Workflow Templates

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 server. Use the Prerequisite manual steps to get started! 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: "Can you help me list the available companies in the collection?" "What do customers say about product deliveries from company X?" "What do customers of company X and company Y say about product ease of use?" Requirements Qdrant for vector store. This can be an a cloud-hosted instance or one you can self-host internally. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow Depending on what queries you'll receive, adjust the tool inputs to make it easier for the agent to set the right parameters. Not interested in Reviews? The techniques shared in this template can be used for other types of collections. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

This workflow sends a message to a Discord channel when a new row is added or a row is updated in a Google Sheet. The message will send all data rows in the Google Sheet. Prerequisites Discord account and Discord credentials. Google account and Google credentials. How it works Using a code node, we can use the obtained Google Sheet data to create a custom message that will be sent to Discord. The message will be sent to the Discord channel specified in the Discord node. Setup This workflow requires that you set up a Discord webhook and have an existing Google Sheet with data. See how to set up a Discord webhook here.

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 to sustainability 🗂️ Filters out irrelevant items 📊 Saves the results in a Google Sheet ✅ Creates a Google Task for each relevant file to review the legislation How it works Trigger manually or on schedule Scrape HTML blocks for scheduled debates Parse each procedure to extract Title, Committee, Rapporteur, PDF link Call GPT-4-turbo to check if the topic matches sustainability criteria Filter responses based on “yes” or “no” Store valid items into Google Sheets Generate tasks in Google Tasks The AI only flags procedures that directly impact the environment, circular economy, or pollution control. What do I need to get started? You’ll need: A Google Sheet connected to your n8n instance An OpenAI account with GPT-4 access A Google Task List Follow the Guide! Follow the sticky notes in the workflow or check my tutorial to configure each node and start using AI to monitor sustainability regulations in Europe. 🎥 Watch My Tutorial Notes AI filters are strict — you can customise the system prompt to match your needs This is ideal for tracking legislative risk for climate regulations This workflow was built using n8n version 1.85.4 Submitted: April 21, 2025

Who this is for? Indeed Data Scraper & Summarization with Airtable, Bright Data and Google Gemini is an automated workflow that extracts company profile information from Indeed using Bright Data Web Unlocker, transforms the data using Google Gemini's LLM, and forward the transformed response with the summary to a specified webhook for downstream use. This workflow is tailored for: Recruiters and HR teams who want quick summaries of companies listed on Indeed. Market researchers and analysts needing structured insights into businesses. Founders, investors, and consultants scouting potential competitors, partners, or clients. No-code enthusiasts looking to automate data extraction and enrichment pipelines without manual scraping or parsing. What problem is this workflow solving? Manually gathering structured information about companies on Indeed is time-consuming and inconsistent. Pages vary in structure, and extracting clean, digestible summaries can require technical scraping expertise. This workflow automates: Extracting company data from Indeed reliably using Bright Data Web Unlocker. Cleaning and summarizing the extracted content using Google Gemini LLM. Storing structured insights directly into Airtable for easy access and further workflows. Eliminates manual research, saves hours, and produces AI-enhanced, easily searchable records. What this workflow does Triggers on-demand. Pulls company page URLs from Airtable. Scrapes content from each Indeed company profile using Bright Data Web Unlocker. Sends the raw HTML to Google Gemini for extraction and summarization. Sends the summarized data to other platforms via a Webhook notification mechanism. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials for Bright Data. The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). In n8n, configure the Airtable Personal Access Token account under Credentials. Update the Webhook Notifier with the Webhook endpoint of your choice. How to customize this workflow to your needs This workflow is built to be flexible - whether you're a company or a market researcher, entrepreneur, or data analyst. Here's how you can adapt it to fit your specific use case: Extend the scraper**: Modify Bright Data targets to pull job listings, salaries, or employee reviews via the Airtable data source. Customize the summary prompt**: Ask Gemini to extract different attributes hiring trends, practices etc. Routing the output to different destinations**: Send summaries or transformed response to Google Sheets, Airtable, or CRMs like HubSpot or Salesforce etc.

Disclaimer This template is only available on n8n self-hosted as it's making use of the community node for MCP Client. Who this is for? The Extract, Transform LinkedIn Data with Bright Data MCP Server & Google Gemini workflow is an automated solution that scrapes LinkedIn content via Bright Data MCP Server then transforms the response using a Gemini LLM. The final output is sent via webhook notification and also persisted on disk. This workflow is tailored for:​ Data Analysts : Who require structured LinkedIn datasets for analytics and reporting. Marketing and Sales Teams : Looking to enrich lead databases, track company updates, and identify market trends. Recruiters and Talent Acquisition Specialists : Who want to automate candidate sourcing and company research. AI Developers : Integrating real-time professional data into intelligent applications. Business Intelligence Teams : Needing current and comprehensive LinkedIn data to drive strategic decisions. What problem is this workflow solving? Gathering structured and meaningful information from the web is traditionally slow, manual, and error-prone. This workflow solves: Reliable web scraping using Bright Data MCP Server LinkedIn tools. LinkedIn person and company web scrapping with AI Agents setup with the Bright Data MCP Server tools. Data extraction and transformation with Google Gemini LLM. Persists the LinkedIn person and company info to disk. Performs a Webhook notification with the LinkedIn person and company info. What this workflow does? This n8n workflow performs the following steps: Trigger: Start manually. Input URL(s): Specify the LinkedIn person and company URL. Web Scraping (Bright Data): Use Bright Data's MCP Server, LinkedIn tools for the person and company data extract. Data Transformation & Aggregation: Uses the Google LLM for handling the data transformation. Store / Output: Save results into disk and also performs a Webhook notification. Pre-conditions Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below. You need to have the Google Gemini API Key. Visit Google AI Studio You need to install the Bright Data MCP Server @brightdata/mcp You need to install the n8n-nodes-mcp Setup Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine. Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below. Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token>. Update the LinkedIn URL person and company workflow. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. Update the file name and path to persist on disk. How to customize this workflow to your needs Different Inputs: Instead of static URLs, accept URLs dynamically via webhook or form submissions. Data Extraction: Modify the LinkedIn Data Extractor node with the suitable prompt to format the data as you wish. Outputs: Update the Webhook endpoints to send the response to Slack channels, Airtable, Notion, CRM systems, etc.

Disclaimer This template is only available on n8n self-hosted as it's making use of the community node for MCP Client. Who this is for? The Scrape Web Data with Bright Data and MCP Automated AI Agent workflow is built for professionals who need to automate large-scale, intelligent data extraction by utilizing the Bright Data MCP Server and Google Gemini. This solution is ideal for: Data Analysts - Who require structured, enriched datasets for analysis and reporting. Marketing Researchers - Seeking fresh market intelligence from dynamic web sources. Product Managers - Who want competitive product and feature insights from various websites. AI Developers - Aiming to feed web data into downstream machine learning models. Growth Hackers - Looking for high-quality data to fuel campaigns, research, or strategic targeting. What problem is this workflow solving? Manually scraping websites, cleaning raw HTML data, and generating useful insights from it can be slow, error-prone, and non-scalable. This workflow solves these problems by: Automating complex web data extraction through Bright Data’s MCP Server. Reducing the human effort needed for cleaning, parsing, and analyzing unstructured web content. Allowing seamless integration into further automation processes. What this workflow does? This n8n workflow performs the following steps: Trigger: Start manually. Input URL(s): Specify the URL to perform the web scrapping. Web Scraping (Bright Data): Use Bright Data’s MCP Server tools to accomplish the web data scrapping with markdown and html format. Store / Output: Save results into disk and also performs a Webhook notification. Setup Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine. Sign up at Bright Data. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below. Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token>. Update the LinkedIn URL person and company workflow. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. Update the file name and path to persist on disk. How to customize this workflow to your needs Different Inputs: Instead of static URLs, accept URLs dynamically via webhook or form submissions. Outputs: Update the Webhook endpoints to send the response to Slack channels, Airtable, Notion, CRM systems, etc.

Disclaimer This template is only available on n8n self-hosted as it's making use of the community node for MCP Client. Who this is for? The Chat Conversations with Bright Data MCP Search Engines & Google Gemini workflow is designed for users who need real-time, AI-enhanced conversations powered by live search engine results. This workflow is tailored for:​ Data Analysts - Who want live, search-based data fused with AI reasoning. Marketing Researchers - Seeking up-to-the-minute market or competitor insights via conversational AI. Product Managers - Exploring user needs, market trends, and competitor analysis in real time. AI Developers - Building dynamic applications that combine live search data with intelligent conversation agents. Growth Hackers - Who need fast, conversational research tools for campaign ideation, outreach, or content creation. What problem is this workflow solving? Traditional chatbots and AI systems often rely on static, outdated data. This workflow enables AI agents to fetch live search engine data and converse intelligently about it, making interactions dynamic, accurate, and highly contextual. This workflow solves the major gaps of: Outdated Knowledge: Regular chatbots lack up-to-date information from live web searches. Manual Search Fatigue: Manually searching for information and interpreting it is time-consuming. Context Bridging: Connecting search results into meaningful, conversational replies requires human-level reasoning. What this workflow does? Accepts a user's conversational query input. Triggers a search request to Bright Data’s MCP Search Engines API (Google, Bing, etc.) based on the query. Waits for the search task to complete. Retrieves real-time search results. Feeds the search results and original question into Google Gemini. Generates a human-like, contextually accurate AI response combining live information and conversational flow. Outputs the response back into a chat app. Pre-conditions Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below. You need to have the Google Gemini API Key. Visit Google AI Studio You need to install the Bright Data MCP Server @brightdata/mcp You need to install the n8n-nodes-mcp Setup Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine. Also, do "Account Setup" as mentioned in the @brightdata/mcp URL. Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below. Make sure to copy the Bright Data Web Unlocker API Token within the Environments textbox above as API_TOKEN=<your-token>. Update the HTTP Request for Webhook Notification node for sending the Webhook notification for chat responses. How to customize this workflow to your needs Change Search Engine: Add or Remove the Search Engine MCP tools based upon the Bright Data MCP Server updates. Expand Outputs: Send AI chat responses to Slack, Discord, custom chat UIs, WhatsApp, or CRM systems. Store conversation logs in a database (PostgreSQL, MongoDB, etc.) for future audits or training.

Who this is for? The Structured Data Extract & Data Mining workflow is crafted for researchers, content analysts, SEO strategists, and AI developers who need to transform semi-structured web data (like markdown content or scraped HTML) into actionable structured datasets. It is ideal for: Content Analysts** - Organizing and mining large volumes of markdown or HTML content. SEO & Trend Researchers** - Exploring topics by location and category. AI Engineers & NLP Developers** - Looking to automate insight extraction from unstructured inputs. Growth Marketers** - Tracking topic-level trends for strategic campaigns. Automation Specialists** - Streamlining workflows from scrape to storage. What problem is this workflow solving? Extracting insights from markdown or HTML documents typically requires manual review, formatting, and parsing. This becomes unscalable when dealing with large datasets or when real-time response is needed. Additionally, trend and topic extraction usually involves external tools, custom scripts, and inconsistent formatting. This workflow solves: Automatic text extraction from markdown or structured content. Location and category-based trend mining with semantic grouping. AI-driven topic extraction and summarization Real-time notification via webhook with rich structured payloads. Persistent storage of mined data to disk for audits or further processing. What this workflow does Receives input: Sets the URL for the data extraction and analysis. Uses Bright Data's Web Unlocker to extract content from relevant sites. A Markdown/Text Extractor node parses the content into clean plaintext The cleaned data is passed to Google Gemini to: Identify trends by location and category Extract key topics and themes Format the response into structured JSON The structured insights are sent via Webhook Notification to external systems (e.g., Slack, Web apps, Zapier) The final output is saved to disk Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. A Google Gemini API key (or access through Vertex AI or proxy). Update the Set URL and Bright Data Zone for setting the brand content URL and the Bright Data Zone name. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. How to customize this workflow to your needs Update Source** : Update the workflow input to read from Google Sheet or Airbase for dynamically tracking multiple brands or topics. Gemini Prompt Customization** : Extract trends within a custom category (e.g., E-commerce design patterns in the US) Output topics with popularity metrics Structure the output as per your database schema (e.g., [{ topic, trend_score, location }]) Webhook Output** : Send notifications to - Slack – with AI summaries in rich blocks Internal APIs – for use in dashboards Zapier/Make – for multi-step automation Persistence** Save output to: Remote FTP or SFTP storage Amazon S3, Google Cloud Storage etc.

n8n Workflow: Sync Workflows with GitLab How It Works This workflow ensures that your self-hosted n8n workflows are version-controlled in a GitLab repository. It compares each current workflow from n8n with its stored counterpart in GitLab. If any differences are detected, the GitLab file is updated with the latest version. Core Logic: Retrieve Workflows – Fetch all workflows from the n8n REST API. Compare with GitLab – For each workflow, fetch the corresponding file from GitLab and compare the JSON. Update if Changed – If differences exist, commit the updated workflow to GitLab using its API. Setup Before using the workflow, ensure the following: Prerequisites: n8n**: Self-hosted instance with access to the /rest/workflows API. GitLab**: A repository where workflows will be stored, and a Personal Access Token (PAT) with api and write_repository permissions. n8n Nodes Required**: HTTP Request (to call n8n and GitLab APIs) Code or Function nodes (for diffing and formatting) Looping (SplitInBatches or similar) Configuration: Set environment variables or workflow credentials for: GITLAB_TOKEN GITLAB_REPO GITLAB_BRANCH (e.g., main) GITLAB_FILE_PATH_PREFIX (e.g., n8n-workflows/) How to Use Import the Workflow into your n8n instance. Configure GitLab API Credentials: Set the GitLab PAT as a header in the HTTP Request node: Private-Token: {{ $env.GITLAB_TOKEN }} Map Workflows to GitLab Paths: Use the workflow name or ID to create the file path. Example: n8n-workflows/workflow-name.json Trigger the Workflow: Can be manually triggered, or scheduled to run at intervals (e.g., daily). Review Commits in GitLab: Each updated workflow will be committed with a message like: "Update workflow: Sample Workflow" Disclaimer This workflow does not handle merge conflicts or manual edits made directly in GitLab. Always ensure proper coordination if multiple sources are modifying workflows. Only structural changes are tracked. Non-functional metadata (like timestamps or IDs) may trigger false positives unless filtered. Use at your own risk. Test in a safe environment before applying to production workflows.

(Image Generation → Hosting → Video Generation) This workflow is designed for creators, automation enthusiasts, and indie hackers who want to generate image-based videos automatically using AI tools — at a low cost. ⚙️ Workflow Overview This automation performs the following steps: Trigger (Schedule or manual) Generate an image using Flux (choose between two APIs) Upload the image to Kraken.io to get a public URL Send the image to Runway ML (choose between two APIs) to generate a video Receive the video as a URL — ready for posting, download, or further automation 🛠️ Step-by-Step Setup 🖼️ Flux (Image Generation) You can use either of the following providers: Option 1: Flux by BlackForest Labs (Direct API) 🔑 Get your API key here: https://docs.bfl.ml/ Paste your API key in the HTTP Request node named Flux (Blackforest) You can customize prompts or styles inside the JSON body Option 2: Flux via RapidAPI 🔑 Subscribe and get your key here: https://rapidapi.com/poorav925/api/ai-text-to-image-generator-flux-free-api/playground/apiendpoint\_e38039ee-1912-4ef9-b4d4-270d72fca851 Enter your RapidAPI key in the X-RapidAPI-Key header Optional: tweak prompts, style, or resolution inside the JSON body 🐙 Kraken.io (Hosting the Image Publicly) Runway ML requires the image to be publicly accessible. We use Kraken.io to host the generated image and return a public URL. 🔑 Get your API credentials: https://kraken.io/account/api-credentials Setup: Copy your API Key and API Secret Open the Kraken Upload node in n8n Replace placeholders with your credentials The node uploads your image and gives back a public image URL for Runway to use 🎬 RunwayML (Video Generation) You also have two options here: Option 1: Runway Official API 🔑 Get your credentials at: https://dev.runwayml.com/ Use the public image URL from Kraken in the JSON body Paste your Bearer token in the Authorization header Customize other settings like video length, style, FPS, etc. Option 2: Runway via RapidAPI 🔑 Subscribe and get your key here: https://rapidapi.com/fortunehoppers/api/runwayml/playground/apiendpoint\_93c8554d-8097-40cd-8252-3d4dec9c0e68 Paste your RapidAPI key in the request header Customize prompt and generation options in the body Use the Kraken-generated image URL as the input source 📤 What to Do with the Video Once the video is generated, you’ll get a direct video URL. You can: Save it to Google Sheets or Notion Send it via email Trigger a YouTube upload automation Or download manually for editing and reposting 💡 Optional Tips & Notes You can schedule this workflow to generate AI videos daily or weekly Combine it with a Google Sheet of prompts for bulk automation Try using a consistent visual style or theme for better branding This workflow is lightweight and affordable — perfect for indie projects or experimental content generation Great for shorts, quote visuals, music loops, AI art promos, etc. 🔗 Resources Flux (Blackforest) Docs Flux on RapidAPI RunwayML Official Docs Runway on RapidAPI Kraken.io API Dashboard 🙋 Need Help? Feel free to reach out: 🐦 Twitter: @juppfy 📧 Email: joseph@uppfy.com If you’d like to hire me for custom n8n workflows or product automations, don’t hesitate to get in touch.

How it works: The n8n flow grabs the needed IDs, fetches the current links, adds your new one, and sends a single HTTP request to NocoDB to update the record’s linked entries. Set up steps: Plan for 10 minutes setup if you’re already running n8n and NocoDB. You’ll need to copy/paste table IDs, set up your HTTP node, and test once. No coding, just copy IDs.

Template: Create an AI Knowledge Base Chatbot with Google Drive and OpenAI GPT (Venio/Salesbear) 📋 Template Overview This comprehensive n8n workflow template creates an intelligent AI chatbot that automatically transforms your Google Drive documents into a searchable knowledge base. The chatbot uses OpenAI's GPT models to provide accurate, context-aware responses based exclusively on your uploaded documents, making it perfect for customer support, internal documentation, and knowledge management systems. 🎯 What This Template Does Automated Knowledge Processing Real-time Document Monitoring**: Automatically detects when files are added or updated in your designated Google Drive folder Intelligent Document Processing**: Converts PDFs, text files, and other documents into searchable vector embeddings Smart Text Chunking**: Breaks down large documents into optimally-sized chunks for better AI comprehension Vector Storage**: Creates a searchable knowledge base that the AI can query for relevant information AI-Powered Chat Interface Webhook Integration**: Receives questions via HTTP requests from any external platform (Venio/Salesbear) Contextual Responses**: Maintains conversation history for natural, flowing interactions Source-Grounded Answers**: Provides responses based strictly on your document content, preventing hallucinations Multi-platform Support**: Works with any chat platform that can send HTTP requests 🔧 Pre-conditions and Requirements Required API Accounts and Permissions 1. Google Drive API Access Google Cloud Platform account Google Drive API enabled OAuth2 credentials configured Read access to your target Google Drive folder 2. OpenAI API Account Active OpenAI account with API access Sufficient API credits for embeddings and chat completions API key with appropriate permissions 3. n8n Instance n8n cloud account or self-hosted instance Webhook functionality enabled Ability to install community nodes (LangChain nodes) 4. Target Chat Platform (Optional) API credentials for your chosen chat platform Webhook capability or API endpoints for message sending Required Permissions Google Drive**: Read access to folder contents and file downloads OpenAI**: API access for text-embedding-ada-002 and gpt-4o-mini models External Platform**: API access for sending/receiving messages (if integrating with existing chat systems) 🚀 Detailed Workflow Operation Phase 1: Knowledge Base Creation File Monitoring: Two trigger nodes continuously monitor your Google Drive folder for new files or updates Document Discovery: When changes are detected, the workflow searches for and identifies the modified files Content Extraction: Downloads the actual file content from Google Drive Text Processing: Uses LangChain's document loader to extract text from various file formats Intelligent Chunking: Splits documents into overlapping chunks (configurable size) for optimal AI processing Vector Generation: Creates embeddings using OpenAI's text-embedding-ada-002 model Storage: Stores vectors in an in-memory vector store for instant retrieval Phase 2: Chat Interaction Question Reception: Webhook receives user questions in JSON format Data Extraction: Parses incoming data to extract chat content and session information AI Processing: AI Agent analyzes the question and determines relevant context Knowledge Retrieval: Searches the vector store for the most relevant document sections Response Generation: OpenAI generates responses based on found content and conversation history Authentication: Validates the request using token-based authentication Response Delivery: Sends the answer back to the originating platform 📚 Usage Instructions After Setup Adding Documents to Your Knowledge Base Upload Files: Simply drag and drop documents into your configured Google Drive folder Supported Formats: PDFs, TXT, DOC, DOCX, and other text-based formats Automatic Processing: The workflow will automatically detect and process new files within minutes Updates: Modify existing files, and the knowledge base will automatically update Integrating with Your Chat Platform Webhook URL: Use the generated webhook URL to send questions POST https://your-n8n-domain/webhook/your-custom-path Content-Type: application/json { "body": { "Data": { "ChatMessage": { "Content": "What are your business hours?", "RoomId": "user-123-session", "Platform": "web", "User": { "CompanyId": "company-456" } } } } } Response Format: The chatbot returns structured responses that your platform can display Testing Your Chatbot Initial Test: Send a simple question about content you know exists in your documents Context Testing: Ask follow-up questions to test conversation memory Edge Cases: Try questions about topics not in your documents to verify appropriate responses Performance: Monitor response times and accuracy 🎨 Customization Options System Message Customization Modify the AI Agent's system message to match your brand and use case: You are a [YOUR_BRAND] customer support specialist. You provide helpful, accurate information based on our documentation. Always maintain a [TONE] tone and [SPECIFIC_GUIDELINES]. Response Behavior Customization Tone and Voice**: Adjust from professional to casual, formal to friendly Response Length**: Configure for brief answers or detailed explanations Fallback Messages**: Customize what the bot says when it can't find relevant information Language Support**: Adapt for different languages or technical terminologies Technical Configuration Options Document Processing Chunk Size**: Adjust from 1000 to 4000 characters based on your document complexity Overlap**: Modify overlap percentage for better context preservation File Types**: Add support for additional document formats AI Model Configuration Model Selection**: Switch between gpt-4o-mini (cost-effective) and gpt-4 (higher quality) Temperature**: Adjust creativity vs. factual accuracy (0.0 to 1.0) Max Tokens**: Control response length limits Memory and Context Conversation Window**: Adjust how many previous messages to remember Session Management**: Configure session timeout and user identification Context Retrieval**: Tune how many document chunks to consider per query Integration Customization Authentication Methods Token-based**: Default implementation with bearer tokens API Key**: Simple API key validation OAuth**: Full OAuth2 implementation for secure access Custom Headers**: Validate specific headers or signatures Response Formatting JSON Structure**: Customize response format for your platform Markdown Support**: Enable rich text formatting in responses Error Handling**: Define custom error messages and codes 🎯 Specific Use Case Examples Customer Support Chatbot Scenario: E-commerce company with product documentation, return policies, and FAQ documents Setup: Upload product manuals, policy documents, and common questions to Google Drive Customization: Professional tone, concise answers, escalation triggers for complex issues Integration: Website chat widget, mobile app, or customer portal Internal HR Knowledge Base Scenario: Company HR department with employee handbook, policies, and procedures Setup: Upload HR policies, benefits information, and procedural documents Customization: Friendly but professional tone, detailed policy explanations Integration: Internal Slack bot, employee portal, or HR ticketing system Technical Documentation Assistant Scenario: Software company with API documentation, user guides, and troubleshooting docs Setup: Upload API docs, user manuals, and technical specifications Customization: Technical tone, code examples, step-by-step instructions Integration: Developer portal, support ticket system, or documentation website Educational Content Helper Scenario: Educational institution with course materials, policies, and student resources Setup: Upload syllabi, course content, academic policies, and student guides Customization: Helpful and encouraging tone, detailed explanations Integration: Learning management system, student portal, or mobile app Healthcare Information Assistant Scenario: Medical practice with patient information, procedures, and policy documents Setup: Upload patient guidelines, procedure explanations, and practice policies Customization: Compassionate tone, clear medical explanations, disclaimer messaging Integration: Patient portal, appointment system, or mobile health app 🔧 Advanced Customization Examples Multi-Language Support // In Edit Fields node, detect language and route accordingly const language = $json.body.Data.ChatMessage.Language || 'en'; const systemMessage = { 'en': 'You are a helpful customer support assistant...', 'es': 'Eres un asistente de soporte al cliente útil...', 'fr': 'Vous êtes un assistant de support client utile...' }; Department-Specific Routing // Route questions to different knowledge bases based on department const department = $json.body.Data.ChatMessage.Department; const vectorStoreKey = vector_store_${department}; Advanced Analytics Integration // Track conversation metrics const analytics = { userId: $json.body.Data.ChatMessage.User.Id, timestamp: new Date().toISOString(), question: $json.body.Data.ChatMessage.Content, response: $json.response, responseTime: $json.processingTime }; 📊 Performance Optimization Tips Document Management Optimal File Size**: Keep documents under 10MB for faster processing Clear Structure**: Use headers and sections for better chunking Regular Updates**: Remove outdated documents to maintain accuracy Logical Organization**: Group related documents in subfolders Response Quality System Message Refinement**: Regularly update based on user feedback Context Tuning**: Adjust chunk size and overlap for your specific content Testing Framework**: Implement systematic testing for response accuracy User Feedback Loop**: Collect and analyze user satisfaction data Cost Management Model Selection**: Use gpt-4o-mini for cost-effective responses Caching Strategy**: Implement response caching for frequently asked questions Usage Monitoring**: Track API usage and set up alerts Batch Processing**: Process multiple documents efficiently 🛡️ Security and Compliance Data Protection Document Security**: Ensure sensitive documents are properly secured Access Control**: Implement proper authentication and authorization Data Retention**: Configure appropriate data retention policies Audit Logging**: Track all interactions for compliance Privacy Considerations User Data**: Minimize collection and storage of personal information Session Management**: Implement secure session handling Compliance**: Ensure adherence to relevant privacy regulations Encryption**: Use HTTPS for all communications 🚀 Deployment and Scaling Production Readiness Environment Variables**: Use environment variables for sensitive configurations Error Handling**: Implement comprehensive error handling and logging Monitoring**: Set up monitoring for workflow health and performance Backup Strategy**: Ensure document and configuration backups Scaling Considerations Load Testing**: Test with expected user volumes Rate Limiting**: Implement appropriate rate limiting Database Scaling**: Consider external vector database for large-scale deployments Multi-Instance**: Configure for multiple n8n instances if needed 📈 Success Metrics and KPIs Quantitative Metrics Response Accuracy**: Percentage of correct answers Response Time**: Average time from question to answer User Satisfaction**: Rating scores and feedback Usage Volume**: Questions per day/week/month Cost Efficiency**: Cost per interaction Qualitative Metrics User Feedback**: Qualitative feedback on response quality Use Case Coverage**: Percentage of user needs addressed Knowledge Gaps**: Identification of missing information Conversation Quality**: Natural flow and context understanding