Vector search and keyword search are two distinct methodologies for retrieving information, each with its unique strengths. Keyword search relies on matching specific terms within the text to the query. This approach is straightforward and effective when the exact words are known. However, it may fall short when users search for concepts or ideas that don't match the exact wording in the data.
In contrast, vector search uses vector embeddings to represent data in a high-dimensional space, capturing semantic meaning beyond mere words. This allows vector search to find semantically similar items, even if they don't share the same keywords. For instance, vector search can identify documents discussing "automobiles" when the query is "cars," thanks to its understanding of semantic similarities.
While keyword search is computationally less demanding, vector search often provides more accurate results in scenarios involving natural language processing and semantic search. It excels in handling unstructured data and understanding the context of queries, making it suitable for applications like question answering and information retrieval.