edits

Author: v-thepet

articles/machine-learning/how-to-retrieval-augmented-generation-cloud-to-local.md

@@ -2,7 +2,6 @@
 title: Work with RAG prompt flows locally (preview)
 titleSuffix: Azure Machine Learning
 description: Learn to work with your Azure Machine Learning (RAG) prompt flows locally by using the Prompt flow extension in Visual Studio Code.
-e prompt flow Visual Studio Code extension.
 services: machine-learning
 ms.service: azure-machine-learning
 ms.subservice: prompt-flow
@@ -30,37 +29,41 @@ In this article, you learn how to transition RAG flows from your Azure Machine L
 
 To complete the procedures in this article, you need the following prerequisites:
 
-- Python 3.9 or above, with the `promptflow` SDK and `promptflow-tools` installed by running `pip install promptflow promptflow-tools`, and the `promptflow-vectordb` tool installed by running `pip install promptflow-vectordb`.
+- Python 3.9 or above installed locally.
+  - The `promptflow` SDK and `promptflow-tools` installed by running `pip install promptflow promptflow-tools`.
+  - The `promptflow-vectordb` tool installed by running `pip install promptflow-vectordb`.
 
 - Visual Studio Code with the **Python** and **Prompt flow** extensions installed.
 
-  :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/vs-code-extension.png" alt-text="Screenshot of the prompt flow VS Code extension in the marketplace." lightbox = "./media/how-to-retrieval-augmented-generation-cloud-to-local/vs-code-extension.png":::
+  :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/vs-code-extension.png" alt-text="Screenshot of the prompt flow VS Code extension in the marketplace.":::
 
 - An [Azure OpenAI account resource](/azure/ai-services/openai/how-to/create-resource#create-a-resource) that has [deployed models](/azure/ai-services/openai/how-to/create-resource#deploy-a-model) for both **chat** and **text-embedding-ada**.
 
-- A [vector index created](how-to-create-vector-index.md) in Azure Machine Learning studio.
+- A [vector index created](how-to-create-vector-index.md) in Azure Machine Learning studio for the example prompt flow to use.
 
 ## Create the prompt flow
 
-This tutorial uses the **Q&A on Your Data** RAG prompt flow, containing a **lookup** node that uses the vector index lookup tool to search questions from the indexed docs. The index docs are stored in the workspace storage blob.
+This tutorial uses the sample **Q&A on Your Data** RAG prompt flow. This flow contains a **lookup** node that uses the vector index lookup tool to search questions from the indexed docs. The index docs are stored in the workspace storage blob.
 
-1. On the **Connections** tab of the Azure Machine Learning studio **Prompt flow** page, [set up a connection](get-started-prompt-flow.md#set-up-connection) to your Azure OpenAI resource if you don't have one.
+1. On the **Connections** tab of the Azure Machine Learning studio **Prompt flow** page, [set up a connection](prompt-flow/get-started-prompt-flow.md#set-up-connection) to your Azure OpenAI resource if you don't have one.
 
 1. Select **Create** on the Azure Machine Learning studio **Prompt flow** page, and on the **Create a new flow** screen, select **Clone** on the **Q&A on Your Data** tile to clone the prompt flow.
 
+   :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/my-flow.png" alt-text="Screenshot of bring your own data QnA in the Azure Machine Learning studio." lightbox = "./media/how-to-retrieval-augmented-generation-cloud-to-local/my-flow.png":::
+
 1. In your cloned flow, populate the **answer_the_question_with_context** step with your **Connection** and **Deployment** information for the **chat** API.
 
-   :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/my-cloud-connection.png" alt-text="Screenshot of answer_the_question_with_context node in studio showing inputs." lightbox = "./media/how-to-retrieval-augmented-generation-cloud-to-local/my-cloud-connection.png":::
+   :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/my-cloud-connection.png" alt-text="Screenshot of answer_the_question_with_context node in studio showing inputs.":::
 
 1. Populate the **mlindex_content** input in the **lookup** step with your vector index information.
 
-   :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/embed-question.png" alt-text="Screenshot of lookup node in studio showing inputs." lightbox = "./media/how-to-retrieval-augmented-generation-cloud-to-local/embed-question.png":::
+   :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/embed-question.png" alt-text="Screenshot of lookup node in studio showing inputs.":::
 
 1. Make sure the example flow runs correctly, and save it.
 
-  :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/my-flow.png" alt-text="Screenshot of bring your own data QnA in the Azure Machine Learning studio." lightbox = "./media/how-to-retrieval-augmented-generation-cloud-to-local/my-flow.png":::
+1. Select the **Download** icon at the upper right of the **Files** section. The flow files download as a ZIP package to your local machine.
 
-1. Select the **Download** icon at the top of the **Files** section of the flow authoring page. The flow files download as a ZIP package to your local machine.
+   :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/download.png" alt-text="Screenshot of the Files section of the authoring page with the Download icon highlighted.":::
 
 1. Unzip the package to a folder.
 
@@ -72,22 +75,24 @@ The rest of this article details how to use the VS Code Prompt flow extension to
 
 1. Select the **Prompt flow** icon in the left menu to open the Prompt flow management pane.
 
+   :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/vs-code-extension.png" alt-text="Screenshot of the prompt flow VS Code extension icon in the VS Code left menu.":::
+
 ### Create the connections
 
 To use the vector index lookup tool locally, you need to create the same connections to the vector index service as you did in the cloud.
 
-1. Expand the **Connections** section at the bottom of the Prompt flow management pane, and select the **+** icon next to the connection type **AzureOpenAI**.
+1. Expand the **Connections** section at the bottom of the Prompt flow management pane, and select the **+** icon next to the **AzureOpenAI** connection type.
 
    :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/vs-code-connection-create.png" alt-text="Screenshot of creating the connections in VS Code." lightbox = "./media/how-to-retrieval-augmented-generation-cloud-to-local/vs-code-connection-create.png":::
 
-1. A *new_AzureOpenAI_connection.yaml* file opens in the editing pane. Edit this file to add your Azure OpenAI connection `name` and `api_base` or endpoint. Note that you don't enter your `api_key` information yet.
+1. A *new_AzureOpenAI_connection.yaml* file opens in the editing pane. Edit this file to add your Azure OpenAI connection `name` and `api_base` or endpoint. Don't enter your `api_key` information yet.
 
    :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/flow-download.png" alt-text="Screenshot of the file for creating an Azure OpenAI connection." lightbox = "./media/how-to-retrieval-augmented-generation-cloud-to-local/flow-download.png":::
 
 1. Select the **Create connection** link at the bottom of the file. The app runs to create the connection. When prompted, enter the API key for your connection in the terminal.
 
 > [!NOTE]
-> There's a new version of the local vector index lookup tool, so you might also need to create a new Azure AI Search connection for the tool to use. For more information, see [Index Lookup tool for Azure Machine Learning (Preview)](prompt-flow/tools-reference/index-lookup-tool.md) and [Package tool isn't found error](prompt-flow/tools-reference/troubleshoot-guidance.md#package-tool-isnt-found-error-occurs-when-you-update-the-flow-for-a-code-first-experience).
+> You might also need to create a new Azure AI Search connection for the new version of the local vector index lookup tool to use. For more information, see [Index Lookup tool for Azure Machine Learning (Preview)](prompt-flow/tools-reference/index-lookup-tool.md) and [Package tool isn't found error](prompt-flow/tools-reference/troubleshoot-guidance.md#package-tool-isnt-found-error-occurs-when-you-update-the-flow-for-a-code-first-experience).
 
 ### Check the files
 
@@ -97,35 +102,38 @@ To use the vector index lookup tool locally, you need to create the same connect
 
 1. In the visual editor version of *flow.dag.yaml*, scroll to the **answer_the_question_with_context** node and make sure the connection is the same as the local connection you created. Check the **deployment_name**, which is the model you use here for the embedding.
 
-   :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/answer-connection.png" alt-text="Screenshot of answer the question with context node with the connection highlighted." lightbox = "./media/how-to-retrieval-augmented-generation-cloud-to-local/answer-connection.png":::
+   :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/answer-connection.png" alt-text="Screenshot of answer the question with context node with the connection highlighted.":::
 
-1. Scroll to the **lookup** node, which consumes the vector index lookup tool in this flow. Check the path of your indexed docs you specify. All publicly accessible paths are supported, such as `https://github.com/Azure/azureml-assets/tree/main/assets/promptflow/data/faiss-index-lookup/faiss_index_sample`.
+1. Scroll to the **lookup** node, which consumes the vector index lookup tool in this flow. Check the path of your indexed docs you specify. All publicly accessible paths are supported.
 
-   :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/search-blob.png" alt-text="Screenshot of search question from indexed docs node in VS Code showing the inputs." lightbox = "./media/how-to-retrieval-augmented-generation-cloud-to-local/search-blob.png":::
+   :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/search-blob.png" alt-text="Screenshot of search question from indexed docs node in VS Code showing the inputs.":::
 
    > [!NOTE]
    > If your indexed docs are a data asset in your workspace, local consumption requires Azure authentication. Make sure you're signed in to Azure and connected to your Azure Machine Learning workspace.
 
 1. Select the **Edit** icon in the **queries** input box, which opens the raw *flow.dag.yaml* file to the `lookup` node definition.
 
-1. Ensure that the value of the `tool` section within this node is set to `promptflow_vectordb.tool.vector_index_lookup.VectorIndexLookup.search`, which is the local version of the vector index lookup tool.
+1. Ensure that the value of the `tool` section in this node is set to `promptflow_vectordb.tool.vector_index_lookup.VectorIndexLookup.search`, which is the local version of the vector index lookup tool.
 
-   :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/search-tool.png" alt-text="Screenshot of the tool section of the lookup node." lightbox = "./media/how-to-retrieval-augmented-generation-cloud-to-local/search-tool.png":::
+   :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/search-tool.png" alt-text="Screenshot of the tool section of the lookup node.":::
 
 1. Scroll to the **generate_prompt_context** node, and in the raw *flow.dag.yaml* file, select the **Open code file** link.
 
 1. In the Python code file, make sure the package name of the vector tool is `promptflow_vectordb`.
 
-    :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/generate-node.png" alt-text="Screenshot of the generate prompt content node in VS Code highlighting the package name." lightbox = "./media/how-to-retrieval-augmented-generation-cloud-to-local/generate-node.png":::
+    :::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/generate-node.png" alt-text="Screenshot of the Python code file with the vector tool package name highlighted.":::
 
 ### Test and run the flow
 
 Scroll up to the top of the flow and fill in the **Inputs** value with a single question for this test run, for example **How to use SDK V2?**, and then select the **Run** icon to run the flow.
 
-:::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/flow-run.png" alt-text="Screenshot of the flow dag YAML file showing inputs and highlighting value of the question input and run button." lightbox = "./media/how-to-retrieval-augmented-generation-cloud-to-local/flow-run.png":::
+:::image type="content" source="./media/how-to-retrieval-augmented-generation-cloud-to-local/flow-run.png" alt-text="Screenshot of the flow dag YAML file showing inputs and highlighting value of the question input and run button.":::
 
-For more information about batch run and evaluation, see [Submit flow run to Azure Machine Learning workspace](prompt-flow/how-to-integrate-with-llm-app-devops.md)
+For more information about batch run and evaluation, see [Submit flow run to Azure Machine Learning workspace](prompt-flow/how-to-integrate-with-llm-app-devops.md).
 
 ## Related content
 
-- [Submit runs to cloud for large scale testing and ops integration](prompt-flow/how-to-integrate-with-llm-app-devops.md#submitting-runs-to-the-cloud-from-local-repository)
+- [Get started with prompt flow](prompt-flow/get-started-prompt-flow.md)
+- [Create and deploy an Azure OpenAI Service resource](/azure/ai-services/openai/how-to/create-resource)
+- [Create a vector index in an Azure Machine Learning prompt flow (preview)](how-to-create-vector-index.md)
+- [Integrate prompt flow with LLM-based application DevOps](prompt-flow/how-to-integrate-with-llm-app-devops.md)