Google Gemini Chat Model and HTTP Request integration

This n8n template demonstrates the beginnings of building your own n8n-powered WhatsApp chatbot! Under the hood, utilise n8n's powerful AI features to handle different message types and use an AI agent to respond to the user. A powerful tool for any use-case! How it works Incoming WhatsApp Trigger provides a way to get messages into the workflow. The message received is extracted and sent through 1 of 4 branches for processing. Each processing branch uses AI to analyse, summarize or transcribe the message so that the AI agent can understand it. The supported types are text, image, audio (voice notes) and video. The AI Agent is used to generate a response generally and uses a wikipedia tool for more complex queries. Finally, the response message is sent back to the WhatsApp user using the WhatsApp node. How to use Once you have setup and configured your WhatsApp account, you'll need to activate your workflow to start processing messages. Good to know: Large media files may negatively impact workflow performance. Requirements WhatsApp Buisness account Google Gemini for LLM. Gemini is used specifically because it can accept audio and video files whereas at time of writing, many other providers like OpenAI's GPT, do not. Customising this workflow For performance reasons, consider detecting large audio and video before sending to the LLM. Pre-processing such files may allow your agent to perform better. Go beyond and create rich and engagement customer experiences by responding using images, audio and video instead of just text!

AppSheet Intelligent Query Orchestrator A friendly, practical tool that makes working with AppSheet data simpler and more efficient. This workflow is your go-to helper for building precise queries without getting lost in a sea of different tables. Background Previously, I built a community node to enable this functionality: Appsheet n8n Community node How It Works This workflow fetches the most up-to-date schema and taxonomy from your Google Sheet mirror and constructs a custom query using key components: TableName**: Specifies exactly which table to query. Selector**: Uses powerful functions like SELECT(), FILTER(), and CONTAINS() to filter data with precision. Columns Required: Extracts only the essential fields, keeping the payload **lean and focused. Natural Language Search Query: Provides a **clear, descriptive context that helps refine and re-rank results. Real-World Use Cases This orchestrator is designed for various industries, making data retrieval effortless: 📦 Supply Chain & Manufacturing Find the right product based on specific attributes. Locate suppliers that meet certain quality or pricing criteria. Obtain details about the lowest-priced raw materials. 🛍 Retail & E-commerce Match customer queries to the most relevant product listings. Identify inventory levels and stock variations. Compare pricing and product features across vendors. 🏥 Healthcare Retrieve patient records based on specific attributes. Track inventory of medical supplies. Schedule and manage appointments dynamically. 🎓 Education Monitor student attendance or performance metrics. Allocate resources and track equipment usage. Manage events and class schedules efficiently. 🔧 Field Services & Maintenance Schedule maintenance tasks by matching service requirements. Track asset conditions and inventory for field equipment. Monitor work orders and dispatch field teams based on real-time data. Examples: Iterative Refinement This workflow operates iteratively, refining the query until it finds the best match—even if it takes multiple rounds. This makes it incredibly versatile for complex inventory management, procurement, and precise data retrieval. In a Nutshell The AppSheet Intelligent Query Orchestrator is like having a smart assistant that: ✅ Understands your data structure ✅ Builds the perfect query every time ✅ Handles a variety of real-world scenarios with ease 🚀 Practical, adaptable, and ready to tackle your toughest data challenges!

This workflow automates the process of discovering and extracting APIs from various services, followed by generating custom schemas. It works in three distinct stages: research, extraction, and schema generation, with each stage tracking progress in a Google Sheet. 🙏 Jim Le deserves major kudos for helping to build this sophisticated three-stage workflow that cleverly automates API documentation processing using a smart combination of web scraping, vector search, and LLM technologies. How it works Stage 1 - Research: Fetches pending services from a Google Sheet Uses Google search to find API documentation Employs Apify for web scraping to filter relevant pages Stores webpage contents and metadata in Qdrant (vector database) Updates progress status in Google Sheet (pending, ok, or error) Stage 2 - Extraction: Processes services that completed research successfully Queries vector store to identify products and offerings Further queries for relevant API documentation Uses Gemini (LLM) to extract API operations Records extracted operations in Google Sheet Updates progress status (pending, ok, or error) Stage 3 - Generation: Takes services with successful extraction Retrieves all API operations from the database Combines and groups operations into a custom schema Uploads final schema to Google Drive Updates final status in sheet with file location Ideal for: Development teams needing to catalog multiple APIs API documentation initiatives Creating standardized API schema collections Automating API discovery and documentation Accounts required: Google account (for Sheets and Drive access) Apify account (for web scraping) Qdrant database Gemini API access Set up instructions: Prepare your Google Sheets document with the services information. Here's an example of a Google Sheet – you can copy it and change or remove the values under the columns. Also, make sure to update Google Sheets nodes with the correct Google Sheet ID. Configure Google Sheets OAuth2 credentials, required third-party services (Apify, Qdrant) and Gemini. Ensure proper permissions for Google Drive access.

Case Study I'm too lazy to record every transaction for my expense tracking. Since all my expenses are digital, I just extract the transactions from bank PDF statements and screenshots into CSV to import into my budgeting software. Read more -> How I used A.I. to track all my expenses What this workflow does Upload your PDF or screenshots into Google Drive It then passes the PDF/image to Vertex Gemini to do some A.I. image recognition It then sends the transactions as CSV and stores it into another Google Drive folder Setup Set up 2 google drive folders. 1 for uploading and 1 for the output. Input your Google Drive crendtials Input your Vertex Gemini credentials How to adjust it to your needs You can upload other types of documents for information extraction. You can extract any text data from any image or PDF You can adjust the A.I. prompt to do different things

The purpose of this workflow, "Sell: Stock Vision," is to create an AI-powered technical analysis agent capable of analyzing financial charts for equity stocks and cryptocurrencies. This workflow provides users with actionable insights into market trends, price movements, candlestick patterns, and technical indicators to support informed trading decisions. How It Works Integration with TradingView:** The workflow uses the Chart-Img.com API to fetch detailed financial charts for the specified stock or cryptocurrency. AI-Powered Analysis:** The workflow employs advanced AI models, including Google's Gemini Chat Model, to analyze the retrieved charts for candlestick patterns, support/resistance levels, and technical indicators like MACD and RSI. News and Sentiment Analysis:** By integrating with SerpAPI, the workflow gathers relevant news about the stock or cryptocurrency to evaluate its potential impact on market movements. Comprehensive Insights:** It provides detailed trading strategies, including buy/sell recommendations, stop-loss levels, and risk-reward evaluations. Continuous Memory:** The AI agent uses buffer memory to retain context for enhanced analysis and continuity. Use Case This workflow is perfect for traders and analysts who need reliable and AI-powered market analysis to make informed trading decisions efficiently. Tutorial Obtain API keys for Chart-Img.com and SerpAPI. Configure the workflow in your n8n instance by inputting the required API keys and connecting the nodes. Trigger the workflow by providing the stock or cryptocurrency symbol, and let the agent do the rest! https://youtu.be/9fR4qNMT5LM

This is a simple workflow that grabs HackerNews front-page headlines from today's date across every year since 2007 and uses a little AI magic (Google Gemini) to sort 'em into themes, sends a neat Markdown summary on Telegram. How it works Runs daily, grabs Hacker News front page for this day across every year since 2007. Pulls headlines & dates. Uses Google Gemini to sort headlines into topics & spot trends. Sends a Markdown summary to Telegram. Set up steps Clone the workflow. Add your Google Gemini API key. Add your Telegram bot token and chat ID. **Built on Day-01 as part of the #100DaysOfAgenticAi Fork it, tweak it, have fun!**

Template Guide for Employee Shortlisting AI Agent Automation Overview This template automates the process of shortlisting job applicants using ERPNext, n8n, and AI-powered decision-making tools like Google Gemini and OpenAI. It reduces manual effort, ensures fast evaluations, and provides justifiable decisions about applicants. This is ideal for businesses aiming to streamline their recruitment process while maintaining accuracy and professionalism. YouTube Tutorial:** For a full walkthrough of this template, visit: Integrate AI in ERPNext: Automate Recruitment Job Applicant Shortlisting in Seconds! What Does This Template Do? Webhook Integration with ERPNext: Automatically triggers the workflow when a job application is created in ERPNext. Resume Validation: Ensures resumes are attached and correctly processes various file formats like PDF and DOC. AI-Powered Evaluation: Uses AI to compare resumes against job descriptions and provides a: Fit Level (Strong, Moderate, or Weak) Score (0–100) Justification for the decision. Automated Decision Making: Based on AI-generated scores: Candidates with a score of 80 or higher are Accepted. Candidates below 80 are Rejected. Applications missing required fields or attachments are put On Hold. ERPNext Integration: Updates applicant records in ERPNext, including custom fields such as justification, fit level, and scores. Notifications: Notifies candidates via email, WhatsApp, or SMS about their application status. Step-by-Step Guide Step 1: Set Up ERPNext Webhook Go to Webhooks in ERPNext. Create a webhook for the Job Applicant DocType. Set the trigger to Insert. Pin and test the webhook to ensure proper data flow. Step 2: Import the Template into n8n Open your n8n instance. Import the provided workflow template. Check all nodes for proper configuration. Step 3: Configure Credentials Add your ERPNext API credentials to the ERPNext nodes. Add credentials for AI services like OpenAI or Google Gemini. Configure additional services like WhatsApp or email if you plan to use them for notifications. Step 4: Test Resume Validation Test how the workflow handles different file types (e.g., PDF, DOC, JPG). Ensure resumes without the proper format or attachment are flagged and rejected. Step 5: AI Evaluation The AI model (Google Gemini or OpenAI) will evaluate resumes against job descriptions. Customize the AI prompt to suit your job evaluation needs. The output will include a Fit Level, Score, Rating, and Justification. Step 6: Decision Automation The workflow automatically categorizes applicants: Accepted for scores ≥ 80. Rejected for scores < 80. On Hold if essential fields or attachments are missing. Step 7: Update ERPNext Records The workflow updates the Job Applicant record in ERPNext with: Status (Accepted, Rejected, On Hold) AI-generated Fit Level, Score, Rating, and Justification. Step 8: Notify Candidates Configure notification nodes (email, WhatsApp, or SMS). Inform candidates about their application status and include feedback if required. How It Works Trigger: The workflow starts when a job application is submitted in ERPNext. Validation: Checks if the resume is attached and in the correct format. AI Evaluation: Compares the resume with the job description and generates a decision. ERPNext Update: Updates the applicant's record with the decision and justification. Notification: Sends a personalized notification to the candidate. Dos and Don’ts Dos: Customize Prompts:** Tailor the AI prompt to match your specific job evaluation requirements. Test the Workflow:** Run sample data to ensure the process works as intended. Secure Your Credentials:** Keep your API credentials safe and do not share them publicly. Optimize for Different Formats:** Ensure the workflow can handle all types of resumes you expect. Don’ts: Avoid Manual Intervention:** Let the workflow handle most of the tasks to ensure efficiency. Do Not Skip Testing:** Always test the workflow with various scenarios to avoid errors. Do Not Overlook Notifications:** Ensure candidates are notified promptly to maintain professionalism. Customization Options Add logic for more file types (e.g., scanned images using OCR). Enhance the AI prompts to analyze more complex resume data. Integrate additional tools like Slack or Trello for recruitment tracking. Resources YouTube Tutorial:** For a full walkthrough of this template, visit: SyncBricks YouTube Channel Detailed Guides and Courses:** Learn more about ERPNext and AI-driven automation at: SyncBricks LMS Support If you encounter issues or want to explore more possibilities with AI-driven automation, feel free to reach out: Email:** [email protected] Website:** ERPNext and Other Courses LinkedIn:** Amjid Ali Let me know if you'd like further details or modifications to the guide!

Who is this for? This workflow is designed for businesses or developers looking to integrate voice-based chat applications with dynamic responses and conversational memory. What problem does this solve? It automates AI-powered voice conversations, maintaining context between sessions and converting speech-to-text and text-to-speech. What this workflow does: The workflow receives audio input, transcribes it using OpenAI, and processes the conversation using Google Gemini Chat Model (you can use OpenAI Chat Model). Responses are converted back to speech using ElevenLabs. Prerequisites: You'll need API keys for: OpenAI (you can obtain it from OpenAI website) ElevenLabs (you can obtain it from their website) Google Gemini (You can obtain it from Google AI Studio) Setup: Configure you API keys Ensure that the value (voice_message) in the "Path" parameter in the Webhook node is used as the name of the parameter that will contain the voice message you are sending via the HTTP Post request.

Learning something new? Endlessly searching to find the best resources? This workflow finds top community-recommended learning resources on any topic from Hacker News, delivered to your inbox. How it works User submits a topic they want to learn via a simple form. The workflow searches for relevant "Ask HN" posts on Hacker News and extracts top-level comments. An LLM analyzes the comments and identifies the best learning resources. A personalized email is sent to the user with a Markdown formatted list of top recommendations, categorized by resource type (e.g., book, course, article) and difficulty level. Set up steps Add your Google Gemini API credentials. You'll need to create a project and enable the Generative Language API. Add your SMTP credentials for sending emails. Customize the Form and email subject (optional) Activate the workflow Screenshots for Workflow, Form and Email Built on Day-03 as part of the #100DaysOfAgenticAi Fork it, tweak it, have fun!

📈 Daily Financial News - Description This workflow automates the process of collecting, organizing, and delivering a daily summary of financial news by following these key steps: Scheduled Activation The workflow starts at 7:00 AM each day, triggered by the Schedule Trigger node. News Retrieval The HTTP Request node fetches the latest financial news from FT.com. Content Extraction The Extract Specific Content node scrapes targeted sections of the HTML (headlines, editor's picks, top stories, etc.) using CSS selectors to locate and capture relevant content. News Aggregation The Set Node gathers and organizes the extracted news data, preparing it for summarization. Categories like "Headline #1," "Editor's Picks," and "Europe News" are all structured into a single data block. Summarization An AI Agent (Google Gemini) takes the aggregated news data and creates a concise, HTML-formatted summary tailored to give investors an insightful market snapshot. Email Delivery Finally, the Microsoft Outlook node sends the summary via email to the designated recipient with the subject "Financial news from today." This process ensures that financial news is efficiently curated, summarized, and delivered without manual intervention.

This n8n workflow demonstrates how we can use Multimodal LLMs to parse and extract from PDF documents in n8n. In this particular scenario, we're passing a candidate's CV/resume to an AI which filters out unqualified applications. However, this sneaky candidate has added in hidden prompt to bypass our bot! Whatever will we do? No fret, using AI Vision is one approach to solve this problem... read on! How it works Our candidate's CV/Resume is a PDF downloaded via Google Drive for this demonstration. The PDF is then converted into an image PNG using a tool called Stirling PDF. Since the hidden prompt has a white font color, it is is invisible in the converted image. The image is then forwarded to a Basic LLM node to process using our multimodal model - in this example, we'll use Google's Gemini 1.5 Pro. In the Basic LLM node, we'll need to set a User Message with the type of Binary. This allows us to directly send the image file in our request. The LLM is now immune to the hidden prompt and its response is has expected. The example CV/Resume with hidden prompt can be found here: https://drive.google.com/file/d/1MORAdeev6cMcTJBV2EYALAwll8gCDRav/view?usp=sharing Requirements Google Gemini API Key. Alternatively, GPT4 will also work for this use-case. Stirling PDF or another service which can convert PDFs into images. Note for data privacy, this example uses a public API and it is recommended that you self-host and use a private instance of Stirling PDF instead. Customising the workflow Swap out the manual trigger for another trigger such as a webhook to integrate into your existing services. This example demonstrates a validation use-case ie. "does the candidate look qualified?". You can try additionally extracting data points instead such as years of experiences, previous companies etc.

Workflow Description This workflow is a powerful, fully automated web query and semantic reranking system that allows users to perform precise, detailed searches, intelligently rank search results and provide high-quality, structured output. Built with AI-powered components, the workflow leverages semantic query generation, result re-ranking, and real-time reporting to deliver actionable insights. It is particularly well-suited for real-time data retrieval, market research, and any domain requiring automated yet customizable search result processing. How It Works Webhook Integration for Input: The workflow begins with a Webhook Node that captures the user's search query as input, enabling seamless integration with other systems. Step 1: Semantic Query Generation (Powered by "Semantic Search - Query Maker"): Using AI (Google Gemini), the initial query is refined and transformed into a context-aware, expert-level search query. The process ensures that the search engine retrieves the most relevant and precise results. Step 2: Web Search Execution: A free Brave Search API processes the refined query to fetch search results, ensuring speed and cost efficiency. Step 3: Semantic Re-Ranking of Results (Powered by "Semantic Search - Result Re-Ranker"): The workflow reranks the search results based on relevance to the original question, prioritizing the most relevant URLs dynamically. Results are passed through AI-powered intelligent reranking to ensure the final output reflects optimal relevance and quality. Step 4: Structured Output Generation: Results are converted into a well-structured, organized JSON format, ranking the top 10 search results with their titles, links, and descriptions. Missing ranks (if fewer than 10 results) are handled gracefully with placeholders, ensuring consistency. Step 5: Real-Time Reporting: The reranked search results are sent back to the user or integrated system via the Webhook Node in a JSON-formatted response. Reports are highly structured and ready for downstream processing or consumption. Key Features AI-Powered Query Refinement: Transforms basic queries into detailed, expert-level search terms for optimal results. Dual-Stage Semantic Search: Combines query generation and result reranking for precise, high-relevance outputs. Top 10 Result Reranking: Dynamically ranks and organizes the top 10 results based on semantic relevance to the query. Customizable Integration: Fully modifiable for alternative APIs or integrations, such as other search engines or custom ranking logic. JSON-Formatted Structured Results: Outputs reranked results in a standardized format, ideal for integration into systems requiring machine-readable data. Webhook-Based Flexibility: Works seamlessly with Webhook inputs for easy deployment in diverse workflows. Cost-Effective API Usage: Pre-integrated with the free Brave Search API, minimizing operational costs while delivering accurate search results. Instructions for API Setup Brave Search API: Visit api.search.brave.com to obtain a free-tier API key for web search. AI Integration (Google Gemini): Visit Google AI Studio and generate an API key for semantic query generation and reranking. Webhook Configuration: Set up the input Webhook to capture search queries and the output Webhook to deliver reranked results. Why Choose This Workflow? Precision and Relevance**: Combines AI-based query generation with advanced reranking for accurate results. Fully Customizable**: Easily adapt the workflow to alternative APIs, search engines, or ranking logic. Real-Time Insights**: Provides structured, real-time output ready for immediate use. Scalable and Modular**: Ideal for businesses, researchers, and data analysts needing a robust, repeatable solution. Tags AI Workflow, Semantic Search, Query Refinement, Search Result Reranking, Real-Time Search, Web Search Automation, Google Search, Brave Search, News Search, API Integration, Market Research, Competitive Intelligence, Business Intelligence,Google Gemini, Anthropic Claude, OpenAI, GPT, LLM

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.

Workflow Description: Product Data Extractor This workflow automates the extraction of product data from Product Hunt by combining webhook interactions, HTML processing, AI-based data analysis, and structured output formatting. It is designed to handle incoming requests dynamically and return detailed JSON responses for further usage. Overview The workflow processes a product name submitted through a webhook. It fetches the corresponding Product Hunt page, extracts and analyzes inline scripts, and structures the data into a well-defined JSON format using AI tools. The final JSON response is returned to the client through the webhook. Workflow Steps Webhook Listener Node:** Receive Product Request Function:** Captures incoming requests containing the product name to process. Details:** Accepts HTTP requests and extracts the product parameter from the query string, such as <custom_webhook_url>/?product=epigram. Fetch Product HTML Node:** Fetch Product HTML Function:** Sends an HTTP request to retrieve the HTML content of the specified Product Hunt page. Details:** Constructs a dynamic URL using the product name and fetches the page data. Extract Inline Scripts Node:** Extract Inline Scripts Function:** Parses the HTML content to extract inline scripts located within the <head> section. Details:** Excludes scripts containing src attributes and validates the presence of inline scripts. Process Data with LLM Node:** Process Script with LLM Function:** Analyzes the extracted scripts using a language model to identify key product data. Details:** Processes the script to derive structured and meaningful insights. Refine Data with Google Gemini Node:** Analyze Script with Google Gemini Function:** Leverages Google Gemini AI for enhanced analysis of script data. Details:** Ensures the extracted data is precise and enriched. Format Product Data to JSON Node:** Format Product Data to JSON Function:** Structures the processed data into a clean JSON format. Details:** Defines a schema to ensure all relevant fields are included in the output. Send JSON Response to Client Node:** Send JSON Response to Client Function:** Returns the final structured JSON response to the client. Details:** Sends the response back via the same webhook that initiated the request. For example, <custom_webhook_url>. Key Features Versatile Use Cases:** This workflow can be used to gather Product Hunt data for creating blog posts or as a tool for AI agents to research products efficiently. Dynamic Processing:** Adapts to various product names through dynamic URL construction. AI Integration:** Utilizes the Gemini 1.5 8B AI model, offering reduced latency and minimal or no cost depending on the use case. Selector Independence:** Functions even if Product Hunt's DOM structure changes, as it does not rely on direct DOM selectors. Reliable Data Output:** A low temperature setting (0) and a precisely defined JSON schema ensure accurate and real data extraction. Dynamic Processing:** Adapts to various product names through dynamic URL construction. AI Integration:** Utilizes advanced language models for data extraction and refinement. Structured Output:** Ensures the output JSON adheres to a predefined schema for consistency. Error Handling:** Includes validations to handle missing or malformed data gracefully. Customization Options Limitations Dependency on Product Hunt:** Significant changes to the way Product Hunt loads data on its pages might require modifications to the workflow. Adaptability:** Even if changes occur, the workflow can be updated to maintain functionality due to its reliance on AI and not direct DOM selectors. Modify the webhook path to suit your application. Adjust the prompt for the language model to include additional fields. Extend the JSON schema to capture more data fields as needed. Expected Output Performance Metrics Response Time:** Typically ~6 seconds per product. Accuracy:** Data extracted with >95% precision due to the pre-defined JSON schema. A JSON object containing detailed information about the specified product. Below is an example of a complete response for the product Epigram: { "id": "861675", "slug": "epigram", "followersCount": 181, "name": "Epigram", "tagline": "Open-Source, Free, and AI-Powered News in Short", "reviewsRating": 0, "logoUuid": "735c2528-554c-467c-9dcf-745ee4b8bbdd.png", "postsCount": 1, "websiteUrl": "https://epigram.news", "websiteDomain": "epigram.news", "metaTitle": "Epigram - Open-source, free, and ai-powered news in short", "postName": "Epigram", "postTagline": "Open-source, free, and ai-powered news in short", "dailyRank": "3", "description": "An open-source, AI-powered news app for busy people. Stay updated with bite-sized news, real-time updates, and in-depth analysis. Experience balanced, trustworthy reporting tailored for fast-paced lifestyles in a sleek, user-friendly interface.", "pricingType": "free", "userName": "Fazle Rahman", "userHeadline": "Co-founder & CEO, Hashnode", "userUsername": "fazlerocks", "userAvatarUrl": "https://ph-avatars.imgix.net/129147/f84e1796-548b-4d6f-9dcf-745ee4b8bbdd.jpeg", "makerName1": "Fazle Rahman", "makerHeadline1": "Co-founder & CEO, Hashnode", "makerUsername1": "fazlerocks", "makerAvatarUrl1": "https://ph-avatars.imgix.net/129147/f84e1796-548b-4d6f-9dcf-745ee4b8bbdd.jpeg", "makerName2": "Sandeep Panda", "makerHeadline2": "Co-Founder @ Hashnode", "makerUsername2": "sandeepg33k", "makerAvatarUrl2": "https://ph-avatars.imgix.net/101872/80b0b618-a540-4110-a6d1-74df39675ad0.jpeg", "primaryLinkUrl": "https://epigram.news/", "media1OriginalHeight": 1080, "media1OriginalWidth": 1440, "media1ImageUuid": "ac426fd1-3854-4734-b43d-34a5e06347ea.gif", "media1MediaType": "video", "media1MetadataUrl": "https://www.loom.com/share/b1a48a9b3cac4ba89ce772a3fbcc2847?sid=75efc771-25fa-4ac0-bb1b-5e38fc447deb", "media1VideoId": "b1a48a9b3cac4ba89ce772a3fbcc2847", "media2OriginalHeight": 630, "media2OriginalWidth": 1200, "media2ImageUuid": "8521a6bd-7640-487b-abd6-29b9f65fee32", "media2MediaType": "image", "media2MetadataUrl": null, "launchState": "featured", "thumbnailImageUuid": "735c2528-554c-467c-9dcf-745ee4b8bbdd.png", "link1StoreName": "Website", "link1WebsiteName": "epigram.news", "link2StoreName": "Github", "link2WebsiteName": "github.com", "latestScore": 233, "launchDayScore": 233, "userId": "129147", "topic1": "News", "topic2": "Open Source", "topic3": "Artificial Intelligence", "weeklyRank": "24", "commentsCount": 20, "postUrl": "https://www.producthunt.com/posts/epigram" } Target Audience This workflow is ideal for developers, marketers, and data analysts seeking to automate the extraction and structuring of product data from Product Hunt for analytics, reporting, or integration with other tools.

This n8n workflow demonstrates how to automate image captioning tasks using Gemini 1.5 Pro - a multimodal LLM which can accept and analyse images. This is a really simple example of how easy it is to build and leverage powerful AI models in your repetitive tasks. How it works For this demo, we'll import a public image from a popular stock photography website, Pexel.com, into our workflow using the HTTP request node. With multimodal LLMs, there is little do preprocess other than ensuring the image dimensions fit within the LLMs accepted limits. Though not essential, we'll resize the image using the Edit image node to achieve fast processing. The image is used as an input to the basic LLM node by defining a "user message" entry with the binary (data) type. The LLM node has the Gemini 1.5 Pro language model attached and we'll prompt it to generate a caption title and text appropriate for the image it sees. Once generated, the generated caption text is positioning over the original image to complete the task. We can calculate the positioning relative to the amount of characters produced using the code node. An example of the combined image and caption can be found here: https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/l5xbb4ze4wyxwwefqmnc Requirements Google Gemini API Key. Access to Google Drive. Customising the workflow Not using Google Gemini? n8n's basic LLM node supports the standard syntax for image content for models that support it - try using GPT4o, Claude or LLava (via Ollama). Google Drive is only used for demonstration purposes. Feel free to swap this out for other triggers such as webhooks to fit your use case.

YouTube Subtitles Report Generator Overview This template enables users to generate analytical reports from YouTube video subtitles, providing insights into the thematic content of the video. Designed for efficiency and simplicity, it processes video subtitles without requiring an API key, making it an accessible solution for content analysis. The system assumes videos already have subtitles available, excluding live streams and videos without subtitle data. Key Features Trigger Webhook: Seamlessly receive video IDs through a webhook trigger. Fetch Video HTML: Retrieves the video’s HTML content directly from YouTube. Extract Subtitles URLs: Processes the HTML content to find and decode subtitle URLs. Fetch Subtitles Content: Downloads the subtitles from the decoded URLs in XML format. Generate Analytical Report: Utilizes an AI model to summarize and analyze the thematic essence of video content. The system supports models such as Google Gemini, OpenAI, and other compatible language models. The quality of the results may vary depending on the model used, with better models providing faster and more accurate summaries. The default prompt focuses on identifying and summarizing the main theme of the video while excluding content related to promotions, subscriptions, or sponsored content. Return Analytical Report: Delivers concise analytical reports as the final response to the webhook, suitable for various use cases like research, content creation, or consumption as plain text. Setup Instructions Step 1: Webhook Configuration Set up the webhook URL in your external system (e.g., app, API) to send YouTube video IDs to this workflow. Step 2: API Access Ensure that your environment has access to YouTube’s public HTML content. An API key is not required since the workflow parses publicly available data. Step 3: AI Integration Verify the connection to the AI model used for report generation. Supported models include Google Gemini and OpenAI. Note that the system can be customized by modifying the provided prompt to suit specific analysis needs. Step 4: Testing Run a sample test with a YouTube video ID to ensure subtitles are correctly retrieved and the report is generated successfully. Expected Outcomes Effortless Content Analysis:** Generate thematic reports with minimal setup. No API Key Dependency:** Simplified access by leveraging YouTube’s public HTML. Actionable Insights:** Gain valuable information on video content for business, research, or personal projects. Cost Considerations:** The execution cost depends primarily on the model used and the length of the video (affecting token usage). Leveraging the free tier of Google Gemini models is recommended to minimize costs. Main Theme Extraction:** The default prompt excludes irrelevant promotional content, providing clear and focused thematic summaries. Estimated setup time: 15–20 minutes with a ready environment. Prompt Description The workflow includes a customizable prompt used to process subtitles in XML format and generate analytical reports. The generated report includes: Title: A brief phrase capturing the essence of the main theme. Body: An analytical description of the main theme, structured into a maximum of three concise paragraphs. It focuses on summarizing key ideas, recurring themes, and connections without referencing the source explicitly (e.g., avoiding phrases like “this video”). The system also removes content related to sales, subscriptions, or promotions to maintain a clear thematic focus. Example Output Title: The Ethical Challenges of Artificial Intelligence Report: Artificial intelligence presents significant challenges in areas such as privacy, fairness, and automated decision-making. Its implementation in critical sectors such as healthcare, justice, and security has sparked debates about inherent biases and lack of transparency. Additionally, there is growing concern over the ethical implications of automation, including its impact on employment and the global economy. Finally, the importance of establishing strong regulatory frameworks is highlighted to balance technological innovation with the protection of human rights.

This n8n workflow is a proof-of-concept template exploring how we might work with multimodal LLMs and their multi-image analysis capabilities. In this demo, we compare 2 screenshots of a webpage taken at different timestamps and pass both to our multimodal LLM for a visual comparison of differences. Handling multiple binary inputs (ie. images) in an AI request is supported by n8n's basic LLM node. How it works This template is intended to run as 2 parts: first to generate the base screenshots and next to run the visual regression test which captures fresh screenshots. Starting with a list of webpages captured in a Google sheet, base screenshots are captured for each using a external web scraping service called Apify.com (I prefer Apify but feel free to use whichever web scraping service available to you) These base screenshots are uploaded to Google Drive and will be referenced later when we run our testing. Phase 2 of the workflow, we'll use a scheduled trigger to fire sometime in the future which will reuse our web scraping service to generate fresh screenshots of our desired webpages. Next, re-download our base screenshots in parallel and with both old and new captures, we'll pass these to our LLM node. In the LLM node's options, we'll define 2 "user message" inputs with the type of binary (data) for our images. Finally, we'll prompt our LLM with our testing criteria and capture the regressions detected. Note, results will vary depending on which LLM you use. A final report can be generated using the LLM's output and is uploaded to Linear. Requirements Apify.com API key for web screenshotting service Google Drive and Sheets access to store list of webpages and captures Customising this workflow Have your own preferred web screenshotting service? Feel free to swap out Apify with your service of choice. If the web screenshot is too large, it may prove difficult for the LLM to spot differences with precision. Try splitting up captures into smaller images instead.