Redis Vector Store node#

Use the Redis Vector Store node to interact with your Redis database as a vector store. You can insert documents into the vector database, get documents from the vector database, retrieve documents using a retriever connected to a chain, or connect it directly to an agent to use as a tool.

On this page, you'll find the node parameters for the Redis Vector Store 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.

Prerequisites#

Before using this node, you need to have a Redis database with the RediSearch module enabled. - Redis Open Source (8.x and later) has RediSearch built-in - Redis Stack (includes RediSearch) for older versions of Redis - Redis Cloud or Redis Enterprise with RediSearch enabled

A new index will be created if you don't have one.

Creating your own indices in advance is only necessary if you want to use a custom index schema or reuse an existing index. Otherwise, you can skip this step and let the node create a new index for you based on the options you specify. Make sure your index follows the schema specified in the LangChain documentation.

Node usage patterns#

You can use the Redis Vector Store node in the following patterns:

Use as a regular node to insert and retrieve documents#

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

You can see an example of this in scenario 1 of this template (the template uses the Supabase Vector Store, but the pattern is the same).

Connect directly to an AI agent as a tool#

You can connect the Redis 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) -> Redis Vector Store node.

Use a retriever to fetch documents#

You can use the Vector Store Retriever node with the Redis Vector Store node to fetch documents from the Redis 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 linked example uses Pinecone, but the pattern is the same) would be: Question and Answer Chain (Retriever connector) -> Vector Store Retriever (Vector Store connector) -> Redis 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 Redis Vector Store node. Rather than connecting the Redis Vector Store directly as a tool, this pattern uses a tool specifically designed to summarizes data in the vector store.

The connections flow (the linked example uses Qdrant, but the pattern is the same) in this case would look like this: AI agent (tools connector) -> Vector Store Question Answer Tool (Vector Store connector) -> Redis Vector store.

Node parameters#

Operation Mode#

This Vector Store node has four modes: Get Many, Insert Documents, Retrieve Documents (As Vector Store for Chain/Tool), and Retrieve Documents (As Tool for AI Agent). 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 is embedded and used for similarity search. The node returns 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.

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#

Redis Index: Enter the name of the Redis vector search index to use. Optionally choose an existing one from the list.
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.

This Operation Mode includes one Node option, the Metadata Filter.

Insert Documents parameters#

Redis Index: Enter the name of the Redis vector search index to use. Optionally choose an existing one from the list.

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

Redis Index: Enter the name of the Redis vector search index to use.

This Operation Mode includes one Node option, the Metadata Filter. Optionally choose an existing one from the list.

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.
Redis Index: Enter the name of the Redis vector search index to use. Optionally choose an existing one from the list.
Limit: Enter how many results to retrieve from the vector store. For example, set this to 10 to get the ten best results.

Include Metadata#

Whether to include document metadata.

You can use this with the Get Many and Retrieve Documents (As Tool for AI Agent) modes.

Node options#

Metadata Filter#

Metadata filters are available for the Get Many, Retrieve Documents (As Vector Store for Chain/Tool), and Retrieve Documents (As Tool for AI Agent) operation modes. This is an OR query. If you specify more than one metadata filter field, at least one 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.

Redis Configuration Options#

Available for all operation modes:

Metadata Key: Enter the key for the metadata field in the Redis hash (default: metadata).
Key Prefix: Enter the key prefix for storing documents (default: doc:).
Content Key: Enter the key for the content field in the Redis hash (default: content).
Embedding Key: Enter the key for the embedding field in the Redis hash (default: embedding).

Insert Options#

Available for the Insert Documents operation mode:

Overwrite Documents: Select whether to overwrite existing documents (turned on) or not (turned off). Also deletes the index.
Time-to-Live: Enter the time-to-live for documents in seconds. Does not expire the index.

Templates and examples#

Refer to:

Redis Vector Search documentation for more information about Redis vector capabilities.
RediSearch documentation for more information about RediSearch.
LangChain's Redis Vector Store documentation for more information about the service.

View n8n's Advanced AI documentation.

Self-hosted AI Starter Kit#

New to working with AI and using self-hosted n8n? Try n8n's self-hosted AI Starter Kit to get started with a proof-of-concept or demo playground using Ollama, Qdrant, and PostgreSQL.

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