Ingest Supabase Files into OpenAI Embeddings and Vector Store
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This workflow automates the ingestion of files from Supabase storage into OpenAI embeddings and a Supabase vector store. It uses the 'Get All files' node to retrieve file names, 'Download' to get file content, and 'Extract Document PDF' or direct text processing to prepare data for 'Embeddings OpenAI' and 'Insert into Supabase Vectorstore'.
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About This Workflow
Overview
This n8n workflow is designed to automate the process of ingesting documents stored in Supabase into an OpenAI-powered embedding system and a Supabase vector store. It addresses the common challenge of making unstructured data (like PDFs and text files) searchable and usable by AI models.
The workflow begins by fetching all files from a specified Supabase storage bucket using the Get All files node. It then iterates through each file, downloading its content. Based on the file type (PDF or other), it uses the Extract Document PDF node or directly processes text. The prepared content is then passed to the Embeddings OpenAI node to generate vector embeddings. Finally, these embeddings, along with the file's metadata, are inserted into a Supabase table designated as a vector store using the Insert into Supabase Vectorstore node. This process enriches your Supabase data with AI-searchable vector representations.
Key Features
- Automates file retrieval from Supabase storage.
- Supports PDF and text file ingestion.
- Generates OpenAI embeddings for semantic search.
- Stores embeddings and document metadata in a Supabase vector store.
- Integrates with Supabase API for seamless data management.
How To Use
- Configure Supabase Credentials: Ensure your Supabase API credentials are set up in n8n.
- Set Supabase Storage URL: In the
Get All filesnode, configure the Supabase URL to point to your storage bucket. - Map File Metadata: In the
Create File record2node, map thenameandidfrom your Supabase files to relevant fields. - Define Vector Store Table: In the
Insert into Supabase Vectorstorenode, specify the Supabase table that will act as your vector store. - Configure OpenAI Credentials: Set up your OpenAI API credentials for embedding generation.
- Adjust Text Splitter Settings: If dealing with large documents, fine-tune the
chunkSizeandchunkOverlapin theRecursive Character Text Splitternode. - Execute the Workflow: Trigger the workflow to start ingesting files.
Apps Used
Workflow JSON
{
"id": "6f3efec0-4037-4370-8ddc-290298fafe40",
"name": "Ingest Supabase Files into OpenAI Embeddings and Vector Store",
"nodes": 0,
"category": "Data Ingestion and AI",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 6f3efec0-4037...
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