This article provides a complete, practical walkthrough of the Extractfromfile Http 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:  
   Effortless File Q&A: Build an AI Agent to Chat with Your Supabase-Stored Documents Using n8n
   
   Meta Description:  
   Learn how to automate document ingestion, vectorization, and smart querying using this n8n workflow that connects Supabase and OpenAI to enable chatbot interactions with your stored PDFs and text files.
   
   Keywords:  
   n8n workflow, Supabase storage, OpenAI embeddings, AI chatbot, document automation, Supabase vector store, PDF extractor, text vectorization, Langchain n8n, automate file analysis, AI agent, chat with files
   
   Third-Party APIs Used:
   
   1. Supabase API - for file storage, record keeping, and vector database (supabaseApi credential)
   2. OpenAI API - for embedding document chunks and interacting via conversational AI (openAiApi credential)
   3. LangChain (n8n LangChain nodes) - for AI components including embedding, data loading, chatbot interaction, text splitting, and vector tools
   
   Article:
   
   Chat With Your Files: An AI-Powered n8n Workflow Using Supabase and OpenAI
   
   Manually combing through documents to find answers is a problem as old as office work itself. What if you could simply upload your files and ask an AI to handle the rest?
   
   That’s exactly what this powerful n8n workflow enables. Designed by the 5minAI community and Mark Shcherbakov, this no-code automation makes Supabase-stored files searchable through a conversational interface powered by OpenAI—and it all happens automatically with easy block-based logic.
   
   Let’s dive into how it works and what you’ll gain from deploying it.
   
   🧩 Overview
   
   The workflow connects Supabase (a backend storage and database platform) with OpenAI’s powerful language and embedding models using n8n, an open-source workflow automation platform. The result is a system that:
   
   - Monitors a Supabase storage bucket
   - Processes and vectorizes new PDF or text files
   - Stores content in a vector database
   - Enables real-time, chatbot-style queries against the file content
   
   This transforms ordinary file storage into a smart knowledge repository you can chat with.
   
   🚀 Step-by-Step Breakdown
   
   1. Fetch Files from Supabase
   
   The workflow starts with a Manual Trigger or Chat Message Trigger. It connects to the Supabase storage bucket and retrieves the list of all files using a Supabase HTTP Request node. A secondary Supabase node fetches all already-processed file records from the "files" database table.
   
   2. Filter Duplicates and Ignore Placeholder Files
   
   A conditional check (If node) filters out duplicates by comparing current files against existing ones in the database, and also skips over special placeholder files often used in cloud storage systems (like .emptyFolderPlaceholder).
   
   3. Download Files Securely
   
   Files identified as new are then downloaded using a secure Supabase HTTP Request. This node ensures the correct file path and credentials are used to fetch documents, especially if stored in a “private” bucket.
   
   4. Determine File Type
   
   Once downloaded, a Switch node checks whether the file is a PDF or a raw text file. Different processing nodes are triggered depending on the type:
   - Text files are passed directly into the text processing pipeline.
   - PDFs go through the Extract Document PDF node to parse embedded text content.
   
   5. Chunk Content for Contextual Embeddings
   
   Using the Recursive Character Text Splitter node from Langchain, the extracted textual content is broken into manageable chunks (default: 500 characters with 200-character overlap). This chunking helps maintain contextual information for downstream AI models.
   
   6. Generate Embeddings with OpenAI
   
   Next, OpenAI’s text-embedding-3-small model is used to generate high-dimensional vector representations (semantic meaning) for each chunk. This allows the AI to later retrieve relevant context when answering queries.
   
   7. Save Metadata and Vectors into Supabase
   
   Each file is logged in the Supabase “files” table for record-keeping (name and storage ID). Simultaneously, the metadata-enriched embeddings are inserted into the Supabase vector store under a “documents” table. This forms the knowledge base your chatbot will query.
   
   8. Chat With Your Files
   
   A separate chatbot flow (triggered via “When chat message received”) kicks off an AI Agent using LangChain’s chat and vector store tools. This agent:
   - Accepts a user’s question
   - Retrieves relevant chunks from the vector store using vector similarity
   - Responds conversationally using OpenAI’s GPT model
   
   It’s like having a smart assistant who’s read all your files and is ready to answer any question about them.
   
   🧠 Use Cases
   
   - Internal knowledge bases
   - Legal document search
   - Research paper navigation
   - Customer support file parsing
   - Team document archives
   
   🔐 Credentials & Customization
   
   You'll need to provide:
   - Creds for Supabase API
   - A working OpenAI API key
   
   Also, be sure to adjust storage bucket names, database table IDs, and schema to match your Supabase structure.
   
   📺 Watch and Learn
   
   Prefer a visual guide? 5minAI offers a recorded walkthrough here:  
   [Watch the 10-minute setup video on YouTube](https://www.youtube.com/watch?v=glWUkdZe_3w)
   
   🌍 Final Thoughts
   
   This n8n workflow empowers anyone—technical or not—to turn their document collections into interactive AI resources. Whether you're a solopreneur, startup team, or part of a large org, it's a plug-and-play system for smarter document interaction. Try integrating it with your cloud workflow and future-proof how you access stored knowledge today!
   
   🔥 Built and shared with love by the 5minAI community. Find the project on [Skool](https://www.skool.com/5minai-2861) or connect with creator [Mark Shcherbakov](https://www.linkedin.com/in/marklowcoding/).

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.