Introduction to n8n and Building an AI Financial Assistant LINE Bot

Technical Articles July 18, 2025

Overview of n8n

n8n is a powerful, open-source automation tool that has recently gained significant popularity online. Pronounced as "nodemation," the name n8n derives from the word "nodemation," combining "node" (the first letter) and "mation" (the last part), with eight letters in between, hence "n8n." This naming convention is similar to "i18n" (Internationalization), where the 18 letters between "i" and "n" are abbreviated.

What is n8n?

n8n, short for nodemation, is composed of two key concepts:

Node: Refers to the node-based interface where workflows are built by connecting individual nodes. Each node represents a specific action or trigger, and n8n is built using Node.js, adding a layer of technical significance to the term.
Mation (Automation): Emphasizes the tool’s core purpose—automating repetitive tasks to streamline workflows.

n8n is a low-code platform, meaning users can create complex workflows with minimal coding. However, it also supports custom code for advanced customization. It integrates with numerous apps and services, such as Google, OpenAI’s ChatGPT, and LINE, allowing users to automate tasks across platforms with a single click. Key advantages include:

Customization: Tailor workflows to specific needs.
Ease of Use: Simplified setup using tools like npm or Docker.
Data Privacy: Host n8n locally to keep data secure and private.

n8n Pricing

n8n offers both cloud-based and self-hosted options. The cloud version requires payment after a 14-day trial, during which users can execute up to 10,000 workflow steps and create up to 15 workflows. If the trial expires without upgrading within 28 days, all projects in the workspace are deleted, so backups are essential. As an open-source tool, n8n can be self-hosted for free, making it cost-effective for local deployment.

Comparison with Other Automation Platforms

n8n is often compared to platforms like Zapier and Make. Here’s a breakdown:

Ease of Use: n8n has a steeper learning curve, requiring some programming knowledge, while Zapier and Make are more beginner-friendly.
Technical Expertise: n8n suits users with coding skills, offering greater flexibility. Zapier and Make cater to non-coders.
Flexibility: n8n’s higher technical threshold allows for more complex workflows.
Pricing: Zapier and Make require paid subscriptions post-trial, while n8n’s open-source nature allows free self-hosting.
Integrations: Zapier supports over 6,000 apps, Make around 1,500, and n8n approximately 1,000 (with growing community contributions).
Complexity: n8n supports advanced logic and branching, ideal for intricate workflows, while Zapier and Make focus on simpler tasks.

n8n Interface

The n8n interface is intuitive, featuring:

Workflows Tab: Displays all created workflows, where nodes are connected to define processes.
Credentials Tab: Stores API keys and other credentials.
Executions Tab: Logs the history of executed workflows.
Node Toolbar: Allows adding nodes or triggers, such as AI modules, Google services, or LINE integrations.
Testing and Execution: Enables testing workflows before deployment.

Node Types in n8n

n8n’s workflows are built using various node types:

Triggers and Actions:
- Manual Trigger: Initiates a workflow manually with a click.
- Scheduled Trigger: Runs workflows at set times (e.g., daily stock updates).
- Chat Message Trigger: Activates workflows based on user input in a chat interface.
Core Nodes: Handle API calls (e.g., LINE or Google APIs) or HTTP requests for data like stock prices.
Cluster Nodes: Aggregate multiple nodes, such as an AI Agent node that integrates models, tools, and parsers.
Credentials: Store API keys securely.
Community Nodes: Allow developers to share custom nodes for additional functionality.

Course Structure: Building an AI Financial Assistant LINE Bot

This course spans four weeks, guiding participants to create an AI-powered financial assistant LINE Bot capable of answering stock-related queries, providing financial insights, and offering buy/sell recommendations.

Week 1: Introduction and Setup

Objective: Understand n8n and set up a local environment.
Topics:
- Introduction to n8n and its node-based automation.
- Overview of Docker, a containerization tool for hosting n8n locally.

Tasks:

Install Docker and set up a local n8n server using commands like:

docker volume create n8n_data
docker run -p 5678:5678 -v n8n_data:/home/node/.n8n n8n

Alternatively, use a docker-compose.yml file for easier management:

services:
  n8n:
    image: n8nio/n8n:latest
    ports:
      - "127.0.0.1:5678:5678"
    volumes:
      - n8n_storage:/home/node/.n8n
volumes:
  n8n_storage:

Access the n8n interface at http://localhost:5678.

Week 2: Integrating LINE

Objective: Build a LINE Bot using n8n.
Key Concepts:
- LINE Messaging API:
  - Push API: The bot proactively sends messages (paid).
  - Reply API: The bot responds to user messages (free).
- Ngrok: Creates a temporary public URL for local n8n servers, enabling webhook functionality.
Tasks:
- Set up a LINE Official Account and configure the Messaging API via LINE Developers.
- Use Ngrok to generate a public URL:
```
ngrok http 5678
```
- Update the n8n docker-compose.yml with the Ngrok URL:
```
services:
  n8n:
    image: n8nio/n8n:latest
    ports:
      - "127.0.0.1:5678:5678"
    environment:
      - WEBHOOK_URL={ngrok_url}
    volumes:
      - n8n_storage:/home/node/.n8n
volumes:
  n8n_storage:
```
- Configure the webhook in LINE Developers with the Production URL from n8n.

Week 3: Stock API Integration

Objective: Connect n8n to a stock API for automated updates.
Tasks:
- Use n8n’s HTTP Request node to fetch stock data.
- Set up scheduled triggers to push stock updates via LINE at specific times (e.g., 8 AM daily).
- Test and refine the workflow.

Week 4: Building the AI Financial Assistant

Objective: Create an AI-powered LINE Bot using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).
Key Concepts:
- LLMs: Advanced AI models like ChatGPT for generating responses.
- RAG (Retrieval-Augmented Generation):
  - Retrieval: Fetch relevant financial documents or data.
  - Augmented: Combine retrieved data with user queries to enhance context.
  - Generation: Use an LLM to generate accurate, context-aware responses.
- RAG Workflow:
  - Chunking: Break documents into smaller pieces.
  - Vectorization: Convert chunks into vectors using an embeddings model.
  - Storage: Store vectors in a vector database.
  - Querying: Compare user queries to vectors for similarity, enhancing the query before passing it to the LLM.
Advantages of RAG:
- Access to up-to-date information via retrieval.
- Reduced hallucinations by grounding responses in verified data.
- Traceable sources for transparency.
Tasks:
- Integrate an LLM (e.g., OpenAI’s GPT) with n8n’s AI Agent node.
- Implement RAG by connecting to a vector database and retrieving financial data.
- Test the bot’s ability to answer stock queries and provide recommendations.

Understanding Docker

Docker is an open-source containerization platform that simplifies the development, deployment, and running of applications. It uses containers to package applications with their dependencies, ensuring consistency across environments (e.g., macOS, Windows, Linux).

Key Docker Concepts

Image: A blueprint containing the application and its dependencies (e.g., n8n’s image from n8nio/n8n).
Container: A running instance of an image, isolated from other containers.
Volume: A storage mechanism for persistent data, preventing data loss when containers stop.
Dockerfile: A script to automate image creation.

Docker Architecture

Docker Client: Sends commands (e.g., docker run, docker pull) to the Docker Daemon.
Docker Daemon: Manages containers and images on the Docker Host.
Docker Registry: Stores images (e.g., Docker Hub for pulling n8n’s image).

Benefits of Docker

Portability: Run applications consistently across different systems.
Efficiency: Containers are lightweight compared to virtual machines, requiring fewer resources.
Speed: Containers start quickly, unlike VMs that emulate entire operating systems.

LINE Messaging API

The LINE Messaging API enables bot communication:

Push API: Sends messages proactively (costs apply).
Reply API: Responds to user messages (free, with a 200-message monthly limit for light users).
Webhook: Allows LINE to notify the bot of new messages, improving efficiency over polling.

LINE Account Setup

LINE Official Account Manager: Manages non-programmatic tasks like coupons or surveys.
LINE Developers: Handles API integrations like Messaging API or LINE Login.
Provider and Channel:
- Provider: The entity (individual or organization) managing services.
- Channel: Specific services (e.g., Messaging API) under a provider. User IDs are consistent within the same provider but differ across providers.

Ngrok for Local Testing

Since local n8n servers (localhost:5678) are inaccessible externally, Ngrok creates a temporary public URL for webhook testing:

Register at Ngrok’s website and download the executable.
Run ngrok http 5678 to generate a public URL.
Update the n8n webhook URL in LINE Developers and test the connection.

Conclusion

This course equips participants with the skills to leverage n8n’s automation capabilities, Docker’s containerization, and LINE’s Messaging API to build a sophisticated AI financial assistant LINE Bot. By integrating stock APIs and RAG-powered LLMs, the bot will deliver accurate, context-aware financial insights, demonstrating the power of modern automation tools.

Shop By Categories