--- title: Redis Vector Store node documentation description: Learn how to use the Redis Vector Store node in n8n. Follow technical documentation to integrate Redis Vector Store node into your workflows. contentType: [integration, reference] priority: medium --- # Redis Vector Store node Use the Redis Vector Store node to interact with your Redis database as a [vector store](/glossary.md#ai-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](/glossary.md#ai-chain), or connect it directly to an [agent](/glossary.md#ai-agent) to use as a [tool](/glossary.md#ai-tool). On this page, you'll find the node parameters for the Redis Vector Store node, and links to more resources. /// note | Credentials You can find authentication information for this node [here](/integrations/builtin/credentials/redis.md). /// --8<-- "_snippets/integrations/builtin/cluster-nodes/sub-node-expression-resolution.md" ## Prerequisites Before using this node, you need a Redis database with the [Redis Query Engine](https://redis.io/docs/latest/develop/ai/search-and-query/?utm_source=n8n&utm_medium=docs) enabled. Use one of the following: - **Redis Open Source (v8.0 and later)** : includes the Redis Query Engine by default - **[Redis Cloud](https://cloud.redis.io/?utm_source=n8n&utm_medium=docs)** : fully managed service - **[Redis Software](https://redis.io/software/?utm_source=n8n&utm_medium=docs)** : self-managed deployment /// note | 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 in [this template](https://n8n.io/workflows/10887-reduce-llm-costs-with-semantic-caching-using-redis-vector-store-and-huggingface/) where the semantic cache is stored in Redis and retrieved using the Redis Vector Store node in the start of the workflow. ### Connect directly to an AI agent as a tool You can connect the Redis Vector Store node directly to the [tool](/glossary.md#ai-tool) connector of an [AI agent](/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/index.md) 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](/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.retrievervectorstore.md) 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](/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainretrievalqa/index.md) node to fetch documents from the vector store that match the given chat input. An [example of the connection flow](https://n8n.io/workflows/1960-ask-questions-about-a-pdf-using-ai/) (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](/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.toolvectorstore.md) 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. This [template](https://n8n.io/workflows/10837-chat-with-github-issues-using-openai-and-redis-vector-search/) shows how to use the Vector Store Question Answer Tool with the Redis Vector Store node. The connections flow in this case would look like this: AI agent (tools connector) -> Vector Store Question Answer Tool (Vector Store connector) -> Redis Vector store. ## Node parameters --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-mode.md" ### Rerank Results --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md" ### 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](#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. Optionally choose an existing one from the list. This Operation Mode includes one **Node option**, the [Metadata Filter](#metadata-filter). ### 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](#get-many-parameters) and [Retrieve Documents (As Tool for AI Agent)](#retrieve-documents-as-tool-for-ai-agent-parameters) modes. ## Node options ### Metadata Filter Metadata filters are available for the [Get Many](#get-many-parameters), [Retrieve Documents (As Vector Store for Chain/Tool)](#retrieve-documents-as-vector-store-for-chaintool-parameters), and [Retrieve Documents (As Tool for AI Agent)](#retrieve-documents-as-tool-for-ai-agent-parameters) 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](/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.documentdefaultdataloader.md) 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](#insert-documents-parameters) 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 [[ templatesWidget(page.title, 'redis-vector-store') ]] ## Related resources Refer to: - [Redis Vector Search documentation](https://redis.io/docs/latest/develop/ai/search-and-query/vectors/) for more information about Redis vector capabilities. - [RediSearch documentation](https://redis.io/docs/latest/develop/interact/search-and-query/) for more information about RediSearch. - [LangChain's Redis Vector Store documentation](https://js.langchain.com/docs/integrations/vectorstores/redis) for more information about the service. --8<-- "_snippets/integrations/builtin/cluster-nodes/langchain-overview-link.md" --8<-- "_snippets/self-hosting/starter-kits/self-hosted-ai-starter-kit.md"