This article provides a complete, practical walkthrough of the Supabase Stickynote Automation Triggered 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:  
   Creating a Chatbot Knowledge System with n8n, OpenAI & Supabase for Ebook Q&A
   
   Meta Description:  
   Discover how to build an AI-powered chatbot using n8n, Supabase, and OpenAI that can answer questions from a Google Drive-hosted ePub book like "How to Transform Your Life" by Geshe Kelsang Gyatso.
   
   Keywords:  
   n8n workflow, OpenAI embeddings, Supabase vector store, AI chatbot, document Q&A, Google Drive automation, RAG pipeline, text embedding, ePub data loader, LangChain, vector database, NLP automation, intelligent retrieval
   
   Third-party APIs Used:
   - OpenAI API (text-embedding-3-small model for embedding generation and chat completions)
   - Google Drive API (for file download)
   - Supabase API (vector storage and document retrieval)
   
   Article:
   
   📘 Building an Intelligent Chatbot to Answer Questions About a Book  
   Using n8n, Supabase, Google Drive, and OpenAI
   
   In an increasingly interconnected world, synthesizing information from documents and enabling human-like interaction with them is becoming a powerful application of AI. This article explores a real-world use case: how to build a Retrieval-Augmented Generation (RAG) chatbot using n8n, OpenAI, and Supabase. Specifically, this bot can answer any question about Geshe Kelsang Gyatso’s book, "How To Transform Your Life," an ePub file hosted on Google Drive.
   
   Using this no-code/low-code solution, even non-programmers can create a sophisticated knowledge assistant that ingests a document, stores its semantic data in a vector database, and allows users to query it intelligently.
   
   Let’s break down this system.
   
   🔗 Step 1: Ingest the Book from Google Drive
   
   It all starts with a Google Drive node in n8n that downloads the ePub file of the book using its Drive URL. This file is then passed to a LangChain-powered node specialized for ePub loading. After loading the content into memory, the book's text is split into smaller, manageable chunks using a recursive character splitter. This step is crucial to ensure efficient embeddings and retrieval performance.
   
   📄 Step 2: Prepare Supabase for Vector Storage
   
   Before inserting data, Supabase must be configured to act as a vector store. This involves:
   
   - Enabling the pgvector extension.
   - Creating a table with essential fields:
     - embedding (VECTOR type, matching the embedding model size),
     - content (TEXT), and
     - metadata (JSONB).
     
   A custom SQL function, match_documents, is created to support vector similarity searches. Proper policies must also be set to allow reading and writing as needed for your use case.
   
   🧠 Step 3: Embed the Document with OpenAI
   
   n8n flows into an Embeddings node powered by OpenAI’s text-embedding-3-small model. Each split chunk of the document is converted into a high-dimensional vector. These embeddings are then inserted into Supabase using the Vector Store Supabase node, preserving both content and metadata.
   
   ⚙️ Step 4: Enable Retrieval through Q&A Chain
   
   Once the document is embedded, you can set up a question-and-answer interaction. For this:
   
   - A chatbot trigger captures incoming user queries.
   - The question is embedded using OpenAI’s same text-embedding-3-small model to ensure dimensional consistency.
   - Supabase’s match_documents function retrieves the top-k relevant chunks based on cosine similarity.
   - These are passed to LangChain’s Retrieval-QA chain, which synthesizes an answer using OpenAI’s chat model.
     
   All of this is fully automated within n8n’s visual workflow builder!
   
   💬 Step 5: Chatbot Interaction
   
   A public webhook triggers the flow when a user sends a message (e.g. “What is the essence of happiness?”). This starts the pipeline:
   
   - Query is embedded.
   - Matching document chunks are retrieved.
   - Response is generated via OpenAI Chat Completion API.
   - Final output is wrapped and returned back to the user.
   
   A "Customize Response" node formats the answer cleanly before sending it back through the webhook.
   
   🛠️ Optional: Upsertion and Deletion
   
   The workflow also allows upsertion (update or insert) so you can refresh your document's content without duplicate entries. Though n8n currently lacks a built-in deletion function for Supabase entries, a Sticky Note outlines a method using the HTTP Request Node to send authorized DELETE requests to Supabase’s REST API.
   
   This includes:
   - Setting appropriate headers (API keys and JWT)
   - Composing the right REST endpoint and query parameters
   - Ensuring permission policies are accurately set in Supabase
   
   🔄 Key Considerations
   
   - Always use the same embedding model (text-embedding-3-small in this case) across insert, update, and retrieval operations to ensure dimensional compatibility.
   - Proper chunking is necessary to preserve semantic meaning while respecting token limits.
   - All data pipelines should be periodically tested to ensure your book’s knowledge base remains consistent and accurate.
   
   ✨ Results
   
   The outcome is a fully interactive chatbot that empowers users to draw insights from a book as if they were speaking to a subject-matter expert. Whether it's philosophical guidance from "How to Transform Your Life" or applying the system to any other body of text—scientific, legal, business, or educational—the potential for use cases is endless.
   
   📦 Conclusion
   
   This n8n-based RAG workflow is a perfect example of how open-source automation platforms, combined with AI and modern cloud databases, can help democratize intelligent applications. With tools like OpenAI, Supabase, and LangChain available at your fingertips, building a chatbot that understands documents is no longer limited to engineers—makers, educators, and content creators can all participate.
   
   Whether for internal documentation, educational content, or spiritual texts like this one, automating knowledge access has never been easier or more scalable.
   
   Now go ahead — ask your document anything. Your AI assistant is ready to answer.
   
   —
   
   For more on how to integrate OpenAI and Supabase with n8n, visit the official docs or explore community templates.

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.