Semantic search is a search technique that understands the meaning and intent behind a query rather than matching exact keywords. It uses vector embeddings and similarity measures to retrieve results that are conceptually relevant, even when they share no words with the query.
Semantic search retrieves information based on the conceptual meaning of a query, not just literal keyword overlap. It contrasts with traditional lexical search (like BM25 or TF-IDF), which scores documents by exact term frequency. A semantic search engine can match the query 'how to fix a flat tire' with a document titled 'puncture repair guide' because it understands they describe the same concept. This is powered by machine learning models that encode language into dense numerical vectors.
At the core of semantic search is the embedding model — a neural network (commonly a Transformer like BERT or Sentence-BERT) that converts text into a fixed-size vector in a high-dimensional space. Texts with similar meanings are mapped to vectors that are geometrically close to each other. Both the query and all documents in the corpus are encoded into this same vector space. Similarity is then measured using metrics like cosine similarity or dot product.
A typical semantic search pipeline has two phases: offline indexing and online retrieval. During indexing, every document is encoded into an embedding vector and stored in a vector database (such as Pinecone, Weaviate, Qdrant, or pgvector). At query time, the user's query is encoded into a vector, and the database runs an Approximate Nearest Neighbor (ANN) search — algorithms like HNSW or IVF-Flat — to find the top-k most similar document vectors efficiently at scale.
Lexical search excels when users query with precise technical terms, identifiers, or rare proper nouns, because embeddings can blur specificity. Semantic search excels for natural-language, paraphrased, or cross-lingual queries. Modern production systems often combine both in a hybrid search approach: lexical search (BM25) retrieves a candidate set, and a semantic re-ranker (a cross-encoder model) re-scores results for final relevance. This balances recall from keyword matching with precision from deep language understanding.
The quality of semantic search is entirely bounded by the embedding model's training data and domain. A general-purpose model (e.g., text-embedding-ada-002 or all-MiniLM-L6-v2) may perform poorly on specialized domains like legal, medical, or code unless fine-tuned or replaced with a domain-specific model. Embedding dimensions also affect the trade-off between accuracy and storage/latency costs. Always evaluate your embedding model on domain-representative data before committing to a production index.
Long documents must be split into smaller chunks (typically 256–512 tokens) before embedding, because embedding models have token limits and long-text embeddings lose fine-grained detail. Chunk size, overlap, and splitting strategy (by sentence, paragraph, or section) significantly impact retrieval quality. Combining vector search with metadata filters — such as date ranges, categories, or user permissions — narrows the search space and improves both speed and precision. This pattern is the foundation of Retrieval-Augmented Generation (RAG) systems used with large language models.
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