Vector search and keyword search differ fundamentally in how they retrieve information. Keyword search relies on exact matches between words in a query and those in a dataset. For example, searching for “dog” returns documents containing “dog” but might miss “canine” or “puppy” unless synonyms are explicitly handled. This approach works well for structured data with predictable terminology, like product SKUs or legal documents. However, it struggles with ambiguity (e.g., “Java” as a programming language vs. coffee) and semantic relationships, such as synonyms or related concepts.
Technically, keyword search uses inverted indexes to map terms to documents. Algorithms like TF-IDF or BM25 rank results based on term frequency and document structure. In contrast, vector search converts text, images, or other data into numerical vectors (embeddings) using neural networks. These vectors capture semantic meaning, allowing similarity comparisons. For instance, a vector for “dog” might be closer to “canine” than to “cat” in vector space. Tools like FAISS, Annoy, or HNSW enable efficient nearest-neighbor searches over these vectors. Developers often use pre-trained models (e.g., BERT, OpenAI embeddings) to generate vectors, which can be indexed and queried using cosine similarity or Euclidean distance.
The choice between the two depends on the use case. Keyword search excels in scenarios requiring precise matches, such as filtering database records or searching codebases. Vector search is better for unstructured data (e.g., images, natural language) where semantic understanding matters. For example, an e-commerce platform might use vector search to recommend products based on user preferences, even if the query terms don’t exactly match product descriptions. Hybrid approaches, combining keyword and vector search, are increasingly common. A support ticket system could first filter by keywords (“login error”) and then use vectors to rank tickets by contextual similarity. Developers should evaluate trade-offs: keyword search is faster for simple queries, while vector search requires more computational resources but handles nuance better.