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Redis Vector Store node documentation

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.

Prerequisites

Before using this node, you need a Redis database with the Redis Query Engine (opens in a new tab) enabled. Use one of the following:

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.

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 (opens in a new tab) (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 (opens in a new tab) (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 (opens in a new tab) (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

Rerank Results

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.

Related resources

Refer to:

Qdrant Vector Store node documentationSentiment Analysis node documentation