Webhook and Google Drive integration

🤖 AI-Powered RAG Chatbot with Google Drive Integration This workflow creates a powerful RAG (Retrieval-Augmented Generation) chatbot that can process, store, and interact with documents from Google Drive using Qdrant vector storage and Google's Gemini AI. How It Works Document Processing & Storage 📚 Retrieves documents from a specified Google Drive folder Processes and splits documents into manageable chunks Extracts metadata using AI for enhanced search capabilities Stores document vectors in Qdrant for efficient retrieval Intelligent Chat Interface 💬 Provides a conversational interface powered by Google Gemini Uses RAG to retrieve relevant context from stored documents Maintains chat history in Google Docs for reference Delivers accurate, context-aware responses Vector Store Management 🗄️ Features secure delete operations with human verification Includes Telegram notifications for important operations Maintains data integrity with proper version control Supports batch processing of documents Setup Steps Configure API Credentials: Set up Google Drive & Docs access Configure Gemini AI API Set up Qdrant vector store connection Add Telegram bot for notifications Add OpenAI Api Key to the 'Delete Qdrant Points by File ID' node Configure Document Sources: Set Google Drive folder ID Define Qdrant collection name Set up document processing parameters Test and Deploy: Verify document processing Test chat functionality Confirm vector store operations Check notification system This workflow is ideal for organizations needing to create intelligent chatbots that can access and understand large document repositories while maintaining context and providing accurate responses through RAG technology.

AI-Powered Tech Radar Advisor This project is built on top of the famous open source ThoughtWorks Tech Radar. You can use this template to build your own AI-Powered Tech Radar Advisor for your company or group of companies. Target Audience This template is perfect for: Tech Audit & Governance Leaders:** Those seeking to build a tech landscape AI platform portal. Tech Leaders & Architects:** Those aiming to provide modern AI platforms that help others understand the rationale behind strategic technology adoption. Product Managers:** Professionals looking to align product innovation with the company's current tech trends. IT & Engineering Teams:** Teams that need to aggregate, analyze, and visualize technology data from multiple sources efficiently. Digital Transformation Experts:** Innovators aiming to leverage AI for actionable insights and strategic recommendations. Data Analysts & Scientists:** Individuals who want to combine structured SQL analysis with advanced semantic search using vector databases. Developers:** Those interested in integrating RAG chatbot functionality with conversation storage. Description Tech Constellation is an AI-powered Tech Radar solution designed to help organizations visualize and steer their technology adoption strategy. It seamlessly ingests data from a Tech Radar Google Sheet—converting it into both a MySQL database and a vector index—to consolidate your tech landscape in one place. The platform integrates an interactive AI chat interface powered by four specialized agents: AI Agent Router:** Analyzes and routes user queries to the most suitable processing agent. SQL Agent:** Executes precise SQL queries on structured data. RAG Agent:** Leverages semantic, vector-based search for in-depth insights. Output Guardrail Agent:** Validates responses to ensure they remain on-topic and accurate. This powerful template is perfect for technology leaders, product managers, engineering teams, and digital transformation experts looking to make data-driven decisions aligned with strategic initiatives across groups of parent-child companies. Features Data Ingestion A Google Sheet containing tech radar data is used as the primary source. The data is ingested and converted into a MySQL database. Simultaneously, the data is indexed into a vector database for semantic (vector-based) search. Interactive AI Chat Chat Integration:** An AI-powered chat interface allows users to ask questions about the tech radar. Customizable AI Agents:** AI Agent Router: Determines the query type and routes it to the appropriate agent. SQL Agent: Processes queries using SQL on structured data. RAG Agent: Performs vector-based searches on document-like data. Output Guardrail Agent: Validates queries and ensures that the responses remain on-topic and accurate. Usage Examples Tell me, is TechnologyABC adopted or on hold, and why? List all the tools that are considered part of the strategic direction for company3 but are not adopted. Project Links & Additional Details GitHub Repository (Frontend Interface Source Code):** github.com/dragonjump/techconstellation Try It:** https://scaler.my

Overview This workflow automatically saves files received via LINE Messaging API into Google Drive and logs the file details into a Google Sheet. It checks the file type against allowed types, organizes files into date-based folders and (optionally) file type–specific subfolders, and sends a reply message back to the LINE user with the file URL or an error message if the file type is not permitted. Who is this for? Developers & IT Administrators: Looking to integrate LINE with Google Drive and Sheets for automated file management. Businesses & Marketing Teams: That want to automatically archive media files and documents received from users via LINE. Anyone Interested in No-Code Automation: Users who want to leverage n8n’s capabilities without heavy coding. What Problem Does This Workflow Solve? Automated File Organization: Files received from LINE are automatically checked for allowed file types, then stored in a structured folder hierarchy in Google Drive (by date and/or file type). Data Logging: Each file upload is recorded in a Google Sheet, providing an audit trail with file names, upload dates, URLs, and types. Instant Feedback: Users receive an immediate reply via LINE confirming the file upload, or an error message if the file type is not allowed. What This Workflow Does Receives Incoming Requests: A webhook node ("LINE Webhook Listener") listens for POST requests from LINE, capturing file upload events and associated metadata. Configuration Loading: A Google Sheets node ("Get Config") reads configuration data (e.g., parent folder ID, allowed file types, folder organization settings, and credentials) from a pre-defined sheet. Data Merging & Processing: The "Merge Event and Config Data" and "Process Event and Config Data" nodes merge and structure the event data with configuration settings. A "Determine Folder Info" node calculates folder names based on the configuration. If Store by Date is enabled, it uses the current date (or a specified date) as the folder name. If Store by File Type is also enabled, it uses the file’s type (e.g., image) for a subfolder. Folder Search & Creation: The workflow searches for an existing date folder ("Search Date Folder"). If the date folder is not found, an IF node ("Check Existing Date Folder") routes to a "Create Date Folder" node. Similarly, for file type organization, the workflow uses a "Search FileType Folder" node (with appropriate conditions) to look for a subfolder, or creates it if not found. The "Set Date Folder ID" and "Set Image Folder ID" nodes capture and merge the resulting folder IDs. Finally, the "Config final ParentId" node sets the final target folder ID based on the configuration conditions: Store by Date: TRUE, Store by File Type: TRUE: Use the file type folder (inside the date folder). Store by Date: TRUE, Store by File Type: FALSE: Use the date folder. Store by Date: FALSE, Store by File Type: TRUE: Use the file type folder. Store by Date: FALSE, Store by File Type: FALSE: Use the Parent Folder ID from the configuration. File Retrieval and Validation: A HTTP Request node ("Get File Binary Content") fetches the file’s binary data from the LINE API. A Function node ("Validate File Type") checks if the file’s MIME type is included in the allowed list (e.g., "audio|image|video"). If not, it throws an error that is captured for the reply. File Upload and Logging: The "Upload File to Google Drive" node uploads the validated binary file to the final target folder. After a successful upload, the "Log File Details to Google Sheet" node logs details such as file name, upload date, Google Drive URL, and file type into a designated Google Sheet. User Feedback: The "Check Reply Enabled Flag" node checks if the reply feature is enabled. Finally, the "Send LINE Reply Message" node sends a reply message back to the LINE user with either the file URL (if the upload was successful) or an error message (if the file type was not allowed). Setup Instructions Google Sheets Setup: Create a Google Sheet with two sheets:** config: Include columns for Parent Folder Path, Parent Folder ID, Store by Date (boolean), Store by File Type (boolean), Allow File Types (e.g., “audio|image|video”), CurrentDate, Reply Enabled, and CHANNEL ACCESS TOKEN. fileList: Create headers for File Name, Date Uploaded, Google Drive URL, and File Type. For an example of the required format, check this Google Sheets template: Google Sheet Template Google Drive Credentials: Set up and authorize your Google Drive credentials in n8n. LINE Messaging API: Configure your LINE Developer Console webhook to point to the n8n Webhook URL ("Line Chat Bot" node). Ensure you have the proper Channel Access Token stored in your Google Sheet. n8n Workflow Import: Import the provided JSON file into your n8n instance. Verify node connections and update any credential references as needed. Test the Workflow: Send a test message via LINE to confirm that files are properly validated, uploaded, logged, and that reply messages are sent. How to Customize This Workflow Allowed File Types: Adjust the "Validate File Type" field in your config sheet to control which file types are accepted. Folder Structure: Modify the logic in the "Determine Folder Info" and subsequent folder nodes to change how folders are structured (e.g., use different date formats or add additional categorization). Logging: Update the "Log File Details to Google Sheet" node if you wish to log additional file metadata. Reply Messages: Customize the reply text in the "Send LINE Reply Message" node to include more detailed information or instructions.

Description This workflow automates document processing using LlamaParse to extract and analyze text from various file formats. It intelligently processes documents, extracts structured data, and delivers actionable insights through multiple channels. How It Works Document Ingestion & Processing 📄 Monitors Gmail for incoming attachments or accepts documents via webhook Validates file formats against supported LlamaParse extensions Uploads documents to LlamaParse for advanced text extraction Stores original documents in Google Drive for reference Intelligent Document Analysis 🧠 Automatically classifies document types (invoices, reports, etc.) Extracts structured data using customized AI prompts Generates comprehensive document summaries with key insights Converts unstructured text into organized JSON data Invoice Processing Automation 💼 Extracts critical invoice details (dates, amounts, line items) Organizes financial data into structured formats Calculates tax breakdowns, subtotals, and payment information Maintains detailed records for accounting purposes Multi-Channel Delivery 📱 Saves extracted data to Google Sheets for tracking and analysis Sends concise summaries via Telegram for immediate review Creates searchable document archives in Google Drive Updates spreadsheets with structured financial information Setup Steps Configure API Credentials 🔑 Set up LlamaParse API connection Configure Gmail OAuth for email monitoring Set up Google Drive and Sheets integrations Add Telegram bot credentials for notifications Customize AI Processing ⚙️ Adjust document classification parameters Modify extraction templates for specific document types Fine-tune summary generation prompts Customize invoice data extraction schema Test and Deploy 🚀 Test with sample documents of various formats Verify data extraction accuracy Confirm notification delivery Monitor processing pipeline performance

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 Interaction: For ongoing conversations, the Window Buffer Memory ensures context retention by maintaining a history of interactions, enhancing the conversational flow. Set up steps: To configure this workflow effectively, follow these detailed setup instructions: ElevenLabs Agent Creation: Begin by creating an agent on ElevenLabs (e.g., named 'test_n8n'). Customize the first message and define the system prompt specific to your use case, such as portraying a character like a waiter at "Pizzeria da Michele". Add a Webhook tool labeled 'test_chatbot_elevenlabs' configured to receive questions via POST requests. Qdrant Collection Initialization: Utilize the HTTP Request nodes ('Create collection' and 'Refresh collection') to initialize and clear existing collections in Qdrant. Ensure you update placeholders QDRANTURL and COLLECTION accordingly. Document Vectorization: Use Google Drive integration to fetch documents from a designated folder. These documents are then downloaded and processed for embedding. Employ the Embeddings OpenAI node to generate embeddings for the downloaded files before storing them into Qdrant via the Qdrant Vector Store node. AI Agent Configuration: Define the system prompt for the AI Agent node which guides its behavior and responses based on the nature of queries expected (e.g., product details, troubleshooting tips). Link necessary models and tools including OpenAI language models and memory buffers to enhance interaction quality. Testing Workflow: Execute test runs of the entire workflow by clicking 'Test workflow' in n8n alongside initiating tests on the ElevenLabs side to confirm all components interact seamlessly. Monitor logs and outputs closely during testing phases to ensure accurate data flow between systems. Integration with Website: Finally, integrate the chatbot widget onto your business website replacing placeholder AGENT_ID with the actual identifier created earlier on ElevenLabs. By adhering to these comprehensive guidelines, users can successfully deploy a sophisticated voice-driven chatbot capable of delivering precise answers utilizing advanced retrieval-augmented generation techniques powered by OpenAI and ElevenLabs technologies.

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-mini) and sends it back to the user via WhatsApp. Set up steps: Create Qdrant Collection: Update the QDRANTURL and COLLECTION variables in the workflow. Use the Create collection HTTP request node to initialize the collection in Qdrant. Vectorize Documents: Configure the Get folder and Download Files nodes to fetch documents from a specified Google Drive folder. Use the Embeddings OpenAI node to generate embeddings for the downloaded files. Store the vectorized documents in Qdrant using the Qdrant Vector Store node. Configure Webhooks: Ensure both Verify and Respond webhooks have the same URL. Set the Verify webhook to use the GET HTTP method and the Respond webhook to use the POST HTTP method. Set Up AI Agent: Define the system prompt for the AI Agent, specifying guidelines for product information, technical support, customer service, and knowledge base usage. Link the AI Agent to the OpenAI chat model and configure any additional tools as needed. Test Workflow: Trigger the workflow manually using the When clicking ‘Test workflow’ node to ensure all components are functioning correctly. Monitor the flow of data through the nodes and verify that responses are being generated and sent accurately. By following these steps, the workflow will be fully operational, enabling a robust AI-powered chatbot capable of handling customer inquiries via WhatsApp.