LLM-assisted vector similarity search

This post introduces a novel approach to enhance search relevance by combining vector similarity search with LLMs. The proposed method involves an initial vector similarity search to shortlist potential results, followed by an LLM that ranks these shortlisted results based on contextual relevance. Experiments on synthetic datasets showed that this LLM-assisted approach significantly outperforms raw vector similarity search for complex queries involving negations or conceptual requirements, as demonstrated by examples from food and tourist spot datasets. The post also notes that for simpler queries, both methods perform comparably.