The Technical Foundation of Better Retrieval

MultiQuery Retriever employs a sophisticated approach to information retrieval:

1. Query Expansion: The integration takes a user's initial question and uses LLMs to generate multiple related queries that explore different aspects and phrasings

2. Parallel Retrieval: Each generated query searches the knowledge base independently, accessing information from different perspectives

3. Result Aggregation: The system combines all retrieved information, removes duplicates, and ranks by relevance

4. Enhanced Context: The aggregated results provide the AI model with a richer, more comprehensive context for generating responses

This process happens seamlessly within your n8n workflows, integrating with your existing data sources and AI models to create a more robust retrieval system.