Automate Supabase File Processing with AI Chatbot in n8n

Opening Problem Statement

Meet Sarah, a knowledge manager at a growing tech startup, juggling the maintenance of hundreds of documents stored in Supabase. Every day, she spends hours manually downloading, processing new PDFs and text files, extracting meaningful data, and trying to build a useful knowledge base her team can query effectively. Duplicate processing causes redundant work, and inconsistent manual workflows risk missed insights. The result? Lost hours, slowed decision-making, and frustrated users.

This specific challenge led Sarah to seek an automated solution that could reliably fetch new files, extract their content intelligently, create searchable vector embeddings, and power an AI chatbot to interact with the stored documents — all without manual intervention.

What This Automation Does ⚙️

This n8n workflow automates the entire process of integrating Supabase file storage with AI-powered search and chat capabilities. When triggered, it:

• ✅ Retrieves the latest list of files from Supabase storage, automatically skipping placeholder and previously processed files to avoid duplicates.
• ✅ Downloads new files from Supabase securely.
• ✅ Uses a smart file type switcher to extract content from PDFs or process text files accordingly.
• ✅ Splits large text contents into manageable chunks for better AI processing.
• ✅ Generates vector embeddings of content using OpenAI’s embedding model.
• ✅ Inserts these embeddings into a Supabase vector store ready for fast, contextual search.
• ✅ Enables an AI chatbot interface to query these documents intelligently in real-time, improving team productivity.

By automating these steps, Sarah saves several hours of manual work weekly, reduces errors, and gives her team a powerful, searchable knowledge hub.

Prerequisites ⚙️

• 🔑 Supabase account with storage bucket and vector store tables configured.
• 🔑 OpenAI account for embedding generation and AI language model access.
• ⏱️ n8n automation platform (cloud or self-hosted). Optional: Self-host with Hostinger.

Step-by-Step Guide to Build This Workflow

1. Trigger the Workflow Manually

Start by adding the Manual Trigger node named When clicking ‘Test workflow’. This node allows you to trigger the workflow manually during testing and development.

Navigation: Add node → Search “Manual Trigger” → Drag and drop.

Expected outcome: Manually initiating the workflow run.

Common mistake: Forgetting to activate the workflow post-setup.

2. Retrieve Current File Records from Supabase Table

Use the Supabase node named Get All Files to fetch all records from the Supabase files table. This gives you the list of files you have already processed.

Navigation: Add node → Select Supabase → Set operation to ‘getAll’ → Choose your files table.

Configuration example: Table ID: ‘files’.

Expected outcome: You get a JSON array of existing records.

Common mistake: Not setting the credentials or table ID correctly.

3. Aggregate Retrieved Data for Comparison

Add the Aggregate node called Aggregate to combine all file records into one data structure for easier comparison.

Expected outcome: A single aggregated item with all fetched file data.

Common mistake: Forgetting to set the aggregation method to aggregate all items.

4. Fetch Latest File List from Supabase Storage

Set up an HTTP Request node called Get All files to call Supabase Storage API and list all files in the storage bucket.

Method: POST.

URL: Use the Supabase storage list endpoint (something like https://.supabase.co/storage/v1/object/list/private).

Body JSON example:

{
 "prefix": "",
 "limit": 100,
 "offset": 0,
 "sortBy": { "column": "name", "order": "asc" }
}

Expected outcome: Retrieves a sorted file list JSON.

Common mistake: Misconfiguring authentication or URL.

5. Loop Over Each File Item

Attach the SplitInBatches node named Loop Over Items with batch size 1 to process each file one at a time.

Purpose: To prevent processing overload and sequentially check each file.

Common mistake: Setting batch size too high causing failures.

6. Check If File Needs Processing

Use an If node named If to decide whether to process the file based on two conditions:

• The file is not already recorded in Supabase (using aggregate data comparison).
• The file name is not a placeholder like “.emptyFolderPlaceholder”.

Expected outcome: True for new valid files, false otherwise.

Common mistake: Logical errors in condition expressions.

7. Download the File

If the file requires processing, use the HTTP Request node named Download to download the file’s content securely from Supabase Storage.

URL example: https://.supabase.co/storage/v1/object/private/{{ $json.name }}

Make sure to authenticate with your Supabase credentials.

Expected outcome: File binary data ready for further processing.

Common mistake: Incorrect URL or missing auth.

8. Switch Node for File Type Processing

Add a Switch node named Switch to branch based on file type:

• txt files: Directly use the text data.
• pdf files: Pass to Extract Document PDF node to extract text.

Common mistake: Not handling other file types might cause errors.

9. PDF Content Extraction

Use the Extract From File node configured to extract PDF text from binary data.

This step converts PDFs into plain text for embedding.

Expected outcome: Extracted text content from the PDF.

Common mistake: Binary data missing or improper file input.

10. Merge Extracted Content

Merge the extracted or direct text content streams with the original item via the Merge node named Merge for unified processing.

Expected outcome: Single unified JSON with file content.

Common mistake: Incorrect merge mode might cause data loss.

11. Split Large Text into Chunks

Use the Recursive Character Text Splitter node to divide large text into chunks (default 500 characters) with overlaps (200) to preserve context.

JavaScript concept: The node recursively splits until chunks meet size criteria.

Expected outcome: Chunked arrays for embedding.

Common mistake: Chunk size too small or too large causing inefficient processing.

12. Load Text Data for Embedding

Add the Default Data Loader node to transform the chunked text arrays into the document format ready for embeddings.

Attach metadata like file_id for traceability.

Expected outcome: Properly formatted documents for vector embeddings.

13. Generate Vector Embeddings Using OpenAI

Connect the Embeddings OpenAI node, configured with text-embedding-3-small model, to generate vector representations of the processed text chunks.

Expected outcome: High-dimensional vectors representing text semantics.

Common mistake: Invalid/expired API key or model name error.

14. Create New File Records in Supabase

Use the Supabase node named Create File record2 to insert new file metadata like name and storage ID after download and processing.

Expected outcome: Updated record keeping to avoid duplicate processing.

15. Insert Vectors into Supabase Vector Store

Finally, use the Vector Store Supabase node with ‘insert’ mode to save embeddings into the vector store table “documents”.

This enables fast, semantic search through your document corpus.

16. AI Chatbot for Query Handling

Set up an AI chatbot with the nodes When chat message received, AI Agent, and underlying Langchain OpenAI chat models and vector store lookup nodes.

This bot retrieves relevant file chunks on demand by querying the vector store, interpreting user intents, and returning human-friendly answers.

Customizations ✏️

• Adjust Chunk Size: In Recursive Character Text Splitter, change chunkSize to a larger or smaller number to optimize context windows for your documents.
• Support More File Types: Extend the Switch node to handle DOCX or CSV files by adding new cases and appropriate parsers.
• Advanced Metadata: In the Default Data Loader, add more metadata fields like upload date, author, or tags to enable richer search filters.
• Custom AI Prompts: Modify the prompt templates in OpenAI Chat Model nodes to tailor chatbot responses specific to your business jargon.
• Authentication Methods: Switch between Supabase API key or OAuth by adjusting credentials in HTTP Request nodes for tighter security.

Troubleshooting 🔧

• Problem: “HTTP 401 Unauthorized” during file list retrieval.

  Cause: Incorrect Supabase credentials or expired token.

  Solution: Recheck and update Supabase API key in the credential manager.

• Problem: “File not downloaded or empty data” after the Download node.

  Cause: Incorrect URL or missing authentication.

  Solution: Verify the URL syntax and ensure node uses Supabase credentials.

• Problem: “No data returned from PDF extraction”.

  Cause: Uploaded file is not a valid PDF or binary data misconfigured.

  Solution: Confirm files are proper PDF format and check upstream binary data flow.

• Problem: “Duplicate file records created in Supabase.”

  Cause: If the comparison logic in the If node fails.

  Solution: Review the conditions that check file presence carefully.

• Problem: “OpenAI embedding errors” or “invalid API key”.

  Cause: Expired or invalid OpenAI credentials.

  Solution: Renew API keys and validate with test calls.

Pre-Production Checklist ✅

• Verify Supabase credentials and storage bucket permissions.
• Test API responses from the Supabase Storage list and download endpoints.
• Confirm file list aggregation correctly identifies new vs processed files.
• Check each file type branch works correctly: txt vs pdf.
• Validate OpenAI API keys and connectivity.
• Test the full flow with a sample file end-to-end before live deployment.
• Backup your existing file metadata tables to enable rollback if needed.

Deployment Guide

After thorough testing, activate your workflow in n8n by toggling the active switch. You can set this to run on a time-based trigger or remain manual depending on how often new files arrive.

Monitor workflow execution logs inside n8n for errors and performance. Leverage Supabase dashboard to check vector store data integrity and update metrics.

FAQs

• Can I use this workflow with other storage providers?
  Yes, but you would need to replace the Supabase Storage HTTP Request nodes with corresponding API calls for your storage provider and adapt schema accordingly.
• Does this automation consume OpenAI credits significantly?
  Embeddings and chat queries with OpenAI will incur costs based on usage. Monitor usage and consider rate limits.
• Is my data safe in this workflow?
  Yes, the workflow authenticates with your own Supabase and OpenAI accounts. Ensure you keep credentials secure and restrict API keys appropriately.
• Can this handle hundreds of files?
  Yes, the workflow processes files one at a time using the SplitInBatches node and can be scaled up or scheduled for larger data sets.

Conclusion

By following this detailed guide, you have automated the tedious and error-prone process of managing Supabase file storage for AI-powered intelligent searching and chatbot querying. Sarah no longer wastes hours manually curating documents; instead, she enjoys instant access to the knowledge contained within her PDFs and text files.

This automation saves considerable time, eliminates duplicate data handling, and empowers user-friendly search experience. Your next steps could be to integrate additional file formats, enhance chatbot conversational skills, or add notification triggers on process completion for better workflow insights.

With n8n, Supabase, and OpenAI combined, efficient document management is no longer a burdensome chore—it’s an opportunity for smarter business agility.