How to Self-Host Flowise with Docker Compose

What Is Flowise?

Flowise is a visual drag-and-drop tool for building LLM-powered chatbots, agents, and RAG pipelines. Instead of writing code, you connect nodes in a canvas — LLM providers, vector databases, document loaders, and tools — to create AI workflows. Built on LangChain.js, Flowise can connect to OpenAI, Anthropic, Ollama, and dozens of other LLM providers.

Prerequisites

• A Linux server (Ubuntu 22.04+ recommended)
• Docker and Docker Compose installed (guide)
• 2 GB+ RAM
• 5 GB free disk space
• An LLM backend (Ollama, OpenAI API key, or similar)

Docker Compose Configuration

Create a docker-compose.yml file:

services:
  flowise:
    image: flowiseai/flowise:3.0.13
    container_name: flowise
    ports:
      - "3000:3000"
    volumes:
      - flowise_data:/root/.flowise
    environment:
      # Authentication
      - FLOWISE_USERNAME=admin
      - FLOWISE_PASSWORD=changeme-use-strong-password
      # API key storage
      - APIKEY_PATH=/root/.flowise
      - SECRETKEY_PATH=/root/.flowise
      # Database (SQLite by default)
      - DATABASE_PATH=/root/.flowise
      # Optional: Log level
      - LOG_LEVEL=info
      # Optional: Execution timeout (ms)
      - EXECUTION_TIMEOUT=300000
    restart: unless-stopped

volumes:
  flowise_data:

For production with PostgreSQL:

services:
  flowise:
    image: flowiseai/flowise:3.0.13
    container_name: flowise
    ports:
      - "3000:3000"
    volumes:
      - flowise_data:/root/.flowise
    environment:
      - FLOWISE_USERNAME=admin
      - FLOWISE_PASSWORD=changeme-use-strong-password
      - DATABASE_TYPE=postgres
      - DATABASE_HOST=flowise-db
      - DATABASE_PORT=5432
      - DATABASE_USER=flowise
      - DATABASE_PASSWORD=flowise-db-password-change-this
      - DATABASE_NAME=flowise
      - APIKEY_PATH=/root/.flowise
      - SECRETKEY_PATH=/root/.flowise
    depends_on:
      flowise-db:
        condition: service_healthy
    restart: unless-stopped

  flowise-db:
    image: postgres:16-alpine
    container_name: flowise-db
    volumes:
      - flowise_db_data:/var/lib/postgresql/data
    environment:
      - POSTGRES_USER=flowise
      - POSTGRES_PASSWORD=flowise-db-password-change-this
      - POSTGRES_DB=flowise
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U flowise"]
      interval: 10s
      timeout: 5s
      retries: 5
    restart: unless-stopped

volumes:
  flowise_data:
  flowise_db_data:

Start the stack:

docker compose up -d

Initial Setup

1. Open http://your-server:3000 in your browser
2. Log in with the credentials you set in FLOWISE_USERNAME / FLOWISE_PASSWORD
3. Click Chatflows → Add New to create your first workflow
4. Drag nodes from the sidebar to build your pipeline

Quick Start: Ollama Chatbot

1. Drag ChatOllama node onto the canvas
2. Set Base URL to http://host.docker.internal:11434 (or your Ollama URL)
3. Set Model Name to llama3.2
4. Drag a Conversational Agent node and connect it
5. Click Save then Chat to test

Configuration

Key Environment Variables

Variable	Default	Description
FLOWISE_USERNAME		Username for UI authentication
FLOWISE_PASSWORD		Password for UI authentication
DATABASE_TYPE	sqlite	Database type: sqlite, postgres, mysql
DATABASE_PATH	/root/.flowise	SQLite database location
APIKEY_PATH	/root/.flowise	API key storage path
SECRETKEY_PATH	/root/.flowise	Secret key storage path
LOG_LEVEL	info	Logging level: error, info, verbose, debug
EXECUTION_TIMEOUT	300000	Flow execution timeout in ms
CORS_ORIGINS	*	Allowed CORS origins

Embedding a Chat Widget

Every chatflow gets an embed code. Click the </> Embed button to get a script tag you can paste into any website:

<script type="module">
  import Chatbot from "https://cdn.jsdelivr.net/npm/flowise-embed/dist/web.js"
  Chatbot.init({
    chatflowid: "your-chatflow-id",
    apiHost: "https://flowise.example.com",
  })
</script>

Advanced Configuration

RAG Pipeline

Build a document-based Q&A chatbot:

1. Document Loader → PDF, CSV, or web page loader
2. Text Splitter → Recursive Character Text Splitter
3. Embeddings → Ollama Embeddings or OpenAI Embeddings
4. Vector Store → Chroma, Pinecone, or Qdrant
5. Retrieval QA Chain → Connects to your LLM

API Access

Every chatflow is accessible via API:

curl -X POST http://localhost:3000/api/v1/prediction/your-chatflow-id \
  -H "Content-Type: application/json" \
  -d '{"question": "What is self-hosting?"}'

Reverse Proxy

Configure your reverse proxy to forward to port 3000. WebSocket support is required for the chat interface. See Reverse Proxy Setup.

Backup

Back up the Flowise data volume:

docker run --rm -v flowise_data:/data -v $(pwd):/backup alpine \
  tar czf /backup/flowise-backup.tar.gz /data

This contains chatflows, credentials, API keys, and the SQLite database (if using SQLite). See Backup Strategy.

Troubleshooting

Cannot Connect to Ollama

Symptom: ChatOllama node fails to connect. Fix: Use http://host.docker.internal:11434 on Docker Desktop, or the Docker bridge IP (http://172.17.0.1:11434) on Linux. Ensure Ollama is listening on 0.0.0.0: set OLLAMA_HOST=0.0.0.0:11434.

Chatflow Execution Times Out

Symptom: Flow returns timeout error. Fix: Increase EXECUTION_TIMEOUT environment variable. Default is 300 seconds (5 minutes). Complex RAG pipelines with large documents may need more.

Authentication Bypass

Symptom: UI accessible without login. Fix: Ensure both FLOWISE_USERNAME and FLOWISE_PASSWORD are set. If either is empty, authentication is disabled.

Resource Requirements

• RAM: 200-500 MB (Flowise itself is lightweight; LLM backends are separate)
• CPU: Low
• Disk: 1-5 GB depending on number of chatflows and stored documents

Verdict

Flowise is the easiest way to build AI chatbots and RAG pipelines without writing code. The drag-and-drop interface makes it accessible to non-developers while remaining powerful enough for complex workflows. The embeddable chat widget is a killer feature for deploying AI chatbots to websites.

Choose Flowise for no-code chatbot building. Choose Langflow if you need Python custom components and more advanced multi-agent orchestration.

Related

• How to Self-Host Langflow
• Flowise vs Langflow
• How to Self-Host Ollama
• How to Self-Host Open WebUI
• Self-Hosted ChatGPT Alternatives
• Best Self-Hosted AI Tools
• Docker Compose Basics