Back to Catalog

Generate and send contract documents with Typeform, Google Docs and Gmail

Abbas AliAbbas Ali
797 views
2/3/2026
Official Page

This workflow is designed for teams or freelancers who want to auto-generate and send contracts based on information gathered from a Typeform (e.g., client name, project scope, deadlines). Perfect for HR onboarding, client agreements, or legal operations.

Prerequisites

  • To use this workflow, you’ll need:
  • A Typeform account and a published form
  • Access to Google Docs (or use a local document template)
  • Gmail or SMTP email integration in n8n
  • n8n Desktop or a hosted n8n instance

How It Works

  • Trigger: Listens for new Typeform submissions.
  • Extract Data: Parses the answers from the form.
  • Generate Contract: Fills a contract template using form inputs.
  • Create PDF: Exports the filled contract as a PDF.
  • Send Email: Sends the PDF to the client’s email address provided in the form.

Nodes Used

  • Typeform Trigger – Triggers on form submission.
  • Set Node – Maps form answers into variables.
  • Google Docs (or HTTP Request) – Uses a template to generate the contract.
  • Google Drive / PDF Converter – Converts to PDF (if needed).
  • Email (Gmail/SMTP) – Sends the completed contract to the recipient.

Tips

  • Replace the Google Docs template ID with your own.
  • Ensure the variable placeholders (like {{client_name}}) match your document.
  • Use the Cron node instead of Typeform Trigger if you want to poll periodically.

Generate and Send Contract Documents with Typeform, Google Docs, and Gmail

This n8n workflow automates the process of generating a contract document based on Typeform submissions, populating it with dynamic data using Google Docs, and then sending the finalized document via Gmail. It streamlines the contract creation and distribution process, reducing manual effort and potential errors.

What it does

This workflow performs the following steps:

  1. Triggers on Typeform Submission: Listens for new form submissions from a specified Typeform.
  2. Prepares Data (Edit Fields): Processes and formats the data received from Typeform, likely extracting relevant fields for the contract.
  3. Generates Document (Google Docs): Uses the processed data to create a new document from a Google Docs template, populating placeholders with the submitted information.
  4. Sends Email (Gmail): Composes and sends an email via Gmail, attaching the newly generated Google Docs contract to the recipient specified in the Typeform submission.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Account/Instance: A running n8n instance.
  • Typeform Account: With a form configured to collect the necessary contract information.
  • Google Docs Account: With a contract template document that contains placeholders for dynamic data.
  • Google Drive Account: To store the generated contract documents.
  • Gmail Account: To send the contract emails.
  • Credentials: Configured n8n credentials for Typeform, Google Drive, Google Docs, and Gmail.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Typeform Trigger:
    • Select your Typeform credential.
    • Choose the specific Typeform you want to trigger the workflow.
    • Activate the workflow.
  3. Configure Edit Fields (Set) Node:
    • Review the expressions to ensure they correctly extract and format data from your Typeform submission for use in Google Docs and Gmail.
  4. Configure Google Docs Node:
    • Select your Google Docs credential.
    • Specify the Google Docs template ID you wish to use for the contract.
    • Map the fields from the previous "Edit Fields" node to the placeholders in your Google Docs template.
    • Configure the output settings, such as the name of the new document and the Google Drive folder where it should be saved.
  5. Configure Google Drive Node:
    • Select your Google Drive credential.
    • Ensure the node is configured to handle the file created by the Google Docs node (e.g., converting it to a PDF if desired, or simply retrieving its ID for attachment).
  6. Configure Gmail Node:
    • Select your Gmail credential.
    • Set the recipient email address (likely from the Typeform data).
    • Define the email subject and body.
    • Attach the document generated by the Google Docs node (or retrieved from Google Drive).
  7. Activate the Workflow: Once all nodes are configured with the correct credentials and settings, activate the workflow to start automating your contract generation and sending process.

Related Templates

AI-powered code review with linting, red-marked corrections in Google Sheets & Slack

Advanced Code Review Automation (AI + Lint + Slack) Who’s it for For software engineers, QA teams, and tech leads who want to automate intelligent code reviews with both AI-driven suggestions and rule-based linting — all managed in Google Sheets with instant Slack summaries. How it works This workflow performs a two-layer review system: Lint Check: Runs a lightweight static analysis to find common issues (e.g., use of var, console.log, unbalanced braces). AI Review: Sends valid code to Gemini AI, which provides human-like review feedback with severity classification (Critical, Major, Minor) and visual highlights (red/orange tags). Formatter: Combines lint and AI results, calculating an overall score (0–10). Aggregator: Summarizes results for quick comparison. Google Sheets Writer: Appends results to your review log. Slack Notification: Posts a concise summary (e.g., number of issues and average score) to your team’s channel. How to set up Connect Google Sheets and Slack credentials in n8n. Replace placeholders (<YOURSPREADSHEETID>, <YOURSHEETGIDORNAME>, <YOURSLACKCHANNEL_ID>). Adjust the AI review prompt or lint rules as needed. Activate the workflow — reviews will start automatically whenever new code is added to the sheet. Requirements Google Sheets and Slack integrations enabled A configured AI node (Gemini, OpenAI, or compatible) Proper permissions to write to your target Google Sheet How to customize Add more linting rules (naming conventions, spacing, forbidden APIs) Extend the AI prompt for project-specific guidelines Customize the Slack message formatting Export analytics to a dashboard (e.g., Notion or Data Studio) Why it’s valuable This workflow brings realistic, team-oriented AI-assisted code review to n8n — combining the speed of automated linting with the nuance of human-style feedback. It saves time, improves code quality, and keeps your team’s review history transparent and centralized.

higashiyama By higashiyama
90

Automate RSS to social media pipeline with AI, Airtable & GetLate for multiple platforms

Overview Automates your complete social media content pipeline: sources articles from Wallabag RSS, generates platform-specific posts with AI, creates contextual images, and publishes via GetLate API. Built with 63 nodes across two workflows to handle LinkedIn, Instagram, and Bluesky—with easy expansion to more platforms. Ideal for: Content marketers, solo creators, agencies, and community managers maintaining a consistent multi-platform presence with minimal manual effort. How It Works Two-Workflow Architecture: Content Aggregation Workflow Monitors Wallabag RSS feeds for tagged articles (to-share-linkedin, to-share-instagram, etc.) Extracts and converts content from HTML to Markdown Stores structured data in Airtable with platform assignment AI Generation & Publishing Workflow Scheduled trigger queries Airtable for unpublished content Routes to platform-specific sub-workflows (LinkedIn, Instagram, Bluesky) LLM generates optimized post text and image prompts based on custom brand parameters Optionally generates AI images and hosts them on Imgbb CDN Publishes via GetLate API (immediate or draft mode) Updates Airtable with publication status and metadata Key Features: Tag-based content routing using Wallabag's native system Swappable AI providers (Groq, OpenAI, Anthropic) Platform-specific optimization (tone, length, hashtags, CTAs) Modular design—duplicate sub-workflows to add new platforms in \~30 minutes Centralized Airtable tracking with 17 data points per post Set Up Steps Setup time: \~45-60 minutes for initial configuration Create accounts and get API keys (\~15 min) Wallabag (with RSS feeds enabled) GetLate (social media publishing) Airtable (create base with provided schema—see sticky notes) LLM provider (Groq, OpenAI, or Anthropic) Image service (Hugging Face, Fal.ai, or Stability AI) Imgbb (image hosting) Configure n8n credentials (\~10 min) Add all API keys in n8n's credential manager Detailed credential setup instructions in workflow sticky notes Set up Airtable database (\~10 min) Create "RSS Feed - Content Store" base Add 19 required fields (schema provided in workflow sticky notes) Get Airtable base ID and API key Customize brand prompts (\~15 min) Edit "Set Custom SMCG Prompt" node for each platform Define brand voice, tone, goals, audience, and image preferences Platform-specific examples provided in sticky notes Configure platform settings (\~10 min) Set GetLate account IDs for each platform Enable/disable image generation per platform Choose immediate publish vs. draft mode Adjust schedule trigger frequency Test and deploy Tag test articles in Wallabag Monitor the first few executions in draft mode Activate workflows when satisfied with the output Important: This is a proof-of-concept template. Test thoroughly with draft mode before production use. Detailed setup instructions, troubleshooting tips, and customization guidance are in the workflow's sticky notes. Technical Details 63 nodes: 9 Airtable operations, 8 HTTP requests, 7 code nodes, 3 LangChain LLM chains, 3 RSS triggers, 3 GetLate publishers Supports: Multiple LLM providers, multiple image generation services, unlimited platforms via modular architecture Tracking: 17 metadata fields per post, including publish status, applied parameters, character counts, hashtags, image URLs Prerequisites n8n instance (self-hosted or cloud) Accounts: Wallabag, GetLate, Airtable, LLM provider, image generation service, Imgbb Basic understanding of n8n workflows and credential configuration Time to customize prompts for your brand voice Detailed documentation, Airtable schema, prompt examples, and troubleshooting guides are in the workflow's sticky notes. Category Tags social-media-automation, ai-content-generation, rss-to-social, multi-platform-posting, getlate-api, airtable-database, langchain, workflow-automation, content-marketing

Mikal Hayden-GatesBy Mikal Hayden-Gates
188

Document RAG & chat agent: Google Drive to Qdrant with Mistral OCR

Knowledge RAG & AI Chat Agent: Google Drive to Qdrant Description This workflow transforms a Google Drive folder into an intelligent, searchable knowledge base and provides a chat agent to query it. It’s composed of two distinct flows: An ingestion pipeline to process documents. A live chat agent that uses RAG (Retrieval-Augmented Generation) and optional web search to answer user questions. This system fully automates the creation of a “Chat with your docs” solution and enhances it with external web-searching capabilities. --- Quick Implementation Steps Import the workflow JSON into your n8n instance. Set up credentials for Google Drive, Mistral AI, OpenAI, and Qdrant. Open the Web Search node and add your Tavily AI API key to the Authorization header. In the Google Drive (List Files) node, set the Folder ID you want to ingest. Run the workflow manually once to populate your Qdrant database (Flow 1). Activate the workflow to enable the chat trigger (Flow 2). Copy the public webhook URL from the When chat message received node and open it in a new tab to start chatting. --- What It Does The workflow is divided into two primary functions: Knowledge Base Ingestion (Manual Trigger) This flow populates your vector database. Scans Google Drive: Lists all files from a specified folder. Processes Files Individually: Downloads each file. Extracts Text via OCR: Uses Mistral AI OCR API for text extraction from PDFs, images, etc. Generates Smart Metadata: A Mistral LLM assigns metadata like documenttype, project, and assignedto. Chunks & Embeds: Text is cleaned, chunked, and embedded via OpenAI’s text-embedding-3-small model. Stores in Qdrant: Text chunks, embeddings, and metadata are stored in a Qdrant collection (docaiauto). AI Chat Agent (Chat Trigger) This flow powers the conversational interface. Handles User Queries: Triggered when a user sends a chat message. Internal RAG Retrieval: Searches Qdrant Vector Store first for answers. Web Search Fallback: If unavailable internally, the agent offers to perform a Tavily AI web search. Contextual Responses: Combines internal and external info for comprehensive answers. --- Who's It For Ideal for: Teams building internal AI knowledge bases from Google Drive. Developers creating AI-powered support, research, or onboarding bots. Organizations implementing RAG pipelines. Anyone making unstructured Google Drive documents searchable via chat. --- Requirements n8n instance (self-hosted or cloud). Google Drive Credentials (to list and download files). Mistral AI API Key (for OCR & metadata extraction). OpenAI API Key (for embeddings and chat LLM). Qdrant instance (cloud or self-hosted). Tavily AI API Key (for web search). --- How It Works The workflow runs two independent flows in parallel: Flow 1: Ingestion Pipeline (Manual Trigger) List Files: Fetch files from Google Drive using the Folder ID. Loop & Download: Each file is processed one by one. OCR Processing: Upload file to Mistral Retrieve signed URL Extract text using Mistral DOC OCR Metadata Extraction: Analyze text using a Mistral LLM. Text Cleaning & Chunking: Split into 1000-character chunks. Embeddings Creation: Use OpenAI embeddings. Vector Insertion: Push chunks + metadata into Qdrant. Flow 2: AI Chat Agent (Chat Trigger) Chat Trigger: Starts when a chat message is received. AI Agent: Uses OpenAI + Simple Memory to process context. RAG Retrieval: Queries Qdrant for related data. Decision Logic: Found → Form answer. Not found → Ask if user wants web search. Web Search: Performs Tavily web lookup. Final Response: Synthesizes internal + external info. --- How To Set Up Import the Workflow Upload the provided JSON into your n8n instance. Configure Credentials Create and assign: Google Drive → Google Drive nodes Mistral AI → Upload, Signed URL, DOC OCR, Cloud Chat Model OpenAI → Embeddings + Chat Model nodes Qdrant → Vector Store nodes Add Tavily API Key Open Web Search node → Parameters → Headers Add your key under Authorization (e.g., tvly-xxxx). Node Configuration Google Drive (List Files): Set Folder ID. Qdrant Nodes: Ensure same collection name (docaiauto). Run Ingestion (Flow 1) Click Test workflow to populate Qdrant with your Drive documents. Activate Chat (Flow 2) Toggle the workflow ON to enable real-time chat. Test Open the webhook URL and start chatting! --- How To Customize Change LLMs: Swap models in OpenAI or Mistral nodes (e.g., GPT-4o, Claude 3). Modify Prompts: Edit the system message in ai chat agent to alter tone or logic. Chunking Strategy: Adjust chunkSize and chunkOverlap in the Code node. Different Sources: Replace Google Drive with AWS S3, Local Folder, etc. Automate Updates: Add a Cron node for scheduled ingestion. Validation: Add post-processing steps after metadata extraction. Expand Tools: Add more functional nodes like Google Calendar or Calculator. --- Use Case Examples Internal HR Bot: Answer HR-related queries from stored policy docs. Tech Support Assistant: Retrieve troubleshooting steps for products. Research Assistant: Summarize and compare market reports. Project Management Bot: Query document ownership or project status. --- Troubleshooting Guide | Issue | Possible Solution | |------------|------------------------| | Chat agent doesn’t respond | Check OpenAI API key and model availability (e.g., gpt-4.1-mini). | | Known documents not found | Ensure ingestion flow ran and both Qdrant nodes use same collection name. | | OCR node fails | Verify Mistral API key and input file integrity. | | Web search not triggered | Re-check Tavily API key in Web Search node headers. | | Incorrect metadata | Tune Information Extractor prompt or use a stronger Mistral model. | --- Need Help or More Workflows? Want to customize this workflow for your business or integrate it with your existing tools? Our team at Digital Biz Tech can tailor it precisely to your use case from automation logic to AI-powered enhancements. We can help you set it up for free — from connecting credentials to deploying it live. Contact: shilpa.raju@digitalbiz.tech Website: https://www.digitalbiz.tech LinkedIn: https://www.linkedin.com/company/digital-biz-tech/ You can also DM us on LinkedIn for any help. ---

DIGITAL BIZ TECHBy DIGITAL BIZ TECH
1409