HTTP Request workflow&#x27;ları

Zacharia Kimotho tarafından

Generating New Keywords and their Search Volumes using the Google Ads API

Generate new keywords for SEO with the monthly Search volumes This workflow is an improvement on the workflows below. It can be used to generate new keywords that you can use for your SEO campaigns or Google ads campaigns Generate SEO Keyword Search Volume Data using Google API and Generating Keywords using Google Autosuggest Usage Send the keywords you need as an array to this workflow Pin the data and map it to the set Keywords node Map the keywords to the Google ads API with the location and Language of your choice Split the results and set them data Pass this to the next nodes as needed for storage Make a copy of this spreedsheet and update the data accordingly Having challenges with the google Ads API? Read this blog Setup Replace the trigger with your desired trigger eg a webhook or manual trigger Map the data correctly to the set Keywords node On the Generate new keywords, Update the {customer_id} on the url and login-customer-id with your actual one. Update the developer-token` also with your values. The url should be corrected as below https://googleads.googleapis.com/v18/customers/{customer-id}:generateKeywordIdeas You should send the headers as below { "name": "content-t

SSL Expiry Alert with SSL-Checker.io

Use Case Managing SSL certificates manually can be time-consuming and error-prone, often leading to unexpected downtime or security risks due to expired certificates. What This Workflow Does This workflow automatically monitors SSL certificates for a list of websites, checks their expiry status using SSL-Checker.io, and sends timely notifications if a certificate is about to expire. Setup Add your credentials for Google Sheets, Gmail, and SSL-Checker.io. Create a Google Sheet with a list of URLs for the websites you want to monitor. Configure the workflow to check the SSL status weekly. Set up email notifications to alert you when a certificate is close to expiry. Activate the workflow to automate monitoring and notification. How to Adjust It to Your Needs Customize the URL Source: Replace Google Sheets with another data source like Airtable or CSV files. Modify Notification Thresholds: Change the expiry threshold (e.g., notify for 14 days instead of 7). Add Additional Actions: Integrate with tools like Slack or Teams for team-wide notifications. Automate Renewal Requests: Add a step to send renewal requests directly to your SSL provider if a certificate is nearing expiry.

Vishal Kumar tarafından

CWS

Davi Saranszky Mesquita tarafından

Make OpenAI Citation for File Retrieval RAG

Make OpenAI Citation for File Retrieval RAG Use case In this example, we will ensure that all texts from the OpenAI assistant search for citations and sources in the vector store files. We can also format the output for Markdown or HTML tags. This is necessary because the assistant sometimes generates strange characters, and we can also use dynamic references such as citations 1, 2, 3, for example. What this workflow does In this workflow, we will use an OpenAI assistant created within their interface, equipped with a vector store containing some files for file retrieval. The assistant will perform the file search within the OpenAI infrastructure and will return the content with citations. We will make an HTTP request to retrieve all the details we need to format the text output. Setup Insert an OpenAI Key How to adjust it to your needs At the end of the workflow, we have a block of code that will format the output, and there we can add Markdown tags to create links. Optionally, we can transform the Markdown formatting into HTML.

Davi Saranszky Mesquita tarafından

Integrating AI with Open-Meteo API for Enhanced Weather Forecasting

Use case Workshop We are using this workflow in our workshops to teach how to use Tools a.k.a functions with artificial intelligence. In this specific case, we will use a generic "AI Agent" node to illustrate that it could use other models from different data providers. Enhanced Weather Forecasting In this small example, it's easy to demonstrate how to obtain weather forecast results from the Open-Meteo site to accurately display the upcoming days. This can be used to plan travel decisions, for example. What this workflow does We will make an HTTP request to find out the geographic coordinates of a city. Then, we will make other HTTP requests to discover the weather for the upcoming days. In this workshop, we demonstrate that the AI will be able to determine which tool to call first—it will first call the geolocation tool and then the weather forecast tool. All of this within a single client conversation call. Setup Insert an OpenAI Key and activate the workflow. by Davi Saranszky Mesquita https://www.linkedin.com/in/mesquitadavi/

Hacker News Throwback Machine - See What Was Hot on This Day, Every Year!

This is a simple workflow that grabs HackerNews front-page headlines from today's date across every year since 2007 and uses a little AI magic (Google Gemini) to sort 'em into themes, sends a neat Markdown summary on Telegram. How it works Runs daily, grabs Hacker News front page for this day across every year since 2007. Pulls headlines & dates. Uses Google Gemini to sort headlines into topics & spot trends. Sends a Markdown summary to Telegram. Set up steps Clone the workflow. Add your Google Gemini API key. Add your Telegram bot token and chat ID. **Built on Day-01 as part of the #100DaysOfAgenticAi Fork it, tweak it, have fun!**

ibrhdotme tarafından

TW-Request-Agri Data Open Platform-Daily Market Sheep Pricing

This workflow automates the process of fetching agricultural transaction data from the Taiwan Agricultural Products Open Data Platform and storing it in a Google Sheets document for further analysis. Key Features Manual Trigger: Allows manual execution of the workflow to control when data is fetched. HTTP Request: Sends a request to the Open Data Platform's API to retrieve detailed transaction data, including: Pricing (Upper, Middle, Lower, Average) Transaction quantities Crop and market details Split Out Node: Processes each record individually, ensuring accurate handling of every data entry. Google Sheets Integration: Appends the data into a structured Google Sheets document for easy access and analysis. Node Configurations 1. Manual Trigger Purpose**: Start the workflow manually. Configuration**: No setup needed. 2. HTTP Request Purpose**: Fetch agricultural data. Configuration**: URL: https://data.moa.gov.tw/api/v1/SheepQuotation Query Parameters: Start_time: 2024/12/01 End_time: 2024/12/31 MarketName: 台北二 api_key: <your_api_key> Headers: accept: application/json 3. Split Out Purpose**: Split the API response data array into individual items. Configuration**: Field to Spl

darrell_tw tarafından

Mark Shcherbakov tarafından

AI Agent for project management and meetings with Airtable and Fireflies

Video Guide I prepared a comprehensive guide detailing how to create a Smart Agent that automates meeting task management by analyzing transcripts, generating tasks in Airtable, and scheduling follow-ups when necessary. Youtube Link Who is this for? This workflow is ideal for project managers, team leaders, and business owners looking to enhance productivity during meetings. It is particularly helpful for those who need to convert discussions into actionable items swiftly and effectively. What problem does this workflow solve? Managing action items from meetings can often lead to missed tasks and poor follow-up. This automation alleviates that issue by automatically generating tasks from meeting transcripts, keeping everyone informed about their responsibilities and streamlining communication. What this workflow does The workflow leverages n8n to create a Smart Agent that listens for completed meeting transcripts, processes them using AI, and generates tasks in Airtable. Key functionalities include: Capturing completed meeting events through webhooks. Extracting relevant meeting details such as transcripts and participants using API calls. Generating structured tasks from meeting d

🔍 Perplexity Research to HTML: AI-Powered Content Creation

Transform simple queries into comprehensive, well-structured content with this n8n workflow that leverages Perplexity AI for research and GPT-4 for content transformation. Create professional blog posts and HTML content automatically while maintaining accuracy and depth. Intelligent Research & Analysis 🚀 Automated Research Pipeline Harnesses Perplexity AI's advanced research capabilities Processes complex topics into structured insights Delivers comprehensive analysis in minutes instead of hours 🧠 Smart Content Organization Automatically structures content with clear hierarchies Identifies and highlights key concepts Maintains technical accuracy while improving readability Creates SEO-friendly content structure Content Transformation Features 📝 Dynamic Content Generation Converts research into professional blog articles Generates clean, responsive HTML output Implements proper semantic structure Includes metadata and categorization 🎨 Professional Formatting Responsive Tailwind CSS styling Clean, modern HTML structure Proper heading hierarchy Mobile-friendly layouts Blockquote highlighting for key insights Perfect For 📚 Content Researchers Save hours of manual research by autom

Joseph LePage tarafından

AI-Powered Social Media Amplifier

> Reach out to me for any setup help/consulting. Automate the curation and sharing of trending GitHub discussions from Hacker News to Twitter and LinkedIn. This workflow leverages AI to generate engaging posts, streamlining your social media content creation and distribution. How it Works Crawl Hacker News for GitHub Posts: The workflow fetches trending GitHub-related discussions from Hacker News. Extract Key Information: Relevant data such as post titles, URLs, and metadata are extracted and filtered to focus only on unposted content. Fetch Additional Details: For each GitHub post, the workflow retrieves extra information from the GitHub repository page to enrich the post content. Generate Social Media Posts: Using AI, the workflow automatically generates tailored posts for Twitter and LinkedIn based on the collected data. Post to Twitter & LinkedIn: The generated content is posted to your Twitter and LinkedIn accounts. Track and Log Posts: Each post is logged in Airtable for tracking, and its status is updated to ensure no duplicate posts are made. Telegram Notification: After posting, a summary of the posts is sent to your Telegram chat for real-time updates. Requirements n8n

Mudit Juneja tarafından

Analyze Email Headers for IP Reputation and Spoofing Detection - Gmail

Analyze Emails for Security Insights Who is this for? This workflow is ideal for IT professionals, security analysts, and organizations looking to enhance their email security practices. It is particularly useful for those who need to analyze Gmail email headers for IP tracking, spoofing detection, and sender reputation assessment. What problem is this workflow solving? Email spoofing and phishing attacks are significant cybersecurity threats. By analyzing email headers, this workflow provides detailed insights into the email's origin, authentication status, and the reputation of the sending IP address. It helps detect potential spoofing attempts and assess the trustworthiness of incoming emails. What this workflow does This n8n workflow automates the process of analyzing email headers received in Gmail. It performs the following key functions: Triggering and Email Header Extraction: It monitors Gmail inboxes for new emails and extracts their headers for analysis. Authentication Analysis: It validates SPF, DKIM, and DMARC authentication results to ensure the email adheres to industry-standard security protocols. IP Analysis: The workflow extracts the originating IP address and eval

Analyze Email Headers for IP Reputation and Spoofing Detection - Outlook

Analyze Emails for Security Insights Who is this for? This workflow is ideal for security teams, IT Ops professionals, and managed service providers (MSPs) responsible for monitoring and validating email traffic. It’s especially useful for organizations that need to identify potential phishing attempts, spam, or compromised accounts by analyzing email headers and IP reputation. What problem is this workflow solving? This workflow helps identify malicious or suspicious emails by verifying email authentication headers (SPF, DKIM, DMARC) and analyzing the reputation of the originating IP address. By automating these checks, it reduces manual analysis time and flags potential threats efficiently. What this workflow does Email Monitoring:** Polls a specified Microsoft Outlook folder for new emails in real-time. Header Analysis:** Retrieves and processes email headers to extract critical information such as authentication results and the sender’s IP address. IP Reputation Check:** Leverages external APIs (IP Quality Score and IP-API) to analyze the originating IP for potential spam or malicious activity. Authentication Validation:** Validates SPF, DKIM, and DMARC headers, determining if

Boriwat Chanruang tarafından

LINE Assistant with Google Calendar and Gmail Integration

Who is this for? This workflow is for small business owners, personal assistants, or project managers who rely on multiple platforms for communication and scheduling. Ideal for users managing customer support, personal scheduling, or group event coordination via LINE, Google Calendar, and Gmail. What problem is this workflow solving? Reduces the manual effort needed to manage conversations, schedule events, and handle email communications. Provides an intelligent system for replying to user messages and fetching relevant calendar or email information in real time. Bridges the gap between messaging platforms and productivity tools, improving efficiency. What this workflow does LINE Chatbot Automation**: Automatically processes and responds to messages received via LINE. Google Calendar Management**: Retrieves upcoming events or schedules new events dynamically based on user queries. Email Retrieval**: Fetches recent emails using Gmail and filters them based on user instructions. AI-Powered Replies**: Uses OpenAI GPT to interpret user queries and provide tailored responses. Setup Prerequisites: LINE Developer account and API access. Google Calendar and Gmail accounts with OAuth crede

🛠️ Auto Workflow Positioning

Check Online Version ! [https://n8n-tools.streamlit.app/](https://n8n-tools.streamlit.app/ ) Who is it for? This workflow is perfect for n8n users who want to maintain clean and organized workflows without manually repositioning nodes. Whether you're building complex workflows or sharing them with a team, maintaining visual clarity is essential for efficiency and collaboration. This template automates the positioning process, saving time and ensuring consistent layout standards. How does it work? The template is divided into two parts: Positioning Engine: A webhook node kicks off the process by receiving a workflow ID. Using the provided workflow ID, an n8n API node fetches the workflow details. The fetched workflow is sent to a processing webhook that calculates optimized positions for the nodes. Finally, an n8n API node updates the workflow with the newly positioned nodes, ensuring a clean and professional layout. Reusable Positioning Block: This is an HTTP Request node that can be seamlessly integrated into any workflow you create. When triggered, it sends the current workflow for automatic positioning via the first part of this template. How to set it up? Enable n8n API Access:

Lucas Peyrin tarafından

Analyze & Sort Suspicious Email Contents with ChatGPT

Analyze & Sort Suspicious Email Contents with ChatGPT and Jira Who is this for? This workflow is tailored for IT security teams, managed service providers (MSPs), and organizations aiming to streamline the detection and reporting of phishing emails. It's especially useful for teams handling high email volumes and requiring quick, automated analysis. What problem is this workflow solving? Phishing emails pose a significant cybersecurity threat, and manual review processes are time-consuming and prone to human error. This workflow automates the identification of malicious emails, provides AI-driven insights, and generates structured reports, enabling faster and more efficient responses to email-based threats. What this workflow does This workflow integrates Gmail or Microsoft Outlook to monitor and capture incoming emails. It processes the email content and headers, converts the email's body to a visual screenshot for clarity, and uses ChatGPT's advanced AI to analyze the email for phishing indicators. Based on the analysis, it categorizes emails as potentially malicious or benign, creating detailed Jira tickets for each case. Attachments, including the email body and screenshots, ar

Analyze Suspicious Email Contents with ChatGPT Vision

Phishing Email Detection and Reporting with n8n Who is this for? This workflow is designed for IT teams, security professionals, and managed service providers (MSPs) looking to automate the process of detecting, analyzing, and reporting phishing emails. What problem is this workflow solving? Phishing emails are a significant cybersecurity threat, and manually detecting and reporting them is time-consuming and prone to errors. This workflow streamlines the process by automating email analysis, generating detailed reports, and logging incidents in a centralized system like Jira. What this workflow does This workflow automates phishing email detection and reporting by integrating Gmail and Microsoft Outlook email triggers, analyzing the content and headers of incoming emails, and generating Jira tickets for flagged phishing emails. Here’s what happens: Email Triggers: Captures incoming emails from Gmail or Microsoft Outlook. Email Analysis: Extracts email content, headers, and metadata for analysis. HTML Screenshot: Converts the email’s HTML body into a visual screenshot. AI Phishing Detection: Leverages ChatGPT to analyze the email and detect potential phishing indicators. Jira Integ

Generate 9:16 Images from Content and Brand Guidelines

Overview This n8n workflow automates the creation of 9:16 aspect ratio images optimized for short-form video content and thumbnails. It integrates multiple tools to retrieve content, generate scripts, and create AI-generated imagery. Key Features Trigger Workflow Manually The workflow starts when triggered manually in n8n. Retrieve Brand Guidelines Fetch brand elements like style, tone, and guidelines from Airtable. SEO Keywords and Blog Post Retrieval Retrieves blog posts and associated SEO keywords from Airtable to form the basis of image content. Content Preparation Uses GPT-4 to prepare a 4-scene script and thumbnail prompts for short-form videos. AI Image Generation Uses Leonardo.ai API to generate: Thumbnail Images Scene-specific Images (9:16 Aspect Ratio) Airtable Asset Management Generated assets (images) are saved back into Airtable with metadata like URLs and file sizes. Tools and Integrations n8n**: Workflow automation platform. OpenAI**: Generates scripts and prompts (GPT-4O-MINI). Leonardo.ai**: AI tool for improving prompts and generating high-quality images. Airtable**: Used as a data source for brand guidelines, blog posts, and to store generated assets. Workflow St

Alex Kim tarafından

Mark Shcherbakov tarafından

Parse PDF with LlamaParse and save to Airtable

Video Guide I prepared a comprehensive guide detailing how to automate the parsing of invoices using n8n and LlamaParse, seamlessly capturing and storing vital billing information. Youtube Link Who is this for? This workflow is ideal for finance teams, accountants, and business operations managers who need to streamline invoice processing. It is particularly helpful for organizations seeking to reduce manual entry errors and improve efficiency in managing billing information. What problem does this workflow solve? Manually processing invoices can be time-consuming and error-prone. This automation eliminates the need for manual data entry by capturing invoice details directly from uploaded documents and storing structured data efficiently. This enhances productivity and accuracy across financial operations. What this workflow does The workflow leverages n8n and LlamaParse to automatically detect new invoices in a designated Google Drive folder, parse essential billing details, and store the extracted data in a structured format. The key functionalities include: Real-time detection of new invoices via Google Drive triggers. Automated HTTP requests to initiate parsing through Lama Clo

Polina Medvedieva tarafından

API Schema Extractor

This workflow automates the process of discovering and extracting APIs from various services, followed by generating custom schemas. It works in three distinct stages: research, extraction, and schema generation, with each stage tracking progress in a Google Sheet. 🙏 Jim Le deserves major kudos for helping to build this sophisticated three-stage workflow that cleverly automates API documentation processing using a smart combination of web scraping, vector search, and LLM technologies. How it works Stage 1 - Research: Fetches pending services from a Google Sheet Uses Google search to find API documentation Employs Apify for web scraping to filter relevant pages Stores webpage contents and metadata in Qdrant (vector database) Updates progress status in Google Sheet (pending, ok, or error) Stage 2 - Extraction: Processes services that completed research successfully Queries vector store to identify products and offerings Further queries for relevant API documentation Uses Gemini (LLM) to extract API operations Records extracted operations in Google Sheet Updates progress status (pending, ok, or error) Stage 3 - Generation: Takes services with successful extraction Retrieves all API o

Vector Database as a Big Data Analysis Tool for AI Agents [2/2 KNN]

Vector Database as a Big Data Analysis Tool for AI Agents Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n". This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (image) datasets to Qdrant Set up meta-variables for anomaly detection in Qdrant Anomaly detection tool KNN classifier tool For anomaly detection The first pipeline to upload an image dataset to Qdrant. The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection. The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset. For KNN (k nearest neighbours) classification The first pipeline to upload an image dataset to Qdrant. This pipeline is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset. To recreate both You'll have to upload crops and lands datasets from Kaggle to your own Google Sto

Vector Database as a Big Data Analysis Tool for AI Agents [3/3 - anomaly]

Vector Database as a Big Data Analysis Tool for AI Agents Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n". This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (image) datasets to Qdrant Set up meta-variables for anomaly detection in Qdrant Anomaly detection tool KNN classifier tool For anomaly detection The first pipeline to upload an image dataset to Qdrant. The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection. 3. This is the third pipeline --- the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset. For KNN (k nearest neighbours) classification The first pipeline to upload an image dataset to Qdrant. The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset. To recreate both You'll have to upload crops and lands datasets from Kaggle to y

Vector Database as a Big Data Analysis Tool for AI Agents [2/3 - anomaly]

Vector Database as a Big Data Analysis Tool for AI Agents Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n". This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (image) datasets to Qdrant Set up meta-variables for anomaly detection in Qdrant Anomaly detection tool KNN classifier tool For anomaly detection The first pipeline to upload an image dataset to Qdrant. 2. This is the second pipeline to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection. The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset. For KNN (k nearest neighbours) classification The first pipeline to upload an image dataset to Qdrant. The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset. To recreate both You'll have to upload crops and lands datasets from Kaggle to your own Googl

Vector Database as a Big Data Analysis Tool for AI Agents [1/3 anomaly][1/2 KNN]

Vector Database as a Big Data Analysis Tool for AI Agents Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n". This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (image) datasets to Qdrant Set up meta-variables for anomaly detection in Qdrant Anomaly detection tool KNN classifier tool For anomaly detection 1. This is the first pipeline to upload an image dataset to Qdrant. The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection. The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset. For KNN (k nearest neighbours) classification 1. This is the first pipeline to upload an image dataset to Qdrant. The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset. To recreate both You'll have to upload crops and lands datasets from Kaggle to