Webhook and Google Gemini Chat Model integration

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

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How to connect Webhook and Google Gemini Chat Model

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.

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

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

You can find Webhook and Google Gemini Chat Model 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 Webhook and Google Gemini Chat Model nodes one by one: input data on the left, parameters in the middle, and output data on the right.

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

Step 3: Connect Webhook and Google Gemini Chat Model

A connection establishes a link between Webhook and Google Gemini Chat Model (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.

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

Step 4: Customize and extend your Webhook and Google Gemini Chat Model 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 Webhook and Google Gemini Chat Model with any of n8n’s 1000+ integrations, and incorporate advanced AI logic into your workflows.

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

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

Save and run the workflow to see if everything works as expected. Based on your configuration, data should flow from Webhook to Google Gemini Chat Model 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.

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

AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs

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.

Nodes used in this workflow

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Popular Webhook and Google Gemini Chat Model workflows

AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs

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.

Creating a AI Slack Bot with Google Gemini

This is an example of how we can build a slack bot in a few easy steps Before you can start, you need to o a few things Create a copy of this workflow Create a slack bot Create a slash command on slack and paste the webhook url to the slack command Note Make sure to configure this webhook using a https:// wrapper and don't use the default http://localhost:5678 as that will not be recognized by your slack webhook. Once the data has been sent to your webhook, the next step will be passing it via an AI Agent to process data based on the queries we pass to our agent. To have some sort of a memory, be sure to set the slack token to the memory node. This way you can refer to other chats from the history. The final message is relayed back to slack as a new message. Since we can not wait longer than 3000 ms for slack response, we will create a new message with reference to the input we passed. We can advance this using the tools or data sources for it to be more custom tailored for your company. Usage To use the slackbot, go to slack and click on your set slash command eg /Bob and send your desired message. This will send the message to your endpoint and get return the processed results as the message. If you would like help setting this up, feel free to reach out to [email protected]

Build your own Webhook and Google Gemini Chat Model integration

Create custom Webhook and Google Gemini Chat Model 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.

Webhook and Google Gemini Chat Model integration details

Webhooks are automatic notifications that apps send when something occurs. They are sent to a certain URL, which is effectively the app's phone number or address, and contain a message or payload. Polling is nearly never quicker than webhooks, and it takes less effort from you.

Webhook node docs + examples

See Webhook integrations

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Development

Google Gemini Chat Model node docs + examples

See Google Gemini Chat Model integrations

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Langchain

Other integrations with Google Gemini Chat Model

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