A semantic search system aims to understand the intent and contextual meaning of a user’s query to return relevant results, even when keywords don’t exactly match. The architecture typically involves three core components: data preprocessing and embedding, vector storage and indexing, and query processing with ranking. Each stage is designed to transform unstructured text into meaningful numerical representations, enable efficient similarity searches, and refine results for accuracy.
The first step involves preprocessing the data and generating embeddings. Raw text (documents, web pages, etc.) is cleaned, normalized, and split into smaller chunks to fit the input limits of embedding models. For example, a PDF might be split into paragraphs or sentences. These chunks are then converted into dense vector representations using models like BERT, Sentence-BERT, or OpenAI’s text-embeddings. These models map text to high-dimensional vectors that capture semantic relationships—similar phrases end up closer in vector space. For instance, the embeddings for “car” and “vehicle” would be nearer than those for “car” and “banana.” Tools like Hugging Face’s Transformers library simplify this step by providing pre-trained models and APIs for generating embeddings.
Next, the vectors are stored in a database optimized for similarity searches. Traditional databases aren’t efficient for high-dimensional vector comparisons, so specialized vector databases like FAISS, Pinecone, or Elasticsearch (with its dense_vector type) are used. These systems index vectors using algorithms like HNSW (Hierarchical Navigable Small World) to enable fast approximate nearest neighbor searches. For example, FAISS uses quantization and partitioning to reduce search latency, making it possible to query millions of vectors in milliseconds. Indexing strategies often involve trade-offs between speed and accuracy, depending on the use case—a product search might prioritize speed, while a research paper retrieval system might favor precision.
Finally, during query processing, the user’s input is converted into an embedding using the same model as the indexed data. The system retrieves the closest vectors from the database and often applies a reranking step to refine results. For instance, a cross-encoder model (like BERT) might re-evaluate the top 100 results to improve relevance by analyzing deeper contextual relationships between the query and each document. Hybrid approaches, combining semantic search with keyword-based techniques (like BM25), are also common. For example, a hybrid system might use BM25 to filter results by exact keywords first, then apply semantic ranking to sort them by meaning. Performance is monitored using metrics like recall@k (how many relevant items are in the top k results) and latency, with caching mechanisms to speed up frequent queries.