Integrate NocoDB with 500+ apps and services

🎶 Add liked songs to a monthly playlist > This Workflow is a port of Add saved songs to a monthly playlist from IFTTT. When you like a song, the workflow will save this song in a monthly playlist. E.g.: It's June 2024, I liked a song. The workflow will save this song in a playlist called June '24. If this playlist does not exist, the workflow will create it for me. ⚙ How it works Each 5 minutes, the workflow will start automatically. He will do 3 things : Get the last 10 songs you saved in the "Liked song" playlist (by clicking on the heart in the app) and save them in a NocoDB table (of course, the workflow avoid to create duplicates). Check if the monthly playlist is already created. Otherwise, the playlist is created. The created playlist is also saved in NocoDB to avoid any problems. Check if the monthly playlist contains all the song liked this month by getting them from NocoDB. If they are not present, add them one by one in the playlist. You may have a question regarding the need of NocoDB. Over the last few weeks/months, I've had duplication problems in my playlists and some playlists have been created twice because Spotify wasn't returning all the information but only partial information. Having the database means I don't have to rely on Spotify's data but on my own, which is accurate and represents reality. 📝 Prerequisites You need to have : Spotify API keys, which you can obtain by creating a Spotify application here: https://developer.spotify.com/dashboard. Create a NocoDB API token 📚 Instructions Follow the instructions below Create your Spotify API credential Create your NocoDB credential Populate all Spotify nodes with your credentials Populate all Spotify nodes with your credentials Enjoy ! If you need help, feel free to ping me on the N8N Discord server or send me a DM at "LucasAlt" Show your support Share your workflow on X and mention @LucasCtrlAlt Consider buying me a coffee 😉

An example workflow for a multilanguage Telegram bot. It allows adding many new languages to the bot without editing the workflow. Important note! Due to some breaking API changes in NocoDB some of its node options are not working at the moment (MAY 2022). These two nodes were replaced by HTTP request nodes. Functionality is still the same.

The News Site from Colt, a telecom company, does not offer an RSS feed, therefore web scraping is the choice to extract and process the news. The goal is to get only the newest posts, a summary of each post and their respective (technical) keywords. Note that the news site offers the links to each news post, but not the individual news. We collect first the links and dates of each post before extracting the newest ones. The result is sent to a SQL database, in this case a NocoDB database. This process happens each week thru a cron job. Requirements: Basic understanding of CSS selectors and how to get them via browser (usually: right click → inspect) ChatGPT API account - normal account is not sufficient A NocoDB database - of course you may choose any type of output target Assumptions: CSS selectors work on the news site The post has a date with own CSS selector - meaning date is not part of the news content "Warnings" Not every site likes to be scraped, especially not in high frequency Each website is structured in different ways, the workflow may then need several adaptations.

This workflow automates the process of gathering LinkedIn advice articles, extracting their content, and generating unique contributions for each article using an AI model. The contributions are then posted to a Slack channel and a NocoDB database for record-keeping. The workflow is triggered weekly to ensure new articles are continuously collected and responded to. Who is this for? This workflow is designed for professionals, marketers, and content creators looking to boost their LinkedIn presence by regularly engaging with LinkedIn advice articles. It’s especially useful for those who want to be seen as a "thought leader" or "top voice" in their niche by contributing relevant and unique advice to trending topics. What problem is this workflow solving? Manually searching for relevant LinkedIn articles, reading through them, and crafting thoughtful contributions can be time-consuming. This workflow solves that by automating the process of finding new articles, extracting key content, and generating AI-powered contributions. It helps users stay consistently active on LinkedIn, contributing value to trending discussions. What this workflow does Triggers Weekly: The workflow is set to run every Monday at 8:00 AM. Search Google for LinkedIn Advice Articles: Uses a predefined Google search URL to find the latest LinkedIn advice articles based on the user's area of expertise. Extract LinkedIn Article Links: A code node extracts all LinkedIn advice article links from the search results. Retrieve Article Content: For each article link, the workflow retrieves the HTML content and extracts the article title, topics, and existing contributions. Generate AI-Powered Contributions: The workflow sends the extracted article content to an AI model, which generates unique, helpful advice for each topic within the article. Post to Slack & NocoDB: The AI-generated contributions, along with the article links, are posted to a designated Slack channel and stored in a NocoDB database for future reference. Setup Google Search URL: Update the Google search URL with the relevant LinkedIn advice query for your field (e.g., "site:linkedin.com/advice 'marketing automation'"). Slack Integration: Connect your Slack account and specify the Slack channel where you want the contributions to be posted. NocoDB Integration: Set up your NocoDB project to store the generated contributions along with the article titles and links. How to customize this workflow Change Search Terms**: Modify the Google search URL to focus on a different LinkedIn topic or expertise area. Adjust Trigger Frequency**: The workflow is set to run weekly, but you can adjust the frequency by changing the schedule trigger. Enhance Contribution Quality**: Customize the AI model's prompt to generate contributions that align with your brand voice or content strategy. Workflow Summary This workflow helps users maintain a consistent presence on LinkedIn by automating the discovery of new advice articles and generating unique contributions using AI. It is ideal for professionals who want to engage with LinkedIn content regularly without spending too much time manually searching and drafting responses.

Template Description WDF Top Keywords: This workflow is designed to streamline keyword research by automating the process of generating, filtering, and analyzing Google and YouTube keyword data. Ensure compliance with local regulations and API terms of service when using this workflow. 📌 Purpose The WDF Top Keywords workflow automates collecting, processing, and managing keyword data for both Google and YouTube platforms. Leveraging multiple data sources and APIs ensures an efficient and scalable approach to identifying high-impact keywords for SEO, content creation, and marketing campaigns. Key Features Automates the generation of keyword suggestions using autocomplete APIs. Integrates with NocoDB to store and manage keyword data. Filters keywords based on monthly search volume and cost-per-click (CPC). Supports bulk import of keyword data into structured databases. Outputs both Google and YouTube keyword insights, enabling informed decision-making. 🎯 Target Audience This workflow is ideal for: Digital marketers aiming to optimize ad campaigns with data-driven insights. SEO specialists looking to identify high-potential keywords efficiently. Content creators seeking trending and relevant topics for their platforms. Agencies managing keyword research for multiple clients. ⚙️ How It Works Trigger: The workflow runs on-demand or at scheduled intervals. Keyword Generation: Retrieves base keywords from NocoDB. Generates autocomplete suggestions for Google and YouTube. Data Processing: Filters and formats keyword data based on specific criteria (e.g., search volume, CPC). Consolidates results for efficient storage and analysis. Storage and Output: Saves data into structured NocoDB tables for tracking and reuse. Bulk imports monthly search volume statistics for detailed analysis. 🛠️ Key APIs and Tools Used NocoDB**: Stores and organizes base and processed keyword data. DataForSEO API**: Provides search volume and keyword performance metrics. Google Autocomplete API**: Suggests relevant Google search terms. YouTube Autocomplete API**: Suggests trending YouTube keywords. Social Flood Docker Instance**: Serves as the local integration hub. Setup Instructions Required Tools: NocoDB n8n DataForSEO Account Social Flood Docker Instance Create the following NocoDB tables: Base Keyword Search Second Order Google Keywords Second Order YouTube Keywords Search Volume This template empowers users to handle complex keyword research tasks effortlessly, saving time and providing actionable insights. Share this template to enhance your workflow efficiency!

Personal Portfolio CV Rag Chatbot - with Conversation Store and Email Summary Target Audience This template is perfect for: Individuals looking to create a working professional and interactive personal portfolio chatbot. Developers interested in integrating RAG Chatbot functionality with conversation storage. 1. Description Create a stunning Personal Portfolio CV with integrated RAG Chatbot capabilities, including conversation storage and daily email summaries. 2.Features: Training: Setup Ingestion stage Upload your CV to Google Drive and let the Drive trigger updates to read your resume cv and convert it into your vector database (RAG purpose). Modify any parts as needed. Chat & Track: Use any frontend/backend interface to call the chat API and chat history API. Reporting Daily Chat Conversations: Receive daily automatic summaries of chat conversations. Data stored via NocoDB. 3.Setup Guide: Step-by-Step Instructions: Ensure all credentials are ready. Follow the notes provided. Ingestion: Upload your CV to Google Drive. The Drive triggers RAG update in your vector database. You can change the folder name, files and indexname of the vector database accordingly. Chat: Use any frontend/backend interface to call the chat API (refer to the notes for details) . [optional] Use any frontend/backend interface to call the update chat history API (refer to the notes for details). 3.Tracking Chat: Get daily automatic summaries of chat conversations.Format email conversations report as you like. You are ready to go!