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diff --git a/docs/versioned_docs/version-v0.17.0/02-Installation/04-kubernetes.md b/docs/versioned_docs/version-v0.17.0/02-Installation/04-kubernetes.md deleted file mode 100644 index 524f6e61..00000000 --- a/docs/versioned_docs/version-v0.17.0/02-Installation/04-kubernetes.md +++ /dev/null @@ -1,71 +0,0 @@ -# Kubernetes - -### Requirements - -- A kubernetes cluster -- kubectl -- kustomize - -### 1. Get the deployment manifests - -You can clone the repository and copy the `/kubernetes` directory into another directory of your choice. - -### 2. Populate the environment variables - -To configure the app, edit the configuration in `.env`. - - -You **should** change the random strings. You can use `openssl rand -base64 36` to generate the random strings. You should also change the `NEXTAUTH_URL` variable to point to your server address. - -Using `HOARDER_VERSION=release` will pull the latest stable version. You might want to pin the version instead to control the upgrades (e.g. `HOARDER_VERSION=0.10.0`). Check the latest versions [here](https://github.com/hoarder-app/hoarder/pkgs/container/hoarder-web). - -### 3. Setup OpenAI - -To enable automatic tagging, you'll need to configure OpenAI. This is optional though but highly recommended. - -- Follow [OpenAI's help](https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key) to get an API key. -- Add the OpenAI API key to the `.env` file: - -``` -OPENAI_API_KEY=<key> -``` - -Learn more about the costs of using openai [here](/openai). - -<details> - <summary>[EXPERIMENTAL] If you want to use Ollama (https://ollama.com/) instead for local inference.</summary> - - **Note:** The quality of the tags you'll get will depend on the quality of the model you choose. Running local models is a recent addition and not as battle tested as using openai, so proceed with care (and potentially expect a bunch of inference failures). - - - Make sure ollama is running. - - Set the `OLLAMA_BASE_URL` env variable to the address of the ollama API. - - Set `INFERENCE_TEXT_MODEL` to the model you want to use for text inference in ollama (for example: `mistral`) - - Set `INFERENCE_IMAGE_MODEL` to the model you want to use for image inference in ollama (for example: `llava`) - - Make sure that you `ollama pull`-ed the models that you want to use. - - -</details> - -### 4. Deploy the service - -Deploy the service by running: - -``` -make deploy -``` - -### 5. Access the service - -By default, these manifests expose the application as a LoadBalancer Service. You can run `kubectl get services` to identify the IP of the loadbalancer for your service. - -Then visit `http://<loadbalancer-ip>:3000` and you should be greated with the Sign In page. - -> Note: Depending on your setup you might want to expose the service via an Ingress, or have a different means to access it. - -### [Optional] 6. Setup quick sharing extensions - -Go to the [quick sharing page](/quick-sharing) to install the mobile apps and the browser extensions. Those will help you hoard things faster! - -## Updating - -Edit the `HOARDER_VERSION` variable in the `kustomization.yaml` file and run `make clean deploy`. |