Flowise Chatbot Setup Guide — Linux/Ubuntu

Build a fully local AI-powered chatbot using Flowise, Ollama, and a Conversational Retrieval QA Chain with RAG (Retrieval-Augmented Generation).

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Table of Contents

1. Prerequisites
2. Install Ollama
3. Pull Required Models
4. Install Flowise
5. Start Flowise
6. Build the Chatbot Flow
   • Step 1: Recursive Character Text Splitter
   • Step 2: Document Store (RAG)
   • Step 3: Ollama Embeddings
   • Step 4: Faiss Vector Store
   • Step 5: Ollama Chat Model
   • Step 6: Buffer Memory
7. Upsert Documents
8. Using Cloud LLM APIs (Optional)
9. Deploy Chatbot to Users
   • Direct Link
   • Embed in Website
   • Session Management
   • Rate Limiting
10. Troubleshooting

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Prerequisites

• Ubuntu 20.04 or later
• At least 8GB RAM (16GB recommended)
• Node.js 18+
• Internet access for first-time model downloads

Install Node.js 18+

curl -fsSL https://deb.nodesource.com/setup_18.x | sudo -E bash -
sudo apt-get install -y nodejs
node -v   # should print v18.x.x or higher

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Install Ollama

Ollama runs LLMs locally on your machine.

curl -fsSL https://ollama.com/install.sh | sh

Make Ollama Accessible on All Interfaces

By default, Ollama only listens on localhost. To allow Flowise (and other services) to reach it:

sudo systemctl edit ollama.service

Add the following inside the file:

[Service]
Environment="OLLAMA_HOST=0.0.0.0"

Save and restart:

sudo systemctl daemon-reload
sudo systemctl restart ollama

Verify it's running:

curl http://localhost:11434/api/tags

You should see a JSON response listing available models.

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Pull Required Models

Chat Model (LLM)

ollama pull qwen2.5:3b        # lightweight, good for most use cases
# OR
ollama pull phi3:mini         # alternative lightweight model
# OR
ollama pull llama3:8b         # better quality, needs more RAM

Embedding Model

ollama pull nomic-embed-text  # required for RAG / vector search

(Optional) Vision Model — for image understanding

ollama pull llava             # allows users to upload screenshots

Verify all models are available:

ollama list

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Install Flowise

npm install -g flowise

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Start Flowise

npx flowise start

Flowise will start on http://localhost:3000

Run as a Background Service (Recommended for Production)

# Install PM2 process manager
npm install -g pm2

# Start Flowise with PM2
pm2 start "npx flowise start" --name flowise

# Auto-start on system reboot
pm2 startup
pm2 save

Set Username and Password (Recommended)

npx flowise start --FLOWISE_USERNAME=admin --FLOWISE_PASSWORD=yourpassword

Or with PM2:

pm2 start "npx flowise start --FLOWISE_USERNAME=admin --FLOWISE_PASSWORD=yourpassword" --name flowise

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Build the Chatbot Flow

Open Flowise at http://localhost:3000 → click Add New to create a new chatflow.

The final flow looks like this:

Document Store
      ↓
Recursive Character Text Splitter
      ↓
Ollama Embeddings → Faiss Vector Store → Conversational Retrieval QA Chain
                                                    ↑
                                              Ollama (Chat)
                                                    ↑
                                             Buffer Memory

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Step 1: Recursive Character Text Splitter

Search for "Recursive Character Text Splitter" in the nodes panel and drag it onto the canvas.

Setting	Recommended Value
Chunk Size	1000
Chunk Overlap	200

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Step 2: Document Store (RAG)

Search for "Document Store" and drag it onto the canvas.

1. Click Select Store → Create New Store
2. Name it (e.g., Product Documentation)
3. Assign the Recursive Character Text Splitter to the Document Store
4. Add your documents:
   • Click the store → Add Document Loader
   • Choose a loader: Text File, PDF File, Docx File, etc.
   • Upload your knowledge base files
5. Connect the Document output of the Document Store to the Faiss node

What to put in your documents: Write plain text files or PDFs describing your software — features, FAQs, installation steps, error explanations, etc. The more detailed your docs, the better the chatbot answers.

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Step 3: Ollama Embeddings

Search for "Ollama Embeddings" and drag it onto the canvas.

Setting	Value
Base URL	http://localhost:11434 (or your server IP)
Model Name	nomic-embed-text:latest

Connect the OllamaEmbeddings output to the Embeddings input of the Faiss node.

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Step 4: Faiss Vector Store

Search for "Faiss" and drag it onto the canvas.

Setting	Value
Base Path to load	/root/.flowise/vectorstore (must be an existing directory)

Create the directory if it doesn't exist:

mkdir -p /root/.flowise/vectorstore

Connect:

• Document output from Document Store → Document input of Faiss
• OllamaEmbeddings → Embeddings input of Faiss

The Faiss Retriever output connects to the Vector Store Retriever input of the QA Chain.

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Step 5: Ollama Chat Model

Search for "ChatOllama" or "Ollama" and drag it onto the canvas.

Setting	Value
Base URL	http://localhost:11434 (or your server IP)
Model Name	qwen2.5:3b
Temperature	0.7 (lower = more factual, higher = more creative)

Important: Use the exact model name with a colon, e.g. qwen2.5:3b not qwen2.5-3b.

Connect the ChatOllama output to the Chat Model input of the QA Chain.

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Step 6: Buffer Memory

Search for "Buffer Memory" and drag it onto the canvas.

Setting	Value
Session ID	(leave empty — auto-uses the session ID passed by the user)
Memory Key	chat_history

Connect the BufferMemory output to the Memory input of the QA Chain.

Buffer Memory keeps conversation history so users can ask follow-up questions naturally. Note: Buffer Memory resets if Flowise restarts. For persistent memory across restarts, use Redis-Backed Memory or MongoDB Memory instead.

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Upsert Documents

Before the chatbot can answer questions, you must index your documents into the vector store.

1. Click the Upsert button (top right of the canvas)
2. Wait for it to complete — you should see a success message
3. Verify the index files were created:

ls /root/.flowise/vectorstore/
# Expected output: faiss.index  faiss.json

Re-upsert every time you update or add documents to the Document Store.

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Using Cloud LLM APIs (Optional)

If you prefer using cloud providers instead of (or in addition to) Ollama:

OpenAI

1. Get your API key at https://platform.openai.com/api-keys
2. In Flowise: Settings → API Keys → Add key
3. Replace the ChatOllama node with a ChatOpenAI node
4. Enter your API key and choose a model (e.g., gpt-4o)

Anthropic (Claude)

1. Get your API key at https://console.anthropic.com
2. Replace the ChatOllama node with a ChatAnthropic node
3. Enter your API key and choose a model (e.g., claude-sonnet-4-5)

Google Gemini

1. Get your API key at https://aistudio.google.com
2. Use the ChatGoogleGenerativeAI node
3. Enter your API key and model name (e.g., gemini-1.5-pro)

For Embeddings with cloud APIs: Replace Ollama Embeddings with OpenAI Embeddings or Google Generative AI Embeddings nodes, using the same API key setup.

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Deploy Chatbot to Users

Direct Link

Share the chatbot link directly — no account needed for users:

http://your-server-ip:3000/chatbot/YOUR-FLOW-ID

Find your flow ID in the URL when editing the flow.

Users just open the link and start chatting immediately. No signup required.

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Embed in Website

Paste this into any HTML page to show a chat bubble:

<script type="module">
  import Chatbot from "https://cdn.jsdelivr.net/npm/flowise-embed/dist/web.js"

  // Generate or reuse a session ID per user
  let sessionId = localStorage.getItem("chatSessionId");
  if (!sessionId) {
    sessionId = crypto.randomUUID();
    localStorage.setItem("chatSessionId", sessionId);
  }

  Chatbot.init({
    chatflowid: "YOUR-FLOW-ID",
    apiHost: "http://your-server-ip:3000",
    chatflowConfig: {
      sessionId: sessionId
    },
    theme: {
      button: {
        backgroundColor: "#your-brand-color",
        right: 20,
        bottom: 20,
      },
      chatWindow: {
        title: "Software Support",
        welcomeMessage: "Hello! How can I help you today?",
        height: 600,
        width: 400,
      }
    }
  })
</script>

Get your embed code from Flowise: open the flow → click the <> (Embed) button (top right).

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Session Management

Each user needs an isolated session to prevent seeing each other's conversation history.

Scenario	Behavior
No sessionId set	All users share memory — dangerous
Unique sessionId per user	Fully isolated conversations
Using your own auth system	Pass your user's ID as the sessionId

// If users are logged into your system, use their user ID
sessionId: currentUser.id  // e.g. "user_12345"

// For anonymous users, use localStorage (persists across page refreshes)
sessionId: localStorage.getItem("chatSessionId") || (() => {
  const id = crypto.randomUUID();
  localStorage.setItem("chatSessionId", id);
  return id;
})()

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Rate Limiting

Limit how many messages a user can send to protect your server.

1. Open your flow in Flowise
2. Click ⚙️ Configuration (top right)
3. Go to the Rate Limiting tab
4. Configure:

Setting	Example Value
Message Limit	20
Duration (seconds)	60
Limit Message	"Too many messages. Please wait a moment."

This limits each IP address to 20 messages per 60 seconds.

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Troubleshooting

❌ fetch failed when chatting

Cause: Flowise cannot reach Ollama.

Fix:

# Verify Ollama is reachable
curl http://localhost:11434/api/tags

# Check Ollama is running
systemctl status ollama

# Check model name is correct (must use colon, not dash)
# ✅ qwen2.5:3b
# ❌ qwen2.5-3b
ollama list

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❌ Cannot read properties of undefined (reading 'startsWith')

Cause: The Base Path to load field in the Faiss node is empty.

Fix:

1. Click the Faiss node
2. Fill in Base Path to load: /root/.flowise/vectorstore
3. Create the directory: mkdir -p /root/.flowise/vectorstore
4. Re-upsert

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❌ could not open faiss.index for reading

Cause: Upsert was never completed successfully — the index file doesn't exist.

Fix:

# Check if the file exists
ls /root/.flowise/vectorstore/

# If empty, fix the Base Path issue above, then Upsert again

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❌ Ollama model not found

Cause: Model name is wrong or model was never pulled.

Fix:

# List available models
ollama list

# Pull the missing model
ollama pull qwen2.5:3b
ollama pull nomic-embed-text:latest

# Always use colon format in Flowise: qwen2.5:3b not qwen2.5-3b

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❌ Flowise crashes or won't start

Fix:

# Check Node.js version (must be 18+)
node -v

# Clear Flowise cache
rm -rf ~/.flowise/cache

# Check port 3000 is not already in use
sudo lsof -i :3000

# Restart with PM2
pm2 restart flowise
pm2 logs flowise

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❌ Out of memory / model too slow

Fix:

# Check available RAM
free -h

# Use a smaller model
ollama pull phi3:mini   # 3.8B — lighter than qwen2.5:3b

# Check what's loaded in Ollama
ollama ps

If RAM is under 8GB, use phi3:mini or qwen2.5:3b (both under 4GB).

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❌ Bot answers incorrectly or doesn't use documents

Cause: Documents are not properly indexed, or chunks are too small/large.

Fix:

1. Make sure you clicked Upsert after adding documents
2. Verify faiss.index and faiss.json exist in the Base Path
3. Try increasing Chunk Size to 1500 and re-upsert
4. Make sure your documents are in plain text, not scanned images

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❌ Users share each other's conversation history

Cause: No sessionId is being passed, or all users get the same ID.

Fix: Use the embed code with a unique sessionId per user (see Session Management).

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Quick Reference

Component	Purpose
Ollama	Runs LLMs locally
nomic-embed-text	Converts text to vectors for search
Faiss	Stores and searches vectors
Document Store	Manages your knowledge base files
Recursive Text Splitter	Breaks documents into searchable chunks
Buffer Memory	Remembers conversation history per user
Conversational Retrieval QA Chain	Ties everything together

Port	Service
3000	Flowise UI and API
11434	Ollama API

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Built with Flowise + Ollama. All models run locally — no data leaves your server.