MultiQuery Retriever consultants

MultiQuery Retriever is an n8n node that improves the accuracy of vector search by generating multiple variations of a user query and retrieving results for each variation. Standard vector search with a single query often misses relevant documents because the query phrasing might not match how the information was written. MultiQuery Retriever addresses this by automatically rephrasing your original question from different angles, running each variation against your vector store, and merging the results into a single, more comprehensive set.

This technique is especially valuable in retrieval-augmented generation (RAG) pipelines where retrieval quality directly determines the quality of your AI output. A user asking “How do I fix a slow database?” might miss documents about “query optimisation” or “index performance tuning” with a single query. MultiQuery Retriever generates those alternative phrasings automatically, significantly improving recall without manual prompt engineering for every possible question.

Osher Digital implements MultiQuery Retriever in AI agent systems where high retrieval accuracy is critical. In knowledge base applications and internal search tools, the difference between finding the right document and returning nothing often comes down to query phrasing. We used this approach in our insurance tech project to improve how the system matched incoming data against reference documents. If your RAG pipeline is returning inconsistent or incomplete results, our AI consulting team can diagnose the retrieval bottleneck and implement fixes like MultiQuery Retriever.