用大模型提升向量搜索的语义理解能力，让复杂查询更准确。
LLM-assisted Vector Similarity Search
- 先用向量搜索粗筛，再用大模型精排结果。
- 在含否定、约束的复杂查询上，准确率显著提升。
- 适合需要理解上下文的智能检索场景。
随着数据检索需求日益复杂，传统方法难以应对语义和概念性查询。向量相似性搜索虽能高效查找语义相近信息，但在处理带有上下文细节的复杂查询时效果下降。本文提出一种结合向量搜索与大语言模型（LLMs）的两阶段混合方法：首先通过向量搜索筛选候选结果，再由大模型进行上下文感知的排序。在结构化数据集上的实验表明，对于简单查询，向量搜索表现良好；而在包含约束、否定或概念性要求的复杂查询中，该方法显著提升准确性。借助大模型的自然语言理解能力，在不牺牲效率的前提下，增强了复杂任务的检索精度。论文还讨论了实际应用，并提出了未来研究方向，以优化并扩展该技术在多样化数据集中的适用性。
原文摘要 · Abstract (English)
As data retrieval demands become increasingly complex, traditional search methods often fall short in addressing nuanced and conceptual queries. Vector similarity search has emerged as a promising technique for finding semantically similar information efficiently. However, its effectiveness diminishes when handling intricate queries with contextual nuances. This paper explores a hybrid approach combining vector similarity search with Large Language Models (LLMs) to enhance search accuracy and relevance. The proposed two-step solution first employs vector similarity search to shortlist potential matches, followed by an LLM for context-aware ranking of the results. Experiments on structured datasets demonstrate that while vector similarity search alone performs well for straightforward queries, the LLM-assisted approach excels in processing complex queries involving constraints, negations, or conceptual requirements. By leveraging the natural language understanding capabilities of LLMs, this method improves the accuracy of search results for complex tasks without sacrificing efficiency. We also discuss real-world applications and propose directions for future research to refine and scale this technique for diverse datasets and use cases. Original article: https://engineering.grab.com/llm-assisted-vector-similarity-search
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