FlowiseAI
English
English
  • Introduction
  • Get Started
  • Contribution Guide
    • Building Node
  • API Reference
    • Assistants
    • Attachments
    • Chat Message
    • Chatflows
    • Document Store
    • Feedback
    • Leads
    • Ping
    • Prediction
    • Tools
    • Upsert History
    • Variables
    • Vector Upsert
  • Using Flowise
    • Agentflow V2
    • Agentflow V1 (Deprecating)
      • Multi-Agents
      • Sequential Agents
        • Video Tutorials
    • API
    • Analytic
      • Arize
      • Langfuse
      • Lunary
      • Opik
      • Phoenix
    • Document Stores
    • Embed
    • Monitoring
    • Streaming
    • Uploads
    • Variables
    • Workspaces
    • Evaluations
  • Configuration
    • Auth
      • App Level
      • Chatflow Level
    • Databases
    • Deployment
      • AWS
      • Azure
      • Alibaba Cloud
      • Digital Ocean
      • Elestio
      • GCP
      • Hugging Face
      • Kubernetes using Helm
      • Railway
      • Render
      • Replit
      • RepoCloud
      • Sealos
      • Zeabur
    • Environment Variables
    • Rate Limit
    • Running Flowise behind company proxy
    • SSO
    • Running Flowise using Queue
    • Running in Production
  • Integrations
    • LangChain
      • Agents
        • Airtable Agent
        • AutoGPT
        • BabyAGI
        • CSV Agent
        • Conversational Agent
        • Conversational Retrieval Agent
        • MistralAI Tool Agent
        • OpenAI Assistant
          • Threads
        • OpenAI Function Agent
        • OpenAI Tool Agent
        • ReAct Agent Chat
        • ReAct Agent LLM
        • Tool Agent
        • XML Agent
      • Cache
        • InMemory Cache
        • InMemory Embedding Cache
        • Momento Cache
        • Redis Cache
        • Redis Embeddings Cache
        • Upstash Redis Cache
      • Chains
        • GET API Chain
        • OpenAPI Chain
        • POST API Chain
        • Conversation Chain
        • Conversational Retrieval QA Chain
        • LLM Chain
        • Multi Prompt Chain
        • Multi Retrieval QA Chain
        • Retrieval QA Chain
        • Sql Database Chain
        • Vectara QA Chain
        • VectorDB QA Chain
      • Chat Models
        • AWS ChatBedrock
        • Azure ChatOpenAI
        • NVIDIA NIM
        • ChatAnthropic
        • ChatCohere
        • Chat Fireworks
        • ChatGoogleGenerativeAI
        • Google VertexAI
        • ChatHuggingFace
        • ChatLocalAI
        • ChatMistralAI
        • IBM Watsonx
        • ChatOllama
        • ChatOpenAI
        • ChatTogetherAI
        • GroqChat
      • Document Loaders
        • API Loader
        • Airtable
        • Apify Website Content Crawler
        • Cheerio Web Scraper
        • Confluence
        • Csv File
        • Custom Document Loader
        • Document Store
        • Docx File
        • File Loader
        • Figma
        • FireCrawl
        • Folder with Files
        • GitBook
        • Github
        • Json File
        • Json Lines File
        • Notion Database
        • Notion Folder
        • Notion Page
        • PDF Files
        • Plain Text
        • Playwright Web Scraper
        • Puppeteer Web Scraper
        • S3 File Loader
        • SearchApi For Web Search
        • SerpApi For Web Search
        • Spider Web Scraper/Crawler
        • Text File
        • Unstructured File Loader
        • Unstructured Folder Loader
        • VectorStore To Document
      • Embeddings
        • AWS Bedrock Embeddings
        • Azure OpenAI Embeddings
        • Cohere Embeddings
        • Google GenerativeAI Embeddings
        • Google VertexAI Embeddings
        • HuggingFace Inference Embeddings
        • LocalAI Embeddings
        • MistralAI Embeddings
        • Ollama Embeddings
        • OpenAI Embeddings
        • OpenAI Embeddings Custom
        • TogetherAI Embedding
        • VoyageAI Embeddings
      • LLMs
        • AWS Bedrock
        • Azure OpenAI
        • Cohere
        • GoogleVertex AI
        • HuggingFace Inference
        • Ollama
        • OpenAI
        • Replicate
      • Memory
        • Buffer Memory
        • Buffer Window Memory
        • Conversation Summary Memory
        • Conversation Summary Buffer Memory
        • DynamoDB Chat Memory
        • MongoDB Atlas Chat Memory
        • Redis-Backed Chat Memory
        • Upstash Redis-Backed Chat Memory
        • Zep Memory
      • Moderation
        • OpenAI Moderation
        • Simple Prompt Moderation
      • Output Parsers
        • CSV Output Parser
        • Custom List Output Parser
        • Structured Output Parser
        • Advanced Structured Output Parser
      • Prompts
        • Chat Prompt Template
        • Few Shot Prompt Template
        • Prompt Template
      • Record Managers
      • Retrievers
        • Extract Metadata Retriever
        • Custom Retriever
        • Cohere Rerank Retriever
        • Embeddings Filter Retriever
        • HyDE Retriever
        • LLM Filter Retriever
        • Multi Query Retriever
        • Prompt Retriever
        • Reciprocal Rank Fusion Retriever
        • Similarity Score Threshold Retriever
        • Vector Store Retriever
        • Voyage AI Rerank Retriever
      • Text Splitters
        • Character Text Splitter
        • Code Text Splitter
        • Html-To-Markdown Text Splitter
        • Markdown Text Splitter
        • Recursive Character Text Splitter
        • Token Text Splitter
      • Tools
        • BraveSearch API
        • Calculator
        • Chain Tool
        • Chatflow Tool
        • Custom Tool
        • Exa Search
        • Google Custom Search
        • OpenAPI Toolkit
        • Code Interpreter by E2B
        • Read File
        • Request Get
        • Request Post
        • Retriever Tool
        • SearchApi
        • SearXNG
        • Serp API
        • Serper
        • Tavily
        • Web Browser
        • Write File
      • Vector Stores
        • AstraDB
        • Chroma
        • Couchbase
        • Elastic
        • Faiss
        • In-Memory Vector Store
        • Milvus
        • MongoDB Atlas
        • OpenSearch
        • Pinecone
        • Postgres
        • Qdrant
        • Redis
        • SingleStore
        • Supabase
        • Upstash Vector
        • Vectara
        • Weaviate
        • Zep Collection - Open Source
        • Zep Collection - Cloud
    • LiteLLM Proxy
    • LlamaIndex
      • Agents
        • OpenAI Tool Agent
        • Anthropic Tool Agent
      • Chat Models
        • AzureChatOpenAI
        • ChatAnthropic
        • ChatMistral
        • ChatOllama
        • ChatOpenAI
        • ChatTogetherAI
        • ChatGroq
      • Embeddings
        • Azure OpenAI Embeddings
        • OpenAI Embedding
      • Engine
        • Query Engine
        • Simple Chat Engine
        • Context Chat Engine
        • Sub-Question Query Engine
      • Response Synthesizer
        • Refine
        • Compact And Refine
        • Simple Response Builder
        • Tree Summarize
      • Tools
        • Query Engine Tool
      • Vector Stores
        • Pinecone
        • SimpleStore
    • Utilities
      • Custom JS Function
      • Set/Get Variable
      • If Else
      • Sticky Note
    • External Integrations
      • Zapier Zaps
  • Migration Guide
    • Cloud Migration
    • v1.3.0 Migration Guide
    • v1.4.3 Migration Guide
    • v2.1.4 Migration Guide
  • Use Cases
    • Calling Children Flows
    • Calling Webhook
    • Interacting with API
    • Multiple Documents QnA
    • SQL QnA
    • Upserting Data
    • Web Scrape QnA
  • Flowise
    • Flowise GitHub
    • Flowise Cloud
Powered by GitBook
On this page
  • Prerequisites
  • Setup Kubernetes Cluster
  • Build and Push the Docker image
  • Deployment to GCP
  • Congratulations!
  • Timeout
Edit on GitHub
  1. Configuration
  2. Deployment

GCP

Learn how to deploy Flowise on GCP

PreviousDigital OceanNextHugging Face

Last updated 4 months ago


Prerequisites

  1. Notedown your Google Cloud [ProjectId]

  2. Install

  3. Install the

  4. Install

Setup Kubernetes Cluster

  1. Create a Kubernetes Cluster if you don't have one.

  1. Name the Cluster, choose the right resource location, use Autopilot mode and keep all other default configs.

  2. Once the Cluster is created, Click the 'Connect' menu from the actions menu

  1. Copy the command and paste into your terminal and hit enter to connect your cluster.

  2. Run the below command and select correct context name, which looks like gke_[ProjectId]_[DataCenter]_[ClusterName]

kubectl config get-contexts
  1. Set the current context

kubectl config use-context gke_[ProjectId]_[DataCenter]_[ClusterName]

Build and Push the Docker image

Run the following commands to build and push the Docker image to GCP Container Registry.

  1. Clone the Flowise

git clone https://github.com/FlowiseAI/Flowise.git
  1. Build the Flowise

cd Flowise
pnpm install
pnpm build
  1. Update the Dockerfile file a little.

Specify the platform of nodejs

FROM --platform=linux/amd64 node:18-alpine

Add python3, make and g++ to install

RUN apk add --no-cache python3 make g++
  1. Build as Docker image, make sure the Docker desktop app is running

docker build -t gcr.io/[ProjectId]/flowise:dev .
  1. Push the Docker image to GCP container registry.

docker push gcr.io/[ProjectId]/flowise:dev

Deployment to GCP

  1. Create a yamls root folder in the project.

  2. Add the deployment.yaml file into that folder.

# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: flowise
  labels:
    app: flowise
spec:
  selector:
    matchLabels:
      app: flowise
  replicas: 1
  template:
    metadata:
      labels:
        app: flowise
    spec:
      containers:
      - name: flowise
        image: gcr.io/[ProjectID]/flowise:dev
        imagePullPolicy: Always
        resources: 
          requests:
            cpu: "1"
            memory: "1Gi"
  1. Add the service.yaml file into that folder.

# service.yaml
apiVersion: "v1"
kind: "Service"
metadata:
  name: "flowise-service"
  namespace: "default"
  labels:
    app: "flowise"
spec:
  ports:
  - protocol: "TCP"
    port: 80
    targetPort: 3000
  selector:
    app: "flowise"
  type: "LoadBalancer"

It will be look like below.

  1. Deploy the yaml files by running following commands.

kubectl apply -f yamls/deployment.yaml
kubectl apply -f yamls/service.yaml
  1. Go to Workloads in the GCP, you can see your pod is running.

  1. Go to Services & Ingress, you can click the Endpoint where the Flowise is hosted.

Congratulations!

Timeout

By default, there is a 30 seconds timeout assigned to the proxy by GCP. This caused issue when the response is taking longer than 30 seconds threshold to return. In order to fix this issue, make the following changes to YAML files:

Note: To set the timeout to be 10 minutes (for example) -- we specify 600 seconds below.

  1. Create a backendconfig.yaml file with the following content:

apiVersion: cloud.google.com/v1
kind: BackendConfig
metadata:
  name: flowise-backendconfig
  namespace: your-namespace
spec:
  timeoutSec: 600
  1. Issue: kubectl apply -f backendconfig.yaml

  2. Update your service.yaml file with the following reference to the BackendConfig:

apiVersion: v1
kind: Service
metadata:
  annotations:
    cloud.google.com/backend-config: '{"default": "flowise-backendconfig"}'
  name: flowise-service
  namespace: your-namespace
...
  1. Issue: kubectl apply -f service.yaml

You have successfully hosted the Flowise apps on GCP

🥳
Git
Google Cloud CLI
Docker Desktop
Click `Clusters` to create one.