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188 lines
5.0 KiB
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188 lines
5.0 KiB
Plaintext
---
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title: Deploy the Langflow development environment on Kubernetes
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slug: /deployment-kubernetes-dev
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---
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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The [Langflow integrated development environment (IDE) Helm chart](https://github.com/langflow-ai/langflow-helm-charts/tree/main/charts/langflow-ide) is designed to provide a complete environment for developers to create, test, and debug their flows. It includes both the Langflow API and visual editor.
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## Prerequisites
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- A [Kubernetes](https://kubernetes.io/docs/setup/) cluster
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- [kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl)
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- [Helm](https://helm.sh/docs/intro/install/)
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## Prepare a Kubernetes cluster
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This example uses [Minikube](https://minikube.sigs.k8s.io/docs/start/), but you can use any Kubernetes cluster.
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1. Create a Kubernetes cluster on Minikube:
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```shell
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minikube start
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```
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2. Set `kubectl` to use Minikube:
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```shell
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kubectl config use-context minikube
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```
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## Install the Langflow IDE Helm chart
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1. Add the repository to Helm, and then update it:
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```shell
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helm repo add langflow https://langflow-ai.github.io/langflow-helm-charts
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helm repo update
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```
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2. Install Langflow with the default options in the `langflow` namespace:
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```shell
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helm install langflow-ide langflow/langflow-ide -n langflow --create-namespace
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```
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3. Check the status of the pods:
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```shell
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kubectl get pods -n langflow
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```
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## Access the Langflow IDE
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Enable local port forwarding to access Langflow from your local machine:
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1. Make the Langflow API accessible from your local machine at port 7860:
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```shell
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kubectl port-forward -n langflow svc/langflow-service-backend 7860:7860
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```
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2. Make the visual editor accessible from your local machine at port 8080:
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```shell
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kubectl port-forward -n langflow svc/langflow-service 8080:8080
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```
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Now you can do the following:
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- Access the Langflow API at `http://localhost:7860`.
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- Access the visual editor at `http://localhost:8080`.
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## Modify your Langflow IDE deployment
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You can modify the Langflow IDE Helm chart's [`values.yaml`](https://github.com/langflow-ai/langflow-helm-charts/blob/main/charts/langflow-ide/values.yaml) file to customize your deployment.
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The following sections describe some common modifications.
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If you need to set secrets, Kubernetes secrets are recommended.
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### Deploy a different Langflow version
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The Langflow IDE Helm chart deploys the latest Langflow version by default.
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To specify a different Langflow version, set the `langflow.backend.image.tag` and `langflow.frontend.image.tag` values to your preferred version.
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For example:
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```yaml
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langflow:
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backend:
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image:
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tag: "1.0.0a59"
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frontend:
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image:
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tag: "1.0.0a59"
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```
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### Use external storage for the Langflow database
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The Langflow IDE Helm chart uses the default Langflow database configuration, specifically a SQLite database stored in a local persistent disk.
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If you want to use an [external PostgreSQL database](/configuration-custom-database), use `postgresql` chart or `externalDatabase` to configure the database connection in `values.yaml`.
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<Tabs>
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<TabItem value="postgresql" label="postgresql" default>
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Use the built-in PostgreSQL chart:
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```yaml
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postgresql:
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enabled: true
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auth:
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username: "langflow"
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password: "langflow-postgres"
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database: "langflow-db"
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```
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</TabItem>
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<TabItem value="externaldatabase" label="externalDatabase">
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If you don't want to use the built-in PostgreSQL chart, set `postgresql.enabled` to `false`, and then configure the database connection in `langflow.backend.externalDatabase`:
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```yaml
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postgresql:
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enabled: false
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langflow:
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backend:
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externalDatabase:
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enabled: true
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driver:
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value: "postgresql"
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host:
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value: "postgresql-svc.langflow.svc.cluster.local"
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port:
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value: "5432"
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user:
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value: "langflow"
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password:
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valueFrom:
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secretKeyRef:
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key: "password"
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name: "your-secret-name"
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database:
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value: "langflow-db"
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sqlite:
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enabled: false
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```
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</TabItem>
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</Tabs>
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### Configure scaling
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To configure scaling for the Langflow IDE Helm chart deployment, you must set `replicaCount` (horizontal scaling) and `resources` (vertical scaling) for both the `langflow.backend` and `langflow.frontend`.
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If your flows rely on a shared state, such as [built-in chat memory](/memory), you must also set up a shared database when scaling horizontally.
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```yaml
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langflow:
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backend:
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replicaCount: 1
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resources:
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requests:
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cpu: 0.5
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memory: 1Gi
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# limits:
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# cpu: 0.5
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# memory: 1Gi
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frontend:
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enabled: true
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replicaCount: 1
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resources:
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requests:
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cpu: 0.3
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memory: 512Mi
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# limits:
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# cpu: 0.3
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# memory: 512Mi
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```
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## See also
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* [Best practices for Langflow on Kubernetes](/deployment-prod-best-practices)
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* [Deploy the Langflow production environment on Kubernetes](/deployment-kubernetes-prod)
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* [Langflow Helm Charts repository](https://github.com/langflow-ai/langflow-helm-charts) |