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integrationGoogle Gemini Chat Model node

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.

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

Popular Webhook and Google Gemini Chat Model workflows

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🤖 AI Powered RAG Chatbot for Your Docs + Google Drive + Gemini + Qdrant

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Personal Portfolio CV Rag Chatbot - with Conversation Store and Email Summary

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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.
+3

Intelligent Web Query and Semantic Re-Ranking Flow using Brave and Google Gemini

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

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

integrationWebhook node
Webhook

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.

Use case

Save engineering resources

Reduce time spent on customer integrations, engineer faster POCs, keep your customer-specific functionality separate from product all without having to code.

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