Skip to content
FlowHubFluxonLab
B
AI Automationfree

Easy Image Captioning with Gemini 1.5 Pro

by Jimleukadapted from n8n official workflow galleryUpdated Aug 2026
RequiresBBasic LLM ChainCCodeEdit ImageEdit ImageGoogle Gemini Chat ModelHTTP RequestHTTP RequestStructured Output ParserStructured Output Parser
Share Post Share
MaWhen clicking ‘Test workflow’When clicking ‘…Google Gemini Chat ModelGoogle Gemini C…OPStructured Output ParserStructured Outp…EIGet InfoEIResize For AICoCalculate PositioningCalculate Posit…EIApply Caption to ImageApply Caption t…MeMerge Image & CaptionMerge Image & C…MeMerge Caption & PositionsMerge Caption &…HRGet ImageCLImage Captioning AgentImage Captionin…123456789101112
1/5
STEPS · 12
Run manually by an operator

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.

Tags

n8nreference-onlychain-llmedit-imagelm-chat-google-geminioutput-parser-structured
Connects
BBasic LLM ChainCCodeeditimageEdit ImageGoogle Gemini Chat ModelWHTTP RequeststructuredoutputparserStructured Output Parser
CategoryAI Automation
Triggermanual
Complexitycomplex
Nodes11
AddedSep 18, 2024

Related workflows

See all AI Automation
BCsheetsgroqchatmodel
free

Confirm and log WhatsApp restaurant orders with Groq and Google Sheets

Quick Overview This workflow triggers on incoming WhatsApp orders, uses Groq to extract items and quantities, validates them against a live Google Sheets menu, logs the result to an orders sheet, and replies to the customer with either a clarification request or a priced confirmation (with optional VIP owner alerts). How it works Triggers when a new WhatsApp message is received on your WhatsApp Business Cloud number. Sends the message text to Groq (Llama 3.3) to extract a structured list of ordered items and quantities. Reads the latest menu (item name, price, availability) from a Google Sheets “Menu” tab. Matches extracted items to the menu, checks availability, calculates line subtotals and the total price, and compiles any validation issues. If there are issues, replies to the customer on WhatsApp asking for a corrected order and appends a “needs_clarification” entry to the Google Sheets “Orders” tab. If the order is valid, appends a “confirmed” entry to the Google Sheets “Orders” tab and sends a WhatsApp confirmation with the item summary and total. If the confirmed total meets or exceeds the VIP threshold, sends a separate WhatsApp notification to the owner number. Setup Conne

by Kanishka Shrivastava
ACWopenaichatmodel
free

Manage Google and Trustpilot reviews with OpenAI and Slack

Quick overview This workflow runs hourly to fetch new Google Business Profile and Trustpilot reviews, deduplicates them, uses OpenAI via a LangChain agent to draft sentiment-aware replies, escalates negative reviews in Slack, and routes positive replies to Slack for approval before posting the reply back to Trustpilot. How it works Runs every hour on a schedule. Fetches the latest reviews from the Google Business Profile API and the Trustpilot API and combines the results. Deduplicates reviews by tracking previously seen review IDs in workflow static data and stops if there are no new reviews. Processes each new review and uses OpenAI (via a LangChain agent) to generate structured JSON containing sentiment, a draft reply, and an optional suggested resolution. Sends negative reviews to a Slack channel for the owner along with the suggested resolution. Sends positive draft replies to Slack for one-tap approval (with a 24-hour timeout) and, if approved, publishes the reply to the Trustpilot API. Sends a Slack alert to the ops channel if the workflow errors. Setup Configure authentication and replace placeholders for the Google Business Profile reviews endpoint (account and location) i

by Redowan Ahmed Farhan
autofixingoutputparserBopenaichatmodelstructuredoutputparser
free

Force AI to use a specific output format

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 n8n Team