Flowise: Install and Build Chatbots and RAG Pipelines with a No-Code Drag-and-Drop Interface

The Problem with Traditional AI Development

I started experimenting with RAG applications early last year — and honestly, the initial phase was pretty time-consuming. Just to set up a simple pipeline (load PDF → chunk → embed → save to vector store → query), I had to write hundreds of lines of Python, debug version conflicts between LangChain and its dependencies, then refactor everything when I wanted to switch from Chroma to Qdrant.

The issue wasn’t that it was too hard. The issue was that every time I changed one component in the pipeline, I had to update code in multiple places, re-run everything, and test again. Developers can live with that, but when you need to prototype quickly for a client or build an internal tool for a non-technical team — that’s a real barrier.

Flowise was built to solve exactly that problem.

What Is Flowise and What Makes It Different

Flowise is an open-source tool that lets you build LLM workflows and chatbots through a drag-and-drop interface. Instead of writing code, you drag “nodes” (LLM, document loader, vector store, memory, tool…) onto a canvas and connect them into a complete flow.

Compared to similar tools like LangFlow or n8n, Flowise has a few standout qualities:

• Runs entirely locally — no cloud required, making it suitable for sensitive enterprise data
• Automatically exposes a REST API — every saved flow instantly gets an API endpoint ready for integration
• Built-in Ollama integration — build a 100% local AI stack with zero API fees
• Supports both Chatflow and Agentflow — from simple chatbots to complex multi-agent workflows

Two Main Flow Types in Flowise

• Chatflow: A linear pipeline — input passes through nodes in a fixed sequence. Used for chatbots, Q&A, and RAG.
• Agentflow: An agent with tools that decides its next step based on context. Used for autonomous tasks and multi-step reasoning.

Installing Flowise

There are three installation options: npm (fastest for development), Docker Compose (recommended for production), or building from source. I typically use Docker for servers and npm for quick testing on a local machine.

Option 1 — npm (Done in 5 Minutes)

npm install -g flowise
npx flowise start

Navigate to http://localhost:3000 and the UI appears immediately. Great for quick testing — no additional configuration needed.

Option 2 — Docker Compose (Recommended)

version: '3.1'
services:
  flowise:
    image: flowiseai/flowise
    restart: always
    environment:
      - PORT=3000
      - FLOWISE_USERNAME=admin
      - FLOWISE_PASSWORD=your_strong_password
    ports:
      - '3000:3000'
    volumes:
      - ~/.flowise:/root/.flowise

docker compose up -d

Configuring Environment Variables

To secure the endpoint and customize the storage path, create a .env file:

PORT=3000
FLOWISE_USERNAME=admin
FLOWISE_PASSWORD=strongpassword123
DATABASE_PATH=/data/flowise
APIKEY_PATH=/data/flowise
LOG_PATH=/data/flowise/logs
FLOWISE_SECRETKEY_OVERWRITE=your_secret_key_here

Hands-On: Building a Chatbot with Ollama

Assuming you already have Ollama running locally (see the Ollama guide on this blog), here’s how to create the simplest chatbot — it takes about 5 minutes.

Creating Your First Chatflow

1. Go to Chatflows → Add New
2. From the left panel, drag 3 nodes onto the canvas:
   • ChatOllama (under Chat Models)
   • ChatPromptTemplate (under Prompts) — optional, to customize the system prompt
   • BufferMemory (under Memory) — so the bot remembers conversation context
3. Configure the ChatOllama node:
   • Base URL: http://localhost:11434
   • Model Name: llama3.2 (or whichever model you’ve pulled)
4. Connect the nodes and click Save
5. Click the chat icon in the top-right corner to test directly in the UI

Calling the Chatbot via API

Each saved Chatflow gets its own unique ID. Call it from any language or client:

curl -X POST http://localhost:3000/api/v1/prediction/{chatflow-id} \
  -H "Content-Type: application/json" \
  -d '{"question": "Hello, how can you help me?"}'

Flowise also auto-generates embed snippets (HTML + React) for embedding the chatbot on a website — just copy and paste, no additional code needed.

Hands-On: A RAG Pipeline for PDF Documents

This is the use case I rely on most in practice — giving a chatbot the ability to “read” internal documents (technical guides, company policies, product catalogs) and answer questions accurately based on their content.

Flow Diagram

PDF File Loader → Recursive Character Text Splitter
                                   ↓
                        Ollama Embeddings
                                   ↓
                        Faiss Vector Store ← (Upsert mode)
                                   ↓
              Conversational Retrieval QA Chain ← ChatOllama

Configuring Each Key Node

Recursive Character Text Splitter:

• Chunk Size: 1000
• Chunk Overlap: 200 — important, to avoid losing context at chunk boundaries

Ollama Embeddings (a free alternative to OpenAI Embeddings):

# Pull the embedding model first
ollama pull nomic-embed-text

• Base URL: http://localhost:11434
• Model Name: nomic-embed-text

Faiss Vector Store: No separate server required — it stores files locally, making it ideal for prototypes and internal tools. Flowise also supports Qdrant, Chroma, Pinecone, and Weaviate when you need to scale.

Upserting Documents and Testing

Once the flow is designed, click the Upsert button. Flowise will:

1. Read the PDF and extract text
2. Split it into chunks based on your configuration
3. Embed each chunk using Ollama
4. Save the vectors into Faiss

Switch to Retrieval mode and ask questions about the PDF content — results are typically quite accurate right out of the gate.

Practical Tips from Real-World Usage

Through real-world use, I’ve found this is one of those skills where the details really matter — it’s not just about knowing how to drag nodes, but understanding when to use which tool and how to avoid common pitfalls.

Back up your flows regularly: Flowise stores data in ~/.flowise/. Export flows as JSON after every major change:

# Back up all Flowise data
tar -czf flowise-backup-$(date +%Y%m%d).tar.gz ~/.flowise/

API Key Management: Flowise has a built-in API key manager (Settings → API Keys). Create a separate key for each client — easy to revoke without affecting other integrations.

Streaming responses: In the Chatflow settings, enable streaming so users see the response appear word by word — a much better UX than waiting for the full response to load at once.

Custom Tools for Agentflow: For more complex agents, Flowise lets you write custom tools in JavaScript and import them into the flow — a powerful way to extend functionality without forking the source code.

Who Flowise Is (and Isn’t) For

I don’t think Flowise replaces code entirely. For production-grade pipelines that require fine-grained control, or teams already comfortable with Python/LangChain — code remains the more flexible choice.

But Flowise shines in these scenarios:

• Rapid prototyping — validate an idea in 30 minutes instead of a few hours
• Internal tools — chatbots for internal docs, system Q&A, lightweight automation
• Non-developer teams — product managers and analysts can build and test flows themselves
• Client demos — a visual flow is far easier to explain than a pile of code

Conclusion

Flowise bridges the gap between “wanting to build an AI app” and “needing to know how to code first.” Fast setup (5 minutes with Docker), an intuitive drag-and-drop interface, automatic API exposure — everything you need to take an idea from whiteboard to working tool in a single morning.

If you need to prototype a chatbot or RAG system, try Flowise before committing to writing code from scratch — you might be surprised by how much you can accomplish without it.

Source code and official documentation: github.com/FlowiseAI/Flowise