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Vector Store Question Answer Tool node#

The Vector Store Question Answer node is a tool that allows an agent to summarize results and answer questions based on chunks from a vector store.

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

Examples and templates

For usage examples and templates to help you get started, refer to n8n's Vector Store Question Answer Tool integrations page.

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.

Node parameters#

Data Name#

Enter the name of the data in the vector store.

Description of Data#

Enter a description of the data in the vector store.

n8n uses the Data Name and Description of Data parameters to populate the tool description for AI agents using the following format:

Useful for when you need to answer questions about [Data Name]. Whenever you need information about [Description of Data], you should ALWAYS use this. Input should be a fully formed question.

Limit#

The maximum number of results to return.

View example workflows and related content on n8n's website.

Refer to LangChain's documentation on tools for more information about tools in LangChain.

View n8n's Advanced AI documentation.

AI glossary#

  • completion: Completions are the responses generated by a model like GPT.
  • hallucinations: Hallucination in AI is when an LLM (large language model) mistakenly perceives patterns or objects that don't exist.
  • vector database: A vector database stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.
  • vector store: 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.