Workflow Templates

Workflow Introduction Core Competence Our invoice extraction workflow is completed end-to-end automatically: Gmail invoice email screening → extraction of key fields from multi-format invoices → automatic archiving of results to Google Drive, replacing the repetitive manual labor of finance staff in opening and entering invoices. Differentiated Advantage High Accuracy: The extraction accuracy of core fields is 91%+, far exceeding that of similar products in the industry; Advantages of table extraction: For invoices containing tables, the line-extract technology has a significant extraction effect; Multi-format Compatibility: Natively supports invoice formats such as PDF, images, Word, Excel, etc., without the need for conversion; Lightweight Integration: Seamlessly integrates with Gmail and Google Drive, Out Of The Box. Company Introduction Laiye ADP (Agentic Document Processing), based on large language models and vision-language models, combined with agent technology, is a new generation platform that enables end-to-end automated document processing. For more information, please visit the official website of Laiye Technology: ++https://laiye.com/++ Use Cases Multi-supplier integration: Efficiently process invoices from multiple suppliers, automatically extract key invoice information for archiving; Accounting firms batch process large amounts of invoice data: they can handle the increased invoice processing. requirements brought about by the growth in the number of clients without adding staff; Cross-border trade enterprises: For multi-language/complex layout overseas invoice scenarios, without the need for manual setup and human processing, achieve understanding of documents and then complete extraction of important data; Small and Medium-sized Technology Enterprises: Quickly identify important information such as invoice date, invoice amount, and invoice number from employee reimbursement invoices, and say goodbye to manual data entry. How it works Step 1: Complete Gmail authorization You need to authorize your Google email. We will automatically obtain your email attachments from Google email. To accurately obtain and identify the invoice attachments, you can set up your email filter configuration by yourself, for example: Emails with attachments and subjects containing keywords like "invoice"; Emails from supplier; Emails under the designated label. Step 2: Automate document filtering We have configured the document automatic filterer for you, which will filter out the documents that meet the automated processing flow. There is no need for manual operation. For those that do not meet the conditions, our workflow will store these documents in the designated folder for quick retrieval during manual processing. There is no need to sift through emails one by one. The documents that we preset for automatic processing include the following conditions (union): The attachment title contains text: invoice, receipt, expenses, fee (any one of them is sufficient. If you want to match other commonly used words in actual business, you can directly modify the {{ $json.attachment_extension }} field in the filter to assign the value. File size: < 50MB File format: .jpeg, .jpg, .png, .bmp, .tiff, .pdf, .doc, .docx, .xls, .xlsx. Step 3: Complete ADP permission configuration You need to go to adp.laiye.com to register for free, after which you can obtain 100 free calls per month Select the Out Of The Box Agent Application-Invoice/Receipt Card, and click the more menu [...] on the card to directly obtain your exclusive API Key, App Secret, and Access Key Simply fill the obtained Key into the configuration item of the 【Laiye ADP HTTP Request Node】 After ADP completes invoice extraction, it will return structured Json data, which includes more than 10 text fields, such as "Invoice Name", "Invoice Number", "Invoice Date", etc., as well as complete invoice table fields, such as "Item Name", "Description", "Quantity", "Unit Price", etc. Step 4: Complete Google Drive authorization Files processed by ADP will be automatically converted into binary data to ensure smooth import into Google Drive (Sheet) Files that have not undergone ADP processing will be saved as the original files to the [Untreated Document] folder. If all files have been automatically processed, this folder will not be created Extracted documents can be automatically saved to any folder you specify. By default, they are stored in MY Drive. If you wish to save them to a custom folder, simply modify the Parent Folder setting Professional Community and Latest News Follow us on LinkedIn for more updates! ++https://www.linkedin.com/company/laiye++ We will share the latest updates on Laiye ADP products; You can share your successful invoice processing cases. Problems & Support If you encounter any issues during use, you can try contacting us for technical support (++global_product@laiye.com++), and your message requesting technical support can include the following content: Describe the problem you encountered in as much detail as possible Your current invoice processing volume and type The specific supplier format or invoice layout you handle Target Accounting Software or System Integration Any technical errors or issues with extraction accuracy Current manual processing workflow and pain points Response time: within 24 hours on working days Professional Areas: Invoice Processing Automation, Order Processing Automation, Receipt Processing Automation

This AI-powered workflow transforms n8n workflow JSON files into publication-ready, SEO-optimized markdown posts for the n8n community. Simply upload your workflow's JSON, and let Google Gemini 2.5 Pro, guided by a LlamaIndex-powered knowledge base of best practices, automatically generate compelling content. Why Use This Workflow? Time Savings: Reduces the time to create a detailed workflow post from over an hour of manual writing to under 2 minutes. Cost Reduction: Eliminates the need for separate AI content subscriptions or outsourcing content creation tasks. Error Prevention: Enforces content quality and structural consistency by using a knowledge base of n8n's official guidelines, minimizing formatting errors. Ideal For n8n Workflow Creators:** To quickly document and share their creations on the community platform without the tedious, time-consuming writing process. Developer Advocates:** To standardize and accelerate the production of technical tutorials and workflow showcases. Content & Marketing Teams:** To streamline the content pipeline for n8n-related blog posts, tutorials, and community engagement initiatives. How It Works Trigger: The process starts when you upload an n8n workflow JSON file via a simple web form. Data Extraction: The workflow automatically extracts the JSON content from the uploaded file. Intelligence Layer: An advanced AI agent, powered by Google Gemini 2.5 Pro, analyzes the structure, nodes, and metadata of your workflow. Knowledge Retrieval: The agent consults a specialized, in-memory knowledge base built from n8n's content guidelines. This knowledge base is created by parsing documents with LlamaIndex and refined with a Cohere Reranker for maximum accuracy. Content Generation: The AI agent synthesizes the technical details from your JSON with the best practices from the knowledge base to write a complete, benefit-driven markdown post. Output & Delivery: The final, polished markdown content is generated as the workflow's output, ready to be copied and pasted into the n8n community platform. Setup Guide Prerequisites | Requirement | Type | Purpose | |-------------|------|---------| | n8n instance | Essential | Workflow execution platform | | Google Gemini API Key | Essential | Powers the core AI content generation | | LlamaIndex Cloud API Key | Essential | Parses documents for the knowledge base | | Cohere API Key | Optional | Improves knowledge base search results | | Google Drive Account | Optional | For automatically updating the knowledge base from a Google Doc | Installation Steps Import the JSON file to your n8n instance. Configure credentials: Google Gemini: In the "GEmini 2.5 pro" node, create and add your Google Gemini API credential. LlamaIndex: In the three HTTP Request nodes named "Parse Document...", "Monitor Document...", and "Retrieve Parsed...", create an HTTP Header Auth credential. The header name is Authorization and the value is Bearer YOUR_LLAMA_INDEX_API_KEY. Cohere: (Optional) In the "Reranker Cohere" node, create and add your Cohere API credential. Google Drive: (Optional) If you plan to auto-update the knowledge base, configure Google Drive OAuth2 credentials for the "Knowledge Base Updated Trigger" and "Download Knowledge Document" nodes. Update environment-specific values: To use the knowledge base auto-update feature, go to the "Knowledge Base Updated Trigger" node and select the Google Drive file containing your content guidelines. Customize settings: The primary system prompt in the "n8ncreator" agent node can be modified to adjust the tone, style, or structure of the generated content. Test execution: Run the workflow manually and use the form to upload a sample n8n workflow JSON file to verify that all connections work correctly. Technical Details Core Nodes | Node | Purpose | Key Configuration | |------|---------|-------------------| | Form Trigger | Initiates the workflow via a file upload. | Set the "Input Json Workflow" field to required. | | Langchain Agent | Orchestrates the entire content creation process. | The system prompt contains all instructions for the AI. | | ChatGoogleGemini | Provides the core generative AI capabilities. | Select your Gemini model of choice (e.g., gemini-2.5-pro). | | VectorStoreInMemory | Acts as the agent's knowledge base tool. | Configured to use embeddings from a Google Gemini model. | | HTTPRequest | Interacts with the LlamaIndex API to parse documents. | Set up with LlamaIndex API endpoint and authentication. | Customization Options Basic Adjustments: Change AI Model:** Replace the ChatGoogleGemini node with another LLM node (e.g., OpenAI, Anthropic) to use a different provider. Adjust System Prompt:** Modify the prompt in the "n8ncreator" node to tailor the output for different platforms (e.g., blog, internal wiki) or change the writing style. Advanced Enhancements: Automated Publishing:** Connect the output of the "n8ncreator" node to a Ghost, WordPress, or GitHub node to automatically publish the generated post. Add Web Search:** Equip the Langchain Agent with a web search tool to allow it to fetch live information about new n8n nodes or services. Batch Processing:** Replace the Form Trigger with a Read Binary Files node to process an entire folder of workflow JSON files in a single run. Performance & Optimization | Metric | Expected Performance | Optimization Tips | |--------|---------------------|-------------------| | Execution time | ~1 minute per run | Largely dependent on the Gemini API response time. | | API calls | 1 LLM call per post | Knowledge base updates trigger LlamaIndex/Google calls separately. | | Error handling | Built-in retry logic for document parsing | Add an error workflow path after the "n8ncreator" node to handle AI generation failures. | Troubleshooting Common Issues: | Problem | Cause | Solution | |---------|-------|----------| | AI output is generic or incomplete | The input JSON file is invalid or lacks key information (e.g., no node names). | Ensure you are uploading a valid, exported n8n workflow JSON. Verify the workflow has been saved with descriptive node names. | | LlamaIndex parsing fails | The LlamaIndex API key is incorrect or the source document is inaccessible. | Double-check your LlamaIndex API credential. Ensure the Google Doc sharing settings allow access. | | Credential Error | API keys are missing or incorrect for Gemini, LlamaIndex, or Cohere. | Go to the specified nodes and verify that the correct credentials have been created and selected. | Created by: khaisa Studio Category: AI Tags: AI, Content Generation, Google Gemini, LlamaIndex, Automation Need custom workflows? Contact us Connect with the creator: Portfolio • Workflows • LinkedIn • Medium • Threads

LOB Underwriting with AI This template ingests borrower documents from OneDrive, extracts text with OCR, classifies each file (ID, paystub, bank statement, utilities, tax forms, etc.), aggregates everything per borrower, and asks an LLM to produce a clear underwriting summary and decision (plus next steps). Good to know AI and OCR usage consume credits (OpenAI + your OCR provider). Folder lookups by name can be ambiguous—use a fixed folderId in production. Scanned image quality drives OCR accuracy; bad scans yield weak text. This flow handles PII—mask sensitive data in logs and control access. Start small: batch size and pagination keep costs/memory sane. How it works Import & locate docs: Manual trigger kicks off a OneDrive folder search (e.g., “LOBs”) and lists files inside. Per-file loop: Download each file → run OCR → classify the document type using filename + extracted text. Aggregate: Combine per-file results into a borrower payload (make BorrowerName dynamic). LLM analysis: Feed the payload to an AI Agent (OpenAI model) to extract underwriting-relevant facts and produce a decision + next steps. Output: Return a human-readable summary (and optionally structured JSON for systems). How to use Start with the Manual Trigger to validate end-to-end on a tiny test folder. Once stable, swap in a Schedule/Cron or Webhook trigger. Review the generated underwriting summary; handle only flagged exceptions (unknown/unreadable docs, low confidence). Setup steps Connect accounts Add credentials for OneDrive, OCR, and OpenAI. Configure inputs In Search a folder, point to your borrower docs (prefer folderId; otherwise tighten the name query). In Get items in a folder, enable pagination if the folder is large. In Split in Batches, set a conservative batch size to control costs. Wire the file path Download a file must receive the current file’s id from the folder listing. Make sure the OCR node receives binary input (PDFs/images). Classification Update keyword rules to match your region/lenders/utilities/tax forms. Keep a fallback Unknown class and log it for review. Combine Replace the hard-coded BorrowerName with: a Set node field, a form input, or parsing from folder/file naming conventions. AI Agent Set your OpenAI model/credentials. Ask the model to output JSON first (structured fields) and Markdown second (readable summary). Keep temperature low for consistent, audit-friendly results. Optional outputs Persist JSON/Markdown to Notion/Docs/DB or write to storage. Customize if needed Doc types: add/remove categories and keywords without touching core logic. Error handling: add IF paths for empty folders, failed downloads, empty OCR, or Unknown class; retry transient API errors. Privacy: redact IDs/account numbers in logs; restrict execution visibility. Scale: add MIME/size filters, duplicate detection, and multi-borrower folder patterns (parent → subfolders).

This n8n template demonstrates how to automatically extract text content from PDF documents received via WhatsApp messages using OCR. It is designed for use cases where users submit documents through WhatsApp and the document content needs to be digitized for further processing — such as document analysis, AI-powered workflows, compliance checks, or data ingestion. Good to know This workflow processes PDF documents only. OCR is handled using AWS Textract, which supports both scanned and digital PDFs. AWS Textract pricing depends on the number of pages processed. Refer to AWS Textract Pricing for up-to-date costs. An AWS S3 bucket is required as an intermediate storage layer for the PDF files. Processing time may vary depending on PDF size and number of pages. How it works The workflow is triggered when an incoming WhatsApp message containing a PDF document is received. The PDF file is downloaded from WhatsApp’s media endpoint using an HTTP Request node. The downloaded PDF is uploaded to an AWS S3 bucket to make it accessible for OCR processing. AWS Textract is invoked to analyze the PDF stored in S3 and extract all readable text content. The Textract response is parsed and consolidated into a clean, ordered text output representing the PDF’s content. How to use The workflow can be triggered using a webhook connected to WhatsApp Cloud API or any compatible WhatsApp integration. Ensure your AWS credentials have permission to upload to S3 and invoke Textract. Once active, simply send a PDF document via WhatsApp to start the extraction process automatically. Requirements WhatsApp integration (e.g. WhatsApp Cloud API or provider webhook) AWS account with: S3 bucket access Textract permissions n8n instance with HTTP Request and AWS nodes configured Customising this workflow Store extracted text in a database or document store. Pass the extracted content to an AI model for summarization, classification, or validation. Split output by pages or sections. Add file type validation or size limits. Extend the workflow to support additional document formats.

Quick overview This workflow listens for CV uploads in a Slack channel, downloads the attached file, extracts key candidate details using easybits Extractor, and replies in the same thread with a formatted summary plus an interactive action card posted via the Slack Web API. How it works Triggers when a new message is posted in the selected Slack channel. Ignores messages posted by the bot itself and skips any messages that do not include a file attachment. Validates that the uploaded file is a supported type (PDF, PNG, or JPEG) and replies in-thread with an error message if not. Downloads the private Slack file using a bearer token and passes the binary to easybits Extractor. Extracts structured CV fields (name, location, years of experience, skills, recent roles, education, salary expectations, and LinkedIn URL) and formats them into a Slack-ready markdown summary and Block Kit buttons payload. Posts the candidate summary as a threaded reply in Slack and sends a second threaded message with an action card (for example, “Save to Sheet” or “Dismiss”) via Slack’s chat.postMessage API. Setup Create and install a Slack app with Event Subscriptions enabled for channel messages, grant scopes such as chat:write and files:read, install it to your workspace, and invite the bot to the target channel. Add Slack OAuth credentials for the Slack trigger/posting and add a bearer token Header Auth credential (Authorization: Bearer xoxb-...) for downloading files and calling Slack’s chat.postMessage API. Install the community node package @easybits/n8n-nodes-extractor, create an easybits Extractor API credential, and ensure your Extractor is configured to return the CV fields used by the workflow. Set the Slack channel ID in the trigger, replace UYOURBOTID with your bot’s Slack user ID, and activate the workflow then paste the trigger Production URL into your Slack app’s Event Subscriptions Request URL.

Description Automatically extract a structured skill matrix from PDF resumes in a Google Drive folder and store results in Google Sheets. Uses Azure OpenAI (GPT-4o-mini) to analyze predefined tech stacks and filters for relevant proficiency. Fast, consistent insights ready for review. 🔍📊 What This Template Does Fetches all resumes from a designated Google Drive folder (“Resume_store”). 🗂️ Downloads each resume file securely via Google Drive API. ⬇️ Extracts text from PDF files for analysis. 📄➡️📝 Analyzes skills with Azure OpenAI (GPT-4o-mini), rating 1–5 and estimating years. 🤖 Parses and filters to include only skills with proficiency > 2, then updates Google Sheets (“Resume store” → “Sheet2”). ✅ Key Benefits Saves hours on manual resume screening. ⏱️ Produces a consistent, structured skill matrix. 📐 Focuses on intermediate to expert skills for faster shortlisting. 🎯 Centralizes candidate data in Google Sheets for easy sharing. 🗃️ Features Predefined tech stack focus: React, Node.js, Angular, Python, Java, SQL, Docker, Kubernetes, AWS, Azure, GCP, HTML, CSS, JavaScript. 🧰 Proficiency scoring (1–5) and estimated years of experience. 📈 PDF-to-text extraction for robust parsing. 🧾 JSON parsing with error handling for invalid outputs. 🛡️ Manual Trigger to run on demand. ▶️ Requirements n8n instance (cloud or self-hosted). Google Drive access with credentials to the “Resume_store” folder. Google Sheets access to the “Resume store” spreadsheet and “Sheet2” tab. Azure OpenAI with GPT-4o-mini deployed and connected via secure credentials. PDF text extraction enabled within n8n. Target Audience HR and Talent Acquisition teams. 👥 Recruiters and staffing agencies. 🧑‍💼 Operations teams managing hiring pipelines. 🧭 Tech hiring managers seeking consistent skill insights. 💡 Step-by-Step Setup Instructions Place candidate resumes (PDF) into Google Drive → “Resume_store”. In n8n, add Google Drive and Google Sheets credentials and authorize access. In n8n, add Azure OpenAI credentials (GPT-4o-mini deployment). Import the workflow, assign credentials to each node, and confirm folder/sheet names. Run the Manual Trigger to execute the flow and verify data in “Resume store” → “Sheet2”.

What is this? This RAG workflow allows you to build a smart chat assistant that can answer user questions based on any collection of documents you provide. It automatically imports and processes files from Google Drive, stores their content in a searchable vector database, and retrieves the most relevant information to generate accurate, context-driven responses. The workflow manages chat sessions and keeps the document database current, making it adaptable for use cases like customer support, internal knowledge bases, or HR assistant etc. How it works 1. Chat RAG Agent Uses OpenAI for responses, referencing only specific data from the vector store (data that is uploaded on google drive folder). Maintains chat history in Postgres using a session key from the chat input. 2. Data Pipeline (File Ingestion) Monitors Google Drive for new/updated files and automatically updates them in vector store Downloads, extracts, and processes file content (PDFs, Google Docs). Generates embeddings and stores them in the Supabase vector store for retrieval. 3. Vector Store Cleanup Scheduled and manual routines to remove duplicate or outdated entries from the Supabase vector store. Ensures only the latest and unique documents are available for retrieval. 4. File Management Handles folder and file creation, upload, and metadata assignment in Google Drive. Ensures files are organized and linked with their corresponding vector store entries. Getting Started Create and connect all relevant credentials Google Drive Postgres Supabase OpenAI Run the table creation nodes first to set up your database tables in Postgres Upload your documents through Google Drive (or swap out for a different file storage solution) The agent will process them automatically (chunking text, storing tabular data in Postgres) Start asking questions that leverage the agent's multiple reasoning approaches Customization (optional) This template provides a solid foundation that you can extend by: Tuning the system prompt for your specific use case Adding document metadata like summaries Implementing more advanced RAG techniques Optimizing for larger knowledge bases Note, if you're using a different nodes eg. file storage, vector store etc the integration may vary a little Prerequisites Google account (google drive) Supabase account OpenAI APIs Postgres account

Youtube Video: https://youtu.be/dEtV7OYuMFQ?si=fOAlZWz4aDuFFovH Workflow Pre-requisites Step 1: Supabase Setup First, replace the keys in the "Save the embedding in DB" & "Search Embeddings" nodes with your new Supabase keys. After that, run the following code snippets in your Supabase SQL editor: Create the table to store chunks and embeddings: CREATE TABLE public."RAG" ( id bigserial PRIMARY KEY, chunk text NULL, embeddings vector(1024) NULL ) TABLESPACE pg_default; Create a function to match embeddings: DROP FUNCTION IF EXISTS public.matchembeddings1(integer, vector); CREATE OR REPLACE FUNCTION public.matchembeddings1( match_count integer, query_embedding vector ) RETURNS TABLE ( chunk text, similarity float ) LANGUAGE plpgsql AS $$ BEGIN RETURN QUERY SELECT R.chunk, 1 - (R.embeddings <=> query_embedding) AS similarity FROM public."RAG" AS R ORDER BY R.embeddings <=> query_embedding LIMIT match_count; END; $$; Step 2: Create Jotform with these fields Your full name email address Upload PDF Document [field where you upload the knowledgebase in PDF] Step 3: Get Together AI API Key Get a Together AI API key and paste it into the "Embedding Uploaded document" node and the "Embed User Message" node. Here is a detailed, node-by-node explanation of the n8n workflow, which is divided into two main parts. Part 1: Ingesting Knowledge from a PDF This first sequence of nodes runs when you submit a PDF through a Jotform. Its purpose is to read the document, process its content, and save it in a specialized database for the AI to use later. JotForm Trigger Type: Trigger What it does: This node starts the entire workflow. It's configured to listen for new submissions on a specific Jotform. When someone uploads a file and submits the form, this node activates and passes the submission data to the next step. Grab New knowledgebase Type: HTTP Request What it does: The initial trigger from Jotform only contains basic information. This node makes a follow-up call to the Jotform API using the submissionID to get the complete details of that submission, including the specific link to the uploaded file. Grab the uploaded knowledgebase file link Type: HTTP Request What it does: Using the file link obtained from the previous node, this step downloads the actual PDF file. It's set to receive the response as a file, not as text. Extract Text from PDF File Type: Extract From File What it does: This utility node takes the binary PDF file downloaded in the previous step and extracts all the readable text content from it. The output is a single block of plain text. Splitting into Chunks Type: Code What it does: This node runs a small JavaScript snippet. It takes the large block of text from the PDF and chops it into smaller, more manageable pieces, or "chunks," each of a predefined length. This is critical because AI models work more effectively with smaller, focused pieces of text. Embedding Uploaded document Type: HTTP Request What it does: This is a key AI step. It sends each individual text chunk to an embeddings API. A specified AI model converts the semantic meaning of the chunk into a numerical list called an embedding or vector. This vector is like a mathematical fingerprint of the text's meaning. Save the embedding in DB Type: Supabase What it does: This node connects to your Supabase database. For every chunk, it creates a new row in a specified table and stores two important pieces of information: the original text chunk and its corresponding numerical embedding (its "fingerprint") from the previous step. Part 2: Answering Questions via Chat This second sequence starts when a user sends a message. It uses the knowledge stored in the database to find relevant information and generate an intelligent answer. When chat message received Type: Chat Trigger What it does: This node starts the second part of the workflow. It listens for any incoming message from a user in a connected chat application. Embend User Message Type: HTTP Request What it does: This node takes the user's question and sends it to the exact same embeddings API and model used in Part 1. This converts the question's meaning into the same kind of numerical vector or "fingerprint." Search Embeddings Type: HTTP Request What it does: This is the "retrieval" step. It calls a custom database function in Supabase. It sends the question's embedding to this function and asks it to search the knowledge base table to find a specified number of top text chunks whose embeddings are mathematically most similar to the question's embedding. Aggregate Type: Aggregate What it does: The search from the previous step returns multiple separate items. This utility node simply bundles those items into a single, combined piece of data. This makes it easier to feed all the context into the final AI model at once. AI Agent & Google Gemini Chat Model Type: LangChain Agent & AI Model What it does: This is the "generation" step where the final answer is created. The AI Agent node is given a detailed set of instructions (a prompt). The prompt tells the Google Gemini Chat Model to act as a professional support agent. Crucially, it provides the AI with the user's original question and the aggregated text chunks from the Aggregate node as its only source of truth. It then instructs the AI to formulate an answer based only on that provided context, format it for a specific chat style, and to say "I don't know" if the answer cannot be found in the chunks. This prevents the AI from making things up.

🧠 Export Tawk.to Help Center Articles to Google Drive as Markdown Files Transform the way you manage your knowledge base with this fully automated N8N workflow! This automation connects directly to your Tawk.to Help Center, reads all published categories and articles, converts them to Markdown (.md) format, and uploads each file to Google Drive 🔹 Key Benefits 🚀 Complete Extraction Automatically captures all categories and articles from your Tawk.to Help Center, even without direct API integration. 🧩 Automatic Conversion Transforms HTML content into clean Markdown files — perfect for editing, version control, or migration to another CMS. ☁️ Native Google Drive Integration Saves each article with a structured filename, avoids duplicates, and organizes them by category. 🔁 Fully Customizable Easily adapt the workflow to export to Notion, GitHub, Dropbox, or any other platform supported by N8N. 💡 Ideal Use Cases Migrating your Tawk.to Help Center Creating automated content backups Integrating documentation across multiple systems ⚙️ Prerequisites Before running this workflow, make sure you have: An active Tawk.to account with access to your Help Center. A Google Drive account (personal or workspace). Access to N8N (self-hosted or cloud). 🧰 Setup Instructions Import the Workflow Download the JSON file from the provided link or your N8N community instance. In N8N, click Import Workflow and upload the file. Authenticate Google Drive Open the Google Drive node. Click Connect, choose your Google account, and allow access. Configure Output Folder Choose or create a target folder in your Google Drive where articles will be saved. Run the Workflow Click Execute Workflow. The automation will read all Help Center articles, convert them to Markdown, and save them to your Drive.

Quick Overview This workflow reindexes Markdown documentation into a Supabase Postgres pgvector table by fetching source docs from an HTTP API, chunking and embedding them via a Supabase Edge Function, upserting the vectors, and deleting stale chunks on a daily schedule or on-demand webhook. How it works Runs daily on a schedule or triggers when a POST request hits the webhook endpoint. Fetches Markdown sources (for example FAQ and blog posts) from a configured HTTP API endpoint. Strips frontmatter, splits content on H2 sections, chunks long sections with overlap, and batches chunks for embedding. For each batch, calls a Supabase Edge Function to generate embeddings for the chunk texts. Upserts each chunk’s source, index, content, and pgvector embedding into a Supabase Postgres rag_chunks table using conflict updates. After processing batches, deletes rows in rag_chunks whose updated_at timestamp is older than the current run to remove stale chunks. Setup Add an HTTP Header Auth credential for the sources API request and for calling the Supabase Edge Function. Add Supabase Postgres credentials with access to the database where the vector table lives. Create a public.rag_chunks table with a pgvector embedding column (matching your model’s dimensions) and a primary key on (source, chunk_idx). Update the sources API URL, the Supabase Edge Function /embed URL, and (optionally) the daily schedule time and batch/chunk sizing constants to match your environment.

🧩 Template Description IP Enrichment & Country Attribution is a lightweight cybersecurity automation that enriches IP addresses with geographic and network intelligence. It validates incoming IPs, filters out private or invalid addresses, and enriches public IPs using an open-source IP enrichment service. 🔄 How It Works Receives an IP address via webhook (API or Slack). Validates the IP format and rejects invalid input. Checks for private or internal IP ranges. Ignores private IPs with a clear response. Enriches public IPs using an open-source IP intelligence service. Normalizes country, ISP, and ASN data and applies a severity label. Slack notifications are sent for enriched public IPs. Returns a structured JSON response. ⚙️ Setup Steps Import & Activate Workflow Import the JSON template into n8n Actvate the workflow Set Up Webhook Copy the webhook URL Send a POST request with the IP in the body, e.g.: { "text" : "8.8.8.8" } Using curl: `curl -X POST https://YOUR_N8N_WEBHOOK_URL \ -H "Content-Type: application/json" \ -d '{"text":"8.8.8.8"}'` Configure Slack (Slack Alert) Create or select Slack credentials in n8n Make sure the bot is in your target channel Update the Slack node with correct channel. Slack Slash Command Setup (Optional) Enable Slash Commands and create new command (for example /ip-enrich). Set the Request URL to your n8n webhook endpoint. Choose POST as the request method. Install the app to your workspace. Usage example: /ip-enrich 8.8.8.8 🎛️ Customization Options Enrichment source: Replace or extend the IP intelligence API with additional providers (for example reputation or abuse scoring). Slack formatting: Customize the Slack message text, emojis, or use threads for better alert grouping. Input sources: Reuse the webhook for other integrations such as SIEM alerts or security tools.

Overview This template automates invoice processing for teams that currently copy data from PDFs into spreadsheets by hand. It is ideal for small businesses, back-office teams, accounting, and operations who want to reduce manual entry, avoid human error, and never miss a payment deadline. The workflow watches a structured Google Drive folder, performs OCR, converts the text into clean structured JSON with an LLM, and appends one row per invoice into Google Sheets. It preserves a link back to the original file for easy review and audit. Designed for small businesses and back-office teams.** Eliminates manual typing** and reduces errors. Prevents missed due dates** by centralizing data. Works with monthly subfolders like "2025年10月分" (meaning "October 2025"). Keeps a Google Drive link to each invoice file. How It Works The workflow runs on a schedule, scans your Drive folder hierarchy, OCRs the PDFs/images, cleans the text, extracts key fields with an LLM, and appends a row to Google Sheets per invoice. Each step is modular so you can swap services or tweak prompts without breaking the flow. Scheduled trigger** runs on a recurring cadence. Scan the parent folder** in Google Drive. Auto-detect the current-month folder** (e.g., a folder named "2025年10月分" meaning "October 2025"). Download PDFs/images** from the detected folder. Extract text** using the OCR.Space API. Clean noise** and normalize with a Code node. Use an OpenAI model** to extract invoice_date, due_date, client_name, line items, totals, and bank info to JSON. Append one row per invoice** to Google Sheets. Requirements Before you start, make sure you have access to the required services and that your Drive is organized into monthly subfolders so the workflow can find the right files. n8n account.** Google Drive access.** Google Sheets access.** OCR.Space API key** (set as <your_ocr_api_key>). OpenAI / LLM API credential** (e.g., <your_openai_credential_name>). Invoice PDFs organized by month** on Google Drive (e.g., folders like "2025年10月分"). Setup Instructions Import the workflow, replace placeholder credentials and IDs with your own, and enable the schedule. You can also run it manually for testing. The parent-folder query and sheet ID must reflect your environment. Replace <your_google_drive_credential_id> and <your_google_drive_credential_name> with your Google Drive Credential. Adjust the parent folder search query to your invoice repository name. Replace the Sheets document ID <your_google_sheet_id> with your spreadsheet ID. Ensure your OpenAI credential <your_openai_credential_name> is selected. Set your OCR.Space key as <your_ocr_api_key>. Enable the Schedule Trigger** after testing. Customization This workflow is easily extensible. You can adapt folder naming rules, enrich the spreadsheet schema, and expand the AI prompt to extract custom fields specific to your company. It also works beyond invoices, covering receipts, quotes, or purchase orders with minor changes. Change the monthly folder naming rule such as {{$now.setZone("Asia/Tokyo").format("yyyy年MM月")}}分 to match your convention. Modify or extend Google Sheets column mappings as needed. Tune the AI prompt to extract project codes, owner names, or custom fields. Repurpose for receipts, quotes, or purchase orders. Localize date formats and tax calculation rules to your standards.