@@ -6,7 +6,7 @@ author: jaep3347
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 11/19/2024
+ms.date: 01/15/2025
 ms.author: lajanuar
 monikerRange: '>=doc-intel-3.1.0'
 ---
@@ -64,14 +64,14 @@ Document Intelligence supports more sophisticated and modular analysis capabilit
 
 |Add-on Capability| Add-On/Free|**2024-11-30 (GA)**|[`2023-07-31` (GA)](/rest/api/aiservices/document-models/analyze-document?view=rest-aiservices-2023-07-31&preserve-view=true&tabs=HTTP)|[`2022-08-31` (GA)](/rest/api/aiservices/document-models/analyze-document?view=rest-aiservices-v3.0%20(2022-08-31)&preserve-view=true&tabs=HTTP)|[v2.1 (GA)](/rest/api/aiservices/analyzer?view=rest-aiservices-v2.1&preserve-view=true)|
 |----------------|-----------|---|--|---|---|
-|Font property extraction|Add-On| ✔️| ✔️| n/a| n/a|
-|Formula extraction|Add-On| ✔️| ✔️| n/a| n/a|
-|High resolution extraction|Add-On| ✔️| ✔️| n/a| n/a|
 |Barcode extraction|Free| ✔️| ✔️| n/a| n/a|
 |Language detection|Free| ✔️| ✔️| n/a| n/a|
 |Key value pairs|Free| ✔️|n/a|n/a| n/a|
-|Query fields|Add-On*| ✔️|n/a|n/a| n/a|
-|Searhable pdf|Add-On**| ✔️|n/a|n/a| n/a|
+|Searchable PDF|Free| ✔️|n/a|n/a| n/a|
+|Font property extraction|**Add-On**| ✔️| ✔️| n/a| n/a|
+|Formula extraction|**Add-On**| ✔️| ✔️| n/a| n/a|
+|High resolution extraction|**Add-On**| ✔️| ✔️| n/a| n/a|
+|Query fields|**Add-On**| ✔️|n/a|n/a| n/a|
 
 ✱ Add-On - Query fields are priced differently than the other add-on features. See [pricing](https://azure.microsoft.com/pricing/details/ai-document-intelligence/) for details. </br>
 ** Add-On - Searchable pdf is available only with Read model as an add-on feature. 

@@ -113,7 +113,7 @@ client = ChatCompletionsClient(
 ```
 
 > [!NOTE]
-> Currently, serverless API endpoints do not support using Microsoft Entra ID for authentication.
+> Currently, serverless API endpoints don't support using Microsoft Entra ID for authentication.
 
 ### Get the model's capabilities
 
@@ -227,7 +227,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormat
+from azure.ai.inference.models import ChatCompletionsResponseFormatText
 
 response = client.complete(
     messages=[
@@ -240,12 +240,12 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format={ "type": ChatCompletionsResponseFormat.TEXT },
+    response_format={ "type": ChatCompletionsResponseFormatText() },
 )
 ```
 
 > [!WARNING]
-> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -272,10 +272,10 @@ The following extra parameters can be passed to Phi-3.5 chat model with vision:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `bool` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `int` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
 
 
 ### Apply content safety
@@ -479,7 +479,7 @@ const client = new ModelClient(
 ```
 
 > [!NOTE]
-> Currently, serverless API endpoints do not support using Microsoft Entra ID for authentication.
+> Currently, serverless API endpoints don't support using Microsoft Entra ID for authentication.
 
 ### Get the model's capabilities
 
@@ -625,7 +625,7 @@ var response = await client.path("/chat/completions").post({
 ```
 
 > [!WARNING]
-> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -657,10 +657,10 @@ The following extra parameters can be passed to Phi-3.5 chat model with vision:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `bool` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `int` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
 
 
 ### Apply content safety
@@ -891,7 +891,7 @@ client = new ChatCompletionsClient(
 ```
 
 > [!NOTE]
-> Currently, serverless API endpoints do not support using Microsoft Entra ID for authentication.
+> Currently, serverless API endpoints don't support using Microsoft Entra ID for authentication.
 
 ### Get the model's capabilities
 
@@ -1037,7 +1037,7 @@ Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}");
 ```
 
 > [!WARNING]
-> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -1066,10 +1066,10 @@ The following extra parameters can be passed to Phi-3.5 chat model with vision:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `bool` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `int` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
 
 
 ### Apply content safety
@@ -1239,7 +1239,7 @@ First, create the client to consume the model. The following code uses an endpoi
 When you deploy the model to a self-hosted online endpoint with **Microsoft Entra ID** support, you can use the following code snippet to create a client.
 
 > [!NOTE]
-> Currently, serverless API endpoints do not support using Microsoft Entra ID for authentication.
+> Currently, serverless API endpoints don't support using Microsoft Entra ID for authentication.
 
 ### Get the model's capabilities
 
@@ -1446,7 +1446,7 @@ Explore other parameters that you can specify in the inference client. For a ful
 ```
 
 > [!WARNING]
-> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -1485,10 +1485,10 @@ The following extra parameters can be passed to Phi-3.5 chat model with vision:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `bool` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `int` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
 
 
 ### Apply content safety
@@ -1542,7 +1542,7 @@ Phi-3.5-vision-Instruct can reason across text and images and generate text comp
 To see this capability, download an image and encode the information as `base64` string. The resulting data should be inside of a [data URL](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/Data_URLs):
 
 > [!TIP]
-> You will need to construct the data URL using an scripting or programming language. This tutorial use [this sample image](../media/how-to/sdks/small-language-models-chart-example.jpg) in JPEG format. A data URL has a format as follows: `data:image/jpg;base64,0xABCDFGHIJKLMNOPQRSTUVWXYZ...`.
+> You need to construct the data URL using a scripting or programming language. This article uses [this sample image](../media/how-to/sdks/small-language-models-chart-example.jpg) in JPEG format. A data URL has a format as follows: `data:image/jpg;base64,0xABCDFGHIJKLMNOPQRSTUVWXYZ...`.
 
 Visualize the image:
 
@@ -1613,14 +1613,11 @@ For more examples of how to use Phi-3 family models, see the following examples
 
 | Description                               | Language          | Sample                                                          |
 |-------------------------------------------|-------------------|-----------------------------------------------------------------|
-| CURL request                              | Bash              | [Link](https://aka.ms/phi-3/webrequests-sample)         |
-| Azure AI Inference package for C#         | C#                | [Link](https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Inference/samples)   |  
-| Azure AI Inference package for JavaScript | JavaScript        | [Link](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-inference-rest/samples)  |
+| Azure AI Inference package for C#         | C#                | [Link](https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Inference/samples)  |
+| Azure AI Inference package for JavaScript | JavaScript        | [Link](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-inference-rest/samples) |
 | Azure AI Inference package for Python     | Python            | [Link](https://aka.ms/azsdk/azure-ai-inference/python/samples)  |
-| Python web requests                       | Python            | [Link](https://aka.ms/phi-3/webrequests-sample)         |
-| OpenAI SDK (experimental)                 | Python            | [Link](https://aka.ms/phi-3/openaisdk)                  |
-| LangChain                                 | Python            | [Link](https://aka.ms/phi-3/langchain-sample)           |
-| LiteLLM                                   | Python            | [Link](https://aka.ms/phi-3/litellm-sample)             | 
+| LangChain                                 | Python            | [Link](https://aka.ms/azureai/langchain)           |
+| Llama-Index                               | Python            | [Link](https://aka.ms/azureai/llamaindex)             | 
 
 
 ## Cost and quota considerations for Phi-3 family models deployed as serverless API endpoints
@@ -1631,7 +1628,7 @@ Quota is managed per deployment. Each deployment has a rate limit of 200,000 tok
 
 Phi-3 family models deployed to managed compute are billed based on core hours of the associated compute instance. The cost of the compute instance is determined by the size of the instance, the number of instances running, and the run duration.
 
-It is a good practice to start with a low number of instances and scale up as needed. You can monitor the cost of the compute instance in the Azure portal.
+It's a good practice to start with a low number of instances and scale up as needed. You can monitor the cost of the compute instance in the Azure portal.
 
 ## Related content
 

@@ -233,7 +233,7 @@ response = client.complete(
 ```
 
 > [!WARNING]
-> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -260,10 +260,10 @@ The following extra parameters can be passed to Phi-3 chat model with vision:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `bool` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `int` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
 
 
 ## Use chat completions with images
@@ -565,7 +565,7 @@ var response = await client.path("/chat/completions").post({
 ```
 
 > [!WARNING]
-> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -597,10 +597,10 @@ The following extra parameters can be passed to Phi-3 chat model with vision:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `bool` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `int` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
 
 
 ## Use chat completions with images
@@ -923,7 +923,7 @@ Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}");
 ```
 
 > [!WARNING]
-> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -952,10 +952,10 @@ The following extra parameters can be passed to Phi-3 chat model with vision:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `bool` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `int` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
 
 
 ## Use chat completions with images
@@ -1278,7 +1278,7 @@ Explore other parameters that you can specify in the inference client. For a ful
 ```
 
 > [!WARNING]
-> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -1317,10 +1317,10 @@ The following extra parameters can be passed to Phi-3 chat model with vision:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `object` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `bool` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `int` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
 
 
 ## Use chat completions with images
@@ -1333,7 +1333,7 @@ Phi-3-vision-128k-Instruct can reason across text and images and generate text c
 To see this capability, download an image and encode the information as `base64` string. The resulting data should be inside of a [data URL](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/Data_URLs):
 
 > [!TIP]
-> You will need to construct the data URL using an scripting or programming language. This tutorial use [this sample image](../media/how-to/sdks/small-language-models-chart-example.jpg) in JPEG format. A data URL has a format as follows: `data:image/jpg;base64,0xABCDFGHIJKLMNOPQRSTUVWXYZ...`.
+> You need to construct the data URL using a scripting or programming language. This article uses [this sample image](../media/how-to/sdks/small-language-models-chart-example.jpg) in JPEG format. A data URL has a format as follows: `data:image/jpg;base64,0xABCDFGHIJKLMNOPQRSTUVWXYZ...`.
 
 Visualize the image:
 
@@ -1404,21 +1404,18 @@ For more examples of how to use Phi-3 family models, see the following examples
 
 | Description                               | Language          | Sample                                                          |
 |-------------------------------------------|-------------------|-----------------------------------------------------------------|
-| CURL request                              | Bash              | [Link](https://aka.ms/phi-3/webrequests-sample)         |
-| Azure AI Inference package for C#         | C#                | [Link](https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Inference/samples)   |  
-| Azure AI Inference package for JavaScript | JavaScript        | [Link](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-inference-rest/samples)  |
+| Azure AI Inference package for C#         | C#                | [Link](https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Inference/samples)                  |
+| Azure AI Inference package for JavaScript | JavaScript        | [Link](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-inference-rest/samples) |
 | Azure AI Inference package for Python     | Python            | [Link](https://aka.ms/azsdk/azure-ai-inference/python/samples)  |
-| Python web requests                       | Python            | [Link](https://aka.ms/phi-3/webrequests-sample)         |
-| OpenAI SDK (experimental)                 | Python            | [Link](https://aka.ms/phi-3/openaisdk)                  |
-| LangChain                                 | Python            | [Link](https://aka.ms/phi-3/langchain-sample)           |
-| LiteLLM                                   | Python            | [Link](https://aka.ms/phi-3/litellm-sample)             | 
+| LangChain                                 | Python            | [Link](https://aka.ms/azureai/langchain)           |
+| Llama-Index                               | Python            | [Link](https://aka.ms/azureai/llamaindex)             |  
 
 
 ## Cost and quota considerations for Phi-3 family models deployed to managed compute
 
 Phi-3 family models deployed to managed compute are billed based on core hours of the associated compute instance. The cost of the compute instance is determined by the size of the instance, the number of instances running, and the run duration.
 
-It is a good practice to start with a low number of instances and scale up as needed. You can monitor the cost of the compute instance in the Azure portal.
+It's a good practice to start with a low number of instances and scale up as needed. You can monitor the cost of the compute instance in the Azure portal.
 
 ## Related content
 

@@ -35,7 +35,7 @@ Phi-3.5 models are lightweight, state-of-the-art open models. These models were
 
 Phi-3.5 Mini uses 3.8B parameters, and is a dense decoder-only transformer model using the same tokenizer as Phi-3 Mini.
 
-Phi-3.5 MoE (mixture-of-expert) uses 16x3.8B parameters with 6.6B active parameters when using 2 experts. The model is a mixture-of-expert decoder-only transformer model, using a tokenizer with vocabulary size of 32,064.
+Phi-3.5 MoE (mixture-of-expert) uses 16x3.8B parameters with 6.6B active parameters when using two experts. The model is a mixture-of-expert decoder-only transformer model, using a tokenizer with vocabulary size of 32,064.
 
 The models underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures. When assessed against benchmarks that test common sense, language understanding, math, code, long context and logical reasoning, Phi-3.5 models showcased robust and state-of-the-art performance among models with less than 13 billion parameters.
 
@@ -147,7 +147,7 @@ client = ChatCompletionsClient(
 ```
 
 > [!NOTE]
-> Currently, serverless API endpoints do not support using Microsoft Entra ID for authentication.
+> Currently, serverless API endpoints don't support using Microsoft Entra ID for authentication.
 
 ### Get the model's capabilities
 
@@ -189,7 +189,7 @@ response = client.complete(
 ```
 
 > [!NOTE]
-> Phi-3.5-Mini-Instruct, Phi-3.5-MoE-Instruct, Phi-3-mini-4k-Instruct, Phi-3-mini-128k-Instruct, Phi-3-small-8k-Instruct, Phi-3-small-128k-Instruct and Phi-3-medium-128k-Instruct don't support system messages (`role="system"`). When you use the Azure AI model inference API, system messages are translated to user messages, which is the closest capability available. This translation is offered for convenience, but it's important for you to verify that the model is following the instructions in the system message with the right level of confidence.
+> Phi-3.5-Mini-Instruct, Phi-3.5-MoE-Instruct, Phi-3-mini-4k-Instruct, Phi-3-mini-128k-Instruct, Phi-3-small-8k-Instruct, Phi-3-small-128k-Instruct, and Phi-3-medium-128k-Instruct don't support system messages (`role="system"`). When you use the Azure AI model inference API, system messages are translated to user messages, which is the closest capability available. This translation is offered for convenience, but it's important for you to verify that the model is following the instructions in the system message with the right level of confidence.
 
 The response is as follows, where you can see the model's usage statistics:
 
@@ -261,7 +261,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormat
+from azure.ai.inference.models import ChatCompletionsResponseFormatText
 
 response = client.complete(
     messages=[
@@ -279,7 +279,7 @@ response = client.complete(
 ```
 
 > [!WARNING]
-> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -306,10 +306,10 @@ The following extra parameters can be passed to Phi-3 family chat models:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `int` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `float` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
 
 
 ### Apply content safety
@@ -363,7 +363,7 @@ Phi-3.5 models are lightweight, state-of-the-art open models. These models were
 
 Phi-3.5 Mini uses 3.8B parameters, and is a dense decoder-only transformer model using the same tokenizer as Phi-3 Mini.
 
-Phi-3.5 MoE (mixture-of-expert) uses 16x3.8B parameters with 6.6B active parameters when using 2 experts. The model is a mixture-of-expert decoder-only transformer model, using a tokenizer with vocabulary size of 32,064.
+Phi-3.5 MoE (mixture-of-expert) uses 16x3.8B parameters with 6.6B active parameters when using two experts. The model is a mixture-of-expert decoder-only transformer model, using a tokenizer with vocabulary size of 32,064.
 
 The models underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures. When assessed against benchmarks that test common sense, language understanding, math, code, long context and logical reasoning, Phi-3.5 models showcased robust and state-of-the-art performance among models with less than 13 billion parameters.
 
@@ -473,7 +473,7 @@ const client = new ModelClient(
 ```
 
 > [!NOTE]
-> Currently, serverless API endpoints do not support using Microsoft Entra ID for authentication.
+> Currently, serverless API endpoints don't support using Microsoft Entra ID for authentication.
 
 ### Get the model's capabilities
 
@@ -517,7 +517,7 @@ var response = await client.path("/chat/completions").post({
 ```
 
 > [!NOTE]
-> Phi-3.5-Mini-Instruct, Phi-3.5-MoE-Instruct, Phi-3-mini-4k-Instruct, Phi-3-mini-128k-Instruct, Phi-3-small-8k-Instruct, Phi-3-small-128k-Instruct and Phi-3-medium-128k-Instruct don't support system messages (`role="system"`). When you use the Azure AI model inference API, system messages are translated to user messages, which is the closest capability available. This translation is offered for convenience, but it's important for you to verify that the model is following the instructions in the system message with the right level of confidence.
+> Phi-3.5-Mini-Instruct, Phi-3.5-MoE-Instruct, Phi-3-mini-4k-Instruct, Phi-3-mini-128k-Instruct, Phi-3-small-8k-Instruct, Phi-3-small-128k-Instruct, and Phi-3-medium-128k-Instruct don't support system messages (`role="system"`). When you use the Azure AI model inference API, system messages are translated to user messages, which is the closest capability available. This translation is offered for convenience, but it's important for you to verify that the model is following the instructions in the system message with the right level of confidence.
 
 The response is as follows, where you can see the model's usage statistics:
 
@@ -619,7 +619,7 @@ var response = await client.path("/chat/completions").post({
 ```
 
 > [!WARNING]
-> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -651,10 +651,10 @@ The following extra parameters can be passed to Phi-3 family chat models:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `int` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `float` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
 
 
 ### Apply content safety
@@ -714,7 +714,7 @@ Phi-3.5 models are lightweight, state-of-the-art open models. These models were
 
 Phi-3.5 Mini uses 3.8B parameters, and is a dense decoder-only transformer model using the same tokenizer as Phi-3 Mini.
 
-Phi-3.5 MoE (mixture-of-expert) uses 16x3.8B parameters with 6.6B active parameters when using 2 experts. The model is a mixture-of-expert decoder-only transformer model, using a tokenizer with vocabulary size of 32,064.
+Phi-3.5 MoE (mixture-of-expert) uses 16x3.8B parameters with 6.6B active parameters when using two experts. The model is a mixture-of-expert decoder-only transformer model, using a tokenizer with vocabulary size of 32,064.
 
 The models underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures. When assessed against benchmarks that test common sense, language understanding, math, code, long context and logical reasoning, Phi-3.5 models showcased robust and state-of-the-art performance among models with less than 13 billion parameters.
 
@@ -839,7 +839,7 @@ client = new ChatCompletionsClient(
 ```
 
 > [!NOTE]
-> Currently, serverless API endpoints do not support using Microsoft Entra ID for authentication.
+> Currently, serverless API endpoints don't support using Microsoft Entra ID for authentication.
 
 ### Get the model's capabilities
 
@@ -882,7 +882,7 @@ Response<ChatCompletions> response = client.Complete(requestOptions);
 ```
 
 > [!NOTE]
-> Phi-3.5-Mini-Instruct, Phi-3.5-MoE-Instruct, Phi-3-mini-4k-Instruct, Phi-3-mini-128k-Instruct, Phi-3-small-8k-Instruct, Phi-3-small-128k-Instruct and Phi-3-medium-128k-Instruct don't support system messages (`role="system"`). When you use the Azure AI model inference API, system messages are translated to user messages, which is the closest capability available. This translation is offered for convenience, but it's important for you to verify that the model is following the instructions in the system message with the right level of confidence.
+> Phi-3.5-Mini-Instruct, Phi-3.5-MoE-Instruct, Phi-3-mini-4k-Instruct, Phi-3-mini-128k-Instruct, Phi-3-small-8k-Instruct, Phi-3-small-128k-Instruct, and Phi-3-medium-128k-Instruct don't support system messages (`role="system"`). When you use the Azure AI model inference API, system messages are translated to user messages, which is the closest capability available. This translation is offered for convenience, but it's important for you to verify that the model is following the instructions in the system message with the right level of confidence.
 
 The response is as follows, where you can see the model's usage statistics:
 
@@ -985,7 +985,7 @@ Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}");
 ```
 
 > [!WARNING]
-> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -1014,10 +1014,10 @@ The following extra parameters can be passed to Phi-3 family chat models:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `int` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `float` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
 
 
 ### Apply content safety
@@ -1077,7 +1077,7 @@ Phi-3.5 models are lightweight, state-of-the-art open models. These models were
 
 Phi-3.5 Mini uses 3.8B parameters, and is a dense decoder-only transformer model using the same tokenizer as Phi-3 Mini.
 
-Phi-3.5 MoE (mixture-of-expert) uses 16x3.8B parameters with 6.6B active parameters when using 2 experts. The model is a mixture-of-expert decoder-only transformer model, using a tokenizer with vocabulary size of 32,064.
+Phi-3.5 MoE (mixture-of-expert) uses 16x3.8B parameters with 6.6B active parameters when using two experts. The model is a mixture-of-expert decoder-only transformer model, using a tokenizer with vocabulary size of 32,064.
 
 The models underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures. When assessed against benchmarks that test common sense, language understanding, math, code, long context and logical reasoning, Phi-3.5 models showcased robust and state-of-the-art performance among models with less than 13 billion parameters.
 
@@ -1156,7 +1156,7 @@ First, create the client to consume the model. The following code uses an endpoi
 When you deploy the model to a self-hosted online endpoint with **Microsoft Entra ID** support, you can use the following code snippet to create a client.
 
 > [!NOTE]
-> Currently, serverless API endpoints do not support using Microsoft Entra ID for authentication.
+> Currently, serverless API endpoints don't support using Microsoft Entra ID for authentication.
 
 ### Get the model's capabilities
 
@@ -1200,7 +1200,7 @@ The following example shows how you can create a basic chat completions request
 ```
 
 > [!NOTE]
-> Phi-3.5-Mini-Instruct, Phi-3.5-MoE-Instruct, Phi-3-mini-4k-Instruct, Phi-3-mini-128k-Instruct, Phi-3-small-8k-Instruct, Phi-3-small-128k-Instruct and Phi-3-medium-128k-Instruct don't support system messages (`role="system"`). When you use the Azure AI model inference API, system messages are translated to user messages, which is the closest capability available. This translation is offered for convenience, but it's important for you to verify that the model is following the instructions in the system message with the right level of confidence.
+> Phi-3.5-Mini-Instruct, Phi-3.5-MoE-Instruct, Phi-3-mini-4k-Instruct, Phi-3-mini-128k-Instruct, Phi-3-small-8k-Instruct, Phi-3-small-128k-Instruct, and Phi-3-medium-128k-Instruct don't support system messages (`role="system"`). When you use the Azure AI model inference API, system messages are translated to user messages, which is the closest capability available. This translation is offered for convenience, but it's important for you to verify that the model is following the instructions in the system message with the right level of confidence.
 
 The response is as follows, where you can see the model's usage statistics:
 
@@ -1363,7 +1363,7 @@ Explore other parameters that you can specify in the inference client. For a ful
 ```
 
 > [!WARNING]
-> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-3 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -1402,10 +1402,10 @@ The following extra parameters can be passed to Phi-3 family chat models:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `int` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `float` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
 
 
 ### Apply content safety
@@ -1457,14 +1457,11 @@ For more examples of how to use Phi-3 family models, see the following examples
 
 | Description                               | Language          | Sample                                                          |
 |-------------------------------------------|-------------------|-----------------------------------------------------------------|
-| CURL request                              | Bash              | [Link](https://aka.ms/phi-3/webrequests-sample)         |
-| Azure AI Inference package for C#         | C#                | [Link](https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Inference/samples)   |  
-| Azure AI Inference package for JavaScript | JavaScript        | [Link](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-inference-rest/samples)  |
+| Azure AI Inference package for C#         | C#                | [Link](https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Inference/samples)                  |
+| Azure AI Inference package for JavaScript | JavaScript        | [Link](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-inference-rest/samples) |
 | Azure AI Inference package for Python     | Python            | [Link](https://aka.ms/azsdk/azure-ai-inference/python/samples)  |
-| Python web requests                       | Python            | [Link](https://aka.ms/phi-3/webrequests-sample)         |
-| OpenAI SDK (experimental)                 | Python            | [Link](https://aka.ms/phi-3/openaisdk)                  |
-| LangChain                                 | Python            | [Link](https://aka.ms/phi-3/langchain-sample)           |
-| LiteLLM                                   | Python            | [Link](https://aka.ms/phi-3/litellm-sample)             | 
+| LangChain                                 | Python            | [Link](https://aka.ms/azureai/langchain)           |
+| Llama-Index                               | Python            | [Link](https://aka.ms/azureai/llamaindex)             |  
 
 
 ## Cost and quota considerations for Phi-3 family models deployed as serverless API endpoints
@@ -1475,11 +1472,11 @@ Quota is managed per deployment. Each deployment has a rate limit of 200,000 tok
 
 Phi-3 family models deployed to managed compute are billed based on core hours of the associated compute instance. The cost of the compute instance is determined by the size of the instance, the number of instances running, and the run duration.
 
-It is a good practice to start with a low number of instances and scale up as needed. You can monitor the cost of the compute instance in the Azure portal.
+It's a good practice to start with a low number of instances and scale up as needed. You can monitor the cost of the compute instance in the Azure portal.
 
 ## Sample notebook
 
-You can use this [sample notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/finetuning/standalone/chat-completion/chat_completion_with_model_as_service.ipynb)  to create a standalone fine-tuning job to enhance a model's ability to summarize dialogues between two people using the Samsum dataset. The training data utilized is the ultrachat_200k dataset, which is divided into four splits suitable for supervised fine-tuning (sft) and generation ranking (gen). The notebook employs the available Azure AI models for the chat-completion task (If you would like to use a different model than what's used in the notebook, you can replace the model name). The notebook includes setting up prerequisites, selecting a model to fine-tune, creating training and validation datasets, configuring and submitting the fine-tuning job, and finally, creating a serverless deployment using the fine-tuned model for sample inference.
+You can use this [sample notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/finetuning/standalone/chat-completion/chat_completion_with_model_as_service.ipynb)  to create a standalone fine-tuning job to enhance a model's ability to summarize dialogues between two people using the `Samsum` dataset. The training data utilized is the `ultrachat_200k` dataset, which is divided into four splits suitable for supervised fine-tuning (sft) and generation ranking (gen). The notebook employs the available Azure AI models for the chat-completion task (If you would like to use a different model than what's used in the notebook, you can replace the model name). The notebook includes setting up prerequisites, selecting a model to fine-tune, creating training and validation datasets, configuring and submitting the fine-tuning job, and finally, creating a serverless deployment using the fine-tuned model for sample inference.
 
 ## Related content
 

@@ -5,9 +5,9 @@ description: Learn how to use Phi-4 family chat models with Azure AI Foundry.
 ms.service: azure-ai-studio
 manager: scottpolly
 ms.topic: how-to
-ms.date: 12/12/2024
-ms.reviewer: fasantia
-reviewer: santiagxf
+ms.date: 01/09/2025
+ms.reviewer: v-vkonjarla
+reviewer: VindyaKonjarla
 ms.author: mopeakande
 author: msakande
 ms.custom: references_regions, generated
@@ -45,6 +45,15 @@ To use Phi-4 family chat models with Azure AI Foundry, you need the following pr
 
 ### A model deployment
 
+**Deployment to serverless APIs**
+
+Phi-4 family chat models can be deployed to serverless API endpoints with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. 
+
+Deployment to a serverless API endpoint doesn't require quota from your subscription. If your model isn't deployed already, use the Azure AI Foundry portal, Azure Machine Learning SDK for Python, the Azure CLI, or ARM templates to [deploy the model as a serverless API](deploy-models-serverless.md).
+
+> [!div class="nextstepaction"]
+> [Deploy the model to serverless API endpoints](deploy-models-serverless.md)
+
 **Deployment to a self-hosted managed compute**
 
 Phi-4 family chat models can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served.
@@ -75,7 +84,7 @@ Read more about the [Azure AI inference package and reference](https://aka.ms/az
 In this section, you use the [Azure AI model inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat.
 
 > [!TIP]
-> The [Azure AI model inference API](https://aka.ms/azureai/modelinference) allows you to talk with most models deployed in Azure AI Foundry with the same code and structure, including Phi-4 family chat models.
+> The [Azure AI model inference API](https://aka.ms/azureai/modelinference) allows you to talk with most models deployed in Azure AI Foundry portal with the same code and structure, including Phi-4 family chat models.
 
 ### Create a client to consume the model
 
@@ -107,6 +116,9 @@ client = ChatCompletionsClient(
 )
 ```
 
+> [!NOTE]
+> Currently, serverless API endpoints don't support using Microsoft Entra ID for authentication.
+
 ### Get the model's capabilities
 
 The `/info` route returns information about the model that is deployed to the endpoint. Return the model's information by calling the following method:
@@ -146,6 +158,9 @@ response = client.complete(
 )
 ```
 
+> [!NOTE]
+> Phi-4 don't support system messages (`role="system"`). When you use the Azure AI model inference API, system messages are translated to user messages, which is the closest capability available. This translation is offered for convenience, but it's important for you to verify that the model is following the instructions in the system message with the right level of confidence.
+
 The response is as follows, where you can see the model's usage statistics:
 
 
@@ -234,7 +249,7 @@ response = client.complete(
 ```
 
 > [!WARNING]
-> Phi-4 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-4 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -261,12 +276,48 @@ The following extra parameters can be passed to Phi-4 family chat models:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `int` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `float` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
 
 
+### Apply content safety
+
+The Azure AI model inference API supports [Azure AI content safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI content safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering (preview) system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions.
+
+The following example shows how to handle events when the model detects harmful content in the input prompt and content safety is enabled.
+
+
+```python
+from azure.ai.inference.models import AssistantMessage, UserMessage, SystemMessage
+
+try:
+    response = client.complete(
+        messages=[
+            SystemMessage(content="You are an AI assistant that helps people find information."),
+            UserMessage(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
+```
+
+> [!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).
+
+> [!NOTE]
+> Azure AI content safety is only available for models deployed as serverless API endpoints.
+
 ::: zone-end
 
 
@@ -292,6 +343,15 @@ To use Phi-4 family chat models with Azure AI Foundry, you need the following pr
 
 ### A model deployment
 
+**Deployment to serverless APIs**
+
+Phi-4 family chat models can be deployed to serverless API endpoints with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. 
+
+Deployment to a serverless API endpoint doesn't require quota from your subscription. If your model isn't deployed already, use the Azure AI Foundry portal, Azure Machine Learning SDK for Python, the Azure CLI, or ARM templates to [deploy the model as a serverless API](deploy-models-serverless.md).
+
+> [!div class="nextstepaction"]
+> [Deploy the model to serverless API endpoints](deploy-models-serverless.md)
+
 **Deployment to a self-hosted managed compute**
 
 Phi-4 family chat models can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served.
@@ -320,7 +380,7 @@ npm install @azure-rest/ai-inference
 In this section, you use the [Azure AI model inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat.
 
 > [!TIP]
-> The [Azure AI model inference API](https://aka.ms/azureai/modelinference) allows you to talk with most models deployed in Azure AI Foundry with the same code and structure, including Phi-4 family chat models.
+> The [Azure AI model inference API](https://aka.ms/azureai/modelinference) allows you to talk with most models deployed in Azure AI Foundry portal with the same code and structure, including Phi-4 family chat models.
 
 ### Create a client to consume the model
 
@@ -352,6 +412,9 @@ const client = new ModelClient(
 );
 ```
 
+> [!NOTE]
+> Currently, serverless API endpoints don't support using Microsoft Entra ID for authentication.
+
 ### Get the model's capabilities
 
 The `/info` route returns information about the model that is deployed to the endpoint. Return the model's information by calling the following method:
@@ -393,6 +456,9 @@ var response = await client.path("/chat/completions").post({
 });
 ```
 
+> [!NOTE]
+> Phi-4 don't support system messages (`role="system"`). When you use the Azure AI model inference API, system messages are translated to user messages, which is the closest capability available. This translation is offered for convenience, but it's important for you to verify that the model is following the instructions in the system message with the right level of confidence.
+
 The response is as follows, where you can see the model's usage statistics:
 
 
@@ -493,7 +559,7 @@ var response = await client.path("/chat/completions").post({
 ```
 
 > [!WARNING]
-> Phi-4 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-4 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -525,11 +591,53 @@ The following extra parameters can be passed to Phi-4 family chat models:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `int` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `float` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
+
+
+### Apply content safety
+
+The Azure AI model inference API supports [Azure AI content safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI content safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering (preview) system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions.
 
+The following example shows how to handle events when the model detects harmful content in the input prompt and content safety is enabled.
+
+
+```javascript
+try {
+    var messages = [
+        { role: "system", content: "You are an AI assistant that helps people find information." },
+        { role: "user", content: "Chopping tomatoes and cutting them into cubes or wedges are great ways to practice your knife skills." },
+    ];
+
+    var response = await client.path("/chat/completions").post({
+        body: {
+            messages: messages,
+        }
+    });
+
+    console.log(response.body.choices[0].message.content);
+}
+catch (error) {
+    if (error.status_code == 400) {
+        var response = JSON.parse(error.response._content);
+        if (response.error) {
+            console.log(`Your request triggered an ${response.error.code} error:\n\t ${response.error.message}`);
+        }
+        else
+        {
+            throw error;
+        }
+    }
+}
+```
+
+> [!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).
+
+> [!NOTE]
+> Azure AI content safety is only available for models deployed as serverless API endpoints.
 
 ::: zone-end
 
@@ -556,6 +664,15 @@ To use Phi-4 family chat models with Azure AI Foundry, you need the following pr
 
 ### A model deployment
 
+**Deployment to serverless APIs**
+
+Phi-4 family chat models can be deployed to serverless API endpoints with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. 
+
+Deployment to a serverless API endpoint doesn't require quota from your subscription. If your model isn't deployed already, use the Azure AI Foundry portal, Azure Machine Learning SDK for Python, the Azure CLI, or ARM templates to [deploy the model as a serverless API](deploy-models-serverless.md).
+
+> [!div class="nextstepaction"]
+> [Deploy the model to serverless API endpoints](deploy-models-serverless.md)
+
 **Deployment to a self-hosted managed compute**
 
 Phi-4 family chat models can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served.
@@ -607,7 +724,7 @@ using System.Reflection;
 In this section, you use the [Azure AI model inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat.
 
 > [!TIP]
-> The [Azure AI model inference API](https://aka.ms/azureai/modelinference) allows you to talk with most models deployed in Azure AI Foundry with the same code and structure, including Phi-4 family chat models.
+> The [Azure AI model inference API](https://aka.ms/azureai/modelinference) allows you to talk with most models deployed in Azure AI Foundry portal with the same code and structure, including Phi-4 family chat models.
 
 ### Create a client to consume the model
 
@@ -631,6 +748,9 @@ client = new ChatCompletionsClient(
 );
 ```
 
+> [!NOTE]
+> Currently, serverless API endpoints don't support using Microsoft Entra ID for authentication.
+
 ### Get the model's capabilities
 
 The `/info` route returns information about the model that is deployed to the endpoint. Return the model's information by calling the following method:
@@ -671,6 +791,9 @@ ChatCompletionsOptions requestOptions = new ChatCompletionsOptions()
 Response<ChatCompletions> response = client.Complete(requestOptions);
 ```
 
+> [!NOTE]
+> Phi-4 don't support system messages (`role="system"`). When you use the Azure AI model inference API, system messages are translated to user messages, which is the closest capability available. This translation is offered for convenience, but it's important for you to verify that the model is following the instructions in the system message with the right level of confidence.
+
 The response is as follows, where you can see the model's usage statistics:
 
 
@@ -772,7 +895,7 @@ Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}");
 ```
 
 > [!WARNING]
-> Phi-4 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-4 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -801,11 +924,53 @@ The following extra parameters can be passed to Phi-4 family chat models:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `int` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `float` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
+
+
+### Apply content safety
 
+The Azure AI model inference API supports [Azure AI content safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI content safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering (preview) system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions.
+
+The following example shows how to handle events when the model detects harmful content in the input prompt and content safety is enabled.
+
+
+```csharp
+try
+{
+    requestOptions = new ChatCompletionsOptions()
+    {
+        Messages = {
+            new ChatRequestSystemMessage("You are an AI assistant that helps people find information."),
+            new ChatRequestUserMessage(
+                "Chopping tomatoes and cutting them into cubes or wedges are great ways to practice your knife skills."
+            ),
+        },
+    };
+
+    response = client.Complete(requestOptions);
+    Console.WriteLine(response.Value.Choices[0].Message.Content);
+}
+catch (RequestFailedException ex)
+{
+    if (ex.ErrorCode == "content_filter")
+    {
+        Console.WriteLine($"Your query has trigger Azure Content Safety: {ex.Message}");
+    }
+    else
+    {
+        throw;
+    }
+}
+```
+
+> [!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).
+
+> [!NOTE]
+> Azure AI content safety is only available for models deployed as serverless API endpoints.
 
 ::: zone-end
 
@@ -832,6 +997,15 @@ To use Phi-4 family chat models with Azure AI Foundry, you need the following pr
 
 ### A model deployment
 
+**Deployment to serverless APIs**
+
+Phi-4 family chat models can be deployed to serverless API endpoints with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. 
+
+Deployment to a serverless API endpoint doesn't require quota from your subscription. If your model isn't deployed already, use the Azure AI Foundry portal, Azure Machine Learning SDK for Python, the Azure CLI, or ARM templates to [deploy the model as a serverless API](deploy-models-serverless.md).
+
+> [!div class="nextstepaction"]
+> [Deploy the model to serverless API endpoints](deploy-models-serverless.md)
+
 **Deployment to a self-hosted managed compute**
 
 Phi-4 family chat models can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served.
@@ -853,14 +1027,17 @@ Models deployed with the [Azure AI model inference API](https://aka.ms/azureai/m
 In this section, you use the [Azure AI model inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat.
 
 > [!TIP]
-> The [Azure AI model inference API](https://aka.ms/azureai/modelinference) allows you to talk with most models deployed in Azure AI Foundry with the same code and structure, including Phi-4 family chat models.
+> The [Azure AI model inference API](https://aka.ms/azureai/modelinference) allows you to talk with most models deployed in Azure AI Foundry portal with the same code and structure, including Phi-4 family chat models.
 
 ### Create a client to consume the model
 
 First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables.
 
 When you deploy the model to a self-hosted online endpoint with **Microsoft Entra ID** support, you can use the following code snippet to create a client.
 
+> [!NOTE]
+> Currently, serverless API endpoints don't support using Microsoft Entra ID for authentication.
+
 ### Get the model's capabilities
 
 The `/info` route returns information about the model that is deployed to the endpoint. Return the model's information by calling the following method:
@@ -902,6 +1079,9 @@ The following example shows how you can create a basic chat completions request
 }
 ```
 
+> [!NOTE]
+> Phi-4 don't support system messages (`role="system"`). When you use the Azure AI model inference API, system messages are translated to user messages, which is the closest capability available. This translation is offered for convenience, but it's important for you to verify that the model is following the instructions in the system message with the right level of confidence.
+
 The response is as follows, where you can see the model's usage statistics:
 
 
@@ -1063,7 +1243,7 @@ Explore other parameters that you can specify in the inference client. For a ful
 ```
 
 > [!WARNING]
-> Phi-4 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Phi-4 family models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs aren't guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -1102,11 +1282,52 @@ The following extra parameters can be passed to Phi-4 family chat models:
 
 | Name           | Description           | Type            |
 | -------------- | --------------------- | --------------- |
-| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
+| `logit_bias` | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. | `float` |
 | `logprobs` | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. | `int` |
 | `top_logprobs` | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. | `float` |
-| `n` | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. | `int` |
+| `n` | How many chat completion choices to generate for each input message. You're charged based on the number of generated tokens across all of the choices. | `int` |
+
+
+### Apply content safety
 
+The Azure AI model inference API supports [Azure AI content safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI content safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering (preview) system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions.
+
+The following example shows how to handle events when the model detects harmful content in the input prompt and content safety is enabled.
+
+
+```json
+{
+    "messages": [
+        {
+            "role": "system",
+            "content": "You are an AI assistant that helps people find information."
+        },
+                {
+            "role": "user",
+            "content": "Chopping tomatoes and cutting them into cubes or wedges are great ways to practice your knife skills."
+        }
+    ]
+}
+```
+
+
+```json
+{
+    "error": {
+        "message": "The response was filtered due to the prompt triggering Microsoft's content management policy. Please modify your prompt and retry.",
+        "type": null,
+        "param": "prompt",
+        "code": "content_filter",
+        "status": 400
+    }
+}
+```
+
+> [!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).
+
+> [!NOTE]
+> Azure AI content safety is only available for models deployed as serverless API endpoints.
 
 ::: zone-end
 
@@ -1116,21 +1337,22 @@ For more examples of how to use Phi-4 family models, see the following examples
 
 | Description                               | Language          | Sample                                                          |
 |-------------------------------------------|-------------------|-----------------------------------------------------------------|
-| CURL request                              | Bash              | [Link](https://aka.ms/phi-3/webrequests-sample)         |
-| Azure AI Inference package for C#         | C#                | [Link](https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Inference/samples)   |  
-| Azure AI Inference package for JavaScript | JavaScript        | [Link](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-inference-rest/samples)  |
+| Azure AI Inference package for C#         | C#                | [Link](https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Inference/samples)                  |
+| Azure AI Inference package for JavaScript | JavaScript        | [Link](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-inference-rest/samples) |
 | Azure AI Inference package for Python     | Python            | [Link](https://aka.ms/azsdk/azure-ai-inference/python/samples)  |
-| Python web requests                       | Python            | [Link](https://aka.ms/phi-3/webrequests-sample)         |
-| OpenAI SDK (experimental)                 | Python            | [Link](https://aka.ms/phi-3/openaisdk)                  |
-| LangChain                                 | Python            | [Link](https://aka.ms/phi-3/langchain-sample)           |
-| LiteLLM                                   | Python            | [Link](https://aka.ms/phi-3/litellm-sample)             | 
+| LangChain                                 | Python            | [Link](https://aka.ms/azureai/langchain)           |
+| Llama-Index                               | Python            | [Link](https://aka.ms/azureai/llamaindex)             |  
+
+
+## Cost and quota considerations for Phi-4 family models deployed as serverless API endpoints
 
+Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per project. Contact Microsoft Azure Support if the current rate limits aren't sufficient for your scenarios.
 
 ## Cost and quota considerations for Phi-4 family models deployed to managed compute
 
 Phi-4 family models deployed to managed compute are billed based on core hours of the associated compute instance. The cost of the compute instance is determined by the size of the instance, the number of instances running, and the run duration.
 
-It is a good practice to start with a low number of instances and scale up as needed. You can monitor the cost of the compute instance in the Azure portal.
+It's a good practice to start with a low number of instances and scale up as needed. You can monitor the cost of the compute instance in the Azure portal.
 
 ## Related content
 

@@ -83,7 +83,7 @@ Gretel | Not available | Gretel-Navigator
 Healthcare AI family Models | MedImageParse<BR>  MedImageInsight<BR>  CxrReportGen<BR>  Virchow<BR>  Virchow2<BR>  Prism<BR>  BiomedCLIP-PubMedBERT<BR>  microsoft-llava-med-v1.5<BR>  m42-health-llama3-med4<BR>  biomistral-biomistral-7b<BR>  microsoft-biogpt-large-pub<BR>  microsoft-biomednlp-pub<BR>  stanford-crfm-biomedlm<BR>  medicalai-clinicalbert<BR>  microsoft-biogpt<BR>  microsoft-biogpt-large<BR>  microsoft-biomednlp-pub<BR> | Not Available
 JAIS | Not available | jais-30b-chat
 Meta Llama family models | Llama-3.3-70B-Instruct<BR> Llama-3.2-3B-Instruct<BR>  Llama-3.2-1B-Instruct<BR>  Llama-3.2-1B<BR>  Llama-3.2-90B-Vision-Instruct<BR>  Llama-3.2-11B-Vision-Instruct<BR>  Llama-3.1-8B-Instruct<BR>  Llama-3.1-8B<BR>  Llama-3.1-70B-Instruct<BR>  Llama-3.1-70B<BR>  Llama-3-8B-Instruct<BR>  Llama-3-70B<BR>  Llama-3-8B<BR>  Llama-Guard-3-1B<BR>  Llama-Guard-3-8B<BR>  Llama-Guard-3-11B-Vision<BR>  Llama-2-7b<BR>  Llama-2-70b<BR>  Llama-2-7b-chat<BR>  Llama-2-13b-chat<BR>  CodeLlama-7b-hf<BR>  CodeLlama-7b-Instruct-hf<BR>  CodeLlama-34b-hf<BR>  CodeLlama-34b-Python-hf<BR>  CodeLlama-34b-Instruct-hf<BR>  CodeLlama-13b-Instruct-hf<BR>  CodeLlama-13b-Python-hf<BR>  Prompt-Guard-86M<BR>  CodeLlama-70b-hf<BR> | Llama-3.3-70B-Instruct<BR> Llama-3.2-90B-Vision-Instruct<br>  Llama-3.2-11B-Vision-Instruct<br>  Llama-3.1-8B-Instruct<br>  Llama-3.1-70B-Instruct<br>  Llama-3.1-405B-Instruct<br>  Llama-3-8B-Instruct<br>  Llama-3-70B-Instruct<br>  Llama-2-7b<br>  Llama-2-7b-chat<br>  Llama-2-70b<br>  Llama-2-70b-chat<br>  Llama-2-13b<br>  Llama-2-13b-chat<br>
-Microsoft Phi family models | Phi-3-mini-4k-Instruct <br> Phi-3-mini-128k-Instruct <br> Phi-3-small-8k-Instruct <br> Phi-3-small-128k-Instruct <br> Phi-3-medium-4k-instruct <br> Phi-3-medium-128k-instruct <br> Phi-3-vision-128k-Instruct <br> Phi-3.5-mini-Instruct <br> Phi-3.5-vision-Instruct <br> Phi-3.5-MoE-Instruct <br> Phi-4| Phi-3-mini-4k-Instruct <br> Phi-3-mini-128k-Instruct <br> Phi-3-small-8k-Instruct <br> Phi-3-small-128k-Instruct <br> Phi-3-medium-4k-instruct <br> Phi-3-medium-128k-instruct <br> <br> Phi-3.5-mini-Instruct <br> Phi-3.5-vision-Instruct <br> Phi-3.5-MoE-Instruct
+Microsoft Phi family models | Phi-3-mini-4k-Instruct <br> Phi-3-mini-128k-Instruct <br> Phi-3-small-8k-Instruct <br> Phi-3-small-128k-Instruct <br> Phi-3-medium-4k-instruct <br> Phi-3-medium-128k-instruct <br> Phi-3-vision-128k-Instruct <br> Phi-3.5-mini-Instruct <br> Phi-3.5-vision-Instruct <br> Phi-3.5-MoE-Instruct <br> Phi-4| Phi-3-mini-4k-Instruct <br> Phi-3-mini-128k-Instruct <br> Phi-3-small-8k-Instruct <br> Phi-3-small-128k-Instruct <br> Phi-3-medium-4k-instruct <br> Phi-3-medium-128k-instruct <br> <br> Phi-3.5-mini-Instruct <br> Phi-3.5-vision-Instruct <br> Phi-3.5-MoE-Instruct <br> Phi-4
 Mistral family models | mistralai-Mixtral-8x22B-v0-1 <br> mistralai-Mixtral-8x22B-Instruct-v0-1 <br> mistral-community-Mixtral-8x22B-v0-1 <br> mistralai-Mixtral-8x7B-v01 <br> mistralai-Mistral-7B-Instruct-v0-2 <br> mistralai-Mistral-7B-v01 <br> mistralai-Mixtral-8x7B-Instruct-v01 <br> mistralai-Mistral-7B-Instruct-v01 | Mistral-large (2402) <br> Mistral-large (2407) <br> Mistral-small <br> Ministral-3B <br> Mistral-NeMo
 Nixtla | Not available | TimeGEN-1
 

@@ -50,10 +50,11 @@ Llama 3.1 8B Instruct <br> Llama 3.1 70B Instruct <br> Llama-3.2-11B-Vision-Inst
 Llama 3.1 405B Instruct  | [Microsoft Managed countries/regions](/partner-center/marketplace/tax-details-marketplace#microsoft-managed-countriesregions)    | East US <br> East US 2 <br> North Central US <br> South Central US <br> West US <br> West US 3     | Not available  |
 
 
-### Microsoft Phi-3 family models
+### Microsoft Phi family models
 
 | Model | Offer Availability Region  | Hub/Project Region for Deployment  | Hub/Project Region for Fine tuning  |
 |---------|---------|---------|---------|
+Phi-4                       | Not applicable | East US <br> East US 2 <br> North Central US <br> South Central US <br> Sweden Central <br> West US <br> West US 3  | Not available       |
 Phi-3.5-vision-Instruct     | Not applicable | East US <br> East US 2 <br> North Central US <br> South Central US <br> Sweden Central <br> West US <br> West US 3  | Not available       |
 Phi-3.5-MoE-Instruct     | Not applicable | East US <br> East US 2 <br> North Central US <br> South Central US <br> Sweden Central <br> West US <br> West US 3  | East US 2       |
 Phi-3.5-Mini-Instruct     | Not applicable | East US <br> East US 2 <br> North Central US <br> South Central US <br> Sweden Central <br> West US <br> West US 3  | East US 2  | East US 2       |
@@ -92,3 +93,9 @@ TimeGEN-1     | [Microsoft Managed countries/regions](/partner-center/marketplac
 |---------|---------|---------|---------|
 AI21-Jamba-1.5-Mini | [Microsoft Managed countries/regions](/partner-center/marketplace/tax-details-marketplace#microsoft-managed-countriesregions) | East US 2 <br> South Central US <br> East US <br> West US 3 <br> West US <br> North Central US       |  Not available       |
 AI21-Jamba-1.5-Large | [Microsoft Managed countries/regions](/partner-center/marketplace/tax-details-marketplace#microsoft-managed-countriesregions) | East US 2 <br> South Central US <br> East US <br> West US 3 <br> West US <br> North Central US       |  Not available       |
+
+### Bria models
+
+|Model  |Offer Availability Region  | Hub/Project Region for Deployment  | Hub/Project Region for Fine tuning  |
+|---------|---------|---------|---------|
+Bria-2.3-Fast   | [Microsoft Managed Countries](/partner-center/marketplace/tax-details-marketplace#microsoft-managed-countriesregions)  | East US 2   | Not available       |

@@ -73,7 +73,6 @@ The API indicates how developers can consume predictions for the following modal
 
 * [Get info](reference-model-inference-info.md): Returns the information about the model deployed under the endpoint.
 * [Text embeddings](reference-model-inference-embeddings.md): Creates an embedding vector representing the input text.
-* [Text completions](reference-model-inference-completions.md): Creates a completion for the provided prompt and parameters.
 * [Chat completions](reference-model-inference-chat-completions.md): Creates a model response for the given chat conversation.
 * [Image embeddings](reference-model-inference-images-embeddings.md): Creates an embedding vector representing the input text and image.
 

@@ -16,7 +16,7 @@ ms.custom: references_regions, build-2024
 
 Azure AI Foundry brings together various Azure AI capabilities that previously were only available as standalone Azure services. While we strive to make all features available in all regions where Azure AI Foundry is supported at the same time, feature availability may vary by region. In this article, you'll learn what Azure AI Foundry features are available across cloud regions.  
 
-## Azure Public regions
+## Azure AI Foundry projects
 
 Azure AI Foundry is currently available in the following Azure regions. You can create [projects in Azure AI Foundry portal](../how-to/create-projects.md) in these regions.
 
@@ -44,30 +44,22 @@ Azure AI Foundry is currently available in the following Azure regions. You can
 - West US
 - West US 3
 
-### Azure Government regions
-
-Azure AI Foundry is currently not available in Azure Government regions or air-gap regions.
-
-## Azure OpenAI
-
-For information on the availability of Azure OpenAI models, see [Azure OpenAI Model summary table and region availability](../../ai-services/openai/concepts/models.md#model-summary-table-and-region-availability).
-
 > [!NOTE]
-> Some models might not be available within the Azure AI Foundry model catalog.
-
-For more information, see [Azure OpenAI quotas and limits](/azure/ai-services/openai/quotas-limits).
-
-## Speech capabilities
-
-Azure AI Speech capabilities including custom neural voice vary in regional availability due to underlying hardware availability. See [Speech service supported regions](../../ai-services/speech-service/regions.md) for an overview.
-
-## Serverless API deployments
+> Azure AI Foundry is currently not available in Azure Government regions or air-gap regions.
 
-Some models in the model catalog can be deployed as a serverless API with pay-as-you-go billing. For information on the regions where each model is available, see [Region availability for models in Serverless API endpoints](../how-to/deploy-models-serverless-availability.md).
+## Azure AI Foundry features
+ 
+You can add features from different regions to your project. You may need to use a different region for a particular feature, based on the region availability of that feature.
 
-## Azure AI Content Safety
+The following table lists the availability of Azure AI Foundry features across Azure regions.
 
-To use the Content Safety APIs, you must create your Azure AI Content Safety resource in a supported region. For a list of supported regions, see [What is Azure AI Content Safety?](../../ai-services/content-safety/overview.md#region-availability)
+| Service                        | Description                                                                                                                                          | Link                                                                                                      |
+|--------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|
+| Azure OpenAI                   | Note that some models might not be available within the Azure AI Foundry model catalog.                                                              | [Azure OpenAI quotas and limits](/azure/ai-services/openai/quotas-limits)
+| Speech capabilities            | Azure AI Speech capabilities including custom neural voice vary in regional availability due to underlying hardware availability.                     | [Speech service supported regions](../../ai-services/speech-service/regions.md)                           |
+| Serverless API deployments     | Some models in the model catalog can be deployed as a serverless API with pay-as-you-go billing.                                                      | [Region availability for models in Serverless API endpoints](../how-to/deploy-models-serverless-availability.md) |
+| Azure AI Content Safety        | To use the Content Safety APIs, you must create your Azure AI Content Safety resource in a supported region.                                           | [What is Azure AI Content Safety?](../../ai-services/content-safety/overview.md#region-availability)       |
+| Azure AI Agent Service         | Azure AI Agent Service supports the same models as the chat completions API in Azure OpenAI.                                                          | [Azure AI Agent Service region availability](../../ai-services/agents/concepts/model-region-support.md#azure-openai-models) |
 
 ## Next steps
 

@@ -453,8 +453,6 @@ items:
           href: reference/reference-model-inference-info.md
         - name: Embeddings
           href: reference/reference-model-inference-embeddings.md
-        - name: Completions
-          href: reference/reference-model-inference-completions.md
         - name: Chat Completions
           href: reference/reference-model-inference-chat-completions.md
         - name: Images Embeddings