fix all links for images and assets

Author: BethanyJep

.github/steps/2-to-azure.md

@@ -26,18 +26,18 @@ In this step, you will learn how to deploy your AI model to Azure AI Foundry aft
 
 1. On the separate tab on the GitHub models playground, click on **Use this model** and select **Language: JavaScript** and **SDK: Azure AI Inference  SDK**. 
 
-   ![Use model](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/use-this-model-button.png?raw=true)
+   ![Use model](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/use-this-model-button.png?raw=true)
 
 2. Under **Create a personal access token**, select **Get production key** and this will take you to ai.azure.com and prompt you to:-
    - Sign in to your Azure account
    - Select your Azure subscription 
    - Deploy model
 
-      ![Select subscription](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/ai-foundry-select-subscription.png?raw=true)
+      ![Select subscription](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/ai-foundry-select-subscription.png?raw=true)
 
 3. The model you selected will be pre-populated in the **Deployment name** field. You can optionally click on **Customize** to change the default configuration on deployment type, model version, tokens per minute (TPM) rate limit etc.
 
-      ![Customize model for deployment](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/deploy-model.png?raw=true)
+      ![Customize model for deployment](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/deploy-model.png?raw=true)
 
 ## 🧰 AI Foundry VS Code Extension
 
@@ -47,15 +47,15 @@ To continue working with your deployed model in VS Code, you will need to instal
 
 2. Once installed, click on the **AI Foundry** icon in the left sidebar and click on **Set Default Project**. Select your project and expand the **Models** section. You should see your deployed model(s) listed there.
 
-      ![Set default project](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/set-default-project.png?raw=true)
+      ![Set default project](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/set-default-project.png?raw=true)
 
 3. Click on the model name to open the model details view, where you can see the model's metadata, including the model version, deployment status, and TPM rate limit.
 
-      ![List of models](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/model-list.png?raw=true)
+      ![List of models](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/model-list.png?raw=true)
 
 4. Right click on your model and select **Open in Playground**. This will open a tab in VS Code with a chat Playground, where you can test your deployed model.
 
-      ![AI Foundry playground](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/ai-foundry-playground.png?raw=true)
+      ![AI Foundry playground](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/ai-foundry-playground.png?raw=true)
 
 5. You can also use the **Compare** feature to compare the performance of your deployed model with other models for manual evaluation. Once you are happy with the performance of your deployed model, right click on the model and select **Open Code File**, then:
    - Select **SDK**: Azure AI Inference SDK/ Azure OpenAI SDK
@@ -89,7 +89,7 @@ To continue working with your deployed model in VS Code, you will need to instal
 
 8. Finally, run `node ai-foundry.js` and observe the output in the terminal. You should see the response from your deployed model.
 
-   ![AI Foundry sample code](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/run-ai-foundry-sample.png?raw=true)
+   ![AI Foundry sample code](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/run-ai-foundry-sample.png?raw=true)
 
 
 ## ✅ Activity: Push `ai-foundry.js` code to your repository

.github/steps/3-add-chat-ui.md

@@ -75,7 +75,7 @@ npm run dev
 
 Navigate to `http://localhost:5173` in your browser to see the chat interface.
 
-![AI Chat Interface](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/vite-lit-ai-chat-interface.png?raw=true)
+![AI Chat Interface](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/vite-lit-ai-chat-interface.png?raw=true)
 
 ## Step 2️⃣: Add your AI model to the chat interface
 
@@ -215,7 +215,7 @@ Rename the `_mockAiCall` function to `_apiCall` and update the `sendMessage` met
 
 With the server running, navigate to `http://localhost:5173` in your browser. You should be able to send messages to the AI model and receive responses.
 
-![AI Chat Interface AI Foundry](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/ai-chat-interface-ai-foundry.png?raw=true)
+![AI Chat Interface AI Foundry](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/ai-chat-interface-ai-foundry.png?raw=true)
 
 ## Step 4️⃣: Deploy to Azure
 
@@ -302,7 +302,7 @@ To deploy the application,
 - Select your Azure subscription,
 - Select a location for the resources.
 
-  ![azd up](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/azd-up.png?raw=true)
+  ![azd up](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/azd-up.png?raw=true)
 
 
 ## ✅ Activity: Push deployment infra code to your repository

.github/steps/4-add-rag.md

@@ -23,7 +23,7 @@ In this step, you will learn how to add RAG (**R**etrieval-**A**ugmented **G**en
 
 To complete this step, you will need to get a sample dataset in any format (e.g., PDF, CSV, JSON) to work with. 
 
-An an example, will use a [sample Contoso Electronics Employee Handbook PDF](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/employee_handbook.pdf) file. **You can bring any file of your choice**, but make sure it contains relevant information that you want your AI app to use for RAG. The code provided here will work with any text-based file.
+An an example, will use a [sample Contoso Electronics Employee Handbook PDF](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/employee_handbook.pdf) file. **You can bring any file of your choice**, but make sure it contains relevant information that you want your AI app to use for RAG. The code provided here will work with any text-based file.
 
 - Create a new folder `data` in the root of your project and move the file in it. To search and read your PDF, you will need to extract the text from it. You can use any PDF parser library of your choice, but for this example, we will use the `pdf-parse` library.
 
@@ -364,24 +364,24 @@ Open your browser to use the app, usually at `http://localhost:5123`.
 2. Ask a question related to the employee handbook, such as _"What is our company's mission statement?"_
    - The expected outcome is that the AI will respond with an answer based on the content of the employee handbook PDF, and the relevant excerpts will be displayed below the response.
 
-      ![AI Foundry RAG with context](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/ai-app-with-rag.png?raw=true)
+      ![AI Foundry RAG with context](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/ai-app-with-rag.png?raw=true)
 
 3. Now ask a question not covered in the employee handbook, such as _"What's the company's stock price?"_
     - The expected outcome is that the AI will respond saying it doesn't have the information, and no excerpts will be displayed.
 
-      ![AI Foundry RAG out of scope](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/ai-app-with-rag-outofscope.png?raw=true)
+      ![AI Foundry RAG out of scope](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/ai-app-with-rag-outofscope.png?raw=true)
 
 ### Test with RAG OFF 🔴 
 1. **Clear chat and uncheck the "Use Employee Handbook" checkbox**.
 2. Ask a question related to the employee handbook, such as _"What is our company's mission statement?"_
    - The expected outcome is that the AI will respond with a generic answer, and likely ask for more context, and no excerpts will be displayed.
 
-      ![AI Foundry no RAG no context](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/no-rag-company.png?raw=true)
+      ![AI Foundry no RAG no context](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/no-rag-company.png?raw=true)
 
 3. Now ask any general question, such as _"What is the capital of Morocco?"_
    - The expected outcome is that the AI will respond with the correct answer, and no excerpts will be displayed.
 
-      ![AI Foundry no RAG general question](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/no-rag-general.png?raw=true)
+      ![AI Foundry no RAG general question](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/no-rag-general.png?raw=true)
 
 Notice how, with RAG enabled, the AI is strictly limited to the handbook and refuses to answer unrelated questions. With RAG disabled, the AI is more flexible and answers any question to the best of its ability.
    

.github/steps/5-frameworks.md

@@ -96,7 +96,7 @@ Currently, the chat model does not remember previous messages. For example, if y
 
 Then ask the model _"Quiz time. What's my name?"_. The model will not remember your name because your name is not passed to the model in the prompt.
 
-![Memory test](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/memory-test.png?raw=true)
+![Memory test](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/memory-test.png?raw=true)
 
 To add memory, you will use LangChain's built-in memory modules - `ChatMessageHistory` and `ConversationSummaryMemory`. Conversation memory allows the AI to reference previous exchanges in a session, enabling more context-aware and coherent responses and LangChain.js provides built-in memory modules that make this easy to implement. With LangChain, you can implement stateful AI app experiences without manually managing chat logs, and you can easily switch between in-memory, Redis, or other storage options.
 
@@ -201,7 +201,7 @@ app.post("/chat", async (req, res) => {
 
 To test this, open the chat UI in your browser and send a message like _"Hey, you can call me Terry. What should I call you?"_ and then ask _"Quiz time. What's my name?"_. The model should remember your name.
 
-  ![Memory test passed](https://github.com/juliamuiruri4/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/memory-test-passed.png?raw=true)
+  ![Memory test passed](https://github.com/Azure-Samples/JS-Journey-to-AI-Foundry/blob/assets/jsai-buildathon-assets/memory-test-passed.png?raw=true)
 
 ## ✅ Activity: Push your updated code to the repository