Elasticsearch supports both vector and full-text search, allowing developers to handle a variety of search scenarios effectively. Full-text search is a traditional approach where documents are indexed based on individual words or phrases. This process includes functionalities like tokenization, stemming, and the ability to weigh different terms based on their relevance. For instance, when you search for the term “developer,” Elasticsearch not only looks for documents containing that exact word but also considers synonyms or related terms, improving the comprehensiveness of the results.
On the other hand, vector search leverages techniques related to machine learning and neural networks. Instead of relying solely on keywords, it represents documents and queries as dense vectors in a high-dimensional space. This allows Elasticsearch to capture the semantic meaning of words and phrases. For example, when searching for "software engineer," vector search can recognize related meanings and terms like "programmer" or "coder," even if they aren’t explicitly stated in the text. By using embeddings—numerical representations of words or phrases—Elasticsearch can compare these vectors and return results that are contextually relevant to the user's query.
Elasticsearch incorporates features like k-NN (k-Nearest Neighbors) for efficient vector search. This means developers can easily implement similarity searches where the system retrieves documents that are not just keyword matches but are also similar in essence to the input query vector. Additionally, the combination of both search types allows for powerful search capabilities. Developers can construct hybrid queries that utilize both vector and full-text search strengths, leading to better user experiences and more relevant search results in applications like e-commerce, content management, and research platforms.