Boosting Vector Search Performance: Leveraging Query Expansion and Relevance Ranking
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🎓 Education: Bachelor's in Engineering (Information Technology), Vidyalankar Institute of Technology, Mumbai (2021)
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Introduction:
Vector search, a fundamental approach in semantic analysis, often encounters challenges due to irrelevant distractors in retrieved data, impacting its performance. To mitigate this, query expansion techniques have been devised, leveraging advanced methodologies such as expansion with generated answers and expansion with multiple queries. However, these techniques introduce the need for relevance ranking to sift through the expanded data effectively. In this blog, we explore the pitfalls of vector search, delve into query expansion methods, and discuss the significance of relevance ranking techniques, including cross-encoder reranking and embedding adapters.
Pitfalls of Vector Search:
Vector search often suffers from the inclusion of distractors in retrieved data, leading to decreased performance in semantic analysis tasks.
Query Expansion Techniques:
Expansion with Generated Answers:
Involves querying an LLM for an imaginary answer, concatenating it with the original query, and retrieving essential context from the vector store after passing this concatenated query.
- The retrieved data is then added with original user query and sent back to the LLM for solution extraction.
Expansion with Multiple Queries:
Utilizes the generation of multiple queries related to the original query by an LLM.
The generated queries are combined with the original query to search relevant documents from the vector store.
After deduplicating retrieved text, the original query is passed along with the relevant text to the LLM for final results extraction.
Relevance Ranking Techniques:
Cross Encoder Reranking:
Utilizes a model to score the relevancy of documents with respect to the query.
- Retrieved documents from the vector store are scored and ranked based on relevance, enhancing the effectiveness of vector search.
Conclusion:
In overcoming the pitfalls of vector search, query expansion techniques play a crucial role in enriching the search context. However, the abundance of expanded data necessitates effective relevance ranking mechanisms. Cross encoder reranking and embedding adapters emerge as potent solutions, offering refined search results by prioritizing relevant content. By integrating these techniques, vector search can enhance its performance, catering to diverse semantic analysis requirements in various domains.
