Redis Vector Store node documentation (#3657)

Co-authored-by: Kartik Balasubramanian <22399046+HumanistSerif@users.noreply.github.com>

docs/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreredis.md

@@ -0,0 +1,139 @@
+---
+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 of this in scenario 1 of [this template](https://n8n.io/workflows/2621-ai-agent-to-chat-with-files-in-supabase-storage/) (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](/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.
+
+The [connections flow](https://n8n.io/workflows/2464-scale-deal-flow-with-a-pitch-deck-ai-vision-chatbot-and-qdrant-vector-store/) (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
+
+--8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-mode.md"
+
+### Rerank Results
+
+--8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
+
+<!-- vale off -->
+### Get Many parameters
+<!-- vale on -->
+
+- **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.
+
+This Operation Mode includes one **Node option**, the [Metadata Filter](#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](#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
+
+<!-- see https://www.notion.so/n8n/Pull-in-templates-for-the-integrations-pages-37c716837b804d30a33b47475f6e3780 -->
+[[ 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"

docs/integrations/builtin/credentials/redis.md

@@ -11,6 +11,7 @@ You can use these credentials to authenticate the following nodes:
 
 - [Redis](/integrations/builtin/app-nodes/n8n-nodes-base.redis.md)
 - [Redis Chat Memory](/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.memoryredischat.md)
+- [Redis Vector Store](/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreredis.md)
 
 ## Supported authentication methods
 

nav.yml

@@ -720,6 +720,7 @@ nav:
           - PGVector Vector Store: integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstorepgvector.md
           - Pinecone Vector Store: integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstorepinecone.md
           - Qdrant Vector Store: integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreqdrant.md
+          - Redis Vector Store: integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreredis.md
           - Supabase Vector Store: integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoresupabase.md
           - Weaviate Vector Store: integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreweaviate.md
           - Zep Vector Store: integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstorezep.md