
+    print(response.choices[0].message.content)
+
+except HttpResponseError as ex:
+    if ex.status_code == 400:
+        response = ex.response.json()
+        if isinstance(response, dict) and "error" in response:
+            print(f"Your request triggered an {response['error']['code']} error:\n\t {response['error']['message']}")
+        else:
+            raise
+    raise
+```
+
+# [Model Inference API (preview)](#tab/inference)
+
 ```python
 from azure.ai.inference.models import AssistantMessage, UserMessage
 
@@ -218,6 +361,7 @@ except HttpResponseError as ex:
             raise
     raise
 ```
+---
 
 > [!TIP]
 > To learn more about how you can configure and control Azure AI Content Safety settings, check the [Azure AI Content Safety documentation](https://aka.ms/azureaicontentsafety).