This article provides a complete, practical walkthrough of the Webhook Code Automate Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Automating Your Resume with AI: How to Build a Personal CV Chatbot Using n8n and Google Gemini
   
   Meta Description:  
   Discover how to create your own intelligent CV chatbot using n8n, Google Gemini, Pinecone, and NocoDB. Learn how to automate resume updates, build conversational AI, and generate daily reports with ease.
   
   Keywords:  
   n8n workflow, AI chatbot, resume automation, CV assistant, personal branding, Google Gemini API, Pinecone vector store, NocoDB, natural language processing, Google Drive automation, LangChain, webhook-based chatbot, Vertex AI, AI resume analysis
   
   Third-Party APIs and Services Used:
   
   1. Google Gemini (PaLM) API (via Vertex AI) – for embeddings and conversational AI
   2. Google Drive API – for file creation and update triggers
   3. Pinecone API – for storing and retrieving resume content as vector embeddings
   4. NocoDB API – for storing and reporting chat session logs
   5. Gmail API – for sending daily conversation reports via email
   
   Article:
   
   Build Your Personal Resume Chatbot with n8n and Google Gemini
   
   In today's competitive job market, your resume isn’t just a static document—it should work for you. What if your résumé could speak on your behalf, answer questions, and intelligently present your skills and achievements? With a low-code automation platform like n8n and AI capabilities from Google's Gemini (PaLM) API, this is now entirely possible.
   
   This article walks you through the key features and structure of a prebuilt n8n workflow titled "Personal Portfolio Resume CV Chatbot." This workflow allows you to automatically process your resume, store its knowledge in a vector database, interact with users via chat endpoints, and even send you daily engagement reports.
   
   Let’s explore how each component works together in this smart CV assistant.
   
   Step 1: Automated Resume Training
   
   The workflow begins with Google Drive triggers that monitor a designated folder for any new or updated resume files. When a file is created or changed, the workflow:
   
   - Downloads the file using the Google Drive API
   - Reads and splits the document into text chunks using LangChain’s splitter
   - Generates dense embeddings of each chunk using Google Gemini’s text embedding model (`models/text-embedding-004`)
   - Stores those embeddings in Pinecone, a high-performance vector database, under an index called "seanrag"
   
   This automatic pipeline ensures that your vector store is always up-to-date with the latest content from your resume.
   
   Step 2: Conversational AI with Context Memory
   
   The chatbot logic operates through an n8n webhook and several LangChain-agent powered nodes.
   
   - A POST request to the /chat webhook carries user-input queries
   - These queries are handled by an AI Agent configured with:
     - Google Gemini Chat Model (`models/gemini-2.0-flash`)
     - Pinecone Vector Tool for document-based retrieval
     - Custom system instructions tuned to respond with facts from Sean Lon's resume
     - Context memory using LangChain’s window buffer
   
   The user receives a well-structured response as if chatting with a smart assistant that knows everything about Sean Lon's professional journey.
   
   Example query:
   
   POST https://n8n.io/webhook-test/chat
   
   {
     "chatInput": "Hi, who is Sean?"
   }
   
   Returns:
   
   [{ "output": "Sean is a skilled engineer who has worked 15 years in the industry" }]
   
   Step 3: Persisting Conversation Logs
   
   An optional webhook (`/update-conversation`) allows frontend interfaces to report chats for long-term storage. This feature:
   
   - Accepts POST data with fields like user input, response, email, and session ID
   - Uses NocoDB API to store the entries in a relational structure
   - Allows session-level tracking and analysis of interactions
   
   Example:
   
   POST https://n8n.io/webhook-test/update-conversation
   
   {
     "user": "What tech stack does Sean use?",
     "email": "visitor@example.com",
     "ai": "Sean is proficient in TypeScript, Python, and Go.",
     "sessionid": "session123"
   }
   
   Step 4: Daily Email Reports
   
   The workflow also features a Schedule Trigger that runs daily (e.g., 6 PM):
   
   - Retrieves all chat conversations from NocoDB for that day
   - Groups them by session and email
   - Formats them into an HTML template
   - Sends a summary email using the Gmail API to a pre-configured address (e.g., lseanlon@gmail.com)
   
   This serves as an insightful engagement report—perfect for candidates or developers who want to monitor how their resume chatbot performs.
   
   What’s Needed To Get Started
   
   To implement this workflow, you’ll need to set up the following components:
   
   - A Google Cloud project with Vertex AI enabled and an API key
   - A Pinecone account with an index (e.g., “seanrag”)
   - A NocoDB instance and a table to hold chat logs
   - A dedicated Google Drive folder for CV/doc uploads
   - n8n credentials set up for:
     - Google Drive (OAuth2)
     - Google Gemini API
     - Pinecone API
     - Gmail API
     - NocoDB API Token
   
   Wrap-Up
   
   This "Personal CV Chatbot" workflow turns your resume into a living document—one that not only adapts but interacts with prospective clients, employers, or visitors directly. Whether you're a freelance developer, a job seeker, or an AI enthusiast wanting to showcase skills in a unique way, this n8n + Gemini + Pinecone + NocoDB stack is powerful, scalable, and personalized.
   
   With just a few simple configurations and connections, you get a resume that talks, learns, and reports—because the future of personal branding is conversational.
   
   Ready to make your CV smart?
   
   Try importing the workflow into your n8n instance, connect your services, and chat with your résumé today.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.