docs: Add documentation for update operations in vector store nodes (#2245)

Co-authored-by: Deborah Barnard <deborah@deborahwrites.com>

_snippets/integrations/builtin/cluster-nodes/common-vector-store-modes.md

@@ -0,0 +1,15 @@
+#### 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.

_snippets/integrations/builtin/cluster-nodes/vector-store-mode-with-update.md

@@ -1,5 +1,3 @@
-### Operation Mode
-
 This Vector Store node has five modes: **Get Many**, **Insert Documents**, **Retrieve Documents (As Vector Store for Chain/Tool)**, **Retrieve Documents (As Tool for AI Agent)**, and **Update Documents**. The mode you select determines the operations you can perform with the node and what inputs and outputs are available.
 
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_snippets/integrations/builtin/cluster-nodes/vector-store-mode.md

@@ -2,20 +2,6 @@
 
 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.
 
-<!-- vale off -->
-#### Get Many
 
-In this mode, you can retrieve multiple documents from your vector database by providing a prompt. The prompt will be embedded and used for similarity search. The node will return 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. 
-<!-- vale on -->
+--8<-- "_snippets/integrations/builtin/cluster-nodes/common-vector-store-modes.md"
 
-#### 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.

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

@@ -1,3 +1 @@
-### 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/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"
 
+### Rerank Results
+
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
 
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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"
 
+### Rerank Results
+
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
 
 <!-- vale from-write-good.Weasel = NO -->

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"
 
+### Rerank Results
+
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
 
 <!-- vale off -->

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"
 
+### Rerank Results
+
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
 
 <!-- vale off -->

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

@@ -8,7 +8,9 @@ priority: medium
 
 # Pinecone Vector Store node
 
-Use the Pinecone node to interact with your Pinecone database as [vector store](/glossary.md#ai-vector-store). You can insert documents into a vector database, get documents from a vector database, retrieve documents to provide them to a retriever connected to a [chain](/glossary.md#ai-chain), or connect directly to an [agent](/glossary.md#ai-agent) as a [tool](/glossary.md#ai-tool).
+
+Use the Pinecone node to interact with your Pinecone database as [vector store](/glossary.md#ai-vector-store). You can insert documents into a vector database, get documents from a vector database, retrieve documents to provide them to a retriever connected to a [chain](/glossary.md#ai-chain), or connect directly to an [agent](/glossary.md#ai-agent) as a [tool](/glossary.md#ai-tool). You can also update an item in a vector database by its ID.
+
 
 On this page, you'll find the node parameters for the Pinecone node, and links to more resources.
 
@@ -48,10 +50,16 @@ The [connections flow](https://n8n.io/workflows/2705-chat-with-github-api-docume
 	
 ## Node parameters
 
+
+### Operation Mode
+
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-mode-with-update.md"
 
+### Rerank Results
+
 --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 -->
@@ -75,6 +83,10 @@ The [connections flow](https://n8n.io/workflows/2705-chat-with-github-api-docume
 * **Pinecone Index**: Select or enter the Pinecone Index to use.
 * **Limit**: Enter how many results to retrieve from the vector store. For example, set this to `10` to get the ten best results.
 
+### Parameters for **Update Documents**
+
+* ID
+
 ## Node options
 
 ### Pinecone Namespace 

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"
 
+### Rerank Results
+
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
 
 <!-- vale from-write-good.Weasel = NO -->

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

@@ -8,7 +8,11 @@ priority: medium
 
 # Supabase Vector Store node
 
-Use the Supabase Vector Store to interact with your Supabase database as [vector store](/glossary.md#ai-vector-store). You can insert documents into a vector database, get documents from a vector database, retrieve documents to provide them to 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).
+
+Use the Supabase Vector Store to interact with your Supabase database as vector store. You can insert documents into a vector database, get many documents from a vector database, and retrieve documents to provide them to a retriever connected to a chain. 
+
+Use the Supabase Vector Store to interact with your Supabase database as [vector store](/glossary.md#ai-vector-store). You can insert documents into a vector database, get documents from a vector database, retrieve documents to provide them to 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). You can also update an item in a vector store by its ID.
+
 
 On this page, you'll find the node parameters for the Supabase node, and links to more resources.
 
@@ -50,8 +54,13 @@ The [connections flow](https://n8n.io/workflows/2621-ai-agent-to-chat-with-files
 
 ## Node parameters
 
+
+### Operation Mode
+
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-mode-with-update.md"
 
+### Rerank Results
+
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
 
 <!-- vale from-write-good.Weasel = NO -->
@@ -66,6 +75,7 @@ The [connections flow](https://n8n.io/workflows/2621-ai-agent-to-chat-with-files
 
 * **Table Name**: Enter the Supabase table to use.
 
+
 ### Retrieve Documents (As Vector Store for Chain/Tool) parameters
 
 * **Table Name**: Enter the Supabase table to use.
@@ -82,6 +92,10 @@ The [connections flow](https://n8n.io/workflows/2621-ai-agent-to-chat-with-files
 * **Table Name**: Enter the Supabase table to use.
 * **ID**: The ID of an embedding entry.
 
+Parameters for **Update Documents**
+
+* ID
+
 ## Node options
 
 ### Query Name

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

@@ -80,6 +80,8 @@ Whether to include document metadata.
 
 You can use this with the [Get Many](#get-many) and [Retrieve Documents (As Tool for AI Agent)](#retrieve-documents-as-tool-for-ai-agent-parameters) modes.
 
+### Rerank Results
+
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
 
 ## Node options

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"
 
+### Rerank Results
+
 --8<-- "_snippets/integrations/builtin/cluster-nodes/vector-store-rerank-results.md"
 
 ### Insert Documents parameters
@@ -86,7 +88,6 @@ Must be the same when embedding the data and when querying it.
 
 This sets the size of the array of floats used to represent the semantic meaning of a text document.
 
-Read more about Zep embeddings in [Zep's embeddings documentation](https://docs.getzep.com/deployment/embeddings/){:target=_blank .external-link}.
 
 ### Is Auto Embedded