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+# 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).
+
+---
+
+## Table of Contents
+
+1. [Prerequisites](#prerequisites)
+2. [Install Ollama](#install-ollama)
+3. [Pull Required Models](#pull-required-models)
+4. [Install Flowise](#install-flowise)
+5. [Start Flowise](#start-flowise)
+6. [Build the Chatbot Flow](#build-the-chatbot-flow)
+   - [Step 1: Recursive Character Text Splitter](#step-1-recursive-character-text-splitter)
+   - [Step 2: Document Store (RAG)](#step-2-document-store-rag)
+   - [Step 3: Ollama Embeddings](#step-3-ollama-embeddings)
+   - [Step 4: Faiss Vector Store](#step-4-faiss-vector-store)
+   - [Step 5: Ollama Chat Model](#step-5-ollama-chat-model)
+   - [Step 6: Buffer Memory](#step-6-buffer-memory)
+   - [Step 7: Conversational Retrieval QA Chain](#step-7-conversational-retrieval-qa-chain)
+7. [Upsert Documents](#upsert-documents)
+8. [Using Cloud LLM APIs (Optional)](#using-cloud-llm-apis-optional)
+9. [Deploy Chatbot to Users](#deploy-chatbot-to-users)
+   - [Direct Link](#direct-link)
+   - [Embed in Website](#embed-in-website)
+   - [Session Management](#session-management)
+   - [Rate Limiting](#rate-limiting)
+10. [Troubleshooting](#troubleshooting)
+
+---
+
+## 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+
+
+```bash
+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
+```
+
+---
+
+## Install Ollama
+
+Ollama runs LLMs locally on your machine.
+
+```bash
+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:
+
+```bash
+sudo systemctl edit ollama.service
+```
+
+Add the following inside the file:
+
+```ini
+[Service]
+Environment="OLLAMA_HOST=0.0.0.0"
+```
+
+Save and restart:
+
+```bash
+sudo systemctl daemon-reload
+sudo systemctl restart ollama
+```
+
+Verify it's running:
+
+```bash
+curl http://localhost:11434/api/tags
+```
+
+You should see a JSON response listing available models.
+
+---
+
+## Pull Required Models
+
+### Chat Model (LLM)
+
+```bash
+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
+
+```bash
+ollama pull nomic-embed-text  # required for RAG / vector search
+```
+
+### (Optional) Vision Model — for image understanding
+
+```bash
+ollama pull llava             # allows users to upload screenshots
+```
+
+Verify all models are available:
+
+```bash
+ollama list
+```
+
+---
+
+## Install Flowise
+
+```bash
+npm install -g flowise
+```
+
+---
+
+## Start Flowise
+
+```bash
+npx flowise start
+```
+
+Flowise will start on **http://localhost:3000**
+
+### Run as a Background Service (Recommended for Production)
+
+```bash
+# 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)
+
+```bash
+npx flowise start --FLOWISE_USERNAME=admin --FLOWISE_PASSWORD=yourpassword
+```
+
+Or with PM2:
+
+```bash
+pm2 start "npx flowise start --FLOWISE_USERNAME=admin --FLOWISE_PASSWORD=yourpassword" --name flowise
+```
+
+---
+
+## 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
+```
+
+---
+
+### 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` |
+
+---
+
+### 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.
+
+---
+
+### 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.
+
+---
+
+### 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:
+
+```bash
+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.
+
+---
+
+### 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.
+
+---
+
+### 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.
+
+---
+
+### Step 7: Conversational Retrieval QA Chain
+
+Search for **"Conversational Retrieval QA Chain"** and drag it onto the canvas.
+
+Connect:
+- **ChatOllama** → **Chat Model**
+- **Faiss Retriever** → **Vector Store Retriever**
+- **BufferMemory** → **Memory**
+
+Leave **Input Moderation** and **Return Source Documents** as optional based on your needs.
+
+**Save the flow** using the save button (top right).
+
+---
+
+## 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:
+
+```bash
+ls /root/.flowise/vectorstore/
+# Expected output: faiss.index  faiss.json
+```
+
+> **Re-upsert every time** you update or add documents to the Document Store.
+
+---
+
+## 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.
+
+---
+
+## 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.
+
+---
+
+### Embed in Website
+
+Paste this into any HTML page to show a chat bubble:
+
+```html
+<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).
+
+---
+
+### 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` |
+
+```javascript
+// 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;
+})()
+```
+
+---
+
+### 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.
+
+---
+
+## Troubleshooting
+
+### ❌ `fetch failed` when chatting
+
+**Cause:** Flowise cannot reach Ollama.
+
+**Fix:**
+```bash
+# 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
+```
+
+---
+
+### ❌ `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
+
+---
+
+### ❌ `could not open faiss.index for reading`
+
+**Cause:** Upsert was never completed successfully — the index file doesn't exist.
+
+**Fix:**
+```bash
+# Check if the file exists
+ls /root/.flowise/vectorstore/
+
+# If empty, fix the Base Path issue above, then Upsert again
+```
+
+---
+
+### ❌ Ollama model not found
+
+**Cause:** Model name is wrong or model was never pulled.
+
+**Fix:**
+```bash
+# 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
+```
+
+---
+
+### ❌ Flowise crashes or won't start
+
+**Fix:**
+```bash
+# 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
+```
+
+---
+
+### ❌ Out of memory / model too slow
+
+**Fix:**
+
+```bash
+# 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).
+
+---
+
+### ❌ 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
+
+---
+
+### ❌ 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](#session-management)).
+
+---
+
+## 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 |
+
+---
+
+*Built with Flowise + Ollama. All models run locally — no data leaves your server.*