Understanding the Basics: Lexical Search vs. Vector Search

In the world of search engines and databases, how we find information can vary greatly depending on the technology used. Two primary methods dominate this landscape: lexical search and vector search. While these terms might sound technical, their underlying concepts are straightforward. Let’s explore what they mean, how they work, and when to use each one.

Lexical search, also known as keyword search, is the traditional method many of us are familiar with. When you type a query into a search engine or a database, lexical search looks for the exact words you’ve entered. For instance, if you search for “best coffee shop,” the search engine scans its index for documents containing the exact terms “best,” “coffee,” and “shop.”

This method is akin to using the “Find” function in a word processor—it’s looking for precise matches. Because of its straightforward nature, lexical search is both simple and fast. It relies on matching text strings, which means it doesn’t need to understand the content’s context or the query’s intent. It just needs to find where the exact terms appear.

However, this simplicity comes with limitations. Lexical search can miss relevant results if they don’t contain the exact words you used. For example, it wouldn’t connect “café” with “coffee shop,” even though they mean the same thing. This method also struggles with variations in language, such as synonyms or different word forms, and it doesn’t grasp the context of complex queries. Despite these drawbacks, lexical search remains highly useful for tasks where precision is key and the queries are straightforward.

Vector search represents a more advanced and nuanced way of finding information. Instead of looking for exact word matches, vector search understands the meaning behind the words. It achieves this by converting words and entire documents into numerical representations called vectors. These vectors capture the semantic essence of the content, meaning they understand the relationships and similarities between different concepts.

Imagine you want to find a “cozy place to drink coffee.” A vector search engine doesn’t just look for documents containing these exact words. Instead, it looks for content that aligns with the idea or meaning of a cozy coffee shop, even if the words used are different, such as “warm café” or “comfortable coffee lounge.”

The main advantage of vector search is its ability to understand context and semantic meaning. It connects related concepts, making it more flexible and “fuzzy”, especially for complex queries. This approach can interpret the intent behind a search query, providing more relevant and comprehensive results. However, vector search is more complex and resource-intensive. It requires significant computational power and storage, and its results can sometimes seem less predictable because they depend on how well the vectors capture the intended meaning.

Lexical search is characterized by its simplicity and speed. It’s highly precise when you know the exact keywords you need. This makes it an excellent choice for situations where you’re looking for specific documents or entries in a database. For example, if you’re searching for a specific file by name or looking up a precise term in a text, lexical search is quick and effective.

However, the major drawback is its limited understanding of language nuances. It doesn’t recognize synonyms or different word forms, which can lead to missing out on relevant information. It’s also less effective with complex queries that require an understanding of context or user intent.

Vector search excels in its ability to understand and interpret the meaning behind words. This makes it highly effective for searches that involve complex language and require contextual understanding. It’s ideal for applications like recommendation systems, where suggesting similar products or content based on user preferences is crucial. It’s also perfect for semantic search engines, which need to understand and respond to user queries more naturally.

The downside of vector search is its complexity and the resources it demands. It requires more computational power and advanced algorithms to process and understand the meaning of text. Additionally, the results can sometimes be less predictable, as they depend on how accurately the vectors represent the intended meanings.

When deciding between lexical and vector search, consider the nature of your search requirements. Lexical search is perfect for simple information retrieval tasks. If you need exact matches, such as finding specific documents, looking up database entries, or performing basic keyword searches on websites or blogs, lexical search is the way to go.

On the other hand, vector search shines in scenarios requiring a deeper understanding of context and meaning. It’s invaluable for recommendation systems, where the goal is to suggest items that are similar in essence to what a user likes. It’s also crucial for semantic search applications, such as research databases or question-answering systems, where understanding the intent behind a query can provide more relevant and comprehensive results.

The choice between lexical and vector search ultimately depends on your needs and resources. If your queries are straightforward and you need exact matches quickly, lexical search is the best choice. It’s efficient and precise, making it ideal for tasks where simplicity and speed are essential.

If your queries are more complex and require a deeper understanding of language and context, vector search is more suitable. It provides greater flexibility and can handle variations in language, making it perfect for advanced applications that demand a higher level of semantic understanding.

Both lexical and vector search have their unique strengths and weaknesses. Lexical search offers simplicity and precision for straightforward queries, while vector search provides a powerful tool for understanding and interpreting complex queries. By understanding these differences, we can choose the right search method for our needs, ensuring that we get the most relevant and accurate results possible.

In this article, we just touched on the basic concepts for lexical and vector search. Be sure to check out our next series of articles where we go a bit deeper into the nuances of both search types. If you want to get your hands dirty with vector search right away, be sure to check out our more technical articles about setting up an end-to-end neural search pipeline with OpenSearch.