A semantic search system relies on three core components to understand and retrieve information based on meaning rather than exact keyword matches: text processing and embedding models, vector storage and indexing, and query handling and ranking. These components work together to transform raw text into searchable representations, store them efficiently, and match user queries to the most relevant results. Let’s break down each part and how they connect.
First, text processing and embedding models convert unstructured text into numerical vectors that capture semantic meaning. This starts with preprocessing steps like tokenization (splitting text into words or subwords), removing stopwords, and normalizing case or punctuation. For example, a sentence like “How do I fix a router?” might be simplified to “fix router.” Next, an embedding model—such as Sentence-BERT, Universal Sentence Encoder, or a transformer-based model—maps the cleaned text into a dense vector. These vectors represent the text’s meaning in a high-dimensional space, where similar phrases (like “repair router” and “troubleshoot network device”) are positioned closer together. Tools like Hugging Face’s Transformers library or OpenAI’s API provide pre-trained models for this step.
The second component, vector storage and indexing, ensures efficient retrieval of embeddings. Once text is converted to vectors, they’re stored in databases optimized for similarity searches, such as FAISS, Annoy, or Elasticsearch with vector plugins. These systems use techniques like approximate nearest neighbor (ANN) search to quickly find vectors close to a query’s embedding without comparing every entry. For instance, FAISS groups vectors into clusters to reduce search time, while Elasticsearch combines keyword and vector search. This step is critical for scaling to large datasets—without efficient indexing, searching millions of vectors would be impractical.
Finally, query handling and ranking processes user inputs and refines results. When a user submits a query, it undergoes the same text processing and embedding steps as the stored data. The system retrieves candidate vectors from storage, then ranks them using similarity metrics like cosine similarity or dot product. Additional filters—such as date ranges or popularity scores—can be applied to prioritize recent or high-quality content. For example, a search for “cloud storage options” might return results ranked by how closely their vectors match the query, but also boost entries tagged as “beginner-friendly” or from trusted sources. This layer ensures the final results balance semantic relevance with practical constraints.