How to build AI chatbots with Flowise and Docker

To build AI chatbots with Flowise and Docker, install Flowise in a Docker container, open the visual dashboard, connect an AI model, create a chatflow, and test the chatbot before embedding it on a website or connecting it to an API.

Flowise is a no-code AI chatbot builder that lets you create chatbot workflows using visual nodes rather than application code. Docker gives Flowise a portable deployment environment, making it easier to run, restart, update, and manage on a VPS.

In this guide, you’ll learn how to install Flowise with Docker, deploy it faster using Hostinger’s Docker template, create a no-code chatbot in the Flowise dashboard, and choose the right chatbot use case for your project.

How to install Flowise with Docker

Installing Flowise with Docker lets you run the visual AI chatbot builder in an isolated container. This setup keeps Flowise separate from your server’s system packages, making it easier to restart, update, or move the application later.

Before starting, make sure your server has Docker installed. You also need terminal access to the server and an open port for Flowise. By default, Flowise runs on port 3000.

1. Create a Docker volume for Flowise data

First, create a Docker volume to store Flowise data outside the container:

docker volume create flowise_data

This volume keeps your chatflows, credentials, and local Flowise configuration available after container restarts or updates.

2. Run the Flowise container

Next, start Flowise with the official Docker image:

docker run -d
--name flowise
-p 3000:3000
-v flowise_data:/root/.flowise
flowiseai/flowise:latest

This command does four things:

• Runs Flowise in the background.
• Names the container flowise.
• Maps port 3000 on your server to port 3000 inside the container.
• Stores Flowise data in the flowise_data Docker volume.

3. Check whether Flowise is running

After starting the container, check its status:

docker ps

If Flowise is running correctly, the output should show a container named flowise using the flowiseai/flowise:latest image.

You can also check the container logs:

docker logs flowise

Use this command if the Flowise dashboard does not load or if you need to confirm that the application started successfully.

4. Open the Flowise dashboard

Open your browser and visit:

http://your-server-ip:3000

Replace your-server-ip with your VPS IP address. If you are running Flowise locally, open:

http://localhost:3000

Flowise will open its web dashboard, where you can create your first AI chatbot workflow.

5. Stop or restart Flowise

To stop the Flowise container, run:

docker stop flowise

To start it again, run:

docker start flowise

To restart Flowise after changing the configuration or troubleshooting the container, run:

docker restart flowise

6. Update Flowise

To update Flowise, pull the latest Docker image:

docker pull flowiseai/flowise:latest

Then stop and remove the current container:

docker stop flowise
docker rm flowise

Finally, start Flowise again with the same volume:

docker run -d
--name flowise
-p 3000:3000
-v flowise_data:/root/.flowise
flowiseai/flowise:latest

Because the container uses the same flowise_data volume, your Flowise data remains available after the update.

How to deploy Flowise with Hostinger’s Docker template

Hostinger’s Flowise Docker template lets you deploy Flowise without manually pulling the Docker image, creating volumes, or running container commands. It installs Flowise on a VPS with a preconfigured Docker setup, so you can open the dashboard and start building AI chatbot workflows faster.

To deploy Flowise with Hostinger’s Docker template:

1. Go to Hostinger’s Flowise Docker template page and choose a VPS plan. An entry-level VPS works for testing a simple chatbot. Choose more CPU, RAM, and NVMe storage if your chatbot will handle multiple users, large knowledge bases, or local AI models.
2. Select the Flowise template during VPS setup. Hostinger will automatically set up the VPS and deploy Flowise with Docker, so you don’t need to run Docker commands manually.
3. Open the server details in hPanel after the VPS setup is complete. From there, use the Docker manager to check the container status, view logs, restart services, or manage updates from the control panel.
4. Open your Flowise instance in a browser using your VPS IP address and the default Flowise port:

http://your-server-ip:3000

Replace your-server-ip with the IP address shown in your Hostinger VPS dashboard.

5. Secure the deployment before building production chatbot workflows. Keep Flowise updated, enable firewall rules, avoid exposing unnecessary ports, and use strong credentials. Store API keys securely because Flowise workflows often connect to model providers, databases, and internal tools.

For production use, connect a domain to the VPS and enable HTTPS with a reverse proxy. This lets users access Flowise via a secure domain rather than an IP address and port.

How to build a no-code AI chatbot in Flowise

After installing Flowise, you can build an AI chatbot from the visual dashboard without writing application code. A basic Flowise chatbot needs a chat model, a prompt, and a connection between the user input and model output. More advanced chatbots can also use memory, uploaded documents, vector databases, and external tools.

To build a no-code AI chatbot in Flowise:

1. Open the Flowise dashboard and select Chatflows. Then, create a new chatflow. A chatflow is the visual workflow that defines how your chatbot receives a message, processes it, and returns an answer.
2. Add a chat model node to the canvas. This node connects Flowise to the large language model (LLM) that generates the chatbot’s responses. Depending on your setup, you can use providers like OpenAI, Anthropic, Google, or a local model provider.
3. Add your model credentials. In the chat model node, enter the required API key or connection details for your chosen provider. Store these credentials carefully, as the chatbot uses them whenever it sends a prompt to the model.
4. Add a prompt node and define the chatbot’s behavior. The prompt should specify the chatbot’s role, the types of answers it should give, and the limits it should follow. For example, a customer support chatbot should answer product questions clearly, avoid unsupported claims, and ask for more details when the user’s question is incomplete.
5. Connect the user input, prompt, and chat model nodes. This connection tells Flowise how to pass the user’s message through the workflow and send the final response back to the chat interface.
6. Add memory if the chatbot needs to remember previous messages in the same conversation. Memory helps the chatbot understand follow-up questions, references, and context from earlier turns.
7. Add a knowledge base if the chatbot needs to answer questions from specific documents or website content. For this setup, use document loaders, text splitters, embeddings, and a vector database. This creates a retrieval-augmented generation (RAG) chatbot that searches your content before generating an answer.
8. Test the chatbot in the Flowise chat window. Ask simple questions first, then test edge cases, unclear prompts, and questions that should use your knowledge base. Adjust the prompt, model settings, memory, or retrieval settings based on the answers.
9. Publish the chatbot using the available embed or API options. The embed option lets you add the chatbot to a website interface, while the API option lets another application send messages to the Flowise workflow.

Start with a simple chatbot before adding memory, tools, or document retrieval. A smaller workflow is easier to test and debug, and it gives you a stable base for building a more advanced no-code AI chatbot later.

What can you build with a Flowise chatbot?

A Flowise chatbot can answer questions, retrieve information from documents, qualify leads, and trigger actions through connected tools or APIs. Since Flowise uses a visual workflow builder, you can create these chatbot experiences by connecting model, prompt, memory, retrieval, and integration nodes.

Customer support chatbot

A customer support chatbot answers product, billing, onboarding, or troubleshooting questions. In Flowise, you can connect the chatbot to help center articles, policy pages, product documentation, or internal support files so it retrieves relevant information before generating an answer.

This setup works well for businesses that receive repeated support questions. For example, an ecommerce store can use a Flowise chatbot to explain return policies, order tracking steps, shipping options, and product details.

Internal knowledge base assistant

An internal knowledge base assistant helps employees search for company information through a chat interface. Instead of manually opening different folders, documents, or SOPs, employees can ask the chatbot a question and receive an answer based on approved internal content.

This use case works well for HR policies, technical documentation, onboarding guides, sales enablement materials, and operational procedures. To build it in Flowise, connect document loaders, embeddings, and a vector database to create a retrieval-based chatbot.

Lead qualification chatbot

A lead qualification chatbot asks visitors structured questions before sending their details to a sales team or CRM. It can collect information such as company size, budget, project timeline, service needs, and contact details.

In Flowise, this workflow can combine prompt instructions with API integrations. For example, the chatbot can ask qualifying questions, summarize the visitor’s answers, and send the lead data to another system through an API endpoint.

Website onboarding assistant

A website onboarding assistant guides visitors through product features, pricing, account setup, or next steps. This type of chatbot helps users find relevant information without searching through multiple pages.

For example, a SaaS company can use a Flowise chatbot to recommend the right plan, explain setup requirements, or direct users to documentation based on their goal.

Document-based RAG chatbot

A document-based retrieval-augmented generation (RAG) chatbot answers questions from PDFs, URLs, text files, or database content. The chatbot retrieves relevant content first, then uses the language model to generate an answer based on that context.

This setup is useful when the chatbot needs to answer from a specific knowledge source instead of relying only on the model’s general training data. In Flowise, a RAG workflow usually includes a document loader, text splitter, embedding model, vector database, retriever, prompt, and chat model.

AI agent with external tools

An AI agent with external tools can perform actions rather than just answering questions. In Flowise, you can connect the agent to APIs, databases, search tools, or automation platforms.

For example, an AI agent can check order details, create support tickets, search a product database, or send structured data to another system. This makes Flowise useful for chatbot workflows that need both conversation and task execution.

Start with one focused chatbot use case before combining several workflows. A customer support chatbot, for example, should first answer a defined set of support questions accurately before adding lead capture, API actions, or advanced agent behavior.

Is Flowise with Docker a good setup for building a no-code chatbot?

Flowise with Docker is a good setup for building a no-code chatbot if you want a self-hosted visual builder, repeatable deployment, and control over your chatbot data. Flowise gives you the drag-and-drop interface for creating chatbot workflows, while Docker keeps the application isolated and easier to manage on a VPS.

This setup is especially useful when you want to build chatbots that connect to your own documents, APIs, databases, or internal tools. For example, you can create a customer support chatbot that searches help center articles, or an internal assistant that answers questions from company documentation.

Docker also makes Flowise easier to maintain after deployment. You can restart the container, check logs, update the image, and preserve chatbot data with a Docker volume. This gives you more control than a fully hosted chatbot builder, especially when your project needs custom configuration or self-hosted infrastructure.

However, Flowise with Docker is not the best option for every chatbot project. If you want a fully managed chatbot tool with no server maintenance, a hosted platform may be simpler. If you need full application-level customization, building a custom chatbot from scratch may offer more flexibility than a visual builder.

For most no-code AI chatbot projects, Flowise with Docker offers a practical middle ground. You get a visual workflow editor, self-hosted deployment, support for LLM providers and knowledge bases, and a deployment setup that can scale from testing to production.

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The author

Domantas Pocius

Domantas is a Content SEO Specialist who focuses on researching, writing, and optimizing content for organic growth. He explores content opportunities through keyword, market, and audience research to create search-driven content that matches user intent. Domantas also manages content workflows and timelines, ensuring SEO content initiatives are delivered accurately and on schedule. Follow him on LinkedIn.

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