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Are you a popular tech startup accelerator (named after a particular higher order function) overwhelmed with 1000s of pitch decks on a daily basis? Wish you could filter through them quickly using AI but the decks are unparseable through conventional means? Then you're in luck! This n8n template uses Multimodal LLMs to parse and extract valuable data from even the most overly designed pitch decks in quick fashion. Not only that, it'll also create the foundations of a RAG chatbot at the end so you or your colleagues can drill down into the details if needed. With this template, you'll scale your capacity to find interesting companies you'd otherwise miss! Requires n8n v1.62.1+ How It Works Airtable is used as the pitch deck database and PDF decks are downloaded from it. An AI Vision model is used to transcribe each page of the pitch deck into markdown. An Information Extractor is used to generate a report from the transcribed markdown and update required information back into pitch deck database. The transcribed markdown is also uploaded to a vector store to build an AI chatbot which can be used to ask questions on the pitch deck. Check out the sample Airtable here: https://airtable.com/appCkqc2jc3MoVqDO/shrS21vGqlnqzzNUc How To Use This template depends on the availability of the Airtable - make a duplicate of the airtable (link) and its columns before running the workflow. When a new pitchdeck is received, enter the company name into the Name column and upload the pdf into the File column. Leave all other columns blank. If you have the Airtable trigger active, the execution should start immediately once the file is uploaded. Otherwise, click the manual test trigger to start the workflow. When manually triggered, all "new" pitch decks will be handled by the workflow as separate executions. Requirements OpenAI for LLM Airtable For Database and Interface Qdrant for Vector Store Customising This Workflow Extend this starter template by adding more AI agents to validate claims made in the pitch deck eg. Linkedin Profiles, Page visits, Reviews etc.

This n8n workflow demonstrates an approach to parsing bank statement PDFs with multimodal LLMs as an alternative to traditional OCR. This allows for much more accurate data extraction from the document especially when it comes to tables and complex layouts. Multimodal Parsing is better than traditiona OCR because: It reduces complexity and overhead by avoiding the need to preprocess the document into text format such as markdown before passing to the LLM. It handles non-standard PDF formats which may produce garbled output via traditional OCR text conversion. It's orders of magnitude cheaper than premium OCR models that still require post-processing cleanup and formatting. LLMs can format to any schema or language you desire! How it works You can use the example bank statement created specifically for this workflow here: https://drive.google.com/file/d/1wS9U7MQDthj57CvEcqG_Llkr-ek6RqGA/view?usp=sharing A PDF bank statement is imported via Google Drive. For this demo, I've created a mock bank statement which includes complex table layouts of 5 columns. Typically, OCR will be unable to align the columns correctly and mistake some deposits for withdrawals. Because multimodal LLMs do not accept PDFs directly, well have to convert the PDF to a series of images. We can achieve this by using a tool such as Stirling PDF. Stirling PDF is self-hostable which is handy for sensitive data such as bank statements. Stirling PDF will return our PDF as a series of JPGs (one for each page) in a zipped file. We can use n8n's decompress node to extract the images and ensure they are ordered by using the Sort node. Next, we'll resize each page using the Edit Image node to ensure the right balance between resolution limits and processing speed. Each resized page image is then passed into the Basic LLM node which will use our multimodal LLM of choice - Gemini 1.5 Pro. In the LLM node's options, we'll add a "user message" of type binary (data) which is how we add our image data as an input. Our prompt will instruct the multimodal LLM to transcribe each page to markdown. Note, you do not need to do this - you can just ask for data points to extract directly! Our goal for this template is to demonstrate the LLMs ability to accurately read the page. Finally, with our markdown version of all pages, we can pass this to another LLM node to extract required data such as deposit line items. Requirements Google Gemini API for Multimodal LLM. Google Drive access for document storage. Stirling PDF instance for PDF to Image conversion Customising the workflow At time of writing, Gemini 1.5 Pro is the most accurate in text document parsing with a relatively low cost. If you are not using Google Gemini however you can switch to other multimodal LLMs such as OpenAI GPT or Antrophic Claude. If you don't need the markdown, simply asking what to extract directly in the LLM's prompt is also acceptable and would save a few extra steps. Not parsing any bank statements any time soon? This template also works for Invoices, inventory lists, contracts, legal documents etc.

Using n8n a lot? Soar above the limitations of the default n8n dashboard! This template gives you an overview of your workflows, nodes, and tags – all in one place. 💪 Built using XML stylesheets and the Bootstrap 5 library, this workflow is self-contained and does not depend on any third-party software. 🙌 It generates a comprehensive overview JSON that can be easily integrated with other BI tools for further analysis and visualization. 📊 Reach out to Eduard if you need help adapting this workflow to your specific use-case! 🚀 Benefits: Workflow Summary** 📈: Instant overview of your workflows, active counts, and triggers. Left-Side Panel** 📋: Quick access to all your workflows, nodes, and tags for seamless navigation. Workflow Details** 🔬: Deep dive into each workflow's nodes, timestamps, and tags. Node Analysis** 🧩: Identify the most frequently used nodes across your workflows. Tag Organization** 🗂️: Workflows are grouped according to their tags. Visually Stunning** 🎨: Clean, intuitive, and easy-to-navigate dashboard design. XML & Bootstrap 5** 🛠️: Built using XML stylesheets and Bootstrap 5, ensuring a self-contained and responsive dashboard. No Dependencies** 🔒: The workflow does not rely on any third-party software. Bootstrap 5 files are loaded via CDN but can be delivered directly from your server. ⚠️ Important note for cloud users Since the cloud version doesn't support environmental variables, please make the following changes: get-nodes-via-jmespath node. Update the instance_url variable: enter your n8n URL instead of {{$env["N8N_PROTOCOL"]}}://{{$env["N8N_HOST"]}} Create HTML node. Please provide the n8n instance URL instead of {{ $env.WEBHOOK_URL }} 🌟Example: Check out our other workflows: n8n.io/creators/eduard n8n.io/creators/yulia

AI Content Generator Workflow Introduction This workflow automates the process of creating high-quality articles using AI, organizing them in Google Drive, and tracking their progress in Google Sheets. It's perfect for marketers, bloggers, and businesses looking to streamline content creation. With minimal setup, you can have a fully operational system to generate, save, and manage your articles in one cohesive workflow. How It Works Collect Inputs: Users fill out a form with details like article title, keywords, and instructions. Generate Content: AI creates an outline and writes the article based on user inputs. Organize Files: Saves the outline and final article in Google Drive for easy access. Track Progress: Updates Google Sheets with links to the generated content for tracking. Set Up Steps Time Required**: Approximately 15–20 minutes to connect all integrations and test the workflow. Steps**: Connect Google Drive and Google Sheets: Authorize access to store files and update the spreadsheet. Set Up OpenAI Integration: Add your OpenAI API key for generating the outline and article content. Customize the Form: Modify the form fields to match the details you want to collect for each article. Test the Workflow: Run the workflow with sample inputs to ensure everything works smoothly. This workflow not only simplifies the process of article creation but also sets a foundation for expanding into additional automations, like posting to social media platforms.

Purpose This workflow allows you to import any workflow from a file or another n8n instance and map the credentials easily. How it works A multi-form setup guides you through the entire process At the beginning you have two options: Upload a workflow file (JSON) Copy workflow from a remote n8n instance If you choose the second option, you get to choose one of your predefined (in the Settings node) remote instances first, then it retrieves a list of all the workflows using the n8n API which you then can choose a workflow from. Now both initial options come together - the workflow file is being processed In parallel all credentials of the current instance are being retrieved using the Execute Command node The next form page enables a mapping of all the credentials used in the workflow. The matching happens between the names (because one workflow can contain different credentials of the same type) of the original credentials and the ones available on the current instance. Every option then shows all available credentials of the same type. In addition the user has always the choice to create a new credential on the fly. For every option which was set to create a new credential, an empty credential is being created on the current instance using the n8n API. An emoji is being appended to the name, which indicates that it needs to be populated. Finally the workflow gets updated with the new credential ID’s and created on the current instance using the n8n API. Then the user gets a message, if the process has succeeded or not. Setup Select your credentials in the nodes which require those Configure your remote instance(s) in the Settings node. (You can skip this step, if you only want to use the File Upload feature) Every instance is defined as object with the keys name, apiKey and baseUrl. Those instances are then wrapped inside an array. You can find an example described within a note on the workflow canvas. How to use Grab the (production) URL of the Form from the first node Open the URL and follow the instructions given in the multi-form Disclaimer Security: Beware, that all credentials are being decrypted and processed within the workflow. Also the API keys to other n8n instances are stored within the workflow. This solution is primarily meant for transferring data between testing environments. For production use consider the n8n enterprise edition which provides a reliable way to deploy workflows between different environments without the need of manual credential mapping.

🚀 Supercharge Your Website Indexing with This Powerful n8n Workflow! 🌐 Google page indexing too slow? Tired of manually clicking through each page in the Google Search Console? 😴 Say goodbye to that tedious process and hello to automation with this n8n workflow! 🎉 **NB: this workflow was tested with sitemap.xml generated by Ghost CMS and WordPress. Reach out to Eduard if you need help adapting this workflow to your specific use-case!** ⚙️ How this automation works 📅 The workflow runs on a schedule or when you click "Test workflow". 🌐 It fetches the website's primary sitemap.xml and extracts all the content-specific sitemaps (this is a typical structure of the sitemap). 📑 Each content-specific sitemap is then parsed to retrieve the individual page data. 🔄 The extracted page data is converted to JSON format for easy manipulation. 🗃️ The lastmod (last modified date) and loc (page URL) fields are assigned to each page entry to ensure compliance with the Sitemap protocol. 🔀 The page entries are sorted by the lastmod field in descending order (newest to oldest). 🔁 The workflow then loops over each page entry and performs the following steps: 🔍 Checks the URL metadata in the Google Indexing API. ✅ If the page is new or has been updated since the last indexing request, it sends a request to the Google Indexing API to update the URL. ⏳ Wait a sec and move on with the next page. 🌟 Benefits ⏰ Save time by automating the indexing process. 🎯 Ensure all your website pages are consistently indexed by Google. 🚀 Improve your website's visibility and search engine rankings. 🛠️ Customize the workflow to fit your specific CMS and requirements. 🔧 Getting started To start using this powerful n8n workflow, follow these steps: ☑️ Make sure to verify the website ownership in the Google Search Console. 👨‍💻 Import the workflow JSON into your n8n instance. Edit the Get sitemap.xml node and update the URL with your website's valid sitemap.xml 🔑 Set up the necessary credentials for the Google Indexing API. 🎚️ Adjust the schedule trigger to run the workflow at your desired frequency. 🎉 Sit back and let the workflow handle the indexing process for you! Ready to take your website indexing to the next level? 🚀 Try this workflow now and see the difference it makes! 😊 ⚠️ IMPORTANT NOTE 1 Need help with connecting Google Cloud Platform to n8n? Check out our article on connecting Google Sheets to n8n. The process is mainly the same. When activating Google APIs, make sure to add Web Search Indexing API. Also, in the credential page of n8n, add the https://www.googleapis.com/auth/indexing scope: Check out Yulia's page for more n8n workflows! ⚠️ IMPORTANT NOTE 2 Free Google Cloud Platform account allows (re)indexing only 200 pages per day. If your website has more, then the workflow will automatically fail on quota limit ⛔. Next day it will skip the previously added items and continue with remaining pages. Example:* Assuming you have a free Google account, 500 pages on your website and they don't change for 3 days: On the first day 200 pages will be added for indexing and the workflow will fail due to quota limits. On the second day, the workflow will check 200 pages again and skip them (because the date of re-indexing is later then the page last modified date). The next 200 pages will be added to indexing. Workflow will fail again due to quota limits. On the third day 400 pages will be checked and skipped, the last 100 pages will be added for indexing and the workflow finishes successfully.