Kyle fixes

Author: Bob Tabor

articles/azure-cache-for-redis/cache-overview-vector-similarity.md

@@ -73,7 +73,7 @@ Azure Cache for Redis can be used effectively as a vector database to store embe
 Redis has a wide range of vector search capabilities through the [RediSearch module](cache-redis-modules.md#redisearch), which is available in the Enterprise tier of Azure Cache for Redis. These include:
 
 - Multiple distance metrics, including `Euclidean`, `Cosine`, and `Internal Product`.
-- Support for both KNN (using `FLAT`) and ANN (using`HNSW`) indexing methods.
+- Support for both KNN (using `FLAT`) and ANN (using `HNSW`) indexing methods.
 - Vector storage in hash or JSON data structures
 - Top K queries
 - [Vector range queries](https://redis.io/docs/interact/search-and-query/search/vectors/#creating-a-vss-range-query) (i.e., find all items within a specific vector distance)

articles/azure-cache-for-redis/cache-tutorial-vector-similarity.md

@@ -31,6 +31,9 @@ In this tutorial, you learn how to:
 >[!IMPORTANT]
 >This tutorial will walk you through building a Jupyter Notebook. You can follow this tutorial with a Python code file (.py) and get *similar* results, but you will need to add all of the code blocks in this tutorial into the `.py` file and execute once to see results. In other words, Jupyter Notebooks provides intermediate results as you execute cells, but this is not behavior you should expect when working in a Python code file.
 
+>[!IMPORTANT]
+>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
 
 * An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
@@ -60,9 +63,6 @@ In this tutorial, you learn how to:
    pip install openai num2words matplotlib plotly scipy scikit-learn pandas tiktoken redis langchain
    ```
 
-   >[!IMPORTANT]
-   >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.
-
 ## Download the dataset
 
 1. In a web browser, navigate to [https://www.kaggle.com/datasets/jrobischon/wikipedia-movie-plots](https://www.kaggle.com/datasets/jrobischon/wikipedia-movie-plots).
@@ -75,7 +75,7 @@ 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 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 Cache for Redis.
 
 1. Go to your Azure Open AI resource in the Azure portal.
 
@@ -156,7 +156,7 @@ Next, you'll read the csv file into a pandas DataFrame.
 
    :::image type="content" source="media/cache-tutorial-vector-similarity/code-cell-4.png" alt-text="Screenshot of results from executing code cell 4, displaying nine columns and a sampling of 10 rows of data." lightbox="media/cache-tutorial-vector-similarity/code-cell-4.png":::
 
-1. Create a function to clean the data by removing whitespace and punctuation, then uses it against the dataframe containing the plot.
+1. Create a function to clean the data by removing whitespace and punctuation, then use it against the dataframe containing the plot.
 
    Add the following code to a new code cell and execute it:
 
@@ -312,7 +312,7 @@ Now that your dataset, Azure OpenAI service API, and Redis instance are set up,
        print(movie_title + ' (Score: ' + similarity_score + ')')
    ```
 
-1. Execute code cell 10. Uou should see the following output:
+1. Execute code cell 10. You should see the following output:
 
    ```output
    Remote Control (Score: 0.8301)
@@ -334,7 +334,7 @@ With Azure Cache for Redis and Azure OpenAI Service, you can use embeddings and
 ## Related Content
 
 * [Learn more about Azure Cache for Redis](cache-overview.md)
-* Learn more about Azure Cache for Redis [vector search capabilities](./cache-redis-modules.md#redisearch)
+* Learn more about Azure Cache for Redis [vector search capabilities](./cache-overview-vector-similarity.md)
 * Learn more about [embeddings generated by Azure OpenAI Service](../ai-services/openai/concepts/understand-embeddings.md)
 * 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/t5/azure-developer-community-blog/vector-similarity-search-with-azure-cache-for-redis-enterprise/ba-p/3822059)