Workflow Templates

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This workflow automates image processing using VLM Run, extracting signed URLs, downloading results, and distributing them via multiple channels (Google Drive & Telegram). ✅ Key Features Upload image files through a Form Trigger. Process images through two VLM Run agents simultaneously: Segmentation Agent → extracts objects. Detection Agent → generates bounding boxes. Webhooks capture completed results asynchronously. Arifact Node** download the images. Downloaded images are automatically: Uploaded to Google Drive Sent to a Telegram chat ⚙️ Workflow Flow User Uploads File → Form Trigger node VLM Run Agents → Segmentation & Detection agents Webhook Nodes → Receive processed results Artifact Node → Download the artifacts Distribution Nodes → Upload to Google Drive & Telegram 📌 Notes Ensure Google Drive OAuth2 credentials have upload permissions. Telegram Bot token and chat ID must be configured correctly. Workflow allows multi-channel sharing of images automatically. ⚠️ Community Node Disclaimer > This workflow uses VLM Run node

Description The Instagram Downloader workflow allows users to download Instagram videos or Reels directly through a Telegram Bot. Simply send an Instagram link to the bot, and it will process the link via a third-party API to fetch the highest quality video, then send it back to your Telegram chat. How It Works Telegram Trigger The workflow starts when the bot receives an Instagram link from a user. HTTP Request – URL Download The link is sent to the API https://www.mediadl.app/api/download to retrieve video metadata. Delay Waits a few seconds to ensure the API response is ready. Filtering URL Only Extracts the direct video file URL from the API result. Delay Adds a short pause to prevent connection errors during download. HTTP Request – Proxy Download Downloads the MP4 video file directly from the filtered URL. Send Video to Telegram Sends the downloaded video back to the user in Telegram. How to Set Up Create & Configure a Telegram Bot Open Telegram, search for BotFather. Send /newbot → choose a bot name & username. Copy the provided Bot Token. Prepare Your n8n Environment Log in to n8n (self-hosted or n8n Cloud). Create Telegram API Credentials using your Bot Token. Import the Workflow In n8n, click Import and select Instagram_Downloader.json. Configure Telegram Nodes Connect your Telegram API credentials in the Telegram Trigger and Send Video nodes. Configure HTTP Request Nodes Ensure the URL and headers in URL Download and Download nodes are correct (already pre-configured). Set responseFormat to file in the final download node. Activate & Test Toggle Activate. Send an Instagram link to your bot to test.

Augment AI chatbot prompts with a knowledge graph reasoning ontology and improve the quality of responses with Graph RAG. In this workflow, we augment the original prompt using the InfraNodus GraphRAG system that will extract a reasoning ontology from a graph that you create (or that you can copy from our repository of public graphs). This additional reasoning logic will improve the user's prompt and make it more descriptive and closely related to the logic you want to use. As the next step, you can send it back to the same graph to generate a high-quality response using Graph RAG or to another graph (or AI model) to apply one type of knowledge in a completely different field. How it works Receives a request from a user (via n8n or a publicly available URL chat bot, you can also connect it to Telegram Sends the request to the knowledge graph in your InfraNodus account that contains a reasoning ontology represented as a knowledge graph. Reformulates the original prompt to include the reasoning logic provided. Sends the request to the knowledge graph in your InfraNodus account (same as the previous one or a new one for cross-disciplinary research) to retrieve a high-quality response using GraphRAG Special sauce: InfraNodus will build a graph from your augmented prompt, then overlap it on the knowledge graph you want to inquire, traverse this graph based on the overlapped parts and extended relations, then retrieve the necessary part of the graph and include it in the context to improve the quality of your response. This helps InfraNodus grasp the relations and nuances that are not usually available through standard RAG. How to use • Just get an InfraNodus API key and add it into your Prompt Augmentation and Knowledge Base InfraNodus HTTP nodes for authentication • Then provide the name of the graphs you want to be using for prompt augmentation and retrieval. Note, these can be two different graphs if you want to apply a reasoning logic from one domain in another (e.g. machine learning in biology or philosophy in electrical engineering). Support If you wan to create your own reasoning ontology graphs, please, refer to this article on generating your own knowledge graph ontologies. You may also be interested to watch this video that explains the logic of this approach in detail: Help article on the same topic: Using knowledge graphs as reasoning experts.

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Assemble response etc.