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https://github.com/srimoyee1212/Kube-GPT-Agent

Use an AI agent to get information about your Kubernetes deployments!
https://github.com/srimoyee1212/Kube-GPT-Agent

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Use an AI agent to get information about your Kubernetes deployments!

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# Kubernetes AI Agent Using Flask and OpenAI's API



This project implements an AI agent capable of interpreting and answering Kubernetes-related queries using natural language. The agent leverages Flask for the web server, Kubernetes Python client for cluster interactions, and OpenAI’s GPT-4 model for natural language processing.



## Approach



### 1. Flask Web Server and Endpoint Setup

The agent runs on a Flask web server and provides a **POST endpoint** (`/query`) for receiving user queries. Queries are submitted in JSON format, and the server responds with relevant information about the Kubernetes cluster.



- **Why Flask?** Flask is lightweight and suitable for microservices, with a minimal setup that allows for efficient request handling and easy API structuring.



### 2. Kubernetes Client Initialization and Error Handling

The agent uses the Kubernetes Python client API (`client.CoreV1Api` and `client.AppsV1Api`) to connect to the Kubernetes cluster, loading the `.kube/config` file for secure configuration access.



- **Error Management:** A custom context manager (`k8s_error_handling`) is implemented to manage API exceptions. This logs both Kubernetes-specific errors and unexpected issues, allowing the application to handle errors gracefully and providing clear logging for debugging.



### 3. Agent Design: Kubernetes Query Processor

The main logic resides within the `KubernetesQueryProcessor` class, which encapsulates Kubernetes-related functions needed to fulfill common queries. The agent maps user queries to functions that can:



- List pods, services, nodes, deployments, and namespaces

- Retrieve status, logs, or pods generated by specific deployments

- Count the number of running pods and nodes



Each function utilizes the Kubernetes API to return data in the expected format, minimizing identifiers. For example, `get_pods_by_deployment` strips any suffixes from pod names, ensuring only base names are returned, as required.



### 4. Natural Language Understanding with GPT-4

OpenAI’s GPT-4 model is used to interpret natural language queries. It translates each query into a structured JSON format containing:



- **operation**: The name of the Kubernetes function to execute

- **parameters**: The parameters needed for the operation



   - **Prompting Technique:** A detailed system prompt provides GPT-4 with a dictionary of valid operations and their parameters. This helps map user queries accurately to specific functions within `KubernetesQueryProcessor`.

   - **Function Invocation:** Once the query is parsed, the agent dynamically calls the required function, passing in parameters as needed.



### 5. Query Processing Flow

Upon receiving a query:

1. It is parsed with GPT-4, which determines the required operation and parameters.

2. The relevant Kubernetes function is invoked with the specified parameters.

3. The response is formatted for clarity and returned to the user.



- **Output Formatting:** For list-based queries, results are presented as comma-separated strings. Counts or single values are returned as plain text.



### 6. Deployment and Testing Recommendations

To ensure robustness, the agent can be tested on Minikube with test applications deployed to confirm responses match expectations. Testing on Minikube provides a realistic Kubernetes environment, allowing potential issues to be identified and resolved before wider deployment.



## Local Setup



### Prerequisites

1. **Python 3.10**: Make sure Python 3.10 is installed.

   - Install Python [here](https://www.python.org/downloads/).

   

2. **Kubernetes Cluster Access**: Ensure you have access to a Kubernetes cluster (e.g., using Minikube or a remote cluster).

   - **Minikube**: You can install Minikube by following [these instructions](https://minikube.sigs.k8s.io/docs/start/).

   - After starting Minikube, confirm the Kubernetes config file is located at `~/.kube/config`.



3. **OpenAI API Key**: You’ll need an API key from OpenAI to use GPT-4.

   - [Sign up for OpenAI](https://platform.openai.com/) and generate an API key.

   - Set the API key as an environment variable:  

     ```bash

     export OPENAI_API_KEY="your_openai_api_key"

     ```



4. **Install Dependencies**: Install the required Python libraries.

   ```bash

   pip install -r requirements.txt

   ```



### Running the AI Agent

**Start the Flask Server**

To start the server, navigate to the directory where app.py is located and run:



```bash

python main.py

```



The server should now be running locally on `http://localhost:8000`. If successful, you should see output similar to:



```bash

 * Running on http://0.0.0.0:8000/ (Press CTRL+C to quit)

```



### Local Testing

esting the Agent Locally

Example Queries Using curl

Once the server is running, you can test the agent by making HTTP POST requests to http://localhost:8000/query. Each query should be submitted in JSON format with a query key, as shown in the examples below.



For each example, open a terminal and replace your_query_here with the specific question or command you want to test.



1. Check the Status of a Pod



```bash

curl -X POST http://localhost:8000/query -H "Content-Type: application/json" -d '{"query": "What is the status of the pod named nginx?"}'

```



2. List All Pods in the Default Namespace



```bash

curl -X POST http://localhost:8000/query -H "Content-Type: application/json" -d '{"query": "List all pods in the default namespace"}'

```



3. Show Logs of a Specific Pod



```bash

curl -X POST http://localhost:8000/query -H "Content-Type: application/json" -d '{"query": "Show me logs for pod nginx"}'

```



4. List All Nodes in the Cluster



```bash

curl -X POST http://localhost:8000/query -H "Content-Type: application/json" -d '{"query": "List all nodes in the cluster"}'

```



5. List All Services in the Default Namespace



```bash

curl -X POST http://localhost:8000/query -H "Content-Type: application/json" -d '{"query": "List all services in the default namespace"}'

```



6. Count Running Pods in the Default Namespace



```bash

curl -X POST http://localhost:8000/query -H "Content-Type: application/json" -d '{"query": "How many pods are running in the default namespace?"}'

```



7. List All Deployments in the Default Namespace



```bash

curl -X POST http://localhost:8000/query -H "Content-Type: application/json" -d '{"query": "List all deployments in the default namespace"}'

```