The Enduring Importance of Lexical Search in the AI Era
Everyone talks about AI and Vector Search at the moment. In this article we delve into the question why lexical search still plays and continues to play a major role when it comes to search.
Reading time: 5 minutes
In today’s tech landscape, AI is the talk of the town, with vector search (also known as semantic search) stealing much of the spotlight. While these advancements are undoubtedly exciting, it’s crucial not to overlook the continued relevance of traditional lexical search. In fact, the future of search technology isn’t about choosing sides – it’s about combining the best of both worlds.
And the best: You are getting both worlds for free with OpenSearch!
What is lexical and what is vector search?
Lexical search, the backbone of traditional information retrieval, operates on a straightforward principle: matching query terms to words in a document. This method has been powering search engines for decades, and for good reason. Its key advantages include precision for exact matches, transparency in result logic, language agnosticism, and efficiency for many types of queries.
Enter vector search, a powerful alternative that has emerged in recent years. By converting text into high-dimensional vectors, it can capture semantic relationships and context, often leading to more nuanced results. Vector search excels in contextual understanding, handling ambiguity, improving relevance for complex queries, and offering cross-lingual capabilities with advanced models.
Lexical Search: The Tried and True Method
Despite the cool factor of vector search, lexical search remains crucial for several reasons. First, it’s unbeatable when it comes to exact matching. Sometimes, you just need to find a specific string – think product codes, error messages, or technical specifications. In these scenarios, lexical search shines.
Explainability is another area where lexical search maintains an edge. In fields like law or academia, being able to explain why certain results appeared is crucial. Lexical search provides this transparency in spades, making it easier to understand and justify search results.
When it comes to rare terms and proper nouns, lexical search often has the upper hand. Newly coined terms or obscure names might not be well-represented in vector models, but lexical search can find them without breaking a sweat.
Resource efficiency is yet another point in favor of lexical search. Not every application needs a supercomputer. For smaller datasets or quick searches, lexical search is often more than sufficient, providing fast and accurate results without the computational overhead of more complex systems.
Lexical search also excels at handling structured data. When dealing with databases or specific document formats, it can leverage that structure effectively to provide precise results.
Lastly, lexical search offers out-of-the-box multilingual support. It works across languages without needing sophisticated models or translations, making it particularly useful for less common languages or specialized technical vocabularies.
The Future is Hybrid
As we look to the future, it’s clear that the most effective search systems will combine both lexical and vector approaches. This hybrid strategy promises more comprehensive, accurate, and flexible search capabilities. Hybrid systems can leverage the complementary strengths of both methods, using lexical search for precision and vector search for context and ambiguity handling.
You are getting the best of both worlds for free with OpenSearch
These combined approaches also allow for improved ranking. By merging signals from both lexical and vector matches, hybrid systems can develop more sophisticated ranking algorithms, leading to results that are both precisely matched and contextually relevant.
Adaptability is another key advantage of hybrid systems. They can adjust based on query type – simple queries might lean on lexical search for speed and accuracy, while complex ones could leverage vector capabilities to understand intent and context.
Different domains and use cases might require different balances of lexical and vector search. A hybrid approach allows for customization based on specific needs, whether it’s a legal research tool requiring high precision or a content discovery system benefiting from semantic understanding.
Lastly, a hybrid approach helps manage edge cases, ensuring the system can fall back on alternative methods when one approach fails. This redundancy leads to more robust and reliable search performance across a wide range of scenarios.
Lexical is Here to Stay
While the buzz around AI and vector search is justified, it’s important to recognize the enduring value of lexical search. Its precision, transparency, and efficiency continue to make it an essential tool in many applications. The future of search technology lies in skillfully combining lexical and vector approaches to create more powerful, flexible, and accurate search systems capable of handling a wide range of user needs and query types.
In the evolving landscape of search technology, lexical search isn’t becoming obsolete – it’s becoming part of a more comprehensive, intelligent search ecosystem.
As we move forward, the challenge for search engineers and AI researchers will be to develop sophisticated hybrid systems that seamlessly integrate lexical and vector search capabilities. This integration will not only enhance search accuracy and relevance but also open up new possibilities for information retrieval across various domains.
In the evolving landscape of search technology, lexical search isn’t becoming obsolete – it’s becoming part of a more comprehensive, intelligent search ecosystem. By embracing this hybrid future, we can ensure that our search tools continue to evolve, providing users with the most effective means of finding the information they need in an increasingly complex digital world.
So, the next time someone tells you lexical search is old news, remind them that in the world of search, it’s not about out with the old and in with the new – it’s about bringing the best of both together to create something even better. The future of search is hybrid, and it’s looking brighter than ever.
Great, but where should I start?
The easiest way to leverage the power of lexical, vector and hybrid search is using OpenSearch. OpenSearch started as a lexical search engine, powered by Lucene and its inverted index approache. However, much has happened in the last years, and OpenSearch has added full vector search capabilities. This means you can use all of the three approaches mentioned in this article with one product - and all that for free!
Also, checkout our article series on how to setup and use vector and hybrid search with OpenSearch!
- Implementing Vector and Hybrid Search with OpenSearch and the Neural Plugin - Part 1
- Implementing Vector and Hybrid Search with OpenSearch and the Neural Plugin - Part 2
- Implementing Vector and Hybrid Search with OpenSearch and the Neural Plugin - Part 3 (Coming soon)
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