by JJ Tham
Generate AI Voiceovers from Scripts and Upload to Google Drive This is the final piece of the AI content factory. This workflow takes your text-based video scripts and automatically generates high-quality audio voiceovers for each one, turning your text into ready-to-use audio assets for your video ads. Go from a spreadsheet of text to a folder of audio files, completely on autopilot. ⚠️ CRITICAL REQUIREMENTS (Read First!) This is an advanced, self-hosted workflow that requires specific local setup: Self-Hosted n8n Only:** This workflow uses the Execute Command and Read/Write Files nodes, which requires you to run your own instance of n8n. It will not work on n8n Cloud. FFmpeg Installation:** You must have FFmpeg installed on the same machine where your n8n instance is running. This is used to convert the audio files to a standard format. What it does This is Part 3 of the AI marketing series. It connects to the Google Sheet where you generated your video scripts (in Part 2). For each script that hasn't been processed, it: Uses the Google Gemini Text-to-Speech (TTS) API to generate a voiceover. Saves the audio file to your local computer. Uses FFmpeg to convert the raw audio into a standard .wav file. Uploads the final .wav file to your Google Drive. Updates the original Google Sheet with a link to the audio file in Drive and marks the script as complete. How to set up IMPORTANT: This workflow is Part 3 of a series and requires the output from Part 2 ("Generate AI Video Ad Scripts"). If you need Part 1 or Part 2 of this workflow series, you can find them for free on my n8n Creator Profile. Connect to Your Scripts Sheet: In the "Getting Video Scripts" node, connect your Google Sheets account and provide the URL to the sheet containing your generated video scripts from Part 2. Configure AI Voice Generation (HTTP Request): In the "HTTP Request To Generate Voice" node, go to the Query Parameters and replace INSERT YOUR API KEY HERE with your Google Gemini API key. In the JSON Body, you can customize the voice prompt (e.g., change <INSERT YOUR DESIRED ACCENT HERE>). Set Your Local File Path: In the first "Read/Write Files from Disk" node, update the File Name field to a valid directory on your local machine where n8n has permission to write files. Replace /Users/INSERT_YOUR_LOCAL_STORAGE_HERE/. Connect Google Drive: In the "Uploading Wav File" node, connect your Google Drive account and choose the folder where your audio files will be saved. Update Your Tracking Sheet: In the final "Uploading Google Drive Link..." node, ensure it's connected to the same Google Sheet from Step 1. This node will update your sheet with the results. Name and Description for Submission Form Here are the name and description, updated with the new information, ready for you to copy and paste. Name: Generate AI Voiceovers from Scripts and Upload to Google Drive Description: Welcome to the final piece of the AI content factory! 🔊 This advanced workflow takes the video ad scripts you've generated and automatically creates high-quality audio voiceovers for each one, completing your journey from strategy to ready-to-use media assets. ⚠️ This is an advanced workflow for self-hosted n8n instances only and requires FFmpeg to be installed locally. ⚙️ How it works This workflow is Part 3 of a series. It reads your video scripts from a Google Sheet, then for each script it: Generates a voiceover using the Google Gemini TTS API. Saves the audio file to your local machine. Converts the file to a standard .wav format using FFmpeg. Uploads the final audio file to Google Drive. Updates your Google Sheet with a link to the new audio file. 👥 Who’s it for? Video Creators & Marketers: Mass-produce voiceovers for video ads, tutorials, or social media content without hiring voice actors. Automation Power Users: A powerful example of how n8n can bridge cloud APIs with local machine commands. Agencies: Drastically speed up the production of audio assets for client campaigns. 🛠️ How to set up This workflow requires specific local setup due to its advanced nature. IMPORTANT: This is Part 3 of a series. To find Part 1 ("Generate a Strategic Plan") and Part 2 ("Generate Video Scripts"), please visit my n8n Creator Profile where they are available for free. Setup involves connecting to your scripts sheet, configuring the AI voice API, setting a local file path for n8n to write to, and connecting your Google Drive.
by Simeon Penev
Who’s it for Content/SEO teams who want a fast, consistent, research-driven brief for a copywriters from a single keyword—without manual review and analysis of the SERP (Google results). How it works / What it does Form Trigger collects the keyword/topic and redirects to Google Drive Folder after the final node. FireCrawl Search & Scrape pulls the top 5 pages for the chosen keyword. AI Agent (with Think + OpenAI Chat Model) analyzes sources and generates an original Markdown brief. Markdown to JSON converts the Markdown into Google Docs batchUpdate requests (H1/H2/H3, lists, links, spacing). Then this is used in Update a document for updating the empty doc. Create a document + Update a document write a Google Doc titled “SEO Brief for ” and update the Google Doc in your target Drive folder. How to set up Add credentials: Firecrawl (Authorization header), OpenAI (Chat), Google Docs OAuth2. Replace placeholders: {{APIKEY}}, {{googledrivefolderid}}, {{googledrivefolderurl}}. Publish and open the Form URL to test. Requirements Firecrawl API key • OpenAI API key • Google account with access to the target Drive folder. Resources Google OAuth2 Credentials Setup - https://docs.n8n.io/integrations/builtin/credentials/google/oauth-generic/ Firecrawl API key - https://take.ms/lGcUp OpenAI API key - https://docs.n8n.io/integrations/builtin/credentials/openai/
by Yaron Been
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automatically analyzes customer lifetime value (CLV) metrics to optimize customer acquisition and retention strategies. It saves you time by eliminating the need to manually calculate CLV and provides data-driven insights for maximizing customer profitability and improving business growth. Overview This workflow automatically scrapes customer data, purchase history, and engagement metrics to calculate and analyze customer lifetime value patterns. It uses Bright Data to access customer analytics platforms and AI to intelligently segment customers, predict CLV, and identify high-value customer characteristics. Tools Used n8n**: The automation platform that orchestrates the workflow Bright Data**: For scraping customer analytics and CRM platforms without being blocked OpenAI**: AI agent for intelligent CLV analysis and customer segmentation Google Sheets**: For storing CLV calculations and customer analysis data How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Bright Data: Add your Bright Data credentials to the MCP Client node Set Up OpenAI: Configure your OpenAI API credentials Configure Google Sheets: Connect your Google Sheets account and set up your CLV analysis spreadsheet Customize: Define customer data sources and CLV calculation parameters Use Cases Customer Success**: Focus retention efforts on high-value customers Marketing Strategy**: Optimize customer acquisition costs based on projected CLV Sales Teams**: Prioritize prospects with higher lifetime value potential Business Strategy**: Make data-driven decisions about customer investments Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Bright Data**: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #customerlifetimevalue #clv #customeranalytics #brightdata #webscraping #customerdata #n8nworkflow #workflow #nocode #customersegmentation #valueanalysis #customerinsights #revenueoptimization #customervalue #clvanalysis #customermetrics #customerprofitability #businessintelligence #customerretention #valueprediction #customeroptimization #revenueanalysis #customerstrategy #lifetimevalue #customerroi #valuedriven #customerworth #profitability
by Deborah
This is a workflow that tries to answer user queries using the standard GPT-4 model. If it can't answer, it sends a message to Slack to ask for human help. It prompts the user to supply an email address. This workflow is used in Advanced AI examples | Ask a human in the documentation. To use this workflow: Load it into your n8n instance. Add your credentials as prompted by the notes. Configure the Slack node to use your Slack details, or swap out Slack for a different service.
by Harshil Agrawal
This is a workflow that sends daily astronomy picture of the day using the NASA node to a channel on Telegram. Cron node: The Cron node triggers the workflow daily at 8 PM. You can update the time in the Cron node to trigger the workflow at your desired time. NASA node: After the Cron node triggers the workflow, the NASA node fetches the Astronomy Picture of the Day from the NASA API. You can also get the binary file of the image. Toggle Download Image to true to get the file. Telegram node: The Telegram node sends the image to a Telegram channel. If you want to share the image on another platform, you can replace the Telegram node with the node of that platform. For example, if you want to post the image on a channel on Slack, replace the Telegram node with the Slack node. You can learn to build this workflow on the documentation page of the NASA node.
by Udit Rawat
Workflow based on the following article. https://www.anthropic.com/news/contextual-retrieval This n8n automation is designed to extract, process, and store content from documents into a Pinecone vector store using context-based chunking. The workflow enhances retrieval accuracy in RAG (Retrieval-Augmented Generation) setups by ensuring each chunk retains meaningful context. Workflow Breakdown: 🔹 Google Drive - Retrieve Document: The automation starts by fetching a source document from Google Drive. This document contains structured content, with predefined boundary markers for easy segmentation. 🔹 Extract Text Content - Once retrieved, the document’s text is extracted for processing. Special section boundary markers are used to divide the text into logical sections. 🔹 Code Node - Create Context-Based Chunks: A custom code node processes the extracted text, identifying section boundaries and splitting the document into meaningful chunks. Each chunk is structured to retain its context within the entire document. 🔹 Loop Node - Process Each Chunk: The workflow loops through each chunk, ensuring they are processed individually while maintaining a connection to the overall document context. 🔹 Agent Node - Generate Context for Each Chunk: We use an Agent node powered by OpenAI’s GPT-4.0-mini via OpenRouter to generate contextual metadata for each chunk, ensuring better retrieval accuracy. 🔹 Prepend Context to Chunks & Create Embeddings - The generated context is prepended to the original chunk, creating context-rich embeddings that improve searchability. 🔹 Google Gemini - Text Embeddings: The processed text is passed through Google Gemini text-embedding-004, which converts the text into semantic vector representations. 🔹 Pinecone Vector Store - Store Embeddings: The final embeddings, along with the enriched chunk content and metadata, are stored in Pinecone, making them easily retrievable for RAG-based AI applications. Use Case: This automation enhances RAG retrieval by ensuring each chunk is contextually aware of the entire document, leading to more accurate AI responses. It’s perfect for applications that require semantic search, AI-powered knowledge management, or intelligent document retrieval. By implementing context-based chunking, this workflow ensures that LLMs retrieve the most relevant data, improving response quality and accuracy in AI-driven applications.
by Harshil Agrawal
This workflow allows you to check for preview for a link and return the preview if it exists. Peekalink node: This node checks if a preview is available for a URL or not. If a preivew is available the node returns true, otherwise false. IF node: The IF node checks the output from the previous node. If the condition is true the node connected to the true branch is executed. If the condition is false the node connected to the false branch is executed. Peekalink1 node: This node will fetch the preview of the URL. Based on your use-case, you can connect the Slack node, Mattermost node etc. to get the response on these platforms. NoOp node: Adding this node here is optional, as the absence of this node won't make a difference to the functioning of the workflow. We've added this as it can sometimes help others with a better understanding of the workflow, visually.
by Dataki
This is the first version of a template for a RAG/GenAI App using WordPress content. As creating, sharing, and improving templates brings me joy 😄, feel free to reach out on LinkedIn if you have any ideas to enhance this template! How It Works This template includes three workflows: Workflow 1**: Generate embeddings for your WordPress posts and pages, then store them in the Supabase vector store. Workflow 2**: Handle upserts for WordPress content when edits are made. Workflow 3**: Enable chat functionality by performing Retrieval-Augmented Generation (RAG) on the embedded documents. Why use this template? This template can be applied to various use cases: Build a GenAI application that requires embedded documents from your website's content. Embed or create a chatbot page on your website to enhance user experience as visitors search for information. Gain insights into the types of questions visitors are asking on your website. Simplify content management by asking the AI for related content ideas or checking if similar content already exists. Useful for internal linking. Prerequisites Access to Supabase for storing embeddings. Basic knowledge of Postgres and pgvector. A WordPress website with content to be embedded. An OpenAI API key Ensure that your n8n workflow, Supabase instance, and WordPress website are set to the same timezone (or use GMT) for consistency. Workflow 1 : Initial Embedding This workflow retrieves your WordPress pages and posts, generates embeddings from the content, and stores them in Supabase using pgvector. Step 0 : Create Supabase tables Nodes : Postgres - Create Documents Table: This table is structured to support OpenAI embedding models with 1536 dimensions Postgres - Create Workflow Execution History Table These two nodes create tables in Supabase: The documents table, which stores embeddings of your website content. The n8n_website_embedding_histories table, which logs workflow executions for efficient management of upserts. This table tracks the workflow execution ID and execution timestamp. Step 1 : Retrieve and Merge WordPress Pages and Posts Nodes : WordPress - Get All Posts WordPress - Get All Pages Merge WordPress Posts and Pages These three nodes retrieve all content and metadata from your posts and pages and merge them. Important: ** **Apply filters to avoid generating embeddings for all site content. Step 2 : Set Fields, Apply Filter, and Transform HTML to Markdown Nodes : Set Fields Filter - Only Published & Unprotected Content HTML to Markdown These three nodes prepare the content for embedding by: Setting up the necessary fields for content embeddings and document metadata. Filtering to include only published and unprotected content (protected=false), ensuring private or unpublished content is excluded from your GenAI application. Converting HTML to Markdown, which enhances performance and relevance in Retrieval-Augmented Generation (RAG) by optimizing document embeddings. Step 3: Generate Embeddings, Store Documents in Supabase, and Log Workflow Execution Nodes: Supabase Vector Store Sub-nodes: Embeddings OpenAI Default Data Loader Token Splitter Aggregate Supabase - Store Workflow Execution This step involves generating embeddings for the content and storing it in Supabase, followed by logging the workflow execution details. Generate Embeddings: The Embeddings OpenAI node generates vector embeddings for the content. Load Data: The Default Data Loader prepares the content for embedding storage. The metadata stored includes the content title, publication date, modification date, URL, and ID, which is essential for managing upserts. ⚠️ Important Note : Be cautious not to store any sensitive information in metadata fields, as this information will be accessible to the AI and may appear in user-facing answers. Token Management: The Token Splitter ensures that content is segmented into manageable sizes to comply with token limits. Aggregate: Ensure the last node is run only for 1 item. Store Execution Details: The Supabase - Store Workflow Execution node saves the workflow execution ID and timestamp, enabling tracking of when each content update was processed. This setup ensures that content embeddings are stored in Supabase for use in downstream applications, while workflow execution details are logged for consistency and version tracking. This workflow should be executed only once for the initial embedding. Workflow 2, described below, will handle all future upserts, ensuring that new or updated content is embedded as needed. Workflow 2: Handle document upserts Content on a website follows a lifecycle—it may be updated, new content might be added, or, at times, content may be deleted. In this first version of the template, the upsert workflow manages: Newly added content** Updated content** Step 1: Retrieve WordPress Content with Regular CRON Nodes: CRON - Every 30 Seconds Postgres - Get Last Workflow Execution WordPress - Get Posts Modified After Last Workflow Execution WordPress - Get Pages Modified After Last Workflow Execution Merge Retrieved WordPress Posts and Pages A CRON job (set to run every 30 seconds in this template, but you can adjust it as needed) initiates the workflow. A Postgres SQL query on the n8n_website_embedding_histories table retrieves the timestamp of the latest workflow execution. Next, the HTTP nodes use the WordPress API (update the example URL in the template with your own website’s URL and add your WordPress credentials) to request all posts and pages modified after the last workflow execution date. This process captures both newly added and recently updated content. The retrieved content is then merged for further processing. Step 2 : Set fields, use filter Nodes : Set fields2 Filter - Only published and unprotected content The same that Step 2 in Workflow 1, except that HTML To Makrdown is used in further Step. Step 3: Loop Over Items to Identify and Route Updated vs. Newly Added Content Here, I initially aimed to use 'update documents' instead of the delete + insert approach, but encountered challenges, especially with updating both content and metadata columns together. Any help or suggestions are welcome! :) Nodes: Loop Over Items Postgres - Filter on Existing Documents Switch Route existing_documents (if documents with matching IDs are found in metadata): Supabase - Delete Row if Document Exists: Removes any existing entry for the document, preparing for an update. Aggregate2: Used to aggregate documents on Supabase with ID to ensure that Set Fields3 is executed only once for each WordPress content to avoid duplicate execution. Set Fields3: Sets fields required for embedding updates. Route new_documents (if no matching documents are found with IDs in metadata): Set Fields4: Configures fields for embedding newly added content. In this step, a loop processes each item, directing it based on whether the document already exists. The Aggregate2 node acts as a control to ensure Set Fields3 runs only once per WordPress content, effectively avoiding duplicate execution and optimizing the update process. Step 4 : HTML to Markdown, Supabase Vector Store, Update Workflow Execution Table The HTML to Markdown node mirrors Workflow 1 - Step 2. Refer to that section for a detailed explanation on how HTML content is converted to Markdown for improved embedding performance and relevance. Following this, the content is stored in the Supabase vector store to manage embeddings efficiently. Lastly, the workflow execution table is updated. These nodes mirros the **Workflow 1 - Step 3 nodes. Workflow 3 : An example of GenAI App with Wordpress Content : Chatbot to be embed on your website Step 1: Retrieve Supabase Documents, Aggregate, and Set Fields After a Chat Input Nodes: When Chat Message Received Supabase - Retrieve Documents from Chat Input Embeddings OpenAI1 Aggregate Documents Set Fields When a user sends a message to the chat, the prompt (user question) is sent to the Supabase vector store retriever. The RPC function match_documents (created in Workflow 1 - Step 0) retrieves documents relevant to the user’s question, enabling a more accurate and relevant response. In this step: The Supabase vector store retriever fetches documents that match the user’s question, including metadata. The Aggregate Documents node consolidates the retrieved data. Finally, Set Fields organizes the data to create a more readable input for the AI agent. Directly using the AI agent without these nodes would prevent metadata from being sent to the language model (LLM), but metadata is essential for enhancing the context and accuracy of the AI’s response. By including metadata, the AI’s answers can reference relevant document details, making the interaction more informative. Step 2: Call AI Agent, Respond to User, and Store Chat Conversation History Nodes: AI Agent** Sub-nodes: OpenAI Chat Model Postgres Chat Memories Respond to Webhook** This step involves calling the AI agent to generate an answer, responding to the user, and storing the conversation history. The model used is gpt4-o-mini, chosen for its cost-efficiency.
by dirogar
Telegram Tasker Bot — это сценарий n8n, который принимает голосовые сообщения в Telegram, автоматически превращает их в текст, извлекает из него ключевые поля задачи и создаёт карточку в нужной доске Trello. Пользователь просто говорит задачу — бот сам оформляет её и присылает ссылку на готовую карточку. Для использования вам потребуется telegram bot. Его можно создать через бота BotFather Так же понадобится доступ к API chatgpt - он используется только для транскрибции аудио в речь. Вы можете использовать любой другой сервис, по вашему выбору. И аккаунт в trello, с доступом к API. !Внимание! ID доски в trello можно взять из url ID столбца на доске трелло можно взять через инструменты разработчика (по крайней мере я так получал эти данные)
by Jonathan
Task: Control your data flow with rate limits and external cues Main use cases: Control the rate of items flow into one or more services in your workflow Wait for external events to occur before continuing with the rest of the workflow
by n8n Team
This workflow is for anyone looking to automatically fetch, validate, and parse complex language-based queries into a structured format. Its unique capability lies in not only processing language but also fixing invalid outputs before structuring them. Note that to use this template, you need to be on n8n version 1.19.4 or later.
by Jonathan
Task: Handle dates and times in your workflow Why: Date and time formats can be hard to work with, we have 2 main ways of doing that with n8n that cover all the main needs Main use cases: Change date format Set custom dates (incl. now and today) Date math