docs: added fetching from notion (#2670)
* Added new Docusaurus instance that fetches automatically from Notion * Add Github workflow to fetch docs from Notion * Added legacy peer deps to solve dependency problems * Fix git ignore and added pages
This commit is contained in:
Lucas Oliveira
GitHub
parent
bab941f3e6
commit
3aa2513a86
354 changed files with 20640 additions and 23291 deletions
361
docs/docs/Deployment/deployment-kubernetes.md
Normal file
361
docs/docs/Deployment/deployment-kubernetes.md
Normal file
|
|
@ -0,0 +1,361 @@
|
|||
---
|
||||
title: Kubernetes
|
||||
sidebar_position: 1
|
||||
slug: /deployment-kubernetes
|
||||
---
|
||||
|
||||
|
||||
|
||||
This guide will help you get LangFlow up and running in Kubernetes cluster, including the following steps:
|
||||
|
||||
- Install [LangFlow as IDE](/deployment-kubernetes) in a Kubernetes cluster (for development)
|
||||
- Install [LangFlow as a standalone application](/deployment-kubernetes) in a Kubernetes cluster (for production runtime workloads)
|
||||
|
||||
## LangFlow (IDE) {#cb60b2f34e70490faf231cb0fe1a4b42}
|
||||
|
||||
|
||||
This solution is designed to provide a complete environment for developers to create, test, and debug their flows. It includes both the API and the UI.
|
||||
|
||||
|
||||
### Prerequisites {#3efd3c63ff8849228c136f9252e504fd}
|
||||
|
||||
- Kubernetes server
|
||||
- kubectl
|
||||
- Helm
|
||||
|
||||
### Step 0. Prepare a Kubernetes cluster {#290b9624770a4c1ba2c889d384b7ef4c}
|
||||
|
||||
|
||||
We use [Minikube](https://minikube.sigs.k8s.io/docs/start/) for this example, but you can use any Kubernetes cluster.
|
||||
|
||||
1. Create a Kubernetes cluster on Minikube.
|
||||
|
||||
```text
|
||||
minikube start
|
||||
```
|
||||
|
||||
2. Set `kubectl` to use Minikube.
|
||||
|
||||
```text
|
||||
kubectl config use-context minikube
|
||||
```
|
||||
|
||||
|
||||
### Step 1. Install the LangFlow Helm chart {#b5c2a35144634a05a392f7e650929efe}
|
||||
|
||||
1. Add the repository to Helm.
|
||||
|
||||
```text
|
||||
helm repo add langflow <https://langflow-ai.github.io/langflow-helm-charts>
|
||||
helm repo update
|
||||
```
|
||||
|
||||
2. Install LangFlow with the default options in the `langflow` namespace.
|
||||
|
||||
```text
|
||||
helm install langflow-ide langflow/langflow-ide -n langflow --create-namespace
|
||||
```
|
||||
|
||||
3. Check the status of the pods
|
||||
|
||||
```text
|
||||
kubectl get pods -n langflow
|
||||
```
|
||||
|
||||
|
||||
```text
|
||||
NAME READY STATUS RESTARTS AGE
|
||||
langflow-0 1/1 Running 0 33s
|
||||
langflow-frontend-5d9c558dbb-g7tc9 1/1 Running 0 38s
|
||||
```
|
||||
|
||||
|
||||
### Step 2. Access LangFlow {#34c71d04351949deb6c8ed7ffe30eafb}
|
||||
|
||||
|
||||
Enable local port forwarding to access LangFlow from your local machine.
|
||||
|
||||
|
||||
```text
|
||||
kubectl port-forward -n langflow svc/langflow-langflow-runtime 7860:7860
|
||||
```
|
||||
|
||||
|
||||
Now you can access LangFlow at [http://localhost:7860/](http://localhost:7860/).
|
||||
|
||||
|
||||
### LangFlow version {#645c6ef7984d4da0bcc4170bab0ff415}
|
||||
|
||||
|
||||
To specify a different LangFlow version, you can set the `langflow.backend.image.tag` and `langflow.frontend.image.tag` values in the `values.yaml` file.
|
||||
|
||||
|
||||
```yaml
|
||||
langflow:
|
||||
backend:
|
||||
image:
|
||||
tag: "1.0.0a59"
|
||||
frontend:
|
||||
image:
|
||||
tag: "1.0.0a59"
|
||||
|
||||
```
|
||||
|
||||
|
||||
### Storage {#6772c00af79147d293c821b4c6905d3b}
|
||||
|
||||
|
||||
By default, the chart will use a SQLLite database stored in a local persistent disk.
|
||||
If you want to use an external PostgreSQL database, you can set the `langflow.database` values in the `values.yaml` file.
|
||||
|
||||
|
||||
```yaml
|
||||
# Deploy postgresql. You can skip this section if you have an existing postgresql database.
|
||||
postgresql:
|
||||
enabled: true
|
||||
fullnameOverride: "langflow-ide-postgresql-service"
|
||||
auth:
|
||||
username: "langflow"
|
||||
password: "langflow-postgres"
|
||||
database: "langflow-db"
|
||||
|
||||
langflow:
|
||||
backend:
|
||||
externalDatabase:
|
||||
enabled: true
|
||||
driver:
|
||||
value: "postgresql"
|
||||
host:
|
||||
value: "langflow-ide-postgresql-service"
|
||||
port:
|
||||
value: "5432"
|
||||
database:
|
||||
value: "langflow-db"
|
||||
user:
|
||||
value: "langflow"
|
||||
password:
|
||||
valueFrom:
|
||||
secretKeyRef:
|
||||
key: "password"
|
||||
name: "langflow-ide-postgresql-service"
|
||||
sqlite:
|
||||
enabled: false
|
||||
|
||||
```
|
||||
|
||||
|
||||
### Scaling {#e1d95ba6551742aa86958dc03b26129e}
|
||||
|
||||
|
||||
You can scale the number of replicas for the LangFlow backend and frontend services by changing the `replicaCount` value in the `values.yaml` file.
|
||||
|
||||
|
||||
```yaml
|
||||
langflow:
|
||||
backend:
|
||||
replicaCount: 3
|
||||
frontend:
|
||||
replicaCount: 3
|
||||
|
||||
```
|
||||
|
||||
|
||||
You can scale frontend and backend services independently.
|
||||
|
||||
|
||||
To scale vertically (increase the resources for the pods), you can set the `resources` values in the `values.yaml` file.
|
||||
|
||||
|
||||
```yaml
|
||||
langflow:
|
||||
backend:
|
||||
resources:
|
||||
requests:
|
||||
memory: "2Gi"
|
||||
cpu: "1000m"
|
||||
frontend:
|
||||
resources:
|
||||
requests:
|
||||
memory: "1Gi"
|
||||
cpu: "1000m"
|
||||
|
||||
```
|
||||
|
||||
|
||||
### Deploy on AWS EKS, Google GKE, or Azure AKS and other examples {#a8c3d4dc4e4f42f49b21189df5e2b851}
|
||||
|
||||
|
||||
Visit the [LangFlow Helm Charts repository](https://github.com/langflow-ai/langflow-helm-charts) for more information.
|
||||
|
||||
|
||||
## LangFlow (Runtime) {#49f2813ad2d3460081ad26a286a65e73}
|
||||
|
||||
|
||||
The runtime chart is tailored for deploying applications in a production environment. It is focused on stability, performance, isolation, and security to ensure that applications run reliably and efficiently.
|
||||
|
||||
|
||||
Using a dedicated deployment for a set of flows is fundamental in production environments to have granular resource control.
|
||||
|
||||
|
||||
### Prerequisites {#3ad3a9389fff483ba8bd309189426a9d}
|
||||
|
||||
- Kubernetes server
|
||||
- kubectl
|
||||
- Helm
|
||||
|
||||
### Step 0. Prepare a Kubernetes cluster {#aaa764703ec44bd5ba64b5ef4599630b}
|
||||
|
||||
|
||||
Follow the same steps as for the LangFlow IDE.
|
||||
|
||||
|
||||
### Step 1. Install the LangFlow runtime Helm chart {#72a18aa8349c421186ba01d73a002531}
|
||||
|
||||
1. Add the repository to Helm.
|
||||
|
||||
```shell
|
||||
helm repo add langflow <https://langflow-ai.github.io/langflow-helm-charts>
|
||||
helm repo update
|
||||
|
||||
```
|
||||
|
||||
2. Install the LangFlow app with the default options in the `langflow` namespace.
|
||||
If you bundled the flow in a docker image, you can specify the image name in the `values.yaml` file or with the `-set` flag:
|
||||
If you want to download the flow from a remote location, you can specify the URL in the `values.yaml` file or with the `-set` flag:
|
||||
|
||||
```shell
|
||||
helm install my-langflow-app langflow/langflow-runtime -n langflow --create-namespace --set image.repository=myuser/langflow-just-chat --set image.tag=1.0.0
|
||||
|
||||
```
|
||||
|
||||
|
||||
```shell
|
||||
helm install my-langflow-app langflow/langflow-runtime -n langflow --create-namespace --set downloadFlows.flows[0].url=https://raw.githubusercontent.com/langflow-ai/langflow/dev/src/backend/base/langflow/initial_setup/starter_projects/Basic%20Prompting%20(Hello%2C%20world!).json
|
||||
|
||||
```
|
||||
|
||||
3. Check the status of the pods.
|
||||
|
||||
```text
|
||||
kubectl get pods -n langflow
|
||||
|
||||
```
|
||||
|
||||
|
||||
### Step 2. Access the LangFlow app API {#e13326fc07734e4aa86dfb75ccfa31f8}
|
||||
|
||||
|
||||
Enable local port forwarding to access LangFlow from your local machine.
|
||||
|
||||
|
||||
```text
|
||||
kubectl port-forward -n langflow svc/langflow-my-langflow-app 7860:7860
|
||||
|
||||
```
|
||||
|
||||
|
||||
Now you can access the API at [http://localhost:7860/api/v1/flows](http://localhost:7860/api/v1/flows) and execute the flow:
|
||||
|
||||
|
||||
```shell
|
||||
id=$(curl -s <http://localhost:7860/api/v1/flows> | jq -r '.flows[0].id')
|
||||
curl -X POST \\
|
||||
"<http://localhost:7860/api/v1/run/$id?stream=false>" \\
|
||||
-H 'Content-Type: application/json'\\
|
||||
-d '{
|
||||
"input_value": "Hello!",
|
||||
"output_type": "chat",
|
||||
"input_type": "chat"
|
||||
}'
|
||||
|
||||
```
|
||||
|
||||
|
||||
### Storage {#09514d2b59064d37b685c7c0acecb861}
|
||||
|
||||
|
||||
In this case, storage is not needed as our deployment is stateless.
|
||||
|
||||
|
||||
### Log level and LangFlow configurations {#ecd97f0be96d4d1cabcc5b77a2d00980}
|
||||
|
||||
|
||||
You can set the log level and other LangFlow configurations in the `values.yaml` file.
|
||||
|
||||
|
||||
```yaml
|
||||
env:
|
||||
- name: LANGFLOW_LOG_LEVEL
|
||||
value: "INFO"
|
||||
|
||||
```
|
||||
|
||||
|
||||
### Configure secrets and variables {#b91929e92acf47c183ea4c9ba9d19514}
|
||||
|
||||
|
||||
To inject secrets and LangFlow global variables, you can use the `secrets` and `env` sections in the `values.yaml` file.
|
||||
|
||||
|
||||
Let's say your flow uses a global variable which is a secret; when you export the flow as JSON, it's recommended to not include it.
|
||||
When importing the flow in the LangFlow runtime, you can set the global variable using the `env` section in the `values.yaml` file.
|
||||
Assuming you have a global variable called `openai_key_var`, you can read it directly from a secret:
|
||||
|
||||
|
||||
```yaml
|
||||
env:
|
||||
- name: openai_key_var
|
||||
valueFrom:
|
||||
secretKeyRef:
|
||||
name: openai-key
|
||||
key: openai-key
|
||||
|
||||
```
|
||||
|
||||
|
||||
or directly from the values file (not recommended for secret values!):
|
||||
|
||||
|
||||
```yaml
|
||||
env:
|
||||
- name: openai_key_var
|
||||
value: "sk-...."
|
||||
|
||||
```
|
||||
|
||||
|
||||
### Scaling {#359b9ea5302147ebbed3ab8aa49dae8d}
|
||||
|
||||
|
||||
You can scale the number of replicas for the LangFlow app by changing the `replicaCount` value in the `values.yaml` file.
|
||||
|
||||
|
||||
```yaml
|
||||
replicaCount: 3
|
||||
|
||||
```
|
||||
|
||||
|
||||
To scale vertically (increase the resources for the pods), you can set the `resources` values in the `values.yaml` file.
|
||||
|
||||
|
||||
```yaml
|
||||
resources:
|
||||
requests:
|
||||
memory: "2Gi"
|
||||
cpu: "1000m"
|
||||
|
||||
```
|
||||
|
||||
|
||||
### Other examples {#8522b4276b51448e9f8f0c6efc731a7c}
|
||||
|
||||
|
||||
Visit the LangFlow Helm Charts repository for more examples and configurations.
|
||||
|
||||
|
||||
Use the default values file as reference for all the options available.
|
||||
|
||||
|
||||
Visit the examples directory to learn more about different deployment options.
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue