OpenAI workflow'ları
630 sonuç — tümü kaynağa bağlı n8n referansı
Daily Personalized Air & Pollen Health Alerts with Ambee API and AI via Email
This workflow fetches real-time air quality and pollen data using Ambee’s APIs and sends a friendly, personalized daily summary by email. It uses a scheduler to automate data collection, AI-generated health tips, and clear, actionable messages—perfect for sensitive users (e.g. kids with asthma, allergy sufferers). Use Case: Ideal for individuals with respiratory conditions, allergies, or those who want to stay informed about environmental conditions affecting their health. Set up steps Estimated time: 10–15 minutes You'll need: Ambee API key (free registration) OpenAI API key Email credentials (Gmail) User Profile 💡 Keep in mind: You’ll need to input your location coordinates (we’ve pre-filled Braunschweig as an example). The AI Agent node uses a ready-made prompt that’s tailored for email—but feel free to adapt it to other messaging platforms.
Multi-Agent AI Clinic Management with WhatsApp, Telegram, and Google Calendar
Healthcare Clinic Assistant with WhatsApp and Telegram Integration Version: 1.1.0 n8n Version: 1.88.0+ License: MIT 📋 Description A comprehensive and modular automation workflow designed for healthcare clinics. It manages patient communication, appointment scheduling, confirmations, rescheduling, internal tasks, and media processing by integrating WhatsApp, Telegram, Google Calendar, and Google Tasks, combined with AI-powered agents for maximum efficiency. This system guarantees proactive communication with patients, streamlined internal clinic management, and consistent data synchronization across platforms. 🌟 Key Features 🤖 AI-Powered Specialized Agents: Distinct agents handle WhatsApp patient support, appointment confirmations, and internal rescheduling tasks. 📱 Omnichannel Communication: Handles patient interactions via WhatsApp and staff commands via Telegram. 📅 Google Calendar Appointment Management: Full synchronization for creating, updating, canceling, and confirming appointments. 📋 Task Management with Google Tasks: Manages shopping lists and administrative tasks efficiently through staff Telegram requests. 🔔 Automated Appointment Reminders: Daily-triggered system
🗞️ AI-powered sustainability newsletter for marketing with Gmail, GPT-4o
Tags: Sustainability, Web Scraping, OpenAI, Google Sheets, Newsletter, Marketing Context Hey! I’m Samir, a Supply Chain Engineer and Data Scientist from Paris, and the founder of LogiGreen Consulting. We use AI, automation, and data to support sustainable business practices for small, medium and large companies. I use this workflow to bring awareness about sustainability and promote my business by delivering automated daily news digests. > Promote your business with a fully automated newsletter powered by AI! This n8n workflow scrapes articles from the official EU news website and sends a daily curated digest, highlighting only the most relevant sustainability news. 📬 For business inquiries, feel free to connect with me on LinkedIn Who is this template for? This workflow is useful for: Business owners** who want to promote their service or products with a fully automated newsletter Sustainability professionals** staying informed on EU climate news Consultants and analysts** working on CSRD, Green Deal, or ESG initiatives Corporate communications teams** tracking relevant EU activity Media curators** building newsletters What does it do? This n8n workflow: ⏰ Triggers automatical
MCP Supabase Server for AI Agent with RAG & Multi-Tenant CRUD
Supabase AI Agent with RAG & Multi-Tenant CRUD Version: 1.0.0 n8n Version: 1.88.0+ Author: Koresolucoes License: MIT Description A stateful AI agent workflow powered by Supabase and Retrieval-Augmented Generation (RAG). Enables persistent memory, dynamic CRUD operations, and multi-tenant data isolation for AI-driven applications like customer support, task orchestration, and knowledge management. Key Features: 🧠 RAG Integration: Leverages OpenAI embeddings and Supabase vector search for context-aware responses. 🗃️ Full CRUD: Manage agent_messages, agent_tasks, agent_status, and agent_knowledge in real time. 📤 Multi-Tenant Ready: Supports per-user/organization data isolation via dynamic table names and webhooks. 🔒 Secure: Role-based access control via Supabase Row Level Security (RLS). Use Cases Customer Support Chatbots: Persist conversation history and resolve queries using institutional knowledge. Automated Task Management: Track and update task statuses dynamically. Knowledge Repositories: Store and retrieve domain-specific information for AI agents. Instructions 1. Import Template Go to n8n > Templates > Import from File and upload this workflow. 2. Configure Credenti
Build a Chatbot, Voice and Phone Agent with Voiceflow, Google Calendar and RAG
Voiceflow is a no-code platform that allows you to design, prototype, and deploy conversational assistants across multiple channels—such as chat, voice, and phone—with advanced logic and natural language understanding. It supports integration with APIs, webhooks, and even tools like Twilio for phone agents. It's perfect for building customer support agents, voice bots, or intelligent assistants. This workflow connects n8n and Voiceflow with tools like Google Calendar, Qdrant (vector database), OpenAI, and an order tracking API to power a smart, multi-channel conversational agent. There are 3 main webhook endpoints in n8n that Voiceflow interacts with: n8n_order – receives user input related to order tracking, queries an API, and responds with tracking status. n8n_appointment – processes appointment booking, reformats date input using OpenAI, and creates a Google Calendar event. n8n_rag – handles general product/service questions using a RAG (Retrieval-Augmented Generation) system backed by: Google Drive document ingestion, Qdrant vector store for search, and OpenAI models for context-based answers. Each webhook is connected to a corresponding "Capture" block inside Voiceflow, which
AI-Powered Telegram Task Manager with MCP Server
Detailed Description The ToDo App workflow is designed to streamline task management through Telegram and Google Tasks integration. This workflow allows users to create, update, and manage tasks via Telegram messages, leveraging AI capabilities to enhance user interaction. The expected outcome is a seamless experience where users can manage their tasks efficiently without needing to switch between applications. Who is this for? This workflow is intended for: Individuals** looking for an efficient way to manage their tasks directly from Telegram. Teams** that require a collaborative task management solution integrated with Google Tasks. Developers** interested in automating task management processes using n8n and Telegram. What problem does this workflow solve? Managing tasks can often be cumbersome, especially when switching between different applications. This workflow addresses the following problems: Fragmented Task Management**: Users can manage tasks directly from Telegram, reducing the need to switch to Google Tasks. Inefficient Communication**: By integrating AI, users can interact with the task management system in a conversational manner, making it more intuitive. Task Upd
WAuto-create and publish AI social videos with Telegram, GPT-4 and Blotato
Auto-create and publish AI social videos with Telegram, GPT-4 and Blotato > ⚠️ Disclaimer: This workflow uses Community Nodes and must be run on a self-hosted instance of n8n. Who is this for? This template is perfect for social media managers, content creators, AI enthusiasts, and automation pros who want to generate short-form videos (Reels) from a simple Telegram message, then publish them across multiple platforms—all without video editing or manual uploads. What problem is this workflow solving? Creating content is only half the job. The real bottleneck comes in: Rendering the video, Adding voice or music, Writing captions and titles, Publishing to multiple platforms. This workflow automates all of that using AI. It saves hours every week and guarantees consistent output. What this workflow does This end-to-end automation handles everything from AI video generation to social publishing: Starts with a Telegram message (text or image prompt) Generates video using Kling or Blotato, based on the input Creates music with Piapi and merges it with the video Generates text overlays and captions with GPT-4 Builds a stylized video using JSON2Video Logs results to Google Sheets Sends
📥 Transform Google Drive Documents into Vector Embeddings
Automatically convert documents from Google Drive into vector embeddings using OpenAI, LangChain, and PGVector — fully automated through n8n. ⚙️ What It Does This workflow monitors a Google Drive folder for new files, supports multiple file types (PDF, TXT, JSON), and processes them into vector embeddings using OpenAI’s text-embedding-3-small model. These embeddings are stored in a Postgres database using the PGVector extension, making them query-ready for semantic search or RAG-based AI agents. After successful processing, files are moved to a separate “vectorized” folder to avoid duplication. 💡 Use Cases Powering Retrieval-Augmented Generation (RAG) AI agents Semantic search across private documents AI assistant knowledge ingestion Automated document pipelines for indexing or classification 🧠 Workflow Highlights Trigger Options:** Manual or Scheduled (3 AM daily by default) Supported File Types:** PDF, TXT, JSON Embedding Stack:** LangChain Text Splitter, OpenAI Embeddings, PGVector Deduplication:** Files are moved after processing License:** CC BY-SA 4.0 Author:** AlexK1919 🛠 What You’ll Need Google Drive OAuth2** credentials (connected to Search Folder, Download File, and Mo
W🌳 EU green legislation tracker with GPT-4o, Google Sheets and Tasks
Tags: EU Legislation, Sustainability, Automation, Web Scraping, OpenAI, Google Sheets, Policy Monitoring, Climate Context Hey! I’m Samir, a Supply Chain Engineer and Data Scientist from Paris, and the founder of LogiGreen Consulting. We use AI, automation, and data to support sustainable business practices for small, medium and large companies. This workflow is part of our broader initiative to monitor and act on sustainability legislation in Europe. > How do you know if new EU laws will impact your business's sustainability goals? This n8n workflow automatically scrapes the EU Parliament’s legislative portal to find and flag procedures related to environmental sustainability. 📬 For business inquiries, feel free to connect with me on LinkedIn Who is this template for? This workflow is useful for: Sustainability consultants** monitoring legal frameworks NGOs and researchers** tracking environmental regulations Companies* aligning with *CSRD* or *EU Green Deal** objectives Policy analysts** looking for automation tools What does it do? This n8n workflow: 🌐 Scrapes the EU Parliament legislative portal for yesterday’s entries 🧠 Uses OpenAI to classify if each procedure is related
W🧑🦯Improve your website accessibility with GPT-4o and Google Sheet
Tags: Accessibility, SEO, Blogging, Marketing, Automation, AI, Web Auditing Context Hey! I’m Samir, a Supply Chain Engineer and Data Scientist from Paris, and the founder of LogiGreen Consulting. In my personal blog, I share insights on how to use AI, automation, and data analytics to improve logistics, operations, and digital sustainability practices. > Have you heard about accessibility? In this workflow, I use n8n to improve the quality of alternative texts for images on my personal website. 📬 For business inquiries, you can connect with me on LinkedIn Who is this template for? This workflow is for: Bloggers* and *website owners* who want to *improve accessibility** SEO professionals** looking to boost page performance Web developers* and *product teams** automating web audits What does it do? This n8n workflow: 🔍 Downloads the HTML of a blog or web page 🖼️ Extracts all ` tags and their alt` attributes 📉 Detects missing or too-short alt texts 🤖 Sends those images to GPT-4o (with vision) to generate new alt descriptions 📄 Saves the results into a Google Sheet, updating the alt text when needed How it works Set a page URL using the Set node Download HTML content Extract i
CDBuild your own Qdrant Vector Store MCP server
This n8n demonstrates how to build your own Qdrant MCP server to extend its functionality beyond that of the official implementation. This n8n implementation exposes other cool API features from Qdrant such as facet search, grouped search and recommendations APIs. With this, we can build an easily customisable and maintainable Qdrant MCP server for business intelligence. This MCP example is based off an official MCP reference implementation which can be found here - https://github.com/qdrant/mcp-server-qdrant How it works A MCP server trigger is used and connected to 5 custom workflow tools. We're using custom workflow tools as there is quite a few nodes required for each task. We use a mix of n8n supported Qdrant nodes for simple operations such as insert documents and similarity search, and HTTP node to hit the Qdrant API directly for Facet search, group search and recommendations. We use "Edit Field" and "Aggregate" nodes to return suitable responses to the MCP client. How to use This Qdrant MCP server allows any compatible MCP client to manage a Qdrant Collection by supporting select and create operations. You will need to have a collection available before you can use this ser
Build your own Google Drive MCP server
This n8n demonstrates how to build a simple Google Drive MCP server to search and get contents of files from Google Drive. This MCP example is based off an official MCP reference implementation which can be found here -https://github.com/modelcontextprotocol/servers/tree/main/src/gdrive How it works A MCP server trigger is used and connected to 1x Google Drive tool and 1x Custom Workflow tool. The Google Drive tool is set to perform a search on files within our Google Drive folder. The Custom Workflow tool downloads target files found in our drive and converts the binaries to their text representation. Eg. PDFs have only their text contents extracted and returned to the MCP client. How to use This Google Drive MCP server allows any compatible MCP client to manage a person or shared Google Drive. Simple select a drive or for better control, specify a folder within the drive to scope the operations to. Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop Try the following queries in your MCP client: "Please help me search for last month's expense reports." "Wh
WScrape Competitor Reviews & Generate Ad Creatives with Bright Data & OpenAI
Scrape Competitor Reviews & Generate Ad Creatives with Bright data and OpenAI How the Flow Runs Fill the Form Enter the Amazon product URL to analyze competitor reviews. Trigger Bright Data Scraper Bright Data scrapes Amazon reviews based on the provided URL. Wait for Snapshot Completion Periodically checks Bright Data until the scraping is complete. Retrieve JSON Data Collects the scraped review data in JSON format. Save Reviews to Google Sheets Automatically appends the scraped reviews to your Google Sheets. Aggregate Reviews Consolidates all reviews into a single summary for simpler analysis. Analyze Reviews with OpenAI LLM Sends the aggregated reviews to OpenAI (GPT-4o mini) to summarize competitors’ main weaknesses clearly. Generate Creative Ad Image OpenAI generates a visually appealing 1080x1080 ad image addressing these identified pain points. Send Ad Creative via Gmail Automatically emails the creative and review summary to your media buying team for immediate use in Meta ads. What You Need Google Sheets:** Template Bright Data:** Dataset and API key: www.brightdata.com OpenAI API Key:** For GPT-4o mini or your preferred LLM Automation Tool:** Ensure it supports HTTP Reque
WScrape Trustpilot reviews using Bright Data & GPT-5.5 for winning ad copy
🔍 Competitor Review Scraper & Ad Copy Generator (Trustpilot + Bright Data + GPT-5.5) 📌 Who It's For Marketers, business owners, and agencies looking to: Analyze competitor pain points Generate high-impact Facebook ad copy Automate manual data processing 🧩 How It Works This n8n-based workflow combines Bright Data, Google Sheets, and OpenAI to scrape, process, and transform Trustpilot reviews into ready-to-use ad copy. 🔹 Step-by-Step Breakdown Trigger (Manual Form Submission) Input required: Competitor’s Trustpilot URL Review timeframe (30d, 3m, 6m, 12m) Fetch Reviews Calls Bright Data’s Dataset API with URL & timeframe Polls until snapshot is ready Retrieve & Store Extracts all reviews Saves them into a structured Google Sheet Filter & Aggregate Filters to only 1–2 star reviews Summarizes common negative feedback Generate Ad Copy Sends the summary to OpenAI GPT-5.5 Produces 3 variations of ad copy targeting pain points Distribute Insights Sends ad copy + summary via email to the marketing team ✅ Requirements -LLM Account -Google Sheets - Copy this sheet: https://docs.google.com/spreadsheets/d/1Zi758ds2_aWzvbDYqwuGiQNaurLgs-leS9wjLWWlbUU/edit?gid=0#gid=0 -Bright Data account ⚙️ S
WFind High-Intent Sales Leads by Scraping Glassdoor with Bright Data & GPT
🔍 Scrape Glassdoor with Bright Data Designed for sales teams, recruiters, and marketers aiming to automate job discovery and prospecting. This workflow scrapes Glassdoor job listings using Bright Data and automatically generates targeted pitches using AI, streamlining lead identification and outreach. 🧩 How It Works This automation leverages n8n, Bright Data, Google Sheets, and OpenAI: 1. Trigger Starts with a custom form input (Location, Keyword, Country). 2. Bright Data Job Scrape Triggers a Bright Data dataset snapshot via HTTP Request. Polls snapshot progress using a Wait node, ensuring data readiness. Retrieves full job listings dataset once ready. 3. Google Sheets Integration Writes detailed job data (company, role, location, overview, metrics) into a Google Sheet. Uses a pre-built template for organized data storage. 4. Automated Pitch Generation (AI) Splits listings into actionable parts: company name, title, and description. Sends data to OpenAI (via LangChain) to generate relevant pitches or icebreakers. Saves generated content back into the same sheet for easy access. ✅ Requirements Ensure you have the following: Google Sheets Google account Template Sheet with columns
WScrape Indeed Job Listings for Hiring Signals Using Bright Data and LLMs
Scrape Indeed Job Listings for Hiring Signals Using Bright Data and LLMs How the flow runs Fill the form with job position you're hunting for. Bright data's scraper will scrape Indeed based on your requirments. Workflow waits for the snapshot. Data returns as JSON. Jobs append to Google Sheets. Each row goes to an LLM to analyze if you're a good fit for the job (based on your prompts). The LLMswrites YES or NO next to each job opportunity, helping you find job posts that are relevant to you. What you need Google Sheets with our template. Bright Data dataset and API key. OpenAI key for GPT‑4o mini (or any other LLM). n8n with required nodes. Form fields To Fill Job Location** – city or region. Keyword** – role or skills. Country** – two‑letter code. Setup steps Copy the sheet template link. Import the JSON workflow. Add your credentials in nodes. Test the form manually. Add a schedule if desired. Bright Data filter example [ { "country": "US", "domain": "indeed.com", "keyword_search": "Growth Marketer", "location": "Miami", "date_posted": "Last 24 hours" } ] Tips -Choose Last 24 hours often. -Increase wait time for big snapshots. -Narrow keywords to save credits. **Need help? **Emai
Log meal nutrients from Telegram to Google Sheets using an AI agent
Who is this for? This workflow is ideal for individuals focused on nutrition tracking, meal planning, or diet optimization—whether you’re a health-conscious individual, fitness coach, or developer working on a healthtech app. It also fits well for anyone who wants to capture their meal data via voice or text, without manually entering everything into a spreadsheet. What problem is this workflow solving? Manually logging meals and breaking down their nutritional content is time-consuming and often skipped. This workflow automates that process using Telegram for input, OpenAI for natural language understanding, and Google Sheets for structured tracking. It enables users to record meals by typing or sending voice messages, which are transcribed, analyzed for nutrients, and automatically stored for tracking and review. What this workflow does This n8n automation lets users send either a text or voice message to a Telegram bot describing their meal. The workflow then: Receives the Telegram message Checks if it’s a voice message • If yes: Downloads the audio file and transcribes it using OpenAI • If no: Uses the text input directly Sends the meal description to OpenAI to extract a struct
Daily AI News Translation & Summary with GPT-4 and Telegram Delivery
📝 What this workflow does Every morning at 8 a.m., this workflow fetches the latest AI-related articles from both GNews and NewsAPI. It merges up to 40 new articles daily, selects the 15 most relevant ones on AI technology and applications, and uses GPT-4.1 to generate concise summaries in accurate Traditional Chinese (while preserving essential English technical terms). Each summary also includes the article link for easy referral. The compiled digest is then posted to your designated Telegram account or group. 👥 Who is this for? AI enthusiasts, professionals, and anyone interested in artificial intelligence news Individuals and teams wanting a concise daily digest of AI developments in Traditional Chinese Telegram users who prefer automated information delivery 🎯 What problem does this workflow solve? With the rapid evolution of AI technology, it can be overwhelming to keep up with new developments. This workflow addresses information overload by automatically collecting, summarizing, and translating the most important AI news each morning — all delivered conveniently to your chosen Telegram channel or group. ⚙️ Setup 🔑 Add NewsAPI and GNews API Keys Register for accounts on
🧠 FloWatch 👁️ Analyze and Diagnose n8n Workflow Errors via OpenAI and Email
🧠 Analyze and Diagnose n8n Workflow Errors Automatically via OpenAI and Email > ⚠️ This template is available on ☁️ Cloud & 🖥️ self-hosted n8n instances with the OpenAI node enabled. 👤 Who is this for? This workflow is designed for n8n developers, automation engineers, and DevOps teams who want to automatically capture and analyze workflow errors, and receive professional HTML-styled diagnostics directly in their inbox. 💥 What problem does this solve? Manually troubleshooting failed workflows in n8n can be time-consuming. This template streamlines error detection by: Capturing workflow failures using the Error Trigger node Diagnosing root causes with the help of OpenAI Sending a fully-formatted, human-readable HTML error report via email Including practical resolutions and next-step suggestions This helps you or your team resolve issues faster and avoid repeated manual debugging. ⚙️ What this workflow does ⚡ Triggers on any n8n workflow error 📦 Extracts relevant error metadata including node, execution ID, and timestamps 🧠 Sends error content to OpenAI for analysis and recommendations 💌 Generates an HTML email report with inline styles and clear formatting 📥 Emails the r
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
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
Travel Planning Assistant with MongoDB Atlas, Gemini LLM and Vector Search
Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration. Until now, these pieces had to be custom-wired. But with the new native n8n nodes for MongoDB Atlas, we reduce that overhead dramatically. With just a few clicks: Store and recall long-term memory from MongoDB Query vector embeddings stored in Atlas Vector Search Use these results in your LLM chains and automation logic In this example we present an ingestion and AI Agent flows that focus around Travel Planning. The different interest points that we want the agent to know about can be ingested into the vector store. The AI Agent will use the vector store tool to get relevant context about those points of interest if it needs to. Prerequisites MongoDB Atlas project and Cluster OpenAI Valid API Key for embeddings (can be other provider) Gemini API Key for the LLM (can be other provider) How it works: There are 2 main flows. One is ingesting flow: Gets a document from a webhook and use MongoDB Vector Atlas to embed the document title and description into points_of_interest collection. Embeddings are stored in a field named embedding Embedding
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.