> For the complete documentation index, see [llms.txt](https://docs.n8n.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstorezep.md).

# Zep Vector Store

{% hint style="warning" %}
**Deprecated**

This node is deprecated, and will be removed in a future version.
{% endhint %}

Use the Zep Vector Store to interact with Zep vector databases. 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[^1], or connect it directly to an agent[^2] to use as a tool[^3].

On this page, you'll find the node parameters for the Zep Vector Store node, and links to more resources.

{% hint style="info" %}
**Credentials**

You can find authentication information for this node [here](/integrations/builtin/credentials/zep.md).
{% endhint %}

{% hint style="info" %}
**Examples and templates**

For usage examples and templates to help you get started, refer to n8n's [Zep Vector Store integrations](https://n8n.io/integrations/zep-vector-store/) page.
{% endhint %}

{% hint style="info" %}
**Parameter resolution in sub-nodes**

Sub-nodes behave differently to other nodes when processing multiple items using an expression.

Most nodes, including root nodes, take any number of items as input, process these items, and output the results. You can use expressions to refer to input items, and the node resolves the expression for each item in turn. For example, given an input of five `name` values, the expression `{{ $json.name }}` resolves to each name in turn.

In sub-nodes, the expression always resolves to the first item. For example, given an input of five `name` values, the expression `{{ $json.name }}` always resolves to the first name.
{% endhint %}

## Node usage patterns <a href="#node-usage-patterns" id="node-usage-patterns"></a>

You can use the Zep Vector Store node in the following patterns.

### Use as a regular node to insert, update, and retrieve documents <a href="#use-as-a-regular-node-to-insert-update-and-retrieve-documents" id="use-as-a-regular-node-to-insert-update-and-retrieve-documents"></a>

You can use the Zep Vector Store as a regular node to insert or get documents. This pattern places the Zep 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 example uses Supabase, but the pattern is the same).

### Connect directly to an AI agent as a tool <a href="#connect-directly-to-an-ai-agent-as-a-tool" id="connect-directly-to-an-ai-agent-as-a-tool"></a>

You can connect the Zep Vector Store node directly to the tool connector of an [AI agent](https://github.com/n8n-io/n8n-docs/blob/main/docs/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent) to use a vector store as a resource when answering queries.

Here, the connection would be: AI agent (tools connector) -> Zep Vector Store node.

### Use a retriever to fetch documents <a href="#use-a-retriever-to-fetch-documents" id="use-a-retriever-to-fetch-documents"></a>

You can use the [Vector Store Retriever](/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.retrievervectorstore.md) node with the Zep Vector Store node to fetch documents from the Zep Vector Store node. This is often used with the [Question and Answer Chain](https://github.com/n8n-io/n8n-docs/blob/main/docs/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainretrievalqa) 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 example uses Pinecone, but the pattern in the same) would be: Question and Answer Chain (Retriever connector) -> Vector Store Retriever (Vector Store connector) -> Zep Vector Store.

### Use the Vector Store Question Answer Tool to answer questions <a href="#use-the-vector-store-question-answer-tool-to-answer-questions" id="use-the-vector-store-question-answer-tool-to-answer-questions"></a>

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 Zep Vector Store node. Rather than connecting the Zep 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/2621-ai-agent-to-chat-with-files-in-supabase-storage/) (this example uses Supabase, 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) -> Zep Vector store.

## Node parameters <a href="#node-parameters" id="node-parameters"></a>

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.

### Rerank Results <a href="#rerank-results" id="rerank-results"></a>

Enables [reranking](/key-concept-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.

### Insert Documents parameters <a href="#insert-documents-parameters" id="insert-documents-parameters"></a>

* **Collection Name**: Enter the collection name to store the data in.

### Get Many parameters <a href="#get-many-parameters" id="get-many-parameters"></a>

* **Collection Name**: Enter the collection name to retrieve the data from.
* **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.

### Retrieve Documents (As Vector Store for Chain/Tool) parameters <a href="#retrieve-documents-as-vector-store-for-chaintool-parameters" id="retrieve-documents-as-vector-store-for-chaintool-parameters"></a>

* **Collection Name**: Enter the collection name to retrieve the data from.

### Retrieve Documents (As Tool for AI Agent) parameters <a href="#retrieve-documents-as-tool-for-ai-agent-parameters" id="retrieve-documents-as-tool-for-ai-agent-parameters"></a>

* **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.
* **Collection Name**: Enter the collection name to retrieve the data from.
* **Limit**: Enter how many results to retrieve from the vector store. For example, set this to `10` to get the ten best results.

## Node options <a href="#node-options" id="node-options"></a>

### Embedding Dimensions <a href="#embedding-dimensions" id="embedding-dimensions"></a>

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.

### Is Auto Embedded <a href="#is-auto-embedded" id="is-auto-embedded"></a>

Available in the **Insert Documents** Operation Mode, enabled by default.

Disable this to configure your embeddings in Zep instead of in n8n.

### Metadata Filter <a href="#metadata-filter" id="metadata-filter"></a>

Available in **Get Many** mode. When searching for data, use this to match with metadata associated with the document.

This is an `AND` query. If you specify more than one metadata filter field, all 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.

## Templates and examples <a href="#templates-and-examples" id="templates-and-examples"></a>

[Browse Zep Vector Store node documentation integration templates](https://n8n.io/integrations/zep-vector-store) or [search all templates](https://n8n.io/workflows/)

## Related resources <a href="#related-resources" id="related-resources"></a>

Refer to [LangChain's Zep documentation](https://js.langchain.com/docs/integrations/vectorstores/zep/) for more information about the service.

View n8n's [Advanced AI](https://github.com/n8n-io/n8n-docs/blob/main/advanced-ai/index.md) documentation.

[^1]: AI chains allow you to interact with large language models (LLMs) and other resources in sequences of calls to components. AI chains in n8n don't use persistent memory, so you can't use them to reference previous context (use AI agents for this).

[^2]: AI agents are artificial intelligence systems capable of responding to requests, making decisions, and performing real-world tasks for users. They use large language models (LLMs) to interpret user input and make decisions about how to best process requests using the information and resources they have available.

[^3]: In an AI context, a tool is an add-on resource that the AI can refer to for specific information or functionality when responding to a request. The AI model can use a tool to interact with external systems or complete specific, focused tasks.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstorezep.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.