VECTOR search understands the context and meaning behind queries, allowing it to retrieve more relevant results for users’ vague searches.
This growth reflects the growing reliance on the internet for various activities and information-seeking purposes. The increasing number of internet users in the Philippines underscores the importance of adapting to the digital age and addressing potential challenges that come when searching for things.
Nearly 90% of users will not return to a site if they have a bad experience. Take a moment to appreciate that staggering statistic. Site reliability engineers are traditionally focused on the “five 9s,” ensuring a website remains up and accessible 99.999% of the time. Yet, that is only a part of the picture guaranteeing a positive user experience. What else can cause a user to click away from a site and never return?
Not being able to discover what they were looking for.
The frustration of trying to search for something and being unable to find it quickly and efficiently may be one of a user’s most disappointing experiences. You want to build a site where that rarely happens. However, users make it very hard. Oftentimes, they do not know exactly what they are looking for. They have a picture in their mind of what they want but lack the precise terms, and their search ends up being submitted with keywords such as: “the thing that tightens screws.” A human respondent to that search will return an index of screwdrivers. What will your keyword-based search return? Articles about tightening techniques, blog posts on different types of screws and tools that have nothing to do with screwdrivers.
This example happens all the time, every single day, countless times a day.
Facing this dilemma requires a new resource to improve the user experience and bring clarity even when users lack it. Vector search offers possibilities that are not feasible with traditional keyword search alone.
HOW VECTOR SEARCH WORKSVector search is a machine learning method that transforms textual data into high-dimensional vectors, capturing semantic relationships between words and phrases. It differs from traditional keyword-based search, which relies on exact matches, by understanding the context and meaning behind queries. This approach enhances the accuracy and relevance of search results, making it a powerful tool for modern information retrieval systems. Vector search interprets the meaning behind queries, identifying relevant documents with related terms. This makes it an invaluable tool for improving user experience by providing precise and accurate search results in response to imprecise or descriptive queries.
Here’s a simple vector search example: -0.024047505110502243.
The process of embedding involves converting textual data into numerical representations, such as vectors, to capture the meaning of words and phrases. This allows models to measure similarity between terms based on their usage and context in large datasets. This transformation leads to more nuanced and context-aware search functionalities, potentially advancing information retrieval and artificial intelligence (AI). For example, a dataset containing the string “Your text string goes here” can be converted into vectors by assigning numerical values to each word, allowing better understanding of relationships and similarities.
These vectors represent the semantic meaning of the words and allow the search functionality to understand and retrieve relevant information based on context rather than just exact keyword matches.
The search engine converts user queries into vector representations using a simple dataset, comparing them with the dataset’s vectors. The vector search identifies that the query’s context and semantics are similar to “Your text string goes here,” allowing the engine to return the most relevant result based on the similarity of the vectors. This process transforms uncertain and unclear user queries into more certainty and clarity.
HOW TO STORE AND RETRIEVE VECTOR EMBEDDINGSVector search is a crucial tool for websites that require quick and cost-effective storage and retrieval of vector embeddings. As a site’s data grows, so do the vector embeddings, making any solution highly scalable. A generic database solution is not suitable for vector search needs, as it must be specialized to handle high-dimensional embeddings efficiently, support rapid similarity searches, and optimize storage for large volumes of vectors. This ensures the search system remains performant and responsive, providing relevant results in real-time even as data scales. A vector search database solution should offer advanced indexing capabilities, support multiple data types, and integrate with popular AI frameworks and embedding generation tools. Additionally, it should provide a quality search experience in offline environments, known as delivering computing “on the edge.” Integrating vector search into a site can improve user experience and ensure repeat visits.
Genie Yuan is the regional vice-president for APAC Japan, Couchbase.
