wip

Author: gitName

articles/api-management/azure-openai-api-from-specification.md

@@ -5,7 +5,7 @@ ms.service: azure-api-management
 author: dlepow
 ms.author: danlep
 ms.topic: how-to
-ms.date: 04/01/2025
+ms.date: 04/30/2025
 ms.collection: ce-skilling-ai-copilot
 ms.custom: template-how-to, build-2024
 ---
@@ -65,11 +65,12 @@ To import an Azure OpenAI API to API Management:
 
         For example, if your API Management gateway endpoint is `https://contoso.azure-api.net`, set a **Base URL** similar to `https://contoso.azure-api.net/my-openai-api/openai`.
     1. Optionally select one or more products to associate with the API. Select **Next**.
-1. On the **Policies** tab, optionally enable policies to monitor and manage Azure OpenAI API token consumption. You can also set or edit policies later.
+1. On the **Policies** tab, optionally enable policies to help monitor and manage Azure OpenAI API token consumption and cache resonses. You can also set or edit policies later.
 
     If selected, enter settings or accept defaults that define the following policies (see linked articles for prerequisites and configuration details):
     * [Manage token consumption](azure-openai-token-limit-policy.md)
     * [Track token usage](azure-openai-emit-token-metric-policy.md) 
+    * [Enable semantic caching of responses](azure-openai-enable-semantic-caching.md)
 
     Select **Review + Create**.
 1. After settings are validated, select **Create**. 

articles/api-management/azure-openai-enable-semantic-caching.md

@@ -18,7 +18,7 @@ ms.collection: ce-skilling-ai-copilot
 Enable semantic caching of responses to Azure OpenAI API requests to reduce bandwidth and processing requirements imposed on the backend APIs and lower latency perceived by API consumers. With semantic caching, you can return cached responses for identical prompts and also for prompts that are similar in meaning, even if the text isn't the same. For background, see [Tutorial: Use Azure Cache for Redis as a semantic cache](../redis/tutorial-semantic-cache.md).
 
 > [!NOTE]
-> The configuration steps in this article enable semantic caching for Azure OpenAI APIs. These steps can be generalized to enable semantic caching for corresponding large language model (LLM) APIs available through the [Azure AI Model Inference API](/azure/ai-studio/reference/reference-model-inference-api). 
+> The configuration steps in this article enable semantic caching for Azure OpenAI APIs. These steps can be generalized to enable semantic caching for corresponding large language model (LLM) APIs available through the [Azure AI Model Inference API](/azure/ai-studio/reference/reference-model-inference-api) or with OpenAI-compatible models served through third-party inference providers. 
 
 ## Prerequisites
 

articles/api-management/genai-gateway-capabilities.md

@@ -7,7 +7,7 @@ author: dlepow
 ms.service: azure-api-management
 ms.collection: ce-skilling-ai-copilot
 ms.topic: concept-article
-ms.date: 04/29/2025
+ms.date: 04/29/20    25
 ms.author: danlep
 ---
 
@@ -36,7 +36,7 @@ The rest of this article describes how Azure API Management can help you address
 
 ## Import Azure OpenAI Service resource as an API
 
-[Import an API from an Azure OpenAI Service endpoint](azure-openai-api-from-specification.md) to Azure API management using a single-click experience. API Management streamlines the onboarding process by automatically importing the OpenAPI schema for the Azure OpenAI API and sets up authentication to the Azure OpenAI endpoint using managed identity, removing the need for manual configuration. Within the same user-friendly experience, you can preconfigure policies for [token limits](#token-limit-policy) and [emitting token metrics](#emit-token-metric-policy). 
+[Import an API from an Azure OpenAI Service endpoint](azure-openai-api-from-specification.md) to Azure API management using a single-click experience. API Management streamlines the onboarding process by automatically importing the OpenAPI schema for the Azure OpenAI API and sets up authentication to the Azure OpenAI endpoint using managed identity, removing the need for manual configuration. Within the same user-friendly experience, you can preconfigure policies for [token limits](#token-limit-policy), [emitting token metrics](#emit-token-metric-policy), and [semantic caching](#semantic-caching-policy). 
 
 :::image type="content" source="media/azure-openai-api-from-specification/azure-openai-api.png" alt-text="Screenshot of Azure OpenAI API tile in the portal.":::
 
@@ -99,15 +99,17 @@ Configure [Azure OpenAI semantic caching](azure-openai-enable-semantic-caching.m
 
 :::image type="content" source="media/genai-gateway-capabilities/semantic-caching.png" alt-text="Diagram of semantic caching in API Management.":::
 
-In API Management, enable semantic caching by using Azure Redis Enterprise or another [external cache](api-management-howto-cache-external.md) compatible with RediSearch and onboarded to Azure API Management. By using the Azure OpenAI Service Embeddings API, the [azure-openai-semantic-cache-store](azure-openai-semantic-cache-store-policy.md) and [azure-openai-semantic-cache-lookup](azure-openai-semantic-cache-lookup-policy.md) policies store and retrieve semantically similar prompt completions from the cache. This approach ensures completions reuse, resulting in reduced token consumption and improved response performance. 
+In API Management, enable semantic caching by using Azure Redis Enterprise, Azure Managed Redis, or another [external cache](api-management-howto-cache-external.md) compatible with RediSearch and onboarded to Azure API Management. By using the Azure OpenAI Service Embeddings API, the [azure-openai-semantic-cache-store](azure-openai-semantic-cache-store-policy.md) and [azure-openai-semantic-cache-lookup](azure-openai-semantic-cache-lookup-policy.md) policies store and retrieve semantically similar prompt completions from the cache. This approach ensures completions reuse, resulting in reduced token consumption and improved response performance. 
 
 > [!TIP]
 > To enable semantic caching for other LLM APIs, API Management provides the equivalent [llm-semantic-cache-store-policy](llm-semantic-cache-store-policy.md) and [llm-semantic-cache-lookup-policy](llm-semantic-cache-lookup-policy.md) policies.
 
 
 ## Content safety policy
 
+To help safeguard users from harmful, offensive, or misleading content, you can automatically moderate all incoming requests to an LLM API by configuring the [llm-content-safety](llm-content-safety-policy.md) policy. The policy enforces content safety checks on LLM prompts by transmitting them first to the [Azure AI Content Safety](azure/ai-services/content-safety/overview) service before sending to the backend LLM API. 
 
+:::image type="content" source="media/genai-gateway-capabilities/content-safety.png" alt-text="Diagram of moderating prompts by Azure AI Content Safety in an API Management policy.":::
 
 ## Labs and samples