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integrationGoogle Gemini Chat Model node
integrationLinear node

Google Gemini Chat Model and Linear integration

Save yourself the work of writing custom integrations for Google Gemini Chat Model and Linear and use n8n instead. Build adaptable and scalable AI, Langchain, and Productivity workflows that work with your technology stack. All within a building experience you will love.

How to connect Google Gemini Chat Model and Linear

  • Step 1: Create a new workflow
  • Step 2: Add and configure nodes
  • Step 3: Connect
  • Step 4: Customize and extend your integration
  • Step 5: Test and activate your workflow

Step 1: Create a new workflow and add the first step

In n8n, click the "Add workflow" button in the Workflows tab to create a new workflow. Add the starting point – a trigger on when your workflow should run: an app event, a schedule, a webhook call, another workflow, an AI chat, or a manual trigger. Sometimes, the HTTP Request node might already serve as your starting point.

Google Gemini Chat Model and Linear integration: Create a new workflow and add the first step

Step 2: Add and configure Google Gemini Chat Model and Linear nodes

You can find Google Gemini Chat Model and Linear in the nodes panel. Drag them onto your workflow canvas, selecting their actions. Click each node, choose a credential, and authenticate to grant n8n access. Configure Google Gemini Chat Model and Linear nodes one by one: input data on the left, parameters in the middle, and output data on the right.

Google Gemini Chat Model and Linear integration: Add and configure Google Gemini Chat Model and Linear nodes

Step 3: Connect Google Gemini Chat Model and Linear

A connection establishes a link between Google Gemini Chat Model and Linear (or vice versa) to route data through the workflow. Data flows from the output of one node to the input of another. You can have single or multiple connections for each node.

Google Gemini Chat Model and Linear integration: Connect Google Gemini Chat Model and Linear

Step 4: Customize and extend your Google Gemini Chat Model and Linear integration

Use n8n's core nodes such as If, Split Out, Merge, and others to transform and manipulate data. Write custom JavaScript or Python in the Code node and run it as a step in your workflow. Connect Google Gemini Chat Model and Linear with any of n8n’s 1000+ integrations, and incorporate advanced AI logic into your workflows.

Google Gemini Chat Model and Linear integration: Customize and extend your Google Gemini Chat Model and Linear integration

Step 5: Test and activate your Google Gemini Chat Model and Linear workflow

Save and run the workflow to see if everything works as expected. Based on your configuration, data should flow from Google Gemini Chat Model to Linear or vice versa. Easily debug your workflow: you can check past executions to isolate and fix the mistake. Once you've tested everything, make sure to save your workflow and activate it.

Google Gemini Chat Model and Linear integration: Test and activate your Google Gemini Chat Model and Linear workflow

Visual Regression Testing with Apify and AI Vision Model

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.

Nodes used in this workflow

Popular Google Gemini Chat Model and Linear workflows

Google Drive node
Google Gemini Chat Model node
+5

Visual Regression Testing with Apify and AI Vision Model

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.

Build your own Google Gemini Chat Model and Linear integration

Create custom Google Gemini Chat Model and Linear workflows by choosing triggers and actions. Nodes come with global operations and settings, as well as app-specific parameters that can be configured. You can also use the HTTP Request node to query data from any app or service with a REST API.

Linear supported actions

Create
Create an issue
Delete
Delete an issue
Get
Get an issue
Get Many
Get many issues
Update
Update an issue

FAQs

  • Can Google Gemini Chat Model connect with Linear?

  • Can I use Google Gemini Chat Model’s API with n8n?

  • Can I use Linear’s API with n8n?

  • Is n8n secure for integrating Google Gemini Chat Model and Linear?

  • How to get started with Google Gemini Chat Model and Linear integration in n8n.io?

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