Merge pull request #132 from anveshmuppeda/dev

🎉 kubectl-ai Blog is out now! Check it out here 🥳

Author: anveshmuppeda

LICENSE

@@ -0,0 +1,8 @@
+{
+  "label": "AI in Kubernetes",
+  "position": 10,
+  "link": {
+    "type": "generated-index",
+    "description": "This section provides a comprehensive guide on how to leverage AI and machine learning in your Kubernetes environment. It covers best practices, tools, and techniques for deploying and managing AI workloads on Kubernetes. Topics include model serving, data pipelines, and integration with popular AI frameworks.",
+  }
+}

README.md

@@ -0,0 +1,224 @@
+---
+// filepath: /Users/anveshmuppeda/Desktop/anvesh/tech/git/kubernetes/docs/ai/kubectl-ai.md
+sidebar_label: "Kubectl-AI"
+sidebar_id: "kubectl-ai"
+sidebar_position: 1
+---
+
+
+# Kubectl-AI: Enhance Kubernetes Management with AI
+
+Below is the trimmed-down, focused documentation covering only the sections you requested, with detailed step-by-step instructions.
+
+---
+
+## kubectl-ai Introduction
+
+`kubectl-ai` is a plugin and CLI tool that turns natural-language queries into Kubernetes commands. Instead of memorizing `kubectl` flags or YAML manifests, you simply ask questions like “show me all pods in the default namespace” or “scale the nginx deployment to 5 replicas,” and `kubectl-ai` figures out the exact `kubectl` commands to run. Under the hood it uses large language models (LLMs) to parse your intent, then executes the corresponding `kubectl` operations and returns both the raw output and an explanation in plain English.
+
+Key benefits:
+- **Natural-language interface:** No more guessing flag names or resource kinds.
+- **Context awareness:** In interactive mode, follow-on questions (“What about in the staging cluster?”) carry context.
+- **Extensible backends:** Works with commercial LLMs (Gemini, OpenAI, Azure OpenAI, Grok) or fully offline local models (Ollama, llama.cpp).
+- **Scriptable & pipable:** Use it in CI, scripts, or combine with shell pipes (`echo … | kubectl-ai`).
+
+---
+
+## Installation
+
+You need a working `kubectl` binary configured against your cluster, plus one of these ways to install `kubectl-ai` itself:
+
+### Quick Install (Linux & macOS)
+
+```bash
+curl -sSL https://raw.githubusercontent.com/GoogleCloudPlatform/kubectl-ai/main/install.sh | bash
+```
+
+This will download the latest release, install the `kubectl-ai` binary into `~/.local/bin` (or similar), and add it to your PATH for the current session.
+
+### Manual Installation (Linux, macOS & Windows)
+
+1. Visit the [releases page](https://github.com/GoogleCloudPlatform/kubectl-ai/releases/latest) and download the archive matching your OS/arch (e.g. `kubectl-ai_Darwin_arm64.tar.gz` for Apple Silicon macOS).
+2. Unpack, make executable, and move into your PATH:
+
+   ```bash
+   tar -zxvf kubectl-ai_Darwin_arm64.tar.gz
+   chmod +x kubectl-ai
+   sudo mv kubectl-ai /usr/local/bin/
+   ```
+
+3. Verify installation:
+
+   ```bash
+   kubectl-ai version
+   ```
+
+---
+
+## Usage
+
+Once installed, you can run `kubectl-ai` in two main modes: using cloud-hosted LLMs (Gemini by default) or local models (Ollama/llama.cpp).
+
+---
+
+### Using Gemini (Default)
+
+1. **Obtain a key**  
+   Go to [Google AI Studio](https://aistudio.google.com), create a project, and copy your Gemini API key.  
+
+2. **Export the environment variable**  
+   ```bash
+   export GEMINI_API_KEY="your_gemini_api_key_here"
+   ```
+
+3. **Run `kubectl-ai`**  
+   - **Interactive shell:**  
+     ```bash
+     kubectl-ai
+     ```
+     You’ll get a `kubectl-ai> ` prompt where you can ask a sequence of questions.
+
+   - **Single-query mode:**  
+     ```bash
+     kubectl-ai --quiet "fetch logs for nginx app in hello namespace"
+     ```
+
+4. **Switch models** (optional)  
+   ```bash
+   # Use a specific Gemini model
+   kubectl-ai --model gemini-2.5-pro-exp-03-25 "list services in default"
+   ```
+
+---
+
+### Using AI Models Running Locally (Ollama or llama.cpp)
+
+You can run entirely offline by hosting your own LLM with either [Ollama](https://ollama.com/) or [llama.cpp](https://github.com/ggml-org/llama.cpp). Below are detailed steps for each:
+
+---
+
+#### A. Ollama
+
+1. **Install Ollama**  
+   ```bash
+   # macOS via Homebrew
+   brew install ollama
+   # or Linux via their install script
+   curl -fsSL https://ollama.com/install.sh | sh
+   ```
+   
+   Alternatively, you can download the Ollama binary directly from their website:
+    1. Visit the Ollama [download page](https://ollama.com/download/mac).
+    2. Click on the Download for macOS button.
+    3. Once the download is complete, locate the .zip file in your ~/Downloads folder.
+    4. Double-click the .zip file to extract its contents. This should create Ollama.app.
+    5. Move the Ollama.app to your Applications folder for easy access.
+    6. Open the Applications folder and double-click on Ollama.app to launch it.
+    7. Follow any on-screen instructions to complete the installation.
+   8. Once installed, you can run the Ollama CLI commands in your terminal.
+
+
+2. **Run the Ollama server**  
+   ```bash
+   ollama serve
+   ```
+   By default, this listens on `http://localhost:11434`.
+
+    ![Ollama Running](img/ollama.running.png)
+
+3. **Pull a model**  
+   ```bash
+   ollama pull gemma3:12b-it-qat
+   ```
+    This downloads the `gemma3:12b-it-qat` model, which is optimized for Kubernetes tasks.
+    
+     ![Ollama Pull](img/ollama.model.pull.png)
+
+4. **List models**  
+   ```bash
+   ollama list
+   ```
+   This command will show you all the models you have downloaded and their status.
+
+    ![Ollama List](img/ollama.list.png)
+   You should see `gemma3:12b-it-qat` in the list.
+
+5. **Configure `kubectl-ai`**  
+   ```bash
+   export OLLAMA_HOST="http://localhost:11434"
+   kubectl-ai \
+     --llm-provider=ollama \
+     --model=gemma3:12b-it-qat \
+     --enable-tool-use-shim
+   ```
+   The `--enable-tool-use-shim` flag ensures the prompt includes the right instructions for invoking `kubectl`.
+    This is important for the model to understand how to interact with Kubernetes.
+    
+     ![Ollama Config](img/ollama.config.png)
+
+6. **Use it**  
+   - Interactive: `kubectl-ai`  
+   - Single query:  
+     ```bash
+     kubectl-ai --quiet "describe deployment my-app"
+     ```
+
+7. **Remove a model**  
+   ```bash
+   ollama remove gemma3:12b-it-qat
+   ```
+   This command will delete the model from your local machine.
+
+8. **Stop the server**  
+   ```bash
+   ollama stop
+   ```
+   This command will stop the Ollama server.
+
+
+---
+
+#### B. llama.cpp
+
+1. **Install or build**  
+   ```bash
+   # Homebrew (macOS/Linux)
+   brew install llama.cpp
+   ```
+   The formula is automatically updated with new llama.cpp releases.
+
+2. **Get a model**  
+   - Download a GGUF (quantized) model from Hugging Face (e.g. `alpaca-7b-q4_0.gguf`).
+   - Place it in `~/models/` or your chosen directory.
+
+3. **Run the server**  
+   ```bash
+   ./main --model ~/models/alpaca-7b-q4_0.gguf --server
+   ```
+   This starts an OpenAI-compatible API on `http://127.0.0.1:8080`.
+
+4. **Configure `kubectl-ai`**  
+   ```bash
+   export LLAMA_CPP_SERVER="http://127.0.0.1:8080"
+   kubectl-ai \
+     --llm-provider=llama.cpp \
+     --model=alpaca-7b-q4_0.gguf \
+     --enable-tool-use-shim
+   ```
+
+5. **Use it**  
+   - Interactive: `kubectl-ai`  
+   - Single query:  
+     ```bash
+     kubectl-ai --quiet "scale deployment nginx to 4 replicas"
+     ```
+
+---
+## References   
+- [GoogleCloudPlatform GitHub Repository](https://github.com/GoogleCloudPlatform/kubectl-ai)
+- [Google AI Studio](https://aistudio.google.com)
+- [Ollama](https://ollama.com/)
+- [llama.cpp](https://github.com/ggml-org/llama.cpp)
+
+With these sections in place, users have everything needed to get started with `kubectl-ai`—from installation through cloud (Gemini) or local (Ollama/llama.cpp) usage.
+

docs/ai/_category_.json

@@ -87,16 +87,27 @@ const config: Config = {
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       },
     ],
+    [
+      '@docusaurus/theme-live-codeblock', // Wrap this plugin in an array
+      {},
+    ],
   ],
 
   themeConfig: {
+    liveCodeBlock: {
+      /**
+       * The position of the live playground, above or under the editor
+       * Possible values: "top" | "bottom"
+       */
+      playgroundPosition: 'bottom',
+    },
     announcementBar: {
       id: 'announcement', // Unique ID for the announcement
-      content:
-        '⭐️ If you like this project, give it a star on <a target="_blank" rel="noopener noreferrer" href="https://github.com/anveshmuppeda/kubernetes">GitHub</a> and follow me on <a target="_blank" rel="noopener noreferrer" href="https://www.linkedin.com/in/anveshmuppeda/">LinkedIn</a> ⭐️',
-      backgroundColor: '#fafbfc', // Background color
-      textColor: '#091E42', // Text color
-      isCloseable: true, // Allow users to close the bar
+      content: 
+        '🎉️ <b><a target="_blank" href="https://github.com/anveshmuppeda/kubernetes/releases/tag/${version}">Docusaurus v${version}</a> is out!</b> 🥳️',
+      backgroundColor: '#fafbfc',
+      textColor: '#091E42',
+      isCloseable: false,
     },
     image: 'img/docusaurus-social-card.jpg',
     navbar: {