MultiQuery Retriever node#
The MultiQuery Retriever node automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query.
On this page, you'll find the node parameters for the MultiQuery Retriever node, and links to more resources.
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 options#
- Query Count: Enter how many different versions of the query to generate.
Templates and examples#
Related resources#
Refer to LangChain's retriever conceptual documentation and LangChain's multiquery retriever API documentation for more information about the service.
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.