semantic-search

Semantic search converts text into vector embeddings that capture meaning, then retrieves conceptually related documents using similarity metrics like cosine distance. This finds synonyms, related concepts, and intent-based matches that keyword matching alone would miss, making it ideal for knowledge bases and document repositories.

Keyword search matches exact words or phrases in text. Semantic search converts both query and documents into embeddings—numerical representations of meaning—then finds items with similar embeddings, capturing intent and conceptual relationships that keyword systems cannot detect.

Embed your text using an embedding model to convert each piece into a vector, then store those vectors in a vector database like Milvus. Query vectors are embedded the same way and compared against stored embeddings to retrieve nearest neighbors, enabling fast similarity-based retrieval at scale.

Yes. Semantic search works with code bases by embedding code snippets and retrieving functionally similar code rather than relying on variable or function names. This is useful for finding alternative implementations, detecting duplicate logic, or discovering related functionality across large codebases.

The choice depends on your domain and language. General-purpose models work across multiple domains, while domain-specific embeddings (legal, medical, code) often perform better on specialized content. The embedding model must match the one used during indexing so queries and stored vectors remain comparable.

Yes. A vector database like Milvus stores and indexes embeddings to enable fast nearest-neighbor retrieval. Without it, searching millions of embeddings sequentially becomes impractical. Vector databases use specialized indexing structures to return semantically similar results in milliseconds.