Supabase Vector Store consultants
We can help you automate your business with Supabase Vector Store and hundreds of other systems to improve efficiency and productivity. Get in touch if you’d like to discuss implementing Supabase Vector Store.
About Supabase Vector Store
The Supabase Vector Store node in n8n connects your workflows to Supabase’s pgvector extension, letting you store, retrieve, and search vector embeddings directly within a PostgreSQL database. For teams already using Supabase as their backend, this node removes the need for a separate vector database — your embeddings live alongside your application data in one place.
Vector search is the backbone of retrieval-augmented generation (RAG) and semantic search applications. Instead of relying on exact keyword matches, you store text as mathematical representations (embeddings) and search by meaning. The Supabase Vector Store node handles both the indexing and retrieval sides, making it straightforward to build AI workflows that understand context rather than just matching strings.
This is particularly useful for organisations building AI agents that need to reference internal knowledge bases, product catalogues, or support documentation. By embedding your content into Supabase and querying it through n8n, you can build assistants that pull the right information before generating a response. Our team used a similar approach when building an AI application processing system for a talent marketplace, where accurate document retrieval was essential.
If you are evaluating vector database options and already run Supabase, this node keeps your architecture simple. Need help designing a RAG pipeline? Talk to our AI development team about building a solution that fits your existing stack.
Supabase Vector Store FAQs
Frequently Asked Questions
Common questions about how Supabase Vector Store consultants can help with integration and implementation
What is the Supabase Vector Store node used for?
Do I need a separate vector database if I use Supabase?
How does semantic search with Supabase differ from keyword search?
What embedding models work with this node?
Can I use this node for both storing and searching embeddings?
Is Supabase Vector Store suitable for production workloads?
How it works
We work hand-in-hand with you to implement Supabase Vector Store
As Supabase Vector Store consultants we work with you hand in hand build more efficient and effective operations. Here’s how we will work with you to automate your business and integrate Supabase Vector Store with integrate and automate 800+ tools.
Step 1
Enable pgvector in Your Supabase Project
Log into your Supabase dashboard and enable the pgvector extension under Database > Extensions. This adds vector data types and similarity search functions to your PostgreSQL instance. You will also need to create a table with a vector column to store your embeddings.
Step 2
Configure Supabase Credentials in n8n
Add your Supabase project URL and service role key as credentials in n8n. The service role key provides the necessary permissions for reading and writing to your database. You can find both values in your Supabase project settings under API.
Step 3
Add the Supabase Vector Store Node to Your Workflow
Search for the Supabase Vector Store node in the n8n editor and add it to your canvas. Choose whether you are performing an insert operation (storing new embeddings) or a search operation (retrieving similar results) based on your workflow needs.
Step 4
Connect an Embeddings Model
Add an embeddings node upstream — such as OpenAI Embeddings or Google Gemini Embeddings — to convert your text into vector representations. The output of this node feeds into the Supabase Vector Store node for storage or provides the query vector for searches.
Step 5
Configure Table and Column Mappings
Specify the Supabase table name, the vector column for embeddings, and any metadata columns you want to store or filter by. Proper column mapping ensures your embeddings are stored correctly and that search results include the metadata you need for downstream processing.
Step 6
Test Retrieval and Tune Results
Run sample queries through your workflow and check that the returned results are relevant. Adjust the number of results, similarity threshold, and metadata filters to improve accuracy. For large datasets, consider adding a vector index in Supabase to speed up queries.
Transform your business with Supabase Vector Store
Unlock hidden efficiencies, reduce errors, and position your business for scalable growth. Contact us to arrange a no-obligation Supabase Vector Store consultation.