How Semantic Search Actually Works: A Technical Deep Dive

Semantic search understands the intent and contextual meaning behind queries, not just surface-level keyword matches. While traditional search engines rely on exact term matching and frequency analysis (TF-IDF), semantic search uses machine learning to map both queries and documents into a shared vector space where meaning can be mathematically compared.

At Hakia, we pioneered semantic search technology back in 2008, long before it became mainstream. Our early work focused on natural language processing and meaning-based retrieval, laying groundwork for what would eventually become the foundation of modern AI search systems used by Google, Bing, and countless enterprise applications today.

The key breakthrough came with the development of dense vector representations—embeddings—that capture semantic relationships. These mathematical representations allow search engines to understand that 'car' and 'automobile' are related, or that 'how to fix a leaky faucet' matches documents about 'repairing dripping taps' even without shared keywords.

Vector embeddings are the mathematical foundation of semantic search. An embedding is a dense vector representation—typically 512 to 4,096 dimensions—that captures the semantic meaning of text. Words, sentences, or entire documents with similar meanings will have similar vector representations in this high-dimensional space.

Modern embedding models like OpenAI's text-embedding-ada-002, Google's Universal Sentence Encoder, or open-source alternatives like sentence-transformers are trained on massive text corpora to learn these representations. The training process uses techniques like contrastive learning, where the model learns to make similar texts have similar embeddings and dissimilar texts have different embeddings.

For a deeper understanding of how these mathematical representations work, see our guide on embeddings explained. The key insight is that semantic similarity becomes geometric similarity—documents about similar topics cluster together in vector space.

A semantic search system operates through a multi-stage pipeline that transforms text into vectors, performs similarity matching, and ranks results. Understanding each stage is crucial for building effective search applications.

1. Document Ingestion: Raw documents are chunked into manageable pieces (typically 200-500 tokens), processed through an embedding model, and stored in a vector database with metadata
2. Query Processing: User queries are processed through the same embedding model used for documents, ensuring queries and documents exist in the same vector space
3. Similarity Search: The query vector is compared against stored document vectors using mathematical similarity measures (cosine similarity, dot product, or Euclidean distance)
4. Ranking & Filtering: Results are ranked by similarity score, often combined with traditional signals like recency, authority, or user preferences
5. Result Presentation: Final results include the original documents plus similarity scores, often with highlighted relevant passages

Vector similarity is the core mechanism that determines search relevance. Three main approaches are used in production systems, each with different mathematical properties and use cases.

Cosine Similarity measures the angle between vectors, ignoring magnitude. It's the most common choice because it focuses purely on direction (meaning) regardless of document length. Values range from -1 to 1, where 1 indicates identical meaning.

import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

# Example: comparing query and document vectors
query_vec = np.array([0.2, 0.8, 0.1, 0.5])
doc_vec = np.array([0.3, 0.7, 0.2, 0.4])
similarity = cosine_similarity(query_vec, doc_vec)
print(f"Similarity: {similarity:.3f}")  # Output: Similarity: 0.932

Dot Product is computationally faster than cosine similarity and works well when vectors are normalized. Many modern embedding models are trained with normalized outputs, making dot product equivalent to cosine similarity but more efficient to compute.

Euclidean Distance measures straight-line distance between points in vector space. Lower values indicate higher similarity. While less common for semantic search, it's useful when magnitude matters or when working with certain types of embeddings.

Building production semantic search requires choosing the right combination of embedding models, vector databases, and search infrastructure. The ecosystem has matured significantly, offering both managed and open-source solutions.

Embedding Models: OpenAI's text-embedding-ada-002 offers excellent quality for $0.0001 per 1K tokens. For open-source alternatives, sentence-transformers provides models like all-MiniLM-L6-v2 (384 dimensions) or all-mpnet-base-v2 (768 dimensions) that run locally. Google's Universal Sentence Encoder and Cohere's embed models are other commercial options.

Vector Databases: Pinecone leads the managed space with serverless scaling and hybrid search capabilities. For self-hosted options, Chroma is Python-native and lightweight, while Weaviate offers advanced filtering and multi-modal support. pgvector extends PostgreSQL with vector capabilities for teams preferring familiar infrastructure.

For comprehensive coverage of vector search architecture and implementation details, see our vector search explained guide. The choice depends on scale, budget, and technical requirements.

While semantic search offers powerful capabilities, keyword search remains valuable for certain use cases. Modern systems often combine both approaches—called hybrid search—to leverage the strengths of each method.

Keyword search excels at exact matches, specific terminology, and cases where precision is critical. It's deterministic, explainable, and computationally lightweight. Legal documents, product codes, or technical specifications often benefit from keyword-based retrieval.

Semantic search shines for natural language queries, conceptual searches, and cases where users might not know exact terminology. It handles synonyms, multilingual content, and conceptual similarity naturally. Customer support, research, and content discovery are ideal use cases.

For a detailed comparison of approaches and implementation strategies, see our analysis of semantic vs keyword search. Most production systems now use hybrid approaches that combine both methods.

Semantic search has moved beyond academic research to power critical applications across industries. Understanding these use cases helps identify opportunities for implementation in your own projects.

Enterprise Knowledge Management: Companies use semantic search to help employees find internal documents, policies, and expertise. Systems can understand queries like 'vacation policy changes' and surface relevant HR documents even if they use different terminology like 'time off updates' or 'leave modifications.'

E-commerce Product Discovery: Online retailers use semantic search to improve product findability. When customers search for 'waterproof hiking boots,' the system can surface products described as 'weather-resistant trail footwear' or 'outdoor adventure shoes' based on semantic understanding rather than keyword matching.

RAG Applications: Retrieval-Augmented Generation systems rely heavily on semantic search to find relevant context for AI responses. Our RAG guide explores this architecture in detail, showing how semantic retrieval grounds AI responses in factual information.

Content Recommendation: Media platforms use semantic search to understand content similarity and user preferences beyond explicit tags. Articles about 'sustainable technology' might be recommended to users interested in 'green innovation' based on semantic similarity.