This article provides a complete, practical walkthrough of the Supabase Stickynote Create 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:  
   How to Build an AI-Powered Q&A Chatbot Using n8n, Supabase, and OpenAI
   
   Meta Description:  
   Discover how to create a smart Q&A chatbot that analyzes and answers queries from an ePub book using n8n, Google Drive, Supabase, and OpenAI’s embedding models. Learn about document loading, vector databases, and retrieval-based AI chains.
   
   Keywords:  
   n8n chatbot, OpenAI embeddings, Supabase vector store, Google Drive ePub, LangChain in n8n, AI Q&A chatbot, text embedding, Supabase pgvector, intelligent document retrieval, ePub loader n8n
   
   Third-Party APIs and Tools Used:
   
   1. Google Drive API – for downloading the source ePub file.
   2. OpenAI API – for generating text embeddings and chat-based responses.
   3. Supabase (with pgvector extension) – for storing and retrieving vector embeddings from a PostgreSQL database.
   4. LangChain (via n8n LangChain nodes) – for chaining document loaders, embedding creation, vector storage/retrieval, and Q&A logic.
   
   —
   
   Article:
   
   Building an Intelligent Book-Based Chatbot with n8n, Supabase & OpenAI
   
   In the age of AI, building interactive systems that understand human questions and respond using existing literature is becoming increasingly achievable. This guide highlights an advanced automation workflow designed using n8n — a powerful low-code tool — in combination with OpenAI’s embedding models and Supabase’s vector capabilities. The result? A fully operational chatbot that can answer user questions based on the content of Venerable Geshe Kelsang Gyatso's book, “How to Transform Your Life”.
   
   Let’s take a deep dive into how this end-to-end AI-powered Q&A system is architected.
   
   Step 1: Preparing the Data
   
   The user starts by providing an ePub of the book stored in Google Drive. Using n8n’s Google Drive node, the workflow automatically downloads this file. This allows us to dynamically load different documents as sources for our chatbot.
   
   Once downloaded, the ePub file is passed through n8n’s powerful LangChain document loader using the epubLoader. This transforms the binary file into structured text data, which can be further processed and split for embeddings.
   
   Step 2: Text Splitting with LangChain
   
   Large documents cannot be embedded in one chunk due to size limitations of embedding models. The Recursive Character Text Splitter node from LangChain breaks the ePub content into manageable pieces that preserve semantic coherence between chunks. This ensures our chatbot will understand the context of individual sections clearly.
   
   Step 3: Embedding Generation Using OpenAI
   
   Using OpenAI’s “text-embedding-3-small” model, each document chunk is transformed into a numerical vector representation. These embeddings form the foundation for similarity-based retrieval, allowing us to later match user queries with the most relevant document chunks.
   
   It’s important to note a best practice here: The same model must be used across all operations — insertion, upsertion, and retrieval — to maintain consistent vector dimensions. In this case, the embedding size is 1,536 dimensions, which is compatible with OpenAI’s text embedding model.
   
   Step 4: Storing Embeddings in Supabase
   
   Supabase serves as our vector database, with the pgvector extension enabled to handle high-dimensional vectors.
   
   A SQL table is manually prepared in Supabase with the following columns:
   
   - embedding (VECTOR[1536])
   - content (TEXT)
   - metadata (JSONB)
   
   A custom SQL function match_documents is also created to facilitate vector similarity searches.
   
   Using Supabase’s LangChain vector store node in n8n, we insert the embeddings along with their original text and metadata. This is crucial for efficient later retrieval.
   
   Step 5: Retrieval and Vector Search
   
   When a user sends a query, the message is caught via the "chatTrigger" node in n8n. This input initializes the retrieval-based Q&A pipeline:
   
   - The user query is embedded using the same OpenAI model.
   - Using the "Retrieve by Query" node, a vector search is triggered on the Supabase table using the match_documents function.
   - Supabase returns the top 10 most relevant chunks based on cosine similarity.
   
   Step 6: Building the Q&A Chain
   
   The core of LangChain's Q&A ability is used here to feed the retrieved documents and the user’s question into OpenAI’s chat model. This generates a detailed, relevant response grounded in the original content of "How to Transform Your Life".
   
   The final answer is formatted and returned to the user via the chat interface, giving the experience of a real-time, intelligent chatbot that knows the book inside out.
   
   Step 7: Updating and Deleting Content
   
   Beyond Q&A, administrators may want to update or delete records. For upserts, the workflow uses the same embedding model and updates vector entries into Supabase using document ID references.
   
   Deletion, however, currently requires a workaround. Since n8n lacks a dedicated Supabase deletion node for vector databases, the HTTP Request Node must be used instead. This is configured to make direct API calls to Supabase using secure bearer tokens and API keys.
   
   Final Thoughts
   
   This powerful n8n setup illustrates how modern automation platforms can enable intelligent AI solutions with minimal or no custom code. By combining Google Drive for document retrieval, OpenAI for embeddings and language understanding, and Supabase for fast vector lookup, even non-traditional developers can build advanced tools like personalized chatbots or search engines.
   
   With a well-designed vector database strategy and LangChain’s modularity, this architecture can be extended to multiple use cases: customer support bots, legal document assistants, internal knowledgebase explorers, and more.
   
   As OpenAI and n8n continue to evolve, the possibilities for accessible, intelligent automations only continue to grow.
   
   —
   
   Whether you're a developer, researcher, or digital creator, building your own intelligent Q&A assistant has never been more achievable. Dive into n8n’s LangChain and AI nodes today to start transforming how you interact with information.

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.