remove bogus information

Author: flang-msft

articles/redis/TOC.yml

@@ -98,10 +98,10 @@ items:
       href: overview-vector-similarity.md
     - name: Connection resilience best practices
       href: best-practices-connection.md
-    - name: Use Azure Functions triggers and bindings
-      items:
-      - name: Create a simple Function app for Redis triggers 
-        href: tutorial-functions-getting-started.md 
+    # - name: Use Azure Functions triggers and bindings
+    #   items:
+    #   - name: Create a simple Function app for Redis triggers 
+    #     href: tutorial-functions-getting-started.md 
 
   - name: Logs and monitoring
     items:
@@ -205,8 +205,8 @@ items:
   items:
   - name: Use Azure Functions triggers and bindings
     items:
-    - name: Create a simple Function app for Redis triggers
-      href: tutorial-functions-getting-started.md
+    # - name: Create a simple Function app for Redis triggers
+    #   href: tutorial-functions-getting-started.md
     - name: Using Azure Functions to create a write-behind cache
       href: tutorial-write-behind.md
   - name: Use Azure Kubernetes Service with a cache

articles/redis/tutorial-vector-similarity.md

@@ -1,6 +1,6 @@
 ---
-title: "Tutorial: Conduct vector similarity search on Azure OpenAI embeddings using Azure Cache for Redis"
-description: In this tutorial, you learn how to use Azure Cache for Redis to store and search for vector embeddings.
+title: "Tutorial: Conduct vector similarity search on Azure OpenAI embeddings using Azure Managed Redis"
+description: In this tutorial, you learn how to use Azure Managed Redis to store and search for vector embeddings.
 ms.date: 02/27/2025
 ms.topic: tutorial
 ms.collection:
@@ -14,8 +14,7 @@ ms.custom:
 
 # Tutorial: Conduct vector similarity search on Azure OpenAI embeddings using Azure Redis
 
-<!-- cawa - need to mention AMR in this tutorial -->
-In this tutorial, you walk through a basic vector similarity search use-case. You use embeddings generated by Azure OpenAI Service and the built-in vector search capabilities of the Enterprise tier of Azure Cache for Redis to query a dataset of movies to find the most relevant match.
+In this tutorial, you walk through a basic vector similarity search use-case. You use embeddings generated by Azure OpenAI Service and the built-in vector search capabilities of Azure Managed Redis to query a dataset of movies to find the most relevant match.
 
 The tutorial uses the [Wikipedia Movie Plots dataset](https://www.kaggle.com/datasets/jrobischon/wikipedia-movie-plots) that features plot descriptions of over 35,000 movies from Wikipedia covering the years 1901 to 2017. The dataset includes a plot summary for each movie, plus metadata such as the year the film was released, the director(s), main cast, and genre. You follow the steps of the tutorial to generate embeddings based on the plot summary and use the other metadata to run hybrid queries.
 
@@ -24,11 +23,11 @@ In this tutorial, you learn how to:
 
 > [!div class="checklist"]
 
-> - Create an Azure Cache for Redis instance configured for vector search
+> - Create an Azure Managed Redis instance configured for vector search
 > - Install Azure OpenAI and other required Python libraries.
 > - Download the movie dataset and prepare it for analysis.
 > - Use the **text-embedding-ada-002 (Version 2)** model to generate embeddings.
-> - Create a vector index in Azure Cache for Redis
+> - Create a vector index in Azure Managed Redis
 > - Use cosine similarity to rank search results.
 > - Use hybrid query functionality through [RediSearch](https://redis.io/docs/interact/search-and-query/) to prefilter the data and make the vector search even more powerful.
 
@@ -39,23 +38,22 @@ In this tutorial, you learn how to:
 >If you would like to follow along in a completed Jupyter notebook instead, [download the Jupyter notebook file named _tutorial.ipynb_](https://github.com/Azure-Samples/azure-cache-redis-samples/tree/main/tutorial/vector-similarity-search-open-ai) and save it into the new _redis-vector_ folder.
 
 ## Prerequisites
-<!-- Continue here. -->
 
 - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
-- Access granted to Azure OpenAI in the desired Azure subscription. Currently, you must apply for access to Azure OpenAI. You can apply for access to Azure OpenAI by completing the form at [https://aka.ms/oai/access](https://aka.ms/oai/access). <!-- I don't know if this is still true -->
+- Access granted to Azure OpenAI in the desired Azure subscription. Currently, you must apply for access to Azure OpenAI. You can apply for access to Azure OpenAI by completing the form at [https://aka.ms/oai/access](https://aka.ms/oai/access). <!-- I don't know if 
 - [Python 3.8 or later version](https://www.python.org/)
 - [Jupyter Notebooks](https://jupyter.org/) (optional)
 - An Azure OpenAI resource with the **text-embedding-ada-002 (Version 2)** model deployed. This model is currently only available in [certain regions](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability). See the [resource deployment guide](/azure/ai-services/openai/how-to/create-resource) for instructions on how to deploy the model.
 
-## Create an Azure Cache for Redis Instance
+## Create an Azure Managed Redis Instance
 
-1. Follow the [Quickstart: Create a Redis Enterprise cache](../azure-cache-for-redis/quickstart-create-redis-enterprise.md) guide, but make sure you add the RedisSearch module at create time.
+1. Follow the [Quickstart](create-manage-cache) guide to create a cache, but make sure you add the RedisSearch module at create time.
 
 1. On the **Advanced** page, make sure that you've added the **RediSearch** module and have chosen the **Enterprise** Cluster Policy. All other settings can match the default described in the quickstart.
 
    It takes a few minutes for the cache to create. You can move on to the next step in the meantime.
 
-    :::image type="content" source="../azure-cache-for-redis/media/cache-create/enterprise-tier-basics.png" alt-text="Screenshot showing the Enterprise tier Basics tab filled out.":::
+    <!-- :::image type="content" source="../azure-cache-for-redis/media/cache-create/enterprise-tier-basics.png" alt-text="Screenshot showing the Enterprise tier Basics tab filled out."::: -->
 
 ## Set up your development environment
 
@@ -82,13 +80,13 @@ In this tutorial, you learn how to:
 
 ## Import libraries and set up connection information
 
-To successfully make a call against Azure OpenAI, you need an **endpoint** and a **key**. You also need an **endpoint** and a **key** to connect to Azure Cache for Redis.
+To successfully make a call against Azure OpenAI, you need an **endpoint** and a **key**. You also need an **endpoint** and a **key** to connect to Azure Managed Redis.
 
 1. Go to your Azure OpenAI resource in the Azure portal.
 
 1. Locate **Endpoint and Keys** in the **Resource Management** section. Copy your endpoint and access key as you'll need both for authenticating your API calls. An example endpoint is: `https://docs-test-001.openai.azure.com`. You can use either `KEY1` or `KEY2`.
 
-1. Go to the **Overview** page of your Azure Cache for Redis resource in the Azure portal. Copy your endpoint.
+1. Go to the **Overview** page of your Azure Managed Redis resource in the Azure portal. Copy your endpoint.
 
 1. Locate **Access keys** in the **Settings** section. Copy your access key. You can use either `Primary` or `Secondary`.
 
@@ -117,7 +115,7 @@ To successfully make a call against Azure OpenAI, you need an **endpoint** and a
 
 1. Update the value of `API_KEY` and `RESOURCE_ENDPOINT` with the key and endpoint values from your Azure OpenAI deployment. `DEPLOYMENT_NAME` should be set to the name of your deployment using the `text-embedding-ada-002 (Version 2)` embeddings model, and `MODEL_NAME` should be the specific embeddings model used.
 
-1. Update `REDIS_ENDPOINT` and `REDIS_PASSWORD` with the endpoint and key value from your Azure Cache for Redis instance.
+1. Update `REDIS_ENDPOINT` and `REDIS_PASSWORD` with the endpoint and key value from your Azure Managed Redis instance.
 
    > [!Important]
    > We strongly recommend using environmental variables or a secret manager like [Azure Key Vault](/azure/key-vault/general/overview) to pass in the API key, endpoint, and deployment name information. These variables are set in plaintext here for the sake of simplicity.
@@ -228,7 +226,7 @@ Load the DataFrame into LangChain using the `DataFrameLoader` class. Once the da
 
 ## Generate embeddings and load them into Redis
 
-Now that the data has been filtered and loaded into LangChain, you'll create embeddings so you can query on the plot for each movie. The following code configures Azure OpenAI, generates embeddings, and loads the embeddings vectors into Azure Cache for Redis.
+Now that the data has been filtered and loaded into LangChain, you'll create embeddings so you can query on the plot for each movie. The following code configures Azure OpenAI, generates embeddings, and loads the embeddings vectors into Azure Managed Redis.
 
 1. Add the following code a new code cell:
 
@@ -265,7 +263,7 @@ Now that the data has been filtered and loaded into LangChain, you'll create emb
    vectorstore.write_schema("redis_schema.yaml")
    ```
 
-1. Execute code cell 8.  This can take over 30 minutes to complete. A `redis_schema.yaml` file is generated as well. This file is useful if you want to connect to your index in Azure Cache for Redis instance without re-generating embeddings.
+1. Execute code cell 8.  This can take over 30 minutes to complete. A `redis_schema.yaml` file is generated as well. This file is useful if you want to connect to your index in Azure Managed Redis instance without re-generating embeddings.
 
 
 > [!Important]
@@ -341,16 +339,14 @@ Now that your dataset, Azure OpenAI service API, and Redis instance are set up,
    Morons from Outer Space (Score: 0.8121)
    ```
 
-With Azure Cache for Redis and Azure OpenAI Service, you can use embeddings and vector search to add powerful search capabilities to your application.
+With Azure Managed Redis and Azure OpenAI Service, you can use embeddings and vector search to add powerful search capabilities to your application.
 
 [!INCLUDE [cache-delete-resource-group](includes/cache-delete-resource-group.md)]
 
 ## Related Content
 
-- [Learn more about Azure Cache for Redis](overview.md)
-- Learn more about Azure Cache for Redis [vector search capabilities](overview-vector-similarity.md)
+- [Learn more about Azure Managed Redis](overview.md)
 - Learn more about [embeddings generated by Azure OpenAI Service](/azure/ai-services/openai/concepts/understand-embeddings)
 - Learn more about [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity)
 - [Read how to build an AI-powered app with OpenAI and Redis](https://techcommunity.microsoft.com/blog/azuredevcommunityblog/vector-similarity-search-with-azure-cache-for-redis-enterprise/3822059)
 - [Build a Q&A app with semantic answers](https://github.com/ruoccofabrizio/azure-open-ai-embeddings-qna)
-