Google Drive and Google Sheets integration

How it Works This workflow automates the process of handling job applications by extracting relevant information from submitted CVs, analyzing the candidate's qualifications against a predefined profile, and storing the results in a Google Sheet. Here’s how it operates: Data Collection and Extraction: The workflow begins with a form submission (On form submission node), which triggers the extraction of data from the uploaded CV file using the Extract from File node. Two informationExtractor nodes (Qualifications and Personal Data) are used to parse specific details such as educational background, work history, skills, city, birthdate, and telephone number from the text content of the CV. Processing and Evaluation: A Merge node combines the extracted personal and qualification data into a single output. This merged data is then passed through a Summarization Chain that generates a concise summary of the candidate’s profile. An HR Expert chain evaluates the candidate against a desired profile (Profile Wanted), assigning a score and providing considerations for hiring. Finally, all collected and processed data including the evaluation results are appended to a Google Sheets document via the Google Sheets node for further review or reporting purposes [[9]]. Set Up Steps To replicate this workflow within your own n8n environment, follow these steps: Configuration: Begin by setting up an n8n instance if you haven't already; you can sign up directly on their website or self-host the application. Import the provided JSON configuration into your n8n workspace. Ensure that all necessary credentials (e.g., Google Drive, Google Sheets, OpenAI API keys) are correctly configured under the Credentials section since some nodes require external service integrations like Google APIs and OpenAI for language processing tasks. Customization: Adjust the parameters of each node according to your specific requirements. For example, modify the fields in the formTrigger node to match what kind of information you wish to collect from applicants. Customize the prompts given to AI models in nodes like Qualifications, Summarization Chain, and HR Expert so they align with the type of analyses you want performed on the candidates' profiles. Update the destination settings in the Google Sheets node to point towards your own spreadsheet where you would like the final outputs recorded.

This workflow automates the generation of multi-speaker podcasts using AI-powered text-to-speech technology. It starts by retrieving a podcast script from a Google Sheets document, where each speaker’s lines are clearly defined. The workflow then processes the script, generates a natural-sounding audio file with different voices for each speaker, and stores the final audio file in Google Drive. The workflow is designed to save time and resources by automating the podcast production process, making it ideal for content creators, marketers, and businesses that need to produce high-quality audio content regularly. How It Works Triggering the Workflow: The workflow starts with the When clicking ‘Test workflow’ node, which can be triggered manually to begin the process. Data Retrieval: The Get Podcast text node retrieves data from a Google Sheets document containing the podcast script. The document includes columns for the speaker's name and the corresponding text. Data Aggregation: The Get all rows node aggregates the data from the Google Sheets document, combining the speaker names and their respective text into a single dataset. Text Formatting: The Full Podcast Text node processes the aggregated data, formatting it into a single string where each speaker's text is prefixed with their name. Audio Generation: The Create Audio node sends a request to the API to generate a multi-speaker podcast audio file. The request includes the formatted text and specifies the voices for each speaker. When you register for the API service you will get 1$ for free. For continuous work add API credits to your account. Status Check: The Get status node checks the status of the audio generation request. If the status is "COMPLETED", the workflow proceeds; otherwise, it waits again. Audio Retrieval: The Get Url Audio node retrieves the URL of the generated audio file. The Get File Audio node downloads the audio file from the provided URL. Audio Storage: The Upload Audio node uploads the generated audio file to a specified Google Drive folder for storage. Key Features Multi-Speaker Support**: Generates podcasts with different voices for each speaker, creating a more dynamic and engaging listening experience. Google Sheets Integration**: Retrieves podcast scripts from a Google Sheets document, making it easy to manage and update content. AI-Powered Text-to-Speech**: Uses advanced AI models to generate natural-sounding audio from text. Automated Audio Generation**: Streamlines the process of creating podcast audio files, reducing the need for manual recording and editing. Google Drive Storage**: Automatically uploads the generated audio files to Google Drive for easy access and sharing. This workflow is ideal for businesses and content creators looking to automate the production of multi-speaker podcasts. It leverages AI to handle the complex task of generating natural-sounding audio, allowing users to focus on creating compelling content.

The best content automation in the market! This advanced workflow not only creates and publishes SEO-optimized blog posts to your WordPress website but also backs up all content and images to a designated folder in your Google Drive. In addition, It generates a unique image for each chapter and a featured image for the overall article, and it automatically collects internal website links—seamlessly inserting them throughout each chapter and the entire article. This integrated approach enhances on-page SEO, improves navigation, and streamlines your content creation process, saving you time while ensuring your work is securely stored. How it works Triggers upon adding a new row to a Google Sheets. Generates a full blog post by writing content based on customizable parameters such as topic, target audience, length, style, and seed keyword. Generates and adds images for each chapter as well as a featured image for the article. Gathers internal website links and strategically embeds them within each chapter and throughout the article, boosting SEO and enhancing user navigation. Publishes the blog post directly to your WordPress website. Saves all content (blog post and images) to Google Drive, organizing them in a folder named after the blog post title. Unique features Full Automation: The workflow is designed to be 100% automated. Once imported and configured, it can run without manual intervention. Simple Activation: It can be easily triggered through the Google Spreadsheets interface. You simply add a new row to a Google Sheet. Customization Options: Offers a wide array of customization options, including topic, category, target audience, word count, number of chapters, length of introduction and conclusion, and writing style. It also allows for the inclusion of calls-to-action (CTAs) and company/product introductions. Automatic Content Saving: After writing a blog post, all content and images are automatically saved to Google Drive, preventing data loss. The folder is even named after the title of the blog post. SEO-Optimized Content: It's designed to create content optimized for SEO using seed keywords. AI Model Flexibility: It’s super easy to switch between different AI models through the Open Router node. Rate Limit Handling: Includes "Wait" nodes to avoid rate limits. Internal Link Limit: Limits the number of internal links to 20 by default. Set up steps Install the workflow template: Import the JSON file into your n8n instance. Connect the workflow to your accounts: This includes linking your WordPress website, Google Drive, and AI models (such as OpenAI GPT-4o). Configure the Google Sheet: Ensure your Google Sheet is set up to trigger the workflow upon adding a new row and that the input data is correctly formatted. Customize the workflow: Adjust parameters like topic, target audience, and writing style to match your specific content needs. Optimize prompts for best results. Test the workflow: Use low-cost AI models and image settings initially to ensure everything runs smoothly. Tailor Further as Needed: Modify workflow elements to align perfectly with your needs and content strategy.

This AI Agent is designed to streamline the process of creating realistic images of clothing products worn by models, eliminating the need for expensive photoshoots. The primary goal is to automate the generation of virtual try-on images for an eCommerce store selling clothing, using advanced image processing technologies. Example of results How It Works Triggering the Workflow: The workflow can be triggered manually using the When clicking ‘Test workflow’ node or automatically via the Schedule Trigger node, which runs the workflow at regular intervals (e.g., every 5 minutes). Data Retrieval: The Get new product node retrieves data from a Google Sheets document containing the URLs of the model image, the clothing product image, and the WooCommerce product ID. The document also includes a column for the resulting virtual try-on image URL, which is initially empty. Setting Up the Request: The Set data node prepares the data for the AI request by assigning the model image URL and the product image URL to variables. AI Image Generation: The Create Image node sends a request to API to generate a virtual try-on image. The request includes the URLs of the model and the clothing product. When you register for the API service you will get 1$ for free. For continuous work add API credits to your account. Image Retrieval: The Get Url image node retrieves the URL of the generated virtual try-on image. The Get File image node downloads the generated image from the provided URL. Image Storage: The Upload Image node uploads the generated image to a specified Google Drive folder for storage. Updating Google Sheets: The Update result node updates the Google Sheets document with the URL of the generated virtual try-on image. Updating WooCommerce: The Update product node updates the corresponding product in WooCommerce by adding the generated virtual try-on image to the product's image gallery. Functionality This AI Agent is designed to streamline the process of creating realistic images of clothing products worn by models, eliminating the need for expensive photoshoots. The primary goal is to automate the generation of virtual try-on images for an eCommerce store selling clothing, using advanced image processing technologies. By offering realistic images of clothing items worn by models, this automation process saves time and resources, making product catalog management more efficient. This approach not only enhances the competitiveness of the eCommerce store but also provides greater flexibility in creating high-quality visual content adaptable to various digital marketing campaigns. Key Features Automated Virtual Try-On**: Generates realistic images of models wearing clothing items using AI. Google Sheets Integration**: Retrieves and updates data in Google Sheets for seamless data management. Image Storage**: Uploads generated images to Google Drive for easy access and archiving. WooCommerce Integration**: Updates product pages with virtual try-on images, enhancing the shopping experience. Scheduled Automation**: Can be triggered manually or run at regular intervals for continuous processing. This workflow is ideal for eCommerce businesses looking to enhance their product catalogs with high-quality, realistic images without the need for costly photoshoots. It leverages AI to provide a cost-effective and efficient solution for virtual try-on image generation.

This n8n workflow creates a financial analysis tool that generates reports on a company's quarterly earnings using the capabilities of OpenAI GPT-4o-mini, Google's Gemini AI and Pinecone's vector search. By analyzing PDFs of any company's earnings reports from their Investor Relations page, this workflow can answer complex financial questions and automatically compile findings into a structured Google Doc. How it works: Data loading and indexing Fetches links to PDF earnings document from a Google Sheet containing a list of file links. Downloads the PDFs from Google Drive. Parses the PDFs, splits the text into chunks, and generates embeddings using the Embeddings Google AI node (text-embedding-004 model). Stores the embeddings and corresponding text chunks in a Pinecone vector database for semantic search. Report generation with AI agent Utilizes an AI Agent node with a specifically crafted system prompt. The agent orchestrates the entire process. The agent uses a Vector Store Tool to access and retrieve information from the Pinecone database. Report delivery Saves the generated report as a Google Doc in a specified Google Drive location. Set up steps Google Cloud Project & Vertex AI API: Create a Google Cloud project. Enable the Vertex AI API for your project. Google AI API key: Obtain a Google AI API key from Google AI Studio. Pinecone account and API key: Create a free account on the Pinecone website. Obtain your API key from your Pinecone dashboard. Create an index named company-earnings in your Pinecone project. Google Drive - download and save financial documents: Go to a company you want to analize and download their quarterly earnings PDFs Save the PDFs in Google Drive Create a Google Sheet that stores a list of file URLs pointing to the PDFs you downloaded and saved to Google Drive Configure credentials in your n8n environment for: Google Sheets OAuth2 Google Drive OAuth2 Google Docs OAuth2 Google Gemini(PaLM) Api (using your Google AI API key) Pinecone API (using your Pinecone API key) Import and configure the workflow: Import this workflow into your n8n instance. Update the List Of Files To Load (Google Sheets) node to point to your Google Sheet. Update the Download File From Google Drive to point to the column where the file URLs are Update the Save Report to Google Docs node to point to your Google Doc where you want the report saved.

This workflow is created by AI developers at WeblineIndia. It streamlines the process of managing content by automatically identifying and fetching the most recently added Google Doc file from your Google Drive. It extracts the content of the document for processing and leverages an AI model to generate a concise and meaningful summary of the extracted text. The summarized content is then stored in a designated Google Sheet, alongside relevant details like the document name and the date it was added, providing an organized and easily accessible reference for future use. This automation simplifies document handling, enhances productivity, and ensures seamless data management. Steps : Fetch the Most Recent Document from Google Drive Action:** Use the Google Drive Node. Details:** List files, filter by date to fetch the most recently added .doc file, and retrieve its file ID and metadata. Extract Content from the Document Action:** Use the Google Docs Node. Details:** Set the operation to "Get Content," pass the file ID, and extract the document's text content. Summarize the Document Using an AI Model Action:** Use an AI Model Node (e.g., OpenAI, ChatGPT). Details:** Provide the extracted text to the AI model, use a prompt to generate a summary, and capture the result. Store the Summarized Content in Google Sheets Action:** Use the Google Sheets Node. Details:** Append a new row to the target sheet with details such as the original document name, summary, and date added. About WeblineIndia WeblineIndia specializes in delivering innovative and custom AI solutions to simplify and automate business processes. If you need any help, please reach out to us.