Vector similarity and keyword matching are two distinct approaches for finding relevant information in text data. The core difference lies in how they interpret and compare content. Keyword matching relies on exact or partial matches of specific words or phrases. For example, a search for “database optimization” using keyword matching would return documents containing those exact terms. It treats text as a set of tokens and ignores context, synonyms, or semantic relationships. This makes it fast and straightforward but limited in handling variations in language or meaning.
Vector similarity, on the other hand, uses mathematical representations (vectors) of text to capture semantic meaning. These vectors are generated using machine learning models like word2vec, BERT, or sentence transformers, which map words, phrases, or entire documents into a high-dimensional space. Similarity is measured by calculating the distance between vectors (e.g., using cosine similarity). For instance, a search for “database optimization” might match a document about “improving SQL query performance” if their vectors are close, even if no keywords overlap. This approach understands context and relationships between concepts, making it more flexible for nuanced queries.
From a technical standpoint, keyword matching is often implemented using inverted indexes (common in search engines like Elasticsearch) to quickly locate documents containing specific terms. It’s efficient for simple queries but struggles with synonyms (“car” vs. “automobile”) or related concepts (“machine learning” vs. “AI”). Vector similarity requires preprocessing text into embeddings, which can be resource-intensive, but enables semantic search. For example, a recommendation system using vectors can suggest articles about “data security” when a user reads “encryption methods,” even if the terms don’t literally match. Developers might combine both approaches: using keyword matching for exact filters and vector similarity for ranking or expanding results based on meaning. The choice depends on the use case—keyword matching excels in speed and simplicity, while vector similarity handles ambiguity and semantic depth.