Google Drive workflow'ları
1.381 sonuç — 188 indirilebilir workflow dosyası, 1.193 kaynağa bağlı n8n referansı
W💡🌐 Essential Multipage Website Scraper with Jina.ai
💡🌐 Essential Multipage Website Scraper with Jina.ai Use responsibly and follow local rules and regulations This N8N workflow enables automated multi-page website scraping using Jina.ai's powerful web scraping capabilities, with seamless integration to Google Drive for content storage. Here's how it works: Main Features The workflow automatically scrapes multiple pages from a website's sitemap and saves each page's content as a separate Google Drive document. Key Components Input Configuration Starts with a sitemap URL (default: https://ai.pydantic.dev/sitemap.xml)** Processes the sitemap to extract individual page URLs Includes filtering options to target specific topics or pages Scraping Process Uses Jina.ai's web scraper to extract content from each URL Converts webpage content into clean markdown format Extracts page titles automatically for document naming Storage Integration Creates individual Google Drive documents for each scraped page Names documents using the format "URL - Page Title" Saves content in markdown format for better readability Usage Instructions Set your target website's sitemap URL in the "Set Website URL" node Configure the "Filter By Topics or Pages" node
🤖🧑💻 AI Agent for Top n8n Creators Leaderboard Reporting
This n8n workflow is designed to automate the aggregation, processing, and reporting of community statistics related to n8n creators and workflows. Its primary purpose is to generate insightful reports that highlight top contributors, popular workflows, and key trends within the n8n ecosystem. Here's how it works and why it's important: How It Works Data Retrieval: The workflow fetches JSON data files from a GitHub repository containing statistics about creators and workflows. It uses HTTP requests to access these files dynamically based on pre-defined global variables. Data Processing: The data is parsed into separate streams for creators and workflows. It processes the data to identify key metrics such as unique weekly and monthly inserters/visitors. Ranking and Filtering: The workflow sorts creators by their weekly inserts and workflows by their popularity. It selects the top 10 creators and top 50 workflows for detailed analysis. Report Generation: Using AI tools like GPT-4 or Google Gemini, the workflow generates a Markdown report summarizing trends, contributors, and workflow statistics. The report includes tables with detailed metrics (e.g., unique visitors, inserters) and i
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Upload to Instagram, TikTok & YouTube from Google Drive
Description This automation template is designed for content creators, digital marketers, and social media managers looking to simplify their video posting workflow. It automates the process of generating engaging video descriptions and uploading content to both Instagram and TikTok, making your social media management more efficient and error-free. Who Is This For? Content Creators & Influencers:** Streamline your video uploads and focus more on creating content. Digital Marketers:** Ensure consistent posting across multiple platforms with minimal manual intervention. Social Media Managers:** Automate repetitive tasks and maintain a steady online presence. What Problem Does This Workflow Solve? Manually creating descriptions and uploading videos to different platforms can be time-consuming and error-prone. This workflow addresses these challenges by: Automating Video Uploads:** Monitors a designated Google Drive folder for new videos. Generating Descriptions:** Uses OpenAI to transcribe video audio and generate engaging, customized social media descriptions. Ensuring Multi-Platform Consistency:** Simultaneously posts your video with the generated description to Instagram and TikTo
Export WordPress Posts to CSV and Upload to Google Drive
Overview This n8n workflow automates the process of extracting published WordPress posts, converting them into a CSV file, and uploading it to Google Drive. It’s perfect for content backups, SEO audits, and data migration. Features Fetches all published posts from a WordPress website Extracts key post details (ID, Title, Link) Converts the extracted data into a CSV file Uploads the CSV file to Google Drive for easy access and storage Use Cases SEO Optimization**: Export post data for keyword analysis and performance tracking Automated Content Backup**: Store WordPress post details in Google Drive. You can add more fields to the Csv file if needed Workflow Steps 1. Trigger Workflow Manually The workflow starts when triggered manually in n8n. 2. Retrieve WordPress Posts The workflow fetches all published posts using the WordPress API. It extracts: Post ID Title Link Rendered Content 3. Format Data The retrieved data is structured to ensure correct CSV formatting. 4. Convert to CSV File The formatted data is transformed into a downloadable CSV file. 5. Upload to Google Drive The CSV file is automatically uploaded to a specified Google Drive folder for easy access and storage. How to U
Backup all n8n workflows to Google Drive every 4 hours
This workflow takes off the task of backing up workflows regularly on Github and uses Google Drive as the main tool to host these. This can be a good way to keep track of your workflows so that you never lose any workflows in case your n8n goes down. How does it work Creates a new folder within a specified folder with the time its backed up Loops around all workflows, converts them to a JSON file and uploads them to the created folder Gets the previous backups and deletes them This has a clean feel and look as it simplifies the backup while not keeping a cache of workflows on your drive. Setup Create a new folder Create new service account credentials Share the folder with the service account email Upload this workflow to your canvas and map the credentials Set the schedule that you need your workflows to run and manage your backups Activate the workflow Happy Productivity! @Imperol
RAG:Context-Aware Chunking | Google Drive to Pinecone via OpenRouter & Gemini
Workflow based on the following article. https://www.anthropic.com/news/contextual-retrieval This n8n automation is designed to extract, process, and store content from documents into a Pinecone vector store using context-based chunking. The workflow enhances retrieval accuracy in RAG (Retrieval-Augmented Generation) setups by ensuring each chunk retains meaningful context. Workflow Breakdown: 🔹 Google Drive - Retrieve Document: The automation starts by fetching a source document from Google Drive. This document contains structured content, with predefined boundary markers for easy segmentation. 🔹 Extract Text Content - Once retrieved, the document’s text is extracted for processing. Special section boundary markers are used to divide the text into logical sections. 🔹 Code Node - Create Context-Based Chunks: A custom code node processes the extracted text, identifying section boundaries and splitting the document into meaningful chunks. Each chunk is structured to retain its context within the entire document. 🔹 Loop Node - Process Each Chunk: The workflow loops through each chunk, ensuring they are processed individually while maintaining a connection to the overall document c
Effortless Email Management with AI-Powered Summarization & Review
How it Works This workflow automates the handling of incoming emails, summarizes their content, generates appropriate responses using a retrieval-augmented generation (RAG) approach, and obtains approval or suggestions before sending replies. Below is an explanation of its functionality divided into two main sections: Email Handling and Summarization: The process begins with the Email Trigger (IMAP) node which listens for new emails in a specified inbox. Once an email is received, the Markdown node converts its HTML content into plain text if necessary, followed by the Email Summarization Chain that uses AI to create a concise summary of up to 100 words. Response Generation and Approval: A Write email node generates a professional response based on the summarized content, ensuring brevity and professionalism while keeping within the word limit. Before sending out any automated replies, the system sends these drafts via Gmail for human review and approval through the Gmail node configured with free-text response options. If approved, the finalized email is sent back to the original sender using the Send Email node; otherwise, it loops back for further edits or manual intervention. A
AI Automated HR Workflow for CV Analysis and Candidate Evaluation
How it Works This workflow automates the process of handling job applications by extracting relevant information from submitted CVs, analyzing the candidate's qualifications against a predefined profile, and storing the results in a Google Sheet. Here’s how it operates: Data Collection and Extraction: The workflow begins with a form submission (On form submission node), which triggers the extraction of data from the uploaded CV file using the Extract from File node. Two informationExtractor nodes (Qualifications and Personal Data) are used to parse specific details such as educational background, work history, skills, city, birthdate, and telephone number from the text content of the CV. Processing and Evaluation: A Merge node combines the extracted personal and qualification data into a single output. This merged data is then passed through a Summarization Chain that generates a concise summary of the candidate’s profile. An HR Expert chain evaluates the candidate against a desired profile (Profile Wanted), assigning a score and providing considerations for hiring. Finally, all collected and processed data including the evaluation results are appended to a Google Sheets document v
AI-Powered Email Automation for Business: Summarize & Respond with RAG
This workflow is ideal for businesses looking to automate their email responses, especially for handling inquiries about company information. It leverages AI to ensure accurate and professional communication. How It Works Email Trigger: The workflow starts with the Email Trigger (IMAP) node, which monitors an email inbox for new messages. When a new email arrives, it triggers the workflow. Email Preprocessing: The Markdown node converts the email's HTML content into plain text for easier processing by the AI models. Email Summarization: The Email Summarization Chain node uses an AI model (DeepSeek R1) to generate a concise summary of the email. The summary is limited to 100 words and is written in Italian. Email Classification: The Email Classifier node categorizes the email into predefined categories (e.g., "Company info request"). If the email does not fit any category, it is classified as "other". Email Response Generation: The Write email node uses an AI model (OpenAI) to draft a professional response to the email. The response is based on the email content and is limited to 100 words. The Review email node uses another AI model (DeepSeek) to review and format the drafted respo
AI Voice Chatbot with ElevenLabs & OpenAI for Customer Service and Restaurants
The "Voice RAG Chatbot with ElevenLabs and OpenAI" workflow in n8n is designed to create an interactive voice-based chatbot system that leverages both text and voice inputs for providing information. Ideal for shops, commercial activities and restaurants How it works: Here's how it operates: Webhook Activation: The process begins when a user interacts with the voice agent set up on ElevenLabs, triggering a webhook in n8n. This webhook sends a question from the user to the AI Agent node. AI Agent Processing: Upon receiving the query, the AI Agent node processes the input using predefined prompts and tools. It extracts relevant information from the knowledge base stored within the Qdrant vector database. Knowledge Base Retrieval: The Vector Store Tool node interfaces with the Qdrant Vector Store to retrieve pertinent documents or data segments matching the user’s query. Text Generation: Using the retrieved information, the OpenAI Chat Model generates a coherent response tailored to the user’s question. Response Delivery: The generated response is sent back through another webhook to ElevenLabs, where it is converted into speech and delivered audibly to the user. Continuous Interactio
Complete business WhatsApp AI-Powered RAG Chatbot using OpenAI
The provided workflow in n8n is designed to create a Business WhatsApp AI RAG (Retrieval-Augmented Generation) Chatbot. How it works: Webhook Setup: The workflow begins by setting up webhooks for verification and response. The Verify webhook receives GET requests and sends back a verification code, while the Respond webhook handles incoming POST requests from Meta regarding WhatsApp messages. Message Handling: Once a message is received, the workflow checks if the incoming JSON contains a user message. If it does, the message is processed further; otherwise, a generic response is sent. AI Agent Interaction: The user's message is passed to the AI Agent node, which uses a conversational agent with a predefined system message tailored for an electronics store. This ensures that the AI provides accurate and professional responses based on the knowledge base. Knowledge Base Utilization: The AI Agent references a knowledge base stored in Qdrant, a vector database. Documents from Google Drive are downloaded, vectorized using OpenAI embeddings, and stored in Qdrant for retrieval during conversations. Response Generation: The AI Agent generates a response using the OpenAI chat model (gpt-4o
Automate SIEM Alert Enrichment with MITRE ATT&CK, Qdrant & Zendesk in n8n
n8n Workflow: Automate SIEM Alert Enrichment with MITRE ATT&CK & Qdrant Who is this for? This workflow is ideal for: Cybersecurity teams & SOC analysts* who want to automate *SIEM alert enrichment**. IT security professionals* looking to integrate *MITRE ATT&CK intelligence** into their ticketing system. Organizations using Zendesk for security incidents* who need enhanced *contextual threat data**. Anyone using n8n and Qdrant* to build *AI-powered security workflows**. What problem does this workflow solve? Security teams receive large volumes of raw SIEM alerts that lack actionable context. Investigating every alert manually is time-consuming and can lead to delayed response times. This workflow solves this problem by: ✔ Automatically enriching SIEM alerts with MITRE ATT&CK TTPs. ✔ Tagging & classifying alerts based on known attack techniques. ✔ Providing remediation steps to guide the response team. ✔ Enhancing security tickets in Zendesk with relevant threat intelligence. What this workflow does 1️⃣ Ingests SIEM alerts (via chatbot or ticketing system like Zendesk). 2️⃣ Queries a Qdrant vector store containing MITRE ATT&CK techniques. 3️⃣ Extracts relevant TTPs (Tactics, Techni
Workflow Results to Markdown Notes in Your Obsidian Vault, via Google Drive
This workflow converts any n8n workflow outputs into Markdown notes that are accessible in your Obsidian Vault through Google Drive synchronization. Setup Requirements Create a designated folder in Google Drive (Desktop). Create a symbolic link between this folder and a new target folder in your Obsidian Vault. Configure Google Drive n8n node settings. Send the output of any workflow to the trigger, and the notes will appear in your Vault folder. Optional Features You can use AI agents to: Write notes in your preferred format (e.g., Zettelkasten). Compose YAML front matter. Suggest tags. Use Cases Convert RSS feed items to notes. Create notes from YouTube video transcripts. Transform tasks in Slack messages into Obsidian tasks. (Requires setting up a corresponding workflow, e.g., RSS trigger, YouTube transcriber, or Slack bot.)
DPersonal Shopper Chatbot for WooCommerce with RAG using Google Drive and openAI
This workflow combines OpenAI, Retrieval-Augmented Generation (RAG), and WooCommerce to create an intelligent personal shopping assistant. It handles two scenarios: Product Search: Extracts user intent (keywords, price ranges, SKUs) and fetches matching products from WooCommerce. General Inquiries: Answers store-related questions (e.g., opening hours, policies) using RAG and documents stored in Google Drive. How It Works 1. Chat Interaction & Intent Detection Chat Trigger**: Starts when a user sends a message ("When chat message received"). Information Extractor**: Uses OpenAI to analyze the message and determine if the user is searching for a product or asking a general question. Extracts: search (true/false). keyword, priceRange, SKU, category (if product-related). Example: { "search": true, "keyword": "red handbags", "priceRange": { "min": 50, "max": 100 }, "SKU": "BAG123", "category": "women's accessories" } 2. Product Search (WooCommerce Integration) AI Agent**: If search: true, routes the request to the personal_shopper tool. WooCommerce Node: Queries the WooCommerce store using extracted parameters (keyword, priceRange, SKU). Filters products in stock (stockStatus: "instock"
SBuild an OpenAI Assistant with Google Drive Integration
Workflow Overview This workflow automates the creation and management of a custom OpenAI Assistant for a travel agency ("Travel with us"), leveraging Google Drive for document storage. How It Works 1. Create the OpenAI Assistant Node**: OpenAI Action: Creates a custom assistant named "Travel with us" Assistant using the gpt-4o-mini model. Instructions: Respond only using the provided document (e.g., agency-specific info). Stay friendly, brief, and focused on travel-related queries. Ignore irrelevant questions politely. Credentials: Requires OpenAI API key. 2. Upload Agency Document Google Drive Node**: Action: Downloads a Google Doc as a PDF. OpenAI2 Node**: Action: Uploads the PDF to OpenAI with purpose: "assistants". Output: Generates a file_id. 3. Update the Assistant with the Document OpenAI Node**: Action: Updates the assistant to include the uploaded file. 4. Chat Interaction Chat Trigger**: Activates when a message is received ("When chat message received"). OpenAI Assistant Node**: Action: Uses the updated assistant to respond to user queries. Memory: Window Buffer Memory retains chat context for coherent conversations. Set Up Steps Prepare the Document: Store your travel a
WAutomated End-to-End Fine-Tuning of OpenAI Models with Google Drive Integration
1. How it Works This n8n workflow automates fine-tuning OpenAI models through these key steps: Manual Trigger**: Starts with the "When clicking ‘Test workflow’" event to initiate the process. Downloads a .jsonl file from Google Drive Upload to OpenAI**: Uploads the .jsonl file to OpenAI via the "Upload File" node (with purpose "fine-tune"). Create Fine-tuning Job**: Sends a POST request to the endpoint https://api.openai.com/v1/fine_tuning/jobs with: { "training_file": "{{ $json.id }}", "model": "gpt-4o-mini-2024-07-18" } OpenAI automatically starts training the model based on the provided file. Interaction with the Trained Model**: An "AI Agent" uses the custom model (e.g., ft:gpt-4o-mini-2024-07-18:n3w-italia::XXXX7B) to respond to chat messages. 2. Set up Steps To configure the workflow: Prepare the Training File: Create a .jsonl file following the specified syntax (e.g., travel assistant Q/A examples). Upload it to Google Drive and update the ID in the "Google Drive" node. Configure Credentials: Google Drive: Connect an account via OAuth2 (googleDriveOAuth2Api). OpenAI: Add your API key in the "OpenAI Chat Model" and "Upload File" nodes. Customize the Model: In the "OpenAI Chat
Remove Personally Identifiable Information (PII) from CSV Files with OpenAI
What this workflow does Monitors Google Drive: The workflow triggers whenever a new CSV file is uploaded. Uses AI to Identify PII Columns: The OpenAI node analyzes the data and identifies PII-containing columns (e.g., name, email, phone). Removes PII: The workflow filters out these columns from the dataset. Uploads Cleaned File: The sanitized file is renamed and re-uploaded to Google Drive, ensuring the original data remains intact. How to customize this workflow to your needs Adjust PII Identification: Modify the prompt in the OpenAI node to align with your specific data compliance requirements. Include/Exclude File Types: Adjust the Google Drive Trigger settings to monitor specific file types (e.g., CSV only). Output Destination: Change the folder in Google Drive where the sanitized file is uploaded. Setup Prerequisites: A Google Drive account. An OpenAI API key. Workflow Configuration: Configure the Google Drive Trigger to monitor a folder for new files. Configure the OpenAI Node to connect with your API Set the Google Drive Upload folder to a different location than the Trigger folder to prevent workflow loops.
Sync New Files From Google Drive with Airtable
This workflow automatically fetches newly uploaded files from a specific folder in Google Drive, shares them via email with specified recipients, and logs the file details (name, ID, created time, modified time) into Airtable for easy tracking. It streamlines the process of file sharing and management while keeping track of important metadata in a central place. Step-by-Step Instructions Google Drive Node (Fetch New File) Action: This node fetches newly uploaded files from the specific folder you’ve mentioned in your Google Drive. Configuration: Set the folder ID in the Google Drive node where the files are uploaded. Use the “New File in Folder” trigger to automatically detect new files added to the folder. Send Email Node (Share File via Email) Action: After detecting the new file, this node shares the file via email with the recipient you specify. Configuration: Set the recipient's email address. Include the file URL from the Google Drive node in the email body, allowing easy access to the file. Add the file name as part of the email subject or body to notify the recipient about the new file. Airtable Node (Store File Metadata) Action: This node stores the file’s metadata, such a
HR Job Posting and Evaluation with AI
Workflow Documentation: HR Job Posting and Evaluation with AI Detailed Description The HR Job Posting and Evaluation with AI workflow is designed to streamline and enhance recruitment for technical roles, such as Automation Specialists. By automating key stages in the hiring process, this workflow ensures a seamless experience for both candidates and HR teams. From collecting applications to evaluating candidates using AI and scheduling interviews, this workflow provides an end-to-end solution for recruitment challenges. Who is this for? This workflow is ideal for: HR Professionals**: Managing multiple job postings and candidates efficiently. Recruitment Teams**: Handling large volumes of applications for technical positions. Hiring Managers**: Ensuring structured and objective candidate evaluations. What problem does this workflow solve? Time-Consuming Processes**: Automates repetitive tasks like data entry, CV management, and scheduling. Fair Candidate Evaluation**: Leverages AI to provide objective insights based on resumes and job descriptions. Streamlined Communication**: Ensures timely and personalized candidate interactions, improving their experience. What this workflow doe
WExtract and process information directly from PDF using Claude and Gemini
Overview This workflow helps you compare Claude 3.5 Sonnet and Gemini 2.0 Flash when extracting data from a PDF This workflow extracts and processes the data within a PDF in one single step, instead of calling an OCR and then an LLM” How it works The initial 2 steps download the PDF and convert it to base64. This base64 string is then sent to both Claude 3.5 Sonnet and Gemini 2.0 Flash to extract information. This workflow is made to let you compare results, latency, and cost (in their dedicated dashboard). How to use it Set up your Google Drive if not already done Select a document on your Google Drive Modify the prompt in "Define Prompt" to extract the information you need and transform it as wanted. Get a Claude API key and/or Gemini API key Note that you can deactivate one of the 2 API calls if you don't want to try both Test the Workflow
RAG Chatbot for Company Documents using Google Drive and Gemini
This workflow implements a Retrieval Augmented Generation (RAG) chatbot that answers employee questions based on company documents stored in Google Drive. It automatically indexes new or updated documents in a Pinecone vector database, allowing the chatbot to provide accurate and up-to-date information. The workflow uses Google's Gemini AI for both embeddings and response generation. How it works The workflow uses two Google Drive Trigger nodes: one for detecting new files added to a specified Google Drive folder, and another for detecting file updates in that same folder. Automated Indexing: When a new or updated document is detected The Google Drive node downloads the file. The Default Data Loader node loads the document content. The Recursive Character Text Splitter node breaks the document into smaller text chunks. The Embeddings Google Gemini node generates embeddings for each text chunk using the text-embedding-004 model. The Pinecone Vector Store node indexes the text chunks and their embeddings in a specified Pinecone index. 7.The Chat Trigger node receives user questions through a chat interface. The user's question is passed to an AI Agent node. The AI Agent node uses a V
AI-Powered RAG Workflow For Stock Earnings Report Analysis
This n8n workflow creates a financial analysis tool that generates reports on a company's quarterly earnings using the capabilities of OpenAI GPT-4o-mini, Google's Gemini AI and Pinecone's vector search. By analyzing PDFs of any company's earnings reports from their Investor Relations page, this workflow can answer complex financial questions and automatically compile findings into a structured Google Doc. How it works: Data loading and indexing Fetches links to PDF earnings document from a Google Sheet containing a list of file links. Downloads the PDFs from Google Drive. Parses the PDFs, splits the text into chunks, and generates embeddings using the Embeddings Google AI node (text-embedding-004 model). Stores the embeddings and corresponding text chunks in a Pinecone vector database for semantic search. Report generation with AI agent Utilizes an AI Agent node with a specifically crafted system prompt. The agent orchestrates the entire process. The agent uses a Vector Store Tool to access and retrieve information from the Pinecone database. Report delivery Saves the generated report as a Google Doc in a specified Google Drive location. Set up steps Google Cloud Project & Vertex
CWParse 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
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