docs: Add documentation for Reranker Cohere node (#3246)

Co-authored-by: Justin Ellingwood <justin@n8n.io>

_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md

@@ -0,0 +1,3 @@
+### Rerank Results
+
+Enables [reranking](/glossary.md#ai-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.

docs/glossary.md

@@ -1,7 +1,7 @@
 ---
 #https://www.notion.so/n8n/Frontmatter-432c2b8dff1f43d4b1c8d20075510fe4
 title: n8n Glossary
-description: A glossary of terms commonly used when working with n8n and releated software.
+description: A glossary of terms commonly used when working with n8n and related software.
 contentType: reference
 ---
 
@@ -17,6 +17,10 @@ AI chains allow you to interact with large language models (LLMs) and other reso
 
 Embeddings are numerical representations of data using vectors. They're used by AI to interpret complex data and relationships by mapping values across many dimensions. Vector databases, or vector stores, are databases designed to store and access embeddings.
 
+#### AI reranking
+
+Reranking is a technique that refines the order of a list of candidate documents to improve the relevance of search results. Retrieval-Augmented Generation (RAG) and other applications use reranking to prioritize the most relevant information for generation or downstream tasks.
+
 #### AI memory
 
 In an AI context, memory allows AI tools to persist message context across interactions. This allows you to have a continuing conversations with AI agents, for example, without submitting ongoing context with each message. In n8n, AI agent nodes can use memory, but AI chains can't.

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

@@ -88,6 +88,8 @@ On n8n Cloud, these values are preset to 100MB (about 8,000 documents, depending
 
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-mode.md"
 
+--8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
+
 <!-- vale from-write-good.Weasel = NO -->
 ### Get Many parameters
 <!-- vale from-write-good.Weasel = YES -->

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

@@ -55,6 +55,8 @@ The connections flow would look like this: AI agent (tools connector) -> Vector
 
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-mode.md"
 
+--8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
+
 <!-- vale from-write-good.Weasel = NO -->
 ### Get Many parameters
 <!-- vale from-write-good.Weasel = YES -->

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

@@ -83,6 +83,8 @@ The [connections flow](https://n8n.io/workflows/2465-building-your-first-whatsap
 
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-mode.md"
 
+--8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
+
 <!-- vale off -->
 ### Get Many parameters
 <!-- vale on -->

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

@@ -49,6 +49,8 @@ The [connections flow](https://n8n.io/workflows/2465-building-your-first-whatsap
 
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-mode.md"
 
+--8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
+
 <!-- vale off -->
 ### Get Many parameters
 <!-- vale on -->

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

@@ -50,6 +50,8 @@ The [connections flow](https://n8n.io/workflows/2705-chat-with-github-api-docume
 
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-mode-with-update.md"
 
+--8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
+
 <!-- vale from-write-good.Weasel = NO -->
 ### Get Many parameters
 <!-- vale from-write-good.Weasel = YES -->

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

@@ -50,6 +50,8 @@ The [connections flow](https://n8n.io/workflows/2464-scale-deal-flow-with-a-pitc
 
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-mode.md"
 
+--8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
+
 <!-- vale from-write-good.Weasel = NO -->
 ### Get Many parameters
 <!-- vale from-write-good.Weasel = YES -->

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

@@ -52,6 +52,8 @@ The [connections flow](https://n8n.io/workflows/2621-ai-agent-to-chat-with-files
 
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-mode-with-update.md"
 
+--8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
+
 <!-- vale from-write-good.Weasel = NO -->
 ### Get Many parameters
 <!-- vale from-write-good.Weasel = YES -->

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

@@ -53,6 +53,8 @@ The [connections flow](https://n8n.io/workflows/2621-ai-agent-to-chat-with-files
 
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-mode.md"
 
+--8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
+
 ### Insert Documents parameters
 
 * **Collection Name**: Enter the collection name to store the data in.

docs/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.rerankercohere.md

@@ -0,0 +1,31 @@
+---
+#https://www.notion.so/n8n/Frontmatter-432c2b8dff1f43d4b1c8d20075510fe4
+title: Reranker Cohere
+description: Learn how to use the Reranker Cohere node in n8n. Follow technical documentation to integrate Cohere reranking into your workflows.
+contentType: [integration, reference]
+---
+
+# Reranker Cohere
+
+The Reranker Cohere node allows you to [rerank](/glossary.md#ai-reranking) the resulting chunks from a [vector store](/glossary.md#ai-vector-store). You can connect this node to a vector store.
+
+The reranker reorders the list of documents retrieved from a vector store for a given query in order of descending relevance.
+
+On this page, you'll find the node parameters for the Reranker Cohere node, and links to more resources.
+
+///  note  | Credentials
+You can find authentication information for this node [here](/integrations/builtin/credentials/cohere.md).
+///
+
+--8<-- "_snippets/integrations/builtin/cluster-nodes/sub-node-expression-resolution.md"
+
+## Node parameters
+
+### Model
+
+Choose the reranking model to use. You can find out more about the available models in [Cohere's model documentation](https://docs.cohere.com/docs/models#rerank).
+
+## Related resources
+
+--8<-- "_snippets/integrations/builtin/cluster-nodes/langchain-overview-link.md"
+--8<-- "_glossary/ai-glossary.md"

docs/integrations/builtin/credentials/cohere.md

@@ -10,6 +10,7 @@ contentType: [integration, reference]
 You can use these credentials to authenticate the following nodes:
 
 * [Cohere](/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmcohere.md)
+* [Reranker Cohere](/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.rerankercohere.md)
 * [Embeddings Cohere](/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.embeddingscohere.md)
 
 ## Prerequisites

nav.yml

@@ -774,6 +774,7 @@ nav:
           - Wikipedia: integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.toolwikipedia.md
           - Wolfram|Alpha: integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.toolwolframalpha.md
           - Call n8n Workflow Tool: integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.toolworkflow.md
+          - Reranker Cohere: integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.rerankercohere.md
       - Credentials:
         - integrations/builtin/credentials/index.md
         - integrations/builtin/credentials/actionnetwork.md

styles/config/vocabularies/default/accept.txt

@@ -200,6 +200,7 @@ Qualys
 Realtime
 Rekognition
 requester
+[Rr]erank(er|ing|ed)?
 Rundeck
 Salesmate
 [Ss]andboxed