docs: collapse folder+index.md guides to flat single-file guides

content/guides/genai-claude-code-mcp/claude-code-mcp-guide.md → content/guides/genai-claude-code-mcp.md

@@ -7,6 +7,7 @@ summary: |
   You’ll enable the Docker Hub MCP server, connect Claude Code, verify MCP access, and create a Node.js + PostgreSQL stack with a conversational prompt.
 aliases:
   - /guides/use-case/genai-claude-code-mcp/
+  - /guides/genai-claude-code-mcp/claude-code-mcp-guide/
 params:
   tags: [ai]
   time: 15 minutes
@@ -73,11 +74,11 @@ Make sure you have:
 1. [Create a read-only personal access token](/security/access-tokens/#create-a-personal-access-token) and enter your access token under **Secrets**
 1. Save the configuration
 
-![Docker Hub](./Images/catalog_docker_hub.avif "Docker Hub")
+![Docker Hub](images/genai-claude-code-mcp-catalog-docker-hub.avif "Docker Hub")
 
 Public images work without credentials. For private repositories, you can add your Docker Hub username and token later.
 
-![Docker Hub Secrets](./Images/dockerhub_secrets.avif "Docker Hub Secrets")
+![Docker Hub Secrets](images/genai-claude-code-mcp-dockerhub-secrets.avif "Docker Hub Secrets")
 
 
 ---
@@ -93,7 +94,7 @@ You can connect from Docker Desktop or using the CLI.
 1. Locate Claude Code  
 1. Select **Connect**
 
-![Docker Connection](./Images/docker-connect-claude.avif)
+![Docker Connection](images/genai-claude-code-mcp-docker-connect-claude.avif)
 
 ### Option B. Connect using the CLI
 
@@ -128,7 +129,7 @@ You should now see:
 - The MCP gateway (for example `MCP_DOCKER`)
 - Tools provided by the Docker Hub MCP server
 
-![mcp-docker](./Images/mcp-servers.avif)
+![mcp-docker](images/genai-claude-code-mcp-mcp-servers.avif)
 
 If not, restart Claude Code or check Docker Desktop to confirm the connection.
 
@@ -268,7 +269,7 @@ Open your browser:
 ```console
 http://localhost:3000
 ```
-![Local Host](./Images/Localhost.avif)
+![Local Host](images/genai-claude-code-mcp-localhost.avif)
 
 Your Node.js app should now be running.
 

content/guides/genai-leveraging-rag/index.md → content/guides/genai-leveraging-rag.md

@@ -132,7 +132,7 @@ When those lines appear in the logs, the web apps are ready to use.
 
 Since the goal is to teach AI about things it does not yet know, begin by asking it a simple question about NiFi at
 [http://localhost:8501/](http://localhost:8501/).
-![alt text](image.png)
+![alt text](images/genai-leveraging-rag-1.png)
 
 ```text
 Question: What is Apache Nifi?
@@ -144,7 +144,7 @@ As shown, the AI does not know anything about this subject because it did not ex
 
 Now it's time to teach the AI some new tricks. First, connect to [http://localhost:8502/](http://localhost:8502/). Instead of using the "neo4j" tag, change it to the "apache-nifi" tag, then select the **Import** button.
 
-![alt text](image-1.png)
+![alt text](images/genai-leveraging-rag-2.png)
 
 After the import is successful, you can access Neo4j to verify the data.
 
@@ -160,7 +160,7 @@ ORDER BY Count DESC;
 
 To execute this query, write in the box on the top and select the blue run button.
 
-![alt text](image-2.png)
+![alt text](images/genai-leveraging-rag-3.png)
 
 Results will appear below. The information shown is downloaded from Stack Overflow and saved in the graph database. RAG will utilize this information to enhance its responses.
 
@@ -178,7 +178,7 @@ CALL db.relationshipTypes()
 
 You're ready to enable the LLM to use this information. Go back to [http://localhost:8501/](http://localhost:8501/), enable the **RAG** checkbox, and ask the same question again. The LLM will provide a more detailed answer.
 
-![alt text](image-3.png)
+![alt text](images/genai-leveraging-rag-4.png)
 
 The system delivers comprehensive, accurate information by pulling from current technical documentation.
 ```text

content/guides/genai-video-bot/index.md → content/guides/genai-video-bot.md

@@ -118,7 +118,7 @@ database. The following steps show how to use the service.
    and select **Submit**. The following example uses
    [https://www.youtube.com/watch?v=yaQZFhrW0fU](https://www.youtube.com/watch?v=yaQZFhrW0fU).
 
-   ![Submitting a video in the yt-whisper service](images/yt-whisper.webp)
+   ![Submitting a video in the yt-whisper service](images/genai-video-bot-yt-whisper.webp)
 
    The yt-whisper service downloads the audio of the video, uses Whisper to
    transcribe it into a WebVTT (`*.vtt`) format (which you can download), then
@@ -129,7 +129,7 @@ database. The following steps show how to use the service.
    you which videos have been indexed in Pinecone. It also provides a button to
    download the transcript.
 
-   ![A processed video in the yt-whisper service](images/yt-whisper-2.webp)
+   ![A processed video in the yt-whisper service](images/genai-video-bot-yt-whisper-2.webp)
 
    You can now access the dockerbot service on port `8504` and ask questions
    about the videos.
@@ -156,7 +156,7 @@ how to use the service.
    example,
    [https://www.youtube.com/watch?v=yaQZFhrW0fU](https://www.youtube.com/watch?v=yaQZFhrW0fU).
 
-   ![Asking a question to the Dockerbot](images/bot.webp)
+   ![Asking a question to the Dockerbot](images/genai-video-bot-bot.webp)
 
    In this example, the Dockerbot answers the question and
    provides links to the video with timestamps, which may contain more
@@ -185,7 +185,7 @@ The following image shows the application's high-level service architecture, whi
 - OpenAI: A remote third-party service.
 - Pinecone: A remote third-party service.
 
-![Application architecture diagram](images/architecture.webp)
+![Application architecture diagram](images/genai-video-bot-architecture.webp)
 
 ## Explore the technologies used and their role
 
@@ -226,7 +226,7 @@ relevance to user input, facilitating efficient search and response generation
 in the application. For more details, see OpenAI's
 [Embeddings](https://platform.openai.com/docs/guides/embeddings) documentation.
 
-![Embedding diagram](images/embeddings.webp)
+![Embedding diagram](images/genai-video-bot-embeddings.webp)
 
 ### Chat completions
 

content/guides/java/index.md → content/guides/java.md

@@ -585,19 +585,19 @@ You should receive the following response:
 
 You’ll use the debugger that comes with the IntelliJ IDEA. You can use the community version of this IDE. Open your project in IntelliJ IDEA, go to the **Run** menu, and then **Edit Configuration**. Add a new Remote JVM Debug configuration similar to the following:
 
-![Java Connect a Debugger](images/connect-debugger.webp)
+![Java Connect a Debugger](images/java-connect-debugger.webp)
 
 Set a breakpoint.
 
 Open `src/main/java/org/springframework/samples/petclinic/vet/VetController.java` and add a breakpoint inside the `showResourcesVetList` function.
 
 To start your debug session, select the **Run** menu and then **Debug _NameOfYourConfiguration_**.
 
-![Debug menu](images/debug-menu.webp?w=300)
+![Debug menu](images/java-debug-menu.webp?w=300)
 
 You should now see the connection in the logs of your Compose application.
 
-![Compose log file ](images/compose-logs.webp)
+![Compose log file ](images/java-compose-logs.webp)
 
 You can now call the server endpoint.
 
@@ -607,7 +607,7 @@ $ curl --request GET --url http://localhost:8080/vets
 
 You should have seen the code break on the marked line and now you are able to use the debugger just like you would normally. You can also inspect and watch variables, set conditional breakpoints, view stack traces and a do bunch of other stuff.
 
-![Debugger code breakpoint](images/debugger-breakpoint.webp)
+![Debugger code breakpoint](images/java-debugger-breakpoint.webp)
 
 Press `ctrl+c` in the terminal to stop your application.
 

content/guides/frameworks/laravel/index.md → content/guides/laravel.md

@@ -6,6 +6,7 @@ description: A guide on using Docker Compose to manage Laravel applications for
 keywords: laravel, php, docker compose, web framework, development, production
 aliases:
   - /frameworks/laravel/
+  - /guides/frameworks/laravel/
   - /guides/frameworks/laravel/common-questions/
   - /guides/frameworks/laravel/development-setup/
   - /guides/frameworks/laravel/prerequisites/