HTTP Request Workflows
458 Ergebnisse — ausschließlich quellenverknüpfte n8n-Referenzen
AI-Powered WhatsApp Chatbot 🤖📲 for Text, Voice, Images & PDFs with memory 🧠
This workflow is a highly advanced multimodal AI assistant designed to operate through WhatsApp. It can understand and respond to text, images, voice messages, and PDF documents by combining OpenAI models with smart logic to adapt to the content received. 🎯 Core Features 📥 1. Automatic Message Type Detection Using the Input type node, the bot detects whether the user has sent: Text Voice messages Images Files (PDF) Other unsupported content 💬 2. Smart Text Message Handling Text messages are processed by an OpenAI GPT-4o-mini agent with a customized system prompt. Replies are concise, accurate, and formatted for mobile readability. 🖼️ 3. Image Analysis & Description Images are downloaded, converted to base64, and analyzed by an image-aware AI model. The output is a rich, structured description, designed for visually impaired users or visual content interpretation. 🎙️ 4. Voice Message Transcription & Reply Audio messages are downloaded and transcribed using OpenAI Whisper. The transcribed text is analyzed and answered by the AI. Optionally, the AI reply can be converted back to voice using OpenAI's text-to-speech, and sent as an audio message. 📄 5. PDF Document Extraction & Sum
High-Level Service Page SEO Blueprint Report Generator
Introduction The "High-Level Service Page SEO Blueprint Report" workflow is a powerful, AI-driven solution designed to generate comprehensive SEO content strategies for service-based businesses. By analyzing competitor websites and user intent, this workflow creates a detailed blueprint that outlines the optimal structure, content, and conversion elements for a service page. The workflow leverages the JINA Reader API to extract content from competitor websites and uses Google Gemini AI to perform deep analysis across multiple dimensions: competitor content structure, user intent, strategic opportunities, and conversion optimization. The final output is a professionally formatted Markdown document that provides actionable guidance for creating a high-performing service page that satisfies both user needs and search engine requirements. This workflow eliminates the time-consuming process of manually analyzing competitors and developing content strategies, providing a data-driven foundation for service page creation that would typically require hours of expert analysis. Who is this for? This workflow is designed for digital marketers, SEO specialists, content strategists, and web deve
Automated Research Report Generation with AI, Wiki, Search & Gmail/Telegram
Automated Research Report Generation with OpenAI, Wikipedia, Google Search, Gmail/Telegram and PDF Output Description What Problem Does This Solve? 🛠️ This workflow automates the process of generating professional research reports for researchers, students, and professionals. It eliminates manual research and report formatting by aggregating data, generating content with AI, and delivering the report as a PDF via Gmail or Telegram. Target audience: Researchers, students, educators, and professionals needing quick, formatted research reports. What Does It Do? 🌟 Aggregates research data from Wikipedia, Google Search, and SerpApi. Refines user queries and generates structured content using OpenAI. Converts the content into a professional HTML report, then to PDF. Sends the PDF report via Gmail or Telegram. Key Features 📋 Real-time data aggregation from multiple sources. AI-driven content generation with OpenAI. Automated HTML-to-PDF conversion for professional reports. Flexible delivery via Gmail or Telegram. Error handling for robust execution. Setup Instructions Prerequisites ⚙️ n8n Instance**: Self-hosted or cloud n8n instance. API Credentials**: OpenAI API: API key with GPT mod
Paul Graham Essay Search & Chat with Milvus Vector Database
Paul Graham Essay Search & Chat with Milvus Vector Database How It Works This workflow creates a RAG (Retrieval-Augmented Generation) system using Milvus vector database to search Paul Graham essays: Scrape & Load: Fetches Paul Graham essays, extracts text, and stores them as vector embeddings in Milvus Chat Interface: Enables semantic search and AI-powered conversations about the essays Set Up Steps Set up Milvus server following the official installation guide, then create a collection Execute the workflow to scrape essays and load them into your Milvus collection Chat with the AI agent using the Milvus tool to query and discuss essay content
WCreate a Paul Graham Essay Q&A System with OpenAI and Milvus Vector Database
Create a Paul Graham Essay Q&A System with OpenAI and Milvus Vector Database How It Works This workflow creates a question-answering system based on Paul Graham essays. It has two main steps: Data Collection & Processing: Scrapes Paul Graham essays Extracts text content Loads them into a Milvus vector store Chat Interaction: Provides a question-answering interface using the stored vector embeddings Utilizes OpenAI embeddings for semantic search Set Up Steps Set up a Milvus server following the official guide Create a collection named "my_collection" Run the workflow to scrape and load Paul Graham essays Start chatting with the QA system The workflow handles the entire process from fetching essays, extracting content, generating embeddings via OpenAI, storing vectors in Milvus, and providing retrieval for question answering.
Create a RAG System with Paul Essays, Milvus, and OpenAI for Cited Answers
Create a RAG System with Paul Essays, Milvus, and OpenAI for Cited Answers This workflow automates the process of creating a document-based AI retrieval system using Milvus, an open-source vector database. It consists of two main steps: Data collection/processing Retrieval/response generation The system scrapes Paul Graham essays, processes them, and loads them into a Milvus vector store. When users ask questions, it retrieves relevant information and generates responses with citations. Step 1: Data Collection and Processing Set up a Milvus server using the official guide Create a collection named "my_collection" Execute the workflow to scrape Paul Graham essays: Fetch essay lists Extract names Split content into manageable items Limit results (if needed) Fetch texts Extract content Load everything into Milvus Vector Store This step uses OpenAI embeddings for vectorization. Step 2: Retrieval and Response Generation When a chat message is received, the system: Sets chunks to send to the model Retrieves relevant information from the Milvus Vector Store Prepares chunks Answers the query based on those chunks Composes citations Generates a comprehensive response This process uses OpenA
WAutomate PDF Image Extraction & Analysis with GPT-4o and Google Drive
Use Case Manually extracting images from PDF files for analysis is often slow and inefficient. Many users resort to taking screenshots of each page, uploading them to an AI tool like OpenAI for image analysis, and then manually copying the insights into a document. This manual process is time-consuming and prone to errors. This workflow streamlines the entire process by automatically extracting images from a PDF, analyzing them using the GPT-4o model, and saving the results in seconds—eliminating the need for manual effort. What This Workflow Does Extracts all images from the uploaded PDF file automatically The workflow scans each page of the PDF and identifies embedded images without manual intervention. Uses the GPT-4o model to analyze each extracted image Each image is processed through GPT-4o to generate descriptive insights, summaries, or context-specific analysis depending on the use case. Saves the analysis results to a .txt file, including image URLs The final output is a plain text file containing both the image URLs (e.g., hosted on cloud storage) and the corresponding GPT-4o analysis, ready for further use or sharing. Setup 1.Set up your credentials when you first open t
WCreate Daily Israeli Economic Newsletter using RSS and GPT-4o
Daily Economic News Brief for Israel (Hebrew, RTL, GPT-4o) Overview Stay ahead of the curve with this AI-powered workflow that delivers a daily economic summary tailored for professionals tracking the Israeli economy. At 8:00 PM Israel Time, this workflow: Retrieves the latest articles from Calcalist and Mako via RSS Filters duplicates and irrelevant stories Uses OpenAI’s GPT-4o to identify the 5 most important stories of the day Summarizes each article in concise, readable Hebrew Generates a fully styled, responsive HTML email (with proper RTL layout) Sends it to your inbox using your preferred SMTP email provider Perfect for economists, analysts, investors, or policymakers who want an actionable and personalized news digest -- no distractions, no fluff. Setup Instructions Estimated setup time: 10 minutes Required credentials: OpenAI API Key SMTP credentials (for email delivery) Steps: Import this template into your n8n instance. Add your OpenAI API Key under credentials. Configure the SMTP Email node with: Host (e.g. smtp.gmail.com) Port (465 or 587) Username (your email) Password (app-specific password or login) Set your target email address in the last node. (Optional) Customiz
WAutomate Hyper-Personalized Outreach at Scale With Bright Data and LLMs
LinkedIn Enrichment & Ice Breaker Generator For SDRs, growth marketers, and founders looking to scale personalized outreach. This workflow enriches LinkedIn profile data using Bright Data and generates AI-powered ice breakers using Claude (Anthropic). It automates research and messaging to help you connect smarter and faster — without manual effort. 🧩 How It Works This workflow combines Google Sheets, Brigt Data, and Claude (Anthropic) to fully automate your outreach research: Trigger Manually trigger the workflow or run it on a schedule (via Manual Trigger or Schedule Trigger). Read Input Sheet Fetches rows from a Google Sheet. Each row must contain at least a Linkedin_URL_Person and row_number. Prepare Input Formats each row for Bright Data’s API using Set and SplitInBatches nodes. Enrich Profile (Bright Data API) Sends LinkedIn URLs to Bright Data’s Dataset API via HTTP Request. Waits for snapshot to be ready using polling logic with Wait, If, and Snapshot Progress nodes. Once ready, retrieves the enriched profile data including: Name City Current company About section Recent posts Update Sheet with Profile Data Writes the retrieved enrichment data into the corresponding row in
Create AI-Ready Vector Datasets for LLMs with Bright Data, Gemini & Pinecone
Who this is for? This workflow enables automated, scalable collection of high-quality, AI-ready data from websites using Bright Data’s Web Unlocker, with a focus on preparing that data for LLM training. Leveraging LLM Chains and AI agents, the system formats and extracts key information, then stores the structured embeddings in a Pinecone vector database. This workflow is tailored for: ML Engineers & Researchers building or fine-tuning domain-specific LLMs. AI Startups needing clean, structured content for product training. Data Teams preparing knowledge bases for enterprise-grade AI apps. LLM-as-a-Service Providers sourcing dynamic web content across niches. What problem is this workflow solving? Training a large language model (LLM) requires vast amounts of clean, relevant, and structured data. Manual collection is slow, error-prone, and lacks scalability. This workflow: Automatically extracts web data from specified URLs. Bypasses anti-bot measures using Bright Data’s Web Unlocker. Formats, cleans, and transforms raw content using LLM agents. Stores semantically searchable vectors in Pinecone. Makes datasets AI-ready for fine-tuning, RAG, or domain-specific training. What this
Generate Company Stories from LinkedIn with Bright Data & Google Gemini
Who this is for? The LinkedIn Company Story Generator is an automated workflow that extracts company profile data from LinkedIn using Bright Data's web scraping infrastructure, then transforms that data into a professionally written narrative or story using a language model (e.g., OpenAI, Gemini). The final output is sent via webhook notification, making it easy to publish, review, or further automate. This workflow is tailored for: Marketing Professionals**: Seeking to generate compelling company narratives for campaigns. Sales Teams**: Aiming to understand potential clients through summarized company insights. Content Creators**: Looking to craft stories or articles based on company data. Recruiters**: Interested in obtaining concise overviews of companies for talent acquisition strategies. What problem is this workflow solving? Manually gathering and summarizing company information from LinkedIn can be time-consuming and inconsistent. This workflow automates the process, ensuring: Efficiency**: Quick extraction and summarization of company data. Consistency**: Standardized summaries for uniformity across use cases. Scalability**: Ability to process multiple companies wit
Extract & Summarize Wikipedia Data with Bright Data and Gemini AI
Who this is for? This workflow automates the process of Wikipedia data extraction using the Bright Data Web Unlocker, parsing and cleaning the data, and then sending the results to a specified webhook URL for downstream processing, reporting, or integration. What problem is this workflow solving? Researchers who need structured information from Wikipedia pages regularly. Data Engineers building knowledge bases or enriching datasets with factual data. Digital Marketers or Content Writers automating fact-checking or content sourcing. Automation Enthusiasts who want to trigger external systems with rich context from Wikipedia. What this workflow does This workflow addresses the challenges of manually retrieving, structuring, and using data from Wikipedia at scale. Workflow Breakdown Trigger Type: Scheduled or Manual Purpose: Starts the workflow either on a fixed schedule (e.g., daily) or on-demand via a manual trigger or incoming webhook. Bright Data Wikipedia Scraping Tool Used: Bright Data Web Unlocker Action: Scrape the HTML content of one or multiple Wikipedia article URLs. Parse & Extract Structured Data The Basic LLM Chain node is responsible for producing a human readable conte
Extract & Summarize Bing Copilot Search Results with Gemini AI and Bright Data
Who is this for? This workflow automates the process of querying Bing's Copilot Search, extracting structured data from the results, summarizing the information, and sending a notification via webhook. It leverages the Microsoft Copilot to retrieve search results and integrates AI-powered tools for data extraction and summarization. What problem is this workflow solving? Data Analysts and Researchers: Who need to gather and summarize information from Bing search results efficiently. Developers and Engineers: Looking to integrate Bing search data into applications or services. Digital Marketers and SEO Specialists: Interested in monitoring search engine results for specific keywords or topics. What this workflow does Manually extracting and summarizing information from search engine results can be time-consuming and error-prone. This workflow automates the process by: Performing Bing searches using Bright Data's Bing Search API. Extracting structured data from the search results. Summarizing the extracted information using AI tools. Sending the summarized data to a specified endpoint via webhook. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web
Search & Summarize Web Data with Perplexity, Gemini AI & Bright Data to Webhooks
Who this is for? This workflow is designed for professionals and teams who need real-time, structured insights from Perplexity Search results without manual effort. What problem is this workflow solving? This n8n workflow solves the problem of automating Perplexity Search result extraction, cleanup, summarization, and AI-enhanced formatting for downstream use like sending the results to a webhook or another system. What this workflow does Automates Perplexity Search via Bright Data Uses Bright Data’s proxy-based SERP API to run a Google Search query programmatically. Makes the process repeatable and scriptable with different search terms and regions/zones. Cleans and Extracts Useful Content The Readable Data Extractor uses LLM-based cleaning to remove HTML/CSS/JS from the response and extract pure text data. Converts messy, unstructured web content into structured, machine-readable format. Summarizes Search Results Through the Gemini Flash + Summarization Chain, it generates a concise summary of the search results. Ideal for users who don’t have time to read full pages of search results. Formats Data Using AI Agent The AI Agent acts like a virtual assistant that: - Understands sear
Google Search Engine Results Page Extraction and Summarization with Bright Data
Who this is for? This workflow is designed for professionals and teams who need real-time, structured insights from Google Search results without manual effort. What problem is this workflow solving? This n8n workflow solves the problem of automating Google Search result extraction, cleanup, summarization, and AI-enhanced formatting for downstream use like sending the results to a webhook or another system. What this workflow does Automates Google Search via Bright Data Uses Bright Data’s proxy-based SERP API to run a Google Search query programmatically. Makes the process repeatable and scriptable with different search terms and regions/zones. Cleans and Extracts Useful Content The Google Search Data Extractor uses LLM based cleaning to remove HTML/CSS/JS from the response and extract pure text data. Converts messy, unstructured web content into structured, machine-readable format. Summarizes Search Results Through the Gemini Flash + Summarization Chain, it generates a concise summary of the search results. Ideal for users who don’t have time to read full pages of search results. Formats Data Using AI Agent The AI Agent acts like a virtual assistant that: Understands search result
Summarize Glassdoor Company Info with Google Gemini and Bright Data Web Scraper
Who is this for? This workflow is designed for HR professionals, employer branding teams, talent acquisition strategists, market researchers, and business intelligence analysts who want to monitor, understand, and act upon employee sentiment and company perception on Glassdoor. It's ideal for organizations that value real-time feedback, are tracking employer brand perception, or need summarized insights for leadership reporting without sifting through thousands of raw reviews. What problem is this workflow solving? Manually reviewing and analyzing Glassdoor reviews is tedious, subjective, and not scalable especially for larger companies or those with many subsidiaries. This workflow: Automates review collection by making a Glassdoor company request via the Bright Data Web Scrapper API. Uses Google Gemini to summarize the content. Sends an actionable summary to HR dashboards, leadership teams, or alert systems via the Webhook notification. What this workflow does Makes an HTTP Request to Glassdoor via the Bright Data Web Scrapper API. Polls the BrightData Glassdoor for the completion of the request. Downloads the Glassdoor response when a new snapshot is ready. Sends the prompt to G
WConvert YouTube Videos into SEO Blog Posts with GPT-4o, Dumpling AI, and Flux
Workflow Description This workflow helps content creators automatically repurpose YouTube videos into SEO-friendly blog posts. It extracts the video transcript, uses AI to generate a full blog post with a relevant image, and sends the complete package via email, ready for publication. Prerequisites/Requirements This workflow relies on external AI services. You will need: OpenAI Account: Used for generating the blog post text (specifically mentioned using GPT-4o in the workflow notes). Credentials: Requires an API key from OpenAI. Cost: OpenAI API usage is typically paid based on the amount of text processed (tokens). Check OpenAI's current pricing. Setup: Sign up at OpenAI and obtain your API key. Dumpling AI Account: Used for retrieving YouTube video transcript and generating the blog post image. Credentials: Requires an API key from Dumpling AI. Cost: Dumpling AI offers 250 free credits to start with and different plans for different levels of usage. Check the pricing page for more details. Setup: Sign up at Dumpling AI and obtain your API key/credentials. Email Account: Credentials for the email service (e.g., Gmail) used to send the final result. How it works Input Video Detail
Compare Sequential, Agent-Based, and Parallel LLM Processing with Claude 3.7
This workflow demonstrates three distinct approaches to chaining LLM operations using Claude 3.7 Sonnet. Connect to any section to experience the differences in implementation, performance, and capabilities. What you'll find: 1️⃣ Naive Sequential Chaining The simplest but least efficient approach - connecting LLM nodes in a direct sequence. Easy to set up for beginners but becomes unwieldy and slow as your chain grows. 2️⃣ Agent-Based Processing with Memory Process a list of instructions through a single AI Agent that maintains conversation history. This structured approach provides better context management while keeping your workflow organized. 3️⃣ Parallel Processing for Maximum Speed Split your prompts and process them simultaneously for much faster results. Ideal when you need to run multiple independent tasks without shared context. Setup Instructions: API Credentials: Configure your Anthropic API key in the credentials manager. This workflow uses Claude 3.7 Sonnet, but you can modify the model in each Anthropic Chat Model node, or pick an entirely different LLM. For Cloud Users: If using the parallel processing method (section 3), replace {{ $env.WEBHOOK_URL }} in the "LLM s
WExtract University Term Dates from Excel using CloudFlare Markdown Conversion
This n8n template imports an XLSX containing terms dates for a university, extracts the relevant events using AI and converts the events to an ICS file which can be imported into iCal, Google Calendar or Outlook. Manually adding important term dates to your calendar by hand? Stop! Automate it with this simple AI/LLM-powered document understanding and extraction template. This cool use-case can be applied to many scenarios where Excel files are predominantly used. How it works The term dates excel file (xlsx) are imported into the workflow from the university's website using the http request node. To parse the excel file, we use an external service - Cloudflare's Markdown Conversion Service. This converts the excel's sheets into markdown tables which our LLM can read. To extract the events and their dates from the markdown, we can use the Information Extractor node for structured output. LLMs are great for this use-case because they can understand the layout; one row may have many data points. With our data, there are endless possibilities to use it! But for this demonstration, we'll generate an ICS file so that we can import the extracted events into our calendar. We use the Python
WGenerate & Auto-Post Social Videos to Multiple Platforms with GPT-4 and Kling AI
AI-Powered Social Video Generator with Auto-Posting to Instagram, TikTok, YouTube, Facebook, LinkedIn, Threads, Pinterest, Twitter (X), and Bluesky Who is this workflow for? This workflow is ideal for content creators, marketers, social media managers, and automation enthusiasts who want to generate, customize, and publish short-form videos across multiple platforms without manual editing or posting. If you use tools like ChatGPT, Kling, or Blotato and want to streamline your content creation process, this workflow is made for you. What problem does this workflow solve? Publishing regular video content on multiple platforms is time-consuming—especially when adding voice-overs, captions, and managing distribution. This workflow solves that by: Automating video generation using AI (Kling + GPT-4) Adding realistic voice narration Styling subtitles for social media Creating titles and social captions Auto-posting to Instagram, TikTok, YouTube, Facebook, Threads, Twitter (X), LinkedIn, Pinterest, and Bluesky All of this is triggered by a simple message sent via Telegram. How the workflow works This end-to-end automation transforms a short Telegram message into a fully produced and publi
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Build an IT Support Assistant Chatbot Leveraging Existing Support Portal
This n8n template demonstrates how you can leverage existing support site search to power your Support Chatbots and agents. Building a support chatbot need not be complicated! If building and indexing vector stores or duplicating data isn't necessarily your thing, an alternative implementation of the RAG approach is to leverage existing knowledge-bases such as support portals. In this way, document management and maintenance of your support agent is significantly reduced. Disclaimer: This template example uses AcuityScheduling's help center website but is not associated, supported nor endorsed by the company. How it works A simple AI agent is connected with chat trigger to receive user queries. The AI agent is instructed to fetch information from the knowledge-base via the attached custom workflow tool (aka "knowledgebase tool"). There is no step to replicate the entire support articles database into a vector store. You may choose not too because of time, cost and maintainence involved. Instead, the tool leverages the existing support portal's search API to retrieve knowledge-base articles. Finally, the search results are formatted before sending an aggregated response back to the
🎨 AI Design Team - Generate and Review AI Images with Ideogram and OpenAI
🎨 AI Graphic Design Team - Generate and Review AI Images with Ideogram and OpenAI Description Who is this for? This workflow is perfect for graphic designers, creative agencies, marketing teams, or freelancers who regularly use AI-generated images in their projects. It's specifically beneficial for teams that want to automate the generation, review, and management of AI-created graphics efficiently. What problem does this workflow solve? Design teams often face time-consuming manual reviews and inconsistent quality checks for AI-generated images. This workflow addresses these challenges by automating image generation and introducing a systematic, AI-driven vetting process. This ensures only high-quality, relevant images reach your team's assets, saving valuable time and enhancing workflow efficiency. What this workflow does AI Image Generation:** Integrates Ideogram via HTTP Request to automatically create AI-generated images based on creative briefs. Automated Image Review:** Uses OpenAI to automatically evaluate and approve images, ensuring they meet your predefined quality standards. Efficient Asset Management:** Automatically creates structured Google Drive folders and compile
Auto-Generate YouTube Chapters with Gemini AI & YouTube Data API v3
Auto-Generate YouTube Chapters with AI-Powered Transcript Analysis Overview This workflow uses YouTube Data API v3 and Google Gemini 1.5 Flash AI to automatically generate timestamped chapters for videos by analyzing SRT captions. It enhances viewer navigation, improves SEO , and saves creators time by automating manual tasks. Prerequisites YouTube API Setup Create a Google Cloud Project Go to the Google Cloud Console. Click Select a project > New Project and name it (e.g., "YouTube Chapters Automation") . Enable YouTube Data API v3 Navigate to APIs & Services > Library. Search for "YouTube Data API v3" and click Enable . Configure OAuth Consent Screen Go to APIs & Services > OAuth consent screen. Select External (public) or Internal (testing), then add required details (app name, support email) . Generate OAuth 2.0 Credentials Under Credentials, click Create Credentials > OAuth client ID. Choose Web app, then download the JSON key file . Add Credentials to n8n Other Requirements Google Gemini API**: Configure access for the gemini-1.5-flash-8b-exp-0924 model by getting the api key. Workflow Steps Set Video ID Input the target video ID (e.g., r1wqsrW2vmE) using the Set
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Daily Newsletter Service using Excel, Outlook and AI
This n8n template builds a newsletter ("daily digest") delivery service which pulls and summarises the latest n8n.io template in select categories defined by subscribers. It's scheduled to run once a day and sends the newsletter directly to subscriber via a nicely formatted email. If you've had trouble keeping up with the latest and greatest templates beign published daily, this workflow can save you a lot of time! How it works A scheduled trigger pulls a list of subscribers (email and category preferences) from an Excel workbook. We work out unique categories amongst all subscribers and only fetch the latest n8n website templates from these categories to save on resources and optimise the number of API calls we make. The fetched templates are summarised via AI to produce a short description which is more suitable for our email format. For each subscriber, we filter and collect only the templates relevant to their category preferences (as defined in the Excel) and ensure that duplicate templates or those which have been "seen before" are omitted. A HTML node is then used to generate the email newsletter. HTML emails are the perfect format since we can add links back to the template