Supabase Vector Store

Use the Supabase Vector Store to interact with your Supabase database as vector store. You can insert documents into a vector database, get many documents from a vector database, and retrieve documents to provide them to a retriever connected to a chain.

Use the Supabase Vector Store to interact with your Supabase database as vector store¹. You can insert documents into a vector database, get documents from a vector database, retrieve documents to provide them to a retriever connected to a chain², or connect it directly to an agent³ to use as a tool⁴. You can also update an item in a vector store by its ID.

On this page, you'll find the node parameters for the Supabase node, and links to more resources.

Supabase provides a quickstart for setting up your vector store. If you use settings other than the defaults in the quickstart, this may affect parameter settings in n8n. Make sure you understand what you're doing.

Node usage patterns

You can use the Supabase Vector Store node in the following patterns.

Use as a regular node to insert, update, and retrieve documents

You can use the Supabase Vector Store as a regular node to insert, update, or get documents. This pattern places the Supabase Vector Store in the regular connection flow without using an agent.

You can see an example of this in scenario 1 of this template.

Connect directly to an AI agent as a tool

You can connect the Supabase Vector Store node directly to the tool connector of an AI agent to use a vector store as a resource when answering queries.

Here, the connection would be: AI agent (tools connector) -> Supabase Vector Store node.

Use a retriever to fetch documents

You can use the Vector Store Retriever node with the Supabase Vector Store node to fetch documents from the Supabase Vector Store node. This is often used with the Question and Answer Chain node to fetch documents from the vector store that match the given chat input.

An example of the connection flow (the example uses Pinecone, but the pattern in the same) would be: Question and Answer Chain (Retriever connector) -> Vector Store Retriever (Vector Store connector) -> Supabase Vector Store.

Use the Vector Store Question Answer Tool to answer questions

Another pattern uses the Vector Store Question Answer Tool to summarize results and answer questions from the Supabase Vector Store node. Rather than connecting the Supabase Vector Store directly as a tool, this pattern uses a tool specifically designed to summarizes data in the vector store.

The connections flow in this case would look like this: AI agent (tools connector) -> Vector Store Question Answer Tool (Vector Store connector) -> Supabase Vector store.

Node parameters

Operation Mode

This Vector Store node has five modes: Get Many, Insert Documents, Retrieve Documents (As Vector Store for Chain/Tool), Retrieve Documents (As Tool for AI Agent), and Update Documents. The mode you select determines the operations you can perform with the node and what inputs and outputs are available.

Get Many

In this mode, you can retrieve multiple documents from your vector database by providing a prompt. The prompt will be embedded and used for similarity search. The node will return the documents that are most similar to the prompt with their similarity score. This is useful if you want to retrieve a list of similar documents and pass them to an agent as additional context.

Insert Documents

Use Insert Documents mode to insert new documents into your vector database.

Retrieve Documents (As Vector Store for Chain/Tool)

Use Retrieve Documents (As Vector Store for Chain/Tool) mode with a vector-store retriever to retrieve documents from a vector database and provide them to the retriever connected to a chain. In this mode you must connect the node to a retriever node or root node.

Retrieve Documents (As Tool for AI Agent)

Use Retrieve Documents (As Tool for AI Agent) mode to use the vector store as a tool resource when answering queries. When formulating responses, the agent uses the vector store when the vector store name and description match the question details.

Update Documents

Use Update Documents mode to update documents in a vector database by ID. Fill in the ID with the ID of the embedding entry to update.

Rerank Results

Enables reranking. If you enable this option, you must connect a reranking node to the vector store. That node will then rerank the results for queries. You can use this option with the Get Many, Retrieve Documents (As Vector Store for Chain/Tool) and Retrieve Documents (As Tool for AI Agent) modes.

Get Many parameters

• Table Name: Enter the Supabase table to use.
• Prompt: Enter the search query.
• Limit: Enter how many results to retrieve from the vector store. For example, set this to 10 to get the ten best results.

Insert Documents parameters

• Table Name: Enter the Supabase table to use.

Retrieve Documents (As Vector Store for Chain/Tool) parameters

• Table Name: Enter the Supabase table to use.

Retrieve Documents (As Tool for AI Agent) parameters

• Name: The name of the vector store.
• Description: Explain to the LLM what this tool does. A good, specific description allows LLMs to produce expected results more often.
• Table Name: Enter the Supabase table to use.
• Limit: Enter how many results to retrieve from the vector store. For example, set this to 10 to get the ten best results.

Update Documents

• Table Name: Enter the Supabase table to use.
• ID: The ID of an embedding entry.

Parameters for Update Documents

• ID

Node options

Query Name

The name of the matching function you set up in Supabase. If you follow the Supabase quickstart, this will be match_documents.

Metadata Filter

Available in Get Many mode. When searching for data, use this to match with metadata associated with the document.

This is an AND query. If you specify more than one metadata filter field, all of them must match.

When inserting data, the metadata is set using the document loader. Refer to Default Data Loader for more information on loading documents.

Templates and examples

Browse Supabase Vector Store node documentation integration templates or search all templates

Related resources

Refer to LangChain's Supabase documentation for more information about the service.

View n8n's Advanced AI documentation.