> 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/sub-nodes/n8n-nodes-langchain.toolvectorstore.md).
# Vector Store Question Answer Tool
The Vector Store Question Answer node is a tool[^1] that allows an agent[^2] to summarize results and answer questions based on chunks from a [vector store](#user-content-fn-3)[^3].
On this page, you'll find the node parameters for the Vector Store Question Answer node, and links to more resources.
{% hint style="info" %}
**Examples and templates**
For usage examples and templates to help you get started, refer to n8n's [Vector Store Question Answer Tool integrations](https://n8n.io/integrations/vector-store-tool/) 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 parameters
### Description of Data
Enter a description of the data in the vector store.
### Limit
The maximum number of results to return.
## How n8n populates the tool description
n8n uses the node name (select the name to edit) and **Description of Data** parameter to populate the tool description for AI agents using the following format:
> Useful for when you need to answer questions about \[node name]. Whenever you need information about \[Description of Data], you should ALWAYS use this. Input should be a fully formed question.
Spaces in the node name are converted to underscores in the tool description.
{% hint style="warning" %}
**Avoid special characters in node names**
Using special characters in the node name will cause errors when the agent runs:
Use only alphanumeric characters, spaces, dashes, and underscores in node names.
{% endhint %}
## Related resources
View [example workflows and related content](https://n8n.io/integrations/vector-store-tool/) on n8n's website.
Refer to [LangChain's documentation on tools](https://langchain-ai.github.io/langgraphjs/how-tos/tool-calling/) for more information about tools in LangChain.
View n8n's [Advanced AI](https://github.com/n8n-io/n8n-docs/blob/main/advanced-ai/index.md) documentation.
[^1]: 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.
[^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]: A vector store, or vector database, stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.
---
# 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/sub-nodes/n8n-nodes-langchain.toolvectorstore.md?ask=&goal=
```
`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.