Automated Document Ingestion and Vectorization for Supabase
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Streamline your document management by automatically ingesting files from Supabase Storage, processing them with AI, and storing them as searchable vectors. This workflow ensures your data is ready for intelligent retrieval and analysis.
About This Workflow
This n8n workflow automates a critical step in building AI-powered applications: getting your data into a format that intelligent models can understand and query. It begins by fetching files from a specified Supabase Storage bucket. For each file, it checks if it's already been processed to avoid duplication. If new, the file is downloaded and its content is loaded. The text is then intelligently split into manageable chunks, and these chunks are converted into numerical vector embeddings using OpenAI. Finally, these embeddings, along with relevant metadata, are stored in a Supabase Vectorstore, making your documents searchable with natural language queries.
Key Features
- Automated File Ingestion: Seamlessly retrieves files from Supabase Storage.
- Smart Deduplication: Prevents reprocessing of existing data.
- AI-Powered Text Processing: Utilizes Langchain and OpenAI for efficient text splitting and embedding generation.
- Supabase Vectorstore Integration: Stores embeddings in Supabase for fast and scalable semantic search.
- Configurable Chunking: Adjusts text chunk size and overlap for optimal embedding quality.
How To Use
- Configure Supabase Credentials: Ensure your Supabase API credentials are set up in n8n.
- Configure OpenAI Credentials: Set up your OpenAI API key in n8n for embedding generation.
- Update Supabase Storage URL: In the 'Get All files' (httpRequest) node, replace
<project_id>with your actual Supabase project ID. - Define Storage Bucket: Ensure the
privatebucket in the 'Get All files' (httpRequest) node is the correct bucket for your files. Modify theurlparameter if necessary. - Map File Metadata: In the 'Default Data Loader' node, ensure the
file_idmetadata is correctly mapped to theidfrom the file object. - Configure Text Splitter: Adjust
chunkSizeandchunkOverlapin the 'Recursive Character Text Splitter' node based on your content type and desired granularity. - Specify Vectorstore Table: In the 'Insert into Supabase Vectorstore' node, ensure the
tableNameis set to your Supabase Vectorstore table (e.g.,documents). - Test Workflow: Trigger the workflow to process files from your Supabase Storage and populate your Vectorstore.
Apps Used
Workflow JSON
{
"id": "d139055b-f35a-4c51-b06f-b7cf1868844c",
"name": "Automated Document Ingestion and Vectorization for Supabase",
"nodes": 5,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: d139055b-f35a...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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