Supabase Vector Store node#

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.

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

Credentials

You can find authentication information for this node here.

Parameter resolution in sub-nodes

Sub-nodes behave differently to other nodes when processing multiple items using an expression.

Most nodes, including root nodes, take any number of items as input, process these items, and output the results. You can use expressions to refer to input items, and the node resolves the expression for each item in turn. For example, given an input of five name values, the expression {{ $json.name }} resolves to each name in turn.

In sub-nodes, the expression always resolves to the first item. For example, given an input of five name values, the expression {{ $json.name }} always resolves to the first name.

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 parameters#

Operation Mode#

This Vector Store node has four modes: Get Many, Insert Documents, Retrieve Documents, 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 (For Agent/Chain)#

Use Retrieve Documents 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.

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.

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 (For Agent/Chain) parameters#

Table Name: Enter the Supabase table to use.

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#

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

View n8n's Advanced AI documentation.

AI glossary#

completion: Completions are the responses generated by a model like GPT.
hallucinations: Hallucination in AI is when an LLM (large language model) mistakenly perceives patterns or objects that don't exist.
vector database: A vector database stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.
vector store: A vector store, or vector database, stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.