AWS S3 and HTTP Request integration

About This Template This workflow creates high-quality, text-rich advertising banners from simple LINE messages. It combines Google Gemini (for marketing-focused prompt engineering) and Nano Banana Pro (accessed via Kie.ai API) to generate images with superior text rendering capabilities. It also handles the asynchronous API polling required for high-quality image generation. How It Works Input: Users send a banner concept via LINE (e.g., "Coffee brand, morning vibe"). Prompt Engineering: Gemini optimizes the request into a detailed prompt, specifying lighting, composition, and Japanese catch-copy placement. Async Generation: The workflow submits a job to Nano Banana Pro (Kie API) and intelligently waits/polls until the image is ready. Hosting: The final image is downloaded and uploaded to a public AWS S3 bucket. Delivery: The image is pushed back to the user on LINE. Who It’s For Marketing teams creating A/B test assets. Japanese market advertisers needing accurate text rendering. Developers looking for an example of Async API Polling patterns in n8n. Requirements n8n** (Cloud or Self-hosted). Kie.ai API Key** (for Nano Banana Pro model). Google Gemini API Key**. AWS S3 Bucket** (Public access enabled). LINE Official Account** (Messaging API). Setup Steps Credentials: Configure the "Header Auth" credential for the Kie.ai nodes (Header: Authorization, Value: Bearer YOUR_API_KEY). AWS: Ensure your S3 bucket allows public read access so LINE can display the image. Webhook: Add the production webhook URL to your LINE Developers console.

About This Template This workflow turns complex data or topics sent via LINE into beautiful, easy-to-understand Infographics. It combines Gemini (to analyze data and structure the visual layout) and Nano Banana Pro (accessed via Kie.ai API) to generate high-quality, data-rich graphics (Charts, timelines, processes). How It Works Input: User sends a topic or data points via LINE (e.g., "Japan's Energy Mix: 20% Solar, 10% Wind..."). Data Visualization Logic: Gemini acts as an Information Designer, deciding the best chart type (Pie, Bar, Flow) and layout for the data. Render: Nano Banana generates a professional 3:4 Vertical Infographic. Smart Polling: The workflow uses a loop to check the API status every 5 seconds, ensuring it waits exactly as long as needed. Delivery: Uploads to S3 and sends the visual report back to LINE. Who It’s For Social Media Managers needing quick visual content. Educators and presenters summarizing data. Consultants creating quick visual reports on the go. Requirements n8n** (Cloud or Self-hosted). Kie.ai API Key** (Nano Banana Pro). Google Gemini API Key**. AWS S3 Bucket** (Public access). LINE Official Account**. Setup Steps Credentials: Configure Header Auth for Kie.ai and your other service credentials. Webhook: Add the production URL to LINE Developers console.

Animate Any Face into a Video with Fal.ai Create stunning deepfake-style videos automatically by swapping a face from an image onto a source video. This workflow provides a powerful, automated pipeline to perform video face-swapping using the Fal.ai API. It's designed to handle the entire asynchronous process: accepting a source video and a target face image, uploading them to cloud storage, initiating the AI job, polling for completion, and retrieving the final, rendered video. | Services Used | Features | | :--- | :--- | | 🤖 Fal.ai | Leverages the powerful Wan 2.2 model for high-quality face animation. | | ☁️ AWS S3 | Uses enterprise-grade cloud storage for reliable public file hosting. | | 🔄 Polling Loop | Intelligently waits for the asynchronous AI job to complete before proceeding. | | 📥 n8n Form Trigger | Provides a simple UI to upload your source image and video. | How It Works ⚙️ 📥 Get User Input: The workflow starts when you upload a source video and a face image via the n8n Form Trigger. ☁️ Upload to Cloud: Both files are automatically uploaded to a specified AWS S3 bucket to generate the publicly accessible URLs required by the AI model. 🚀 Start AI Job: The public URLs for the video and image are sent in an HTTP Request to the Fal.ai API, which starts the asynchronous face animation process and returns a request_id. ⏳ Wait & Check: The workflow enters a polling loop. It Waits for one minute, then makes another HTTP Request to the Fal.ai status endpoint using the request_id. ✅ Check for Completion: An IF node checks if the job status is COMPLETED. If not, the workflow loops back to the Wait node. 🎬 Retrieve Final Video: Once the job is complete, the workflow makes a final HTTP Request to fetch the finished animated video. 🛠️ How to Set Up 🔑 Set Up Fal.ai Credentials: Get your API Key from Fal.ai. In n8n, go to Credentials, add a new Header Auth credential, and save your key. Connect this credential to all three HTTP Request nodes in the workflow. ☁️ Configure AWS S3: Add your AWS credentials in n8n. In the two AWS S3 nodes (Upload Video1 and Upload Image1), update the Bucket Name parameter to your own S3 bucket. Ensure your bucket permissions allow for public reads. ▶️ Activate and Run: Activate the workflow. Open the Form Trigger URL from the n8n editor, upload your files, and submit. The final video will be available in the execution log of the Get Final Video node. Requirements An active Fal.ai account and API key. An AWS account with an S3 bucket configured for public access. Alternative Storage:* For a personal setup, you can replace the AWS S3 nodes with Cloudinary* nodes. Just ensure the output is a public URL. 💬 Need Help or Want to Learn More? Join my Skool community for n8n + AI automation tutorials, live Q&A sessions, and exclusive workflows: 👉 https://www.skool.com/n8n-ai-automation-champions Template Author: Sandeep Patharkar Category: Content Generation / Content Marketing Difficulty: Intermediate Estimated Setup Time: ⏱️ 20 minutes

Salesforce to S3 File Migration & Cleanup Automate archiving old Salesforce files to Amazon S3, log them back in Salesforce, and free up org storage — all from a scheduled n8n workflow. 🔧 How It Works (High-Level) Schedule Trigger kicks off (e.g., daily). Query Salesforce for ContentDocument records older than 365 days. Loop Each File → download binary via REST. Upload to S3 with the original filename. Lookup Links (ContentDocumentLink) to keep the parent record reference. Filter Out Users (ignore LinkedEntityId starting with 005). Create S3_File__c record in Salesforce for traceability. Delete Original File from Salesforce to reclaim storage. Notify via Slack when the batch is done. 🚀 Set Up Steps (Time: ~45–90 mins) Import n8n Workflow JSON and wire up credentials (Salesforce OAuth2, AWS S3, Slack). Install Salesforce Unmanaged Package (Custom Object S3_File__c, Apex controller, LWC, settings). Fill S3Settings__c (bucket, region, keys, expiry) or swap to Named Credentials. Test with a Sandbox Batch (e.g., small date range) and verify upload/delete. Schedule & Monitor (tweak interval, Slack channel). 💖 Why you’ll love it 💸 Slash storage costs — offload gigabytes to S3 🔍 Full traceability — every file still tracked in Salesforce 🧰 Plug & play — import JSON, install package, plug in creds 🧱 Modular & extensible — swap S3, add approvals, build an uploader UI ⏱ Set it & forget it — scheduled automation + Slack alerts 📦 What’s Included n8n JSON Flow** – ready to import. Salesforce Unmanaged Package** – Apex (S3FilesController.cls), LWC (s3FilesViewer), S3_File__c, S3Settings__c. S3 + Salesforce Setup Guide** – quick reference for configuring keys, permissions, and the LWC. All components are editable — extend, replace, or integrate with your own processes. 🧱 Requirements n8n instance (self-hosted or Cloud) with HTTP Request, AWS S3, Slack, and Salesforce nodes. Salesforce org with API access & permission to install unmanaged packages. You have to have Query All Files permission. Setup-> Permission Sets / Profile -> App Permission -> Content -> Query All Files. Allows View All Data users to SOQL query all files in the org. AWS S3 bucket + IAM user/role with GetObject/PutObject (and optional ListBucket).

Monthly AI SEO Content Engine: 34 Assets to WordPress, LinkedIn & Socials Summary This workflow is an end-to-end, enterprise-grade content engine. It automates the entire lifecycle of a brand's content marketing: capturing business context via an n8n Form Trigger to generate a master strategy document, using real-time analytics to write 34 targeted assets, packaging them into a styled .docx file for review, and automatically generating images to publish the content across WordPress, LinkedIn, Facebook, and Instagram. Key features Automated Brand Strategy:** Uses an n8n Form Trigger to capture business details and generates a 16-section master Markdown strategy document saved to Google Drive. Data-Driven Content Planning:** Integrates Google Analytics 4 (GA4) and localized Google Trends to identify content gaps (high-bounce pages) and rising topics to inform the content plan. High-Volume AI Generation:** Leverages native n8n Google Gemini nodes to write 34 distinct assets per run: 6 SEO blogs, 12 LinkedIn posts, 8 educational social posts, 4 newsletters, and 4 long-form guides. Native Document Assembly:** Bypasses basic text outputs by dynamically assembling the generated content into a professionally styled, downloadable Microsoft Word (.docx) package with a generated table of contents. AI Image Generation:** Uses Google Gemini 2.5 Flash Image to generate custom, photorealistic featured images and social media graphics for every post natively within the flow. Omnichannel Publishing:** Parses the finalized .docx file, extracts the targeted content, and schedules it automatically to WordPress, LinkedIn, Facebook Pages, and Instagram Business with clamped timestamp validation. Automated Error Logging & Alerts:** Built-in error trapping that sends a detailed summary to your Gmail if any API fails, or if prerequisite files are missing before publishing begins. How it works This template is divided into three distinct pipelines that run sequentially: Pipeline A: Strategy Generation Trigger:** An n8n Form Trigger collects the company name, mission, audience, and core services. Process:** The native Google Gemini node generates a strict JSON strategy object, which a Code node formats into a comprehensive Markdown document. Storage:** The Google Drive node saves this context document directly to the root of your Google Drive. Pipeline B: Content Generation & Assembly Trigger:** Triggered manually via the Pipeline B trigger, or swap in a Schedule Trigger to run monthly. Context Gathering:** Fetches the Markdown strategy from Drive, pulls the last 30 days of GA4 data, and fetches current Google Trends via RSS. Generation:** Google Gemini is prompted in stages: first to build a Content Plan JSON, then to write the batches of blogs, social posts, and guides. Assembly:** A robust Code node compiles the raw text into a valid, styled .zip/.docx file and uploads it to the root of your Google Drive. Notification:** A Gmail node sends the team an email notification with a direct link to the finalized .docx package. Pipeline C: Multi-Platform Publishing Trigger:** Schedule Trigger set to run on the 1st of each month — adjust the time to run after Pipeline B completes. Extraction:** Searches Google Drive for the latest .docx package, unzips it, and extracts the raw XML text. (If missing, it safely aborts and alerts you). Processing:** Code nodes split the text into individual arrays for Blogs, LinkedIn, and Social posts, safely clamping publish dates to prevent API scheduling errors. Media & Publishing:** * Generates an image for each asset using Gemini 2.5 Flash Image. Uploads blog images to WordPress Media and creates a future-dated WordPress post. Publishes text and images natively to LinkedIn, Facebook, and Instagram (via AWS S3 temporary hosting). Nodes used n8n Form Trigger Schedule Trigger Code (Heavy JavaScript for data merging, XML parsing, and DOCX assembly) Google Gemini (Langchain Native Nodes for Text and Image) Google Drive Google Analytics Gmail LinkedIn Facebook Graph API AWS S3 (Temporary public storage for Instagram image ingestion) Compression, Set, If, Merge, HTTP Request (WordPress API) Setup instructions Credentials Setup: You will need active credentials for Google Drive, Google Analytics (OAuth2), Gmail, LinkedIn, Facebook Graph API, AWS S3, and a Google Gemini API key. Your LinkedIn credential must have the openid scope enabled to fetch the user URN dynamically. Run Pipeline A: Run the n8n Form Trigger manually, fill out your company details, and let it generate your initial Markdown strategy file in the root of your Google Drive. Configure Pipeline B: Open the Config: Pipeline B node and enter your GA4 Property ID, notification email address, the direct Drive link to the generated Markdown file, and your preferred Google Trends geographic/language codes. Configure Pipeline C: Open the Config: Pipeline C node and enter your WordPress domain, Facebook Page ID, Instagram Business ID, your S3 bucket details (used to temporarily host images for Instagram), and a notification email address. WordPress Auth: Ensure your WordPress site has Application Passwords enabled to use Basic Auth for the Upload to WP Media node. Activate: Turn the workflow on. Pipeline B and C will run automatically on their schedules. Requirements n8n version 1.60+** (Required for the node types and versions used in this template). AWS S3 bucket with public-read access** (Required to host image URLs temporarily so Instagram's Graph API can fetch them). Google Gemini API credentials** (Budget for ~100k-150k output tokens per monthly run). Google Workspace (Analytics, Drive, Gmail). WordPress site with Basic Auth enabled. LinkedIn account, Facebook Page, and Instagram Business account. Customization ideas Adjust Output Volume:** Modify the JSON prompt in the Build Content Plan Prompt node to generate weekly content (e.g., 2 blogs, 4 social posts) instead of massive monthly batches. Change the Tone:** Update the "Brand Voice" restrictions in the n8n Form Trigger or tweak the system prompts in the Code nodes to match a specific industry style (e.g., highly technical vs. casual). Add Human-in-the-Loop:** Replace the direct link between Pipeline B and C with a Wait node that pauses publishing until a manager clicks an approval webhook link sent via email. Use cases Marketing Agencies:** Scaling monthly content deliverables for multiple clients while ensuring the AI strictly adheres to different brand guidelines. SEO Consultants:** Automating high-volume, keyword-targeted blog clusters backed by Google Analytics gap data. Lean Startups:** Maintaining a massive omnichannel social media and blog presence without hiring a dedicated content team.

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.