Back to Integrations
integrationWebhook node
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

Scrape ProductHunt using Google Gemini

Workflow Description: Product Data Extractor This workflow automates the extraction of product data from Product Hunt by combining webhook interactions, HTML processing, AI-based data analysis, and structured output formatting. It is designed to handle incoming requests dynamically and return detailed JSON responses for further usage. Overview The workflow processes a product name submitted through a webhook. It fetches the corresponding Product Hunt page, extracts and analyzes inline scripts, and structures the data into a well-defined JSON format using AI tools. The final JSON response is returned to the client through the webhook. Workflow Steps Webhook Listener Node:** Receive Product Request Function:** Captures incoming requests containing the product name to process. Details:** Accepts HTTP requests and extracts the product parameter from the query string, such as <custom_webhook_url>/?product=epigram. Fetch Product HTML Node:** Fetch Product HTML Function:** Sends an HTTP request to retrieve the HTML content of the specified Product Hunt page. Details:** Constructs a dynamic URL using the product name and fetches the page data. Extract Inline Scripts Node:** Extract Inline Scripts Function:** Parses the HTML content to extract inline scripts located within the <head> section. Details:** Excludes scripts containing src attributes and validates the presence of inline scripts. Process Data with LLM Node:** Process Script with LLM Function:** Analyzes the extracted scripts using a language model to identify key product data. Details:** Processes the script to derive structured and meaningful insights. Refine Data with Google Gemini Node:** Analyze Script with Google Gemini Function:** Leverages Google Gemini AI for enhanced analysis of script data. Details:** Ensures the extracted data is precise and enriched. Format Product Data to JSON Node:** Format Product Data to JSON Function:** Structures the processed data into a clean JSON format. Details:** Defines a schema to ensure all relevant fields are included in the output. Send JSON Response to Client Node:** Send JSON Response to Client Function:** Returns the final structured JSON response to the client. Details:** Sends the response back via the same webhook that initiated the request. For example, <custom_webhook_url>. Key Features Versatile Use Cases:** This workflow can be used to gather Product Hunt data for creating blog posts or as a tool for AI agents to research products efficiently. Dynamic Processing:** Adapts to various product names through dynamic URL construction. AI Integration:** Utilizes the Gemini 1.5 8B AI model, offering reduced latency and minimal or no cost depending on the use case. Selector Independence:** Functions even if Product Hunt's DOM structure changes, as it does not rely on direct DOM selectors. Reliable Data Output:** A low temperature setting (0) and a precisely defined JSON schema ensure accurate and real data extraction. Dynamic Processing:** Adapts to various product names through dynamic URL construction. AI Integration:** Utilizes advanced language models for data extraction and refinement. Structured Output:** Ensures the output JSON adheres to a predefined schema for consistency. Error Handling:** Includes validations to handle missing or malformed data gracefully. Customization Options Limitations Dependency on Product Hunt:** Significant changes to the way Product Hunt loads data on its pages might require modifications to the workflow. Adaptability:** Even if changes occur, the workflow can be updated to maintain functionality due to its reliance on AI and not direct DOM selectors. Modify the webhook path to suit your application. Adjust the prompt for the language model to include additional fields. Extend the JSON schema to capture more data fields as needed. Expected Output Performance Metrics Response Time:** Typically ~6 seconds per product. Accuracy:** Data extracted with >95% precision due to the pre-defined JSON schema. A JSON object containing detailed information about the specified product. Below is an example of a complete response for the product Epigram: { "id": "861675", "slug": "epigram", "followersCount": 181, "name": "Epigram", "tagline": "Open-Source, Free, and AI-Powered News in Short", "reviewsRating": 0, "logoUuid": "735c2528-554c-467c-9dcf-745ee4b8bbdd.png", "postsCount": 1, "websiteUrl": "https://epigram.news", "websiteDomain": "epigram.news", "metaTitle": "Epigram - Open-source, free, and ai-powered news in short", "postName": "Epigram", "postTagline": "Open-source, free, and ai-powered news in short", "dailyRank": "3", "description": "An open-source, AI-powered news app for busy people. Stay updated with bite-sized news, real-time updates, and in-depth analysis. Experience balanced, trustworthy reporting tailored for fast-paced lifestyles in a sleek, user-friendly interface.", "pricingType": "free", "userName": "Fazle Rahman", "userHeadline": "Co-founder & CEO, Hashnode", "userUsername": "fazlerocks", "userAvatarUrl": "https://ph-avatars.imgix.net/129147/f84e1796-548b-4d6f-9dcf-745ee4b8bbdd.jpeg", "makerName1": "Fazle Rahman", "makerHeadline1": "Co-founder & CEO, Hashnode", "makerUsername1": "fazlerocks", "makerAvatarUrl1": "https://ph-avatars.imgix.net/129147/f84e1796-548b-4d6f-9dcf-745ee4b8bbdd.jpeg", "makerName2": "Sandeep Panda", "makerHeadline2": "Co-Founder @ Hashnode", "makerUsername2": "sandeepg33k", "makerAvatarUrl2": "https://ph-avatars.imgix.net/101872/80b0b618-a540-4110-a6d1-74df39675ad0.jpeg", "primaryLinkUrl": "https://epigram.news/", "media1OriginalHeight": 1080, "media1OriginalWidth": 1440, "media1ImageUuid": "ac426fd1-3854-4734-b43d-34a5e06347ea.gif", "media1MediaType": "video", "media1MetadataUrl": "https://www.loom.com/share/b1a48a9b3cac4ba89ce772a3fbcc2847?sid=75efc771-25fa-4ac0-bb1b-5e38fc447deb", "media1VideoId": "b1a48a9b3cac4ba89ce772a3fbcc2847", "media2OriginalHeight": 630, "media2OriginalWidth": 1200, "media2ImageUuid": "8521a6bd-7640-487b-abd6-29b9f65fee32", "media2MediaType": "image", "media2MetadataUrl": null, "launchState": "featured", "thumbnailImageUuid": "735c2528-554c-467c-9dcf-745ee4b8bbdd.png", "link1StoreName": "Website", "link1WebsiteName": "epigram.news", "link2StoreName": "Github", "link2WebsiteName": "github.com", "latestScore": 233, "launchDayScore": 233, "userId": "129147", "topic1": "News", "topic2": "Open Source", "topic3": "Artificial Intelligence", "weeklyRank": "24", "commentsCount": 20, "postUrl": "https://www.producthunt.com/posts/epigram" } Target Audience This workflow is ideal for developers, marketers, and data analysts seeking to automate the extraction and structuring of product data from Product Hunt for analytics, reporting, or integration with other tools.

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

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]

YouTube Report Generator

YouTube Subtitles Report Generator Overview This template enables users to generate analytical reports from YouTube video subtitles, providing insights into the thematic content of the video. Designed for efficiency and simplicity, it processes video subtitles without requiring an API key, making it an accessible solution for content analysis. The system assumes videos already have subtitles available, excluding live streams and videos without subtitle data. Key Features Trigger Webhook: Seamlessly receive video IDs through a webhook trigger. Fetch Video HTML: Retrieves the video’s HTML content directly from YouTube. Extract Subtitles URLs: Processes the HTML content to find and decode subtitle URLs. Fetch Subtitles Content: Downloads the subtitles from the decoded URLs in XML format. Generate Analytical Report: Utilizes an AI model to summarize and analyze the thematic essence of video content. The system supports models such as Google Gemini, OpenAI, and other compatible language models. The quality of the results may vary depending on the model used, with better models providing faster and more accurate summaries. The default prompt focuses on identifying and summarizing the main theme of the video while excluding content related to promotions, subscriptions, or sponsored content. Return Analytical Report: Delivers concise analytical reports as the final response to the webhook, suitable for various use cases like research, content creation, or consumption as plain text. Setup Instructions Step 1: Webhook Configuration Set up the webhook URL in your external system (e.g., app, API) to send YouTube video IDs to this workflow. Step 2: API Access Ensure that your environment has access to YouTube’s public HTML content. An API key is not required since the workflow parses publicly available data. Step 3: AI Integration Verify the connection to the AI model used for report generation. Supported models include Google Gemini and OpenAI. Note that the system can be customized by modifying the provided prompt to suit specific analysis needs. Step 4: Testing Run a sample test with a YouTube video ID to ensure subtitles are correctly retrieved and the report is generated successfully. Expected Outcomes Effortless Content Analysis:** Generate thematic reports with minimal setup. No API Key Dependency:** Simplified access by leveraging YouTube’s public HTML. Actionable Insights:** Gain valuable information on video content for business, research, or personal projects. Cost Considerations:** The execution cost depends primarily on the model used and the length of the video (affecting token usage). Leveraging the free tier of Google Gemini models is recommended to minimize costs. Main Theme Extraction:** The default prompt excludes irrelevant promotional content, providing clear and focused thematic summaries. Estimated setup time: 15–20 minutes with a ready environment. Prompt Description The workflow includes a customizable prompt used to process subtitles in XML format and generate analytical reports. The generated report includes: Title: A brief phrase capturing the essence of the main theme. Body: An analytical description of the main theme, structured into a maximum of three concise paragraphs. It focuses on summarizing key ideas, recurring themes, and connections without referencing the source explicitly (e.g., avoiding phrases like “this video”). The system also removes content related to sales, subscriptions, or promotions to maintain a clear thematic focus. Example Output Title: The Ethical Challenges of Artificial Intelligence Report: Artificial intelligence presents significant challenges in areas such as privacy, fairness, and automated decision-making. Its implementation in critical sectors such as healthcare, justice, and security has sparked debates about inherent biases and lack of transparency. Additionally, there is growing concern over the ethical implications of automation, including its impact on employment and the global economy. Finally, the importance of establishing strong regulatory frameworks is highlighted to balance technological innovation with the protection of human rights.
+2

Proxmox AI Agent with n8n and Generative AI Integration

Proxmox AI Agent with n8n and Generative AI Integration This template automates IT operations on a Proxmox Virtual Environment (VE) using an AI-powered conversational agent built with n8n. By integrating Proxmox APIs and generative AI models (e.g., Google Gemini), the workflow converts natural language commands into API calls, enabling seamless management of your Proxmox nodes, VMs, and clusters. Watch Video on Youtube How It Works Trigger Mechanism The workflow can be triggered through multiple channels like chat (Telegram, email, or n8n's built-in chat). Interact with the AI agent conversationally. AI-Powered Parsing A connected AI model (Google Gemini or other compatible models like OpenAI or Claude) processes your natural language input to determine the required Proxmox API operation. API Call Generation The AI parses the input and generates structured JSON output, which includes: response_type: The HTTP method (GET, POST, PUT, DELETE). url: The Proxmox API endpoint to execute. details: Any required payload parameters for the API call. Proxmox API Execution The structured output is used to make HTTP requests to the Proxmox VE API. The workflow supports various operations, such as: Retrieving cluster or node information. Creating, deleting, starting, or stopping VMs. Migrating VMs between nodes. Updating or resizing VM configurations. Response Formatting The workflow formats API responses into a user-friendly summary. For example: Success messages for operations (e.g., "VM started successfully"). Error messages with missing parameter details. Extensibility You can enhance the workflow by connecting additional triggers, external services, or AI models. It supports: Telegram/Slack integration for real-time notifications. Backup and restore workflows. Cloud monitoring extensions. Key Features Multi-Channel Input**: Use chat, email, or custom triggers to communicate with the AI agent. Low-Code Automation**: Easily customize the workflow to suit your Proxmox environment. Generative AI Integration**: Supports advanced AI models for precise command interpretation. Proxmox API Compatibility**: Fully adheres to Proxmox API specifications for secure and reliable operations. Error Handling**: Detects and informs you of missing or invalid parameters in your requests. Example Use Cases Create a Virtual Machine Input: "Create a VM with 4 cores, 8GB RAM, and 50GB disk on psb1." Action: Sends a POST request to Proxmox to create the VM with specified configurations. Start a VM Input: "Start VM 105 on node psb2." Action: Executes a POST request to start the specified VM. Retrieve Node Details Input: "Show the memory usage of psb3." Action: Sends a GET request and returns the node's resource utilization. Migrate a VM Input: "Migrate VM 202 from psb1 to psb3." Action: Executes a POST request to move the VM with optional online migration. Pre-Requisites Proxmox API Configuration Enable the Proxmox API and generate API keys in the Proxmox Data Center. Use the Authorization header with the format: PVEAPIToken=<user>@<realm>!<token-id>=<token-value> n8n Setup Add Proxmox API credentials in n8n using Header Auth. Connect a generative AI model (e.g., Google Gemini) via the relevant credential type. Access the Workflow Import this template into your n8n instance. Replace placeholder credentials with your Proxmox and AI service details. Additional Notes This template is designed for Proxmox 7.x and above. For advanced features like backup, VM snapshots, and detailed node monitoring, you can extend this workflow. Always test with a non-production Proxmox environment before deploying in live systems.

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.

Learn more

FAQs

  • Can Webhook connect with Google Gemini Chat Model?

  • Can I use Webhook’s API with n8n?

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

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

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

Need help setting up your Webhook and Google Gemini Chat Model integration?

Discover our latest community's recommendations and join the discussions about Webhook and Google Gemini Chat Model integration.
Benjamin Hatton
Albert Ashkhatoyan
Víctor González
Salomão
sg tech

Looking to integrate Webhook and Google Gemini Chat Model in your company?

Over 3000 companies switch to n8n every single week

Why use n8n to integrate Webhook with Google Gemini Chat Model

Build complex workflows, really fast

Build complex workflows, really fast

Handle branching, merging and iteration easily.
Pause your workflow to wait for external events.

Code when you need it, UI when you don't

Simple debugging

Your data is displayed alongside your settings, making edge cases easy to track down.

Use templates to get started fast

Use 1000+ workflow templates available from our core team and our community.

Reuse your work

Copy and paste, easily import and export workflows.

Implement complex processes faster with n8n

red iconyellow iconred iconyellow icon