This article provides a complete, practical walkthrough of the Code Googledocs Automation 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 Tech Radar Intelligence with n8n: A Workflow for RAG + SQL-Based AI Agents
   
   Meta Description:  
   Discover how a fully automated n8n workflow transforms a Tech Radar spreadsheet into structured and unstructured AI-readable data, enabling smart querying via SQL or RAG AI agents. Includes Google Sheets, Docs, Pinecone, MySQL, and Google Gemini integration.
   
   Keywords:  
   n8n, AI agent, RAG architecture, SQL agent, Tech Radar, LLM, Pinecone, Google Gemini, Vertex AI, MySQL, Google Sheets automation, Google Docs, workflow automation, vector database, intelligent assistant, LangChain, vector embeddings
   
   Third-party APIs and Services Used:
   
   1. Google Sheets API (OAuth2)
   2. Google Docs API (via Service Account)
   3. Google Drive API (OAuth2)
   4. Google Vertex AI / Gemini API (formerly PaLM)
   5. Pinecone Vector Database API
   6. MySQL (n8n built-in MySQL node for SQL API actions)
   7. Groq AI API (for Qwen and Deepseek models)
   8. Anthropic Claude API
   9. LangChain components (used in n8n AI agent modules)
   
   Article:
   
   Automating Tech Radar Intelligence: Building a Hybrid RAG & SQL AI Agent with n8n
   
   As organizations increasingly adopt emerging technologies, maintaining a living document—like a Tech Radar—to track tools, platforms, and strategic directions has become essential. But how do you efficiently query and derive insights from such a dynamic and human-readable document? That’s where the power of low-code automation and AI integration shines.
   
   In this article, we break down a powerful n8n workflow designed to transform raw Tech Radar data into structured and unstructured formats, enabling smart query processing using Retrieval-Augmented Generation (RAG) and SQL-based language models.
   
   Overview of the Workflow
   
   This n8n workflow named “Tech Radar” transforms a Google Sheet-based radar into two parallel data systems: a MySQL database for structured queries and a Pinecone vector database for RAG-based semantic searches. It then intelligently routes user questions between two AI agents based on context—ensuring the most accurate and domain-relevant response.
   
   The workflow is divided into three main phases:
   
   1. Setup Phase – Data Preprocessing  
   2. Storage Phase – Structured (SQL) + Unstructured (Vector DB)  
   3. Chat Phase – Query Input Routing & AI Response
   
   Let’s unpack each phase.
   
   1. Setup Phase: Data Extraction & Transformation
   
   The journey begins with reading data from a shared Google Sheets document titled “Tech Constellation Compass.” This sheet lists various technologies alongside metadata including:
   
   - Name
   - Ring (Adopt, Trial, Hold, etc.)
   - Quadrant (Tools, Platforms, Techniques, etc.)
   - Strategic Direction status
   - Adoption across 3 different companies
   
   Each row from the sheet is parsed using a code node that converts the structured tabular data into simple text blocks, which are then used for natural language understanding downstream.
   
   Additionally, these blocks are inserted into a standard Google Doc to serve as a canonical unstructured version of the radar. Periodic updates with the Google Docs API keep the document version fresh.
   
   2. Storage Phase: Dual Database Integration
   
   Structured Database (MySQL)
   Every month, a Cron node triggers a refresh operation by:
   - Deleting all MySQL data from the 'techradar' table
   - Reloading updated rows from Google Sheets
   
   This ensures your SQL AI agent has access to current data capable of responding to simple SELECT-style questions like “Is Backstage used by Company2?”
   
   Unstructured Vector Database (Pinecone)
   Simultaneously, the updated Google Doc is monitored for changes using the Google Drive API. When a change is detected, the document is downloaded, split into smaller text chunks using LangChain’s Character Splitter, and sent through Google Gemini text embedding. These embeddings are then inserted into the Pinecone vector DB.
   
   3. Chat Phase: Conversational AI Workflow
   
   This is where it gets exciting. A webhook endpoint (e.g., /radar-rag) exposed via n8n allows your frontend app to send chat queries to the workflow.
   
   Here’s what happens behind the scenes:
   
   - An LLM “Input Router” evaluates whether the question is best suited for structured (SQL) or unstructured (RAG) response.
   - If SQL is more appropriate, the SQL Agent workflow is invoked. This uses LangChain tools sequentially to: 
      - Analyze schema
      - Fetch table definitions
      - Formulate and run a custom SQL query on your MySQL table
   - If RAG is better, the RAG Agent uses Pinecone vector search, backed by Gemini embeddings, to retrieve relevant context from the GDoc and generate a robust answer.
   
   The final response is processed through a LangChain output guardrail agent that reformats and verifies the reply, ensuring consistency with guardrails for tone, domain scope, and accuracy—important when functioning as an “AI Architect.”
   
   Why Use Two Agents?
   
   SQL Agent is ideal for questions that have binary or relational answers, such as:
   - “How many tools are in ‘Adopt’ quadrant?”
   - “Which technologies are strategic direction but not used by Company3?”
   
   RAG Agent is better at context-rich questions like:
   - “Explain why RAG is preferred as a strategy.”
   - “What are the key points mentioned about OpenAI integration?”
   
   Together, they enable a hybrid AI assistant that combines precision of databases with semantic understanding of documentation.
   
   Final Thoughts: A Modular Blueprint for Enterprise AI
   
   This n8n workflow represents more than just automation—it’s a modular AI architecture tailored for enterprise environments. With components like Google Cloud, Pinecone vector DB, LangChain agents, and LLM evaluation routing, you get the best of both structured and unstructured query processing.
   
   Whether you're building an internal tech advisor, an onboarding knowledge assistant, or a smart documentation system, this opens the doors to a truly intelligent enterprise.
   
   Try the demo or clone the GitHub frontend (listed in the sticky notes) to see how it integrates with real-time conversations and Tech Radar insight generation.
   
   Welcome to the future of real-time organizational intelligence.

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.