LLM/n8n-install
1
0
Fork 0
mirror of https://github.com/kossakovsky/n8n-install.git synced 2026-03-07 22:33:11 +00:00
Code Issues Packages Projects Releases Wiki Activity

Remove outdated documentation for project structure and business logic from ai-docs

Browse Source 
- Deleted basic-structure.md and business-logic.md files as they are no longer relevant to the current project setup and documentation needs.
This commit is contained in:
Yury Kossakovsky
2025-07-17 13:30:09 -06:00
parent 194b3b5215
commit 3709f2218d
2 changed files with 0 additions and 126 deletions
Download Patch File Download Diff File Expand all files Collapse all files

83
ai-docs/basic-structure.md
View File

@@ -1,83 +0,0 @@
# Project Structure: n8n Ecosystem Installer
This document outlines the basic structure of the n8n Ecosystem Installer project, focusing on key directories and files and their purposes.
## 1. Overview
The project is designed to deploy a comprehensive development environment centered around n8n, using Docker Compose for service orchestration. It includes n8n, Flowise, Open WebUI, Supabase, Qdrant, Langfuse, SearXNG, Prometheus, Grafana, Crawl4ai, and Caddy for reverse proxying and SSL.
## 2. Root Directory Key Files
- **`README.md`**: Provides a high-level overview of the project, its components, installation instructions, and usage guidelines.
- **`docker-compose.yml`**: The primary Docker Compose file. It defines most of the services (n8n, Flowise, Open WebUI, Qdrant, Langfuse, Caddy, SearXNG, monitoring tools, Crawl4ai, etc.), their configurations, volumes, and networks. Note that the full Supabase stack is managed by a separate Docker Compose file (see Section 4.8).
- **`.env` (Generated)**: This critical file is generated by the installation script (`scripts/03_generate_secrets.sh`). It stores all necessary environment variables, including API keys, domain names, user credentials, and other configuration parameters for the various services. **It should not be committed to version control.**
- **`Caddyfile`**: Configuration file for the Caddy web server. It defines how incoming traffic is routed to the appropriate backend services (n8n, Flowise, Supabase, etc.) and handles automatic HTTPS/SSL certificate management via Let's Encrypt.
- **`start_services.py`**: A Python script that offers an alternative method for starting services. It notably handles the cloning of the Supabase Docker configuration from its official repository into a local `supabase/docker/` directory, prepares its `.env` file, and starts Supabase services before the main stack. It also includes logic for SearXNG secret key generation and initial setup.
## 3. Core Directories
### 3.1. `scripts/`
This directory contains shell scripts essential for the installation, configuration, and maintenance of the ecosystem.
- **`install.sh`**: The main entry point for setting up the entire environment. It orchestrates the execution of other scripts in a specific sequence.
- **`01_system_preparation.sh`**: Prepares the host system (e.g., updates, firewall configuration).
- **`02_install_docker.sh`**: Installs Docker and Docker Compose if not already present.
- **`03_generate_secrets.sh`**: Prompts the user for necessary information (domain, email, API keys) and generates the `.env` file with all required secrets and configurations.
- **`04_run_services.sh`**: Starts all the services defined in `docker-compose.yml` using `docker compose up`.
- **`05_final_report.sh`**: Displays a summary of access URLs and credentials for the deployed services after successful installation.
- **`update.sh`**: Script to pull the latest changes from the Git repository and update the Docker images for all services.
- **`utils.sh`**: Contains helper functions used by the other shell scripts.
### 3.2. `n8n/`
Configuration and data related to the n8n service.
- **`backup/`**: This subdirectory is used to store n8n workflow JSON files. Workflows placed here can be automatically imported into the n8n instance during the initial setup or via the `n8n-import` service defined in `docker-compose.yml`.
- **`n8n_import_script.sh`**: A shell script executed by the `n8n-import` Docker service to process and import workflows from the `backup/` directory into n8n.
### 3.3. `shared/`
This directory (created if it doesn't exist) is intended to be mounted as a shared volume into n8n containers (typically at `/data/shared` inside the container). Its purpose is to facilitate file exchange between the host machine and n8n workflows (e.g., reading local files, writing outputs).
### 3.4. `searxng/`
Configuration for the SearXNG private metasearch engine.
- **`settings-base.yml`**: A base template for SearXNG's `settings.yml` configuration file. The `start_services.py` script or a similar setup step copies this to `settings.yml` and populates it, for instance, with a generated secret key. The `docker-compose.yml` mounts this directory into the SearXNG container.
### 3.5. `prometheus/`
Configuration for the Prometheus monitoring system.
- **`prometheus.yml`**: The main configuration file for Prometheus, defining scrape targets and rules. This file is mounted directly into the Prometheus container.
### 3.6. `grafana/`
Configuration and provisioning files for the Grafana observability platform.
- **`dashboards/`**: Contains JSON files defining pre-configured Grafana dashboards (e.g., `1860_rev39.json`).
- **`provisioning/`**:
- `datasources/main.yml`: Defines data sources to be automatically provisioned in Grafana (e.g., connecting to the Prometheus service).
- `dashboards/` (subdirectory): Can contain YAML files to provision dashboards from the `grafana/dashboards` directory.
### 3.7. `flowise/` (Project Root Directory)
This directory in the project root contains example/template files for Flowise.
- **`*.json` files**: These are typically exported Flowise chatflow definitions (e.g., `Web Search + n8n Agent Chatflow.json`) and custom tool configurations.
- **Note**: The actual runtime data and configuration for the Flowise service (defined in `docker-compose.yml`) are stored in a volume mapped from `~/.flowise` on the host machine to `/root/.flowise` inside the container. The files in this project directory serve as starting points or examples.
### 3.8. `supabase/` (Dynamically Created)
This directory is **not** present in the initial project checkout. It is created by the `start_services.py` script (or a similar process within the main installer) by cloning parts of the official Supabase GitHub repository.
- **`docker/`**: Contains Supabase's own Docker setup.
- `docker-compose.yml`: A Docker Compose file specific to Supabase services (Postgres, Kong, GoTrue, etc.).
- `.env`: Supabase-specific environment variables, typically populated by copying relevant values from the main project's `.env` file.
The Supabase stack is started and managed somewhat independently but is integrated into the overall project (e.g., accessible via Caddy and its database can be used by other services like n8n).
## 4. Service Data and Volumes
Most services persist their data in Docker named volumes, which are defined at the beginning of the main `docker-compose.yml` file (e.g., `n8n_storage`, `qdrant_storage`, `langfuse_postgres_data`, etc.). This ensures data is retained across container restarts.

43
ai-docs/business-logic.md
View File

@@ -1,43 +0,0 @@
# Business Logic: n8n Ecosystem Installer
## 1. General Product Idea
The **n8n Ecosystem Installer** is an open-source project that provides a Docker Compose-based template to rapidly deploy a comprehensive, self-hosted development and automation environment. The core of this environment is **n8n**, a powerful workflow automation tool, augmented by **Flowise**, a low-code platform for building Large Language Model (LLM) applications.
The product's main idea is to offer a turnkey solution that bundles n8n and Flowise with a suite of essential supporting services. These services include:
- **Open WebUI**: A user-friendly interface for interacting with n8n agents and LLMs.
- **Supabase**: An open-source Firebase alternative, providing database, vector storage, and authentication capabilities.
- **Qdrant**: A high-performance vector database for AI applications.
- **Langfuse**: An LLM engineering platform for observability and debugging of LLM applications.
- **SearXNG**: A private metasearch engine.
- **Crawl4ai**: A web crawler optimized for AI data extraction.
- **Prometheus & Grafana**: For monitoring and visualizing system metrics.
- **Caddy**: A web server that automatically handles HTTPS/SSL for all exposed services.
By pre-configuring these tools to work together, the installer significantly reduces the complexity and time required to set up such an integrated environment from scratch.
## 2. Target Users
The primary target users for the n8n Ecosystem Installer are:
- **Developers and Engineers**: Individuals who need a robust, self-hosted platform for building, testing, and deploying workflow automations and AI-powered applications.
- **AI Enthusiasts and Researchers**: Users who want to experiment with n8n's AI capabilities, Flowise, and various LLM tools in a private and controlled environment.
- **Low-code/No-code Builders**: Citizen developers or power users who want to leverage the visual interfaces of n8n and Flowise to create sophisticated automations and AI agents without extensive coding.
- **Users Prioritizing Self-Hosting and Data Privacy**: Individuals or organizations that prefer to host their tools and data on their own infrastructure for security, customization, or compliance reasons.
- **Small to Medium-sized Businesses (SMBs)**: Companies looking for a cost-effective way to implement powerful automation and AI solutions without relying on expensive SaaS subscriptions for each individual tool.
## 3. Problems Solved
The n8n Ecosystem Installer addresses several key challenges:
- **Complexity of Setup**: Manually installing and configuring multiple interconnected services (n8n, databases, AI tools, reverse proxies, monitoring) is time-consuming, error-prone, and requires significant technical expertise. The installer automates this process.
- **Integration Effort**: Ensuring that different tools work seamlessly together often involves custom scripting and configuration. The installer provides a pre-integrated stack.
- **Time to Value**: By simplifying setup and integration, users can get their development environment up and running quickly, allowing them to focus on building automations and AI applications rather than on infrastructure management.
- **Accessibility of Advanced AI Tools**: It makes a suite of powerful AI development tools (vector stores, LLM observability, AI agent builders) more accessible by bundling them in an easy-to-deploy package.
- **Secure Hosting**: With Caddy's automatic HTTPS, the project provides a secure way to expose services, which is often a hurdle for self-hosted solutions.
- **Learning Curve for n8n**: For new n8n users, it offers an environment where they can immediately start exploring advanced features, including AI agents and community workflows (with an option to auto-import hundreds of them).
- **Vendor Lock-in**: By promoting open-source tools, it provides an alternative to proprietary platforms, giving users more control and flexibility.
- **Resource Management**: Using Docker Compose allows for efficient management of the various services, making it easier to start, stop, and update components of the ecosystem.
In essence, the project democratizes access to a powerful, integrated n8n-centered automation and AI development stack by radically simplifying its deployment and initial configuration.