Semantic Search
Semantic search uses vector embeddings to find documents based on meaning rather than keyword matching. It converts queries and documents into high-dimensional vectors, then finds the closest matches using distance metrics like cosine similarity. This approach understands synonyms, paraphrases, and conceptual relationships that keyword search completely misses.
What Is Semantic Search?
Traditional keyword search (like Elasticsearch BM25) works by matching exact words between the query and documents. If you search for "car maintenance schedule," it will not find a document about "vehicle servicing intervals" because the words do not overlap. Semantic search solves this by converting both the query and documents into numerical vectors (embeddings) that capture meaning. Since "car maintenance schedule" and "vehicle servicing intervals" have similar meanings, their vectors will be close together in the embedding space.
The process works in two stages. First, during indexing, you generate embeddings for all your documents (or document chunks) using an embedding model like OpenAI text-embedding-3-large, Cohere embed-v3, or open-source models like BGE or E5. These vectors are stored in a vector database optimized for similarity search. Second, during query time, you embed the user's query using the same model and search for the nearest vectors using approximate nearest neighbor (ANN) algorithms like HNSW or IVF.
Embedding quality directly determines search quality. Larger embedding models (1,536 to 3,072 dimensions) generally produce more accurate results than smaller ones but cost more per embedding and require more storage. Domain-specific fine-tuned embeddings outperform general-purpose models for specialized content. For example, a medical embedding model will better capture the relationship between clinical terms than a general-purpose model trained primarily on web text.
Semantic search has limitations that practitioners must understand. It can struggle with negation ("documents NOT about Python"), exact string matching (serial numbers, email addresses), and highly technical jargon not well-represented in the embedding model's training data. These limitations are why hybrid search (combining semantic and keyword approaches) is the recommended production strategy. Salt Technologies AI evaluates semantic search as one component of a broader retrieval strategy, never as the sole retrieval mechanism for production systems.
Real-World Use Cases
Intelligent Help Center
A SaaS company replaces their keyword-based help center with semantic search. Users who type "I can't log in" now find articles titled "Authentication Troubleshooting" and "Password Reset Guide" that keyword search would miss entirely. Support ticket volume drops by 35%.
Patent Prior Art Search
A patent law firm uses semantic search to find relevant prior art across 10 million patents. By searching for concepts rather than keywords, they discover relevant patents using different terminology, reducing the risk of missed prior art and strengthening patent applications.
Talent Matching
A recruiting platform uses semantic search to match job descriptions with candidate resumes. The system understands that "led a team of 5 engineers" is relevant to a search for "management experience," even without exact keyword overlap. Match quality improves by 40% over keyword-based matching.
Common Misconceptions
Semantic search understands language like a human does.
Semantic search captures statistical patterns of meaning from training data, not true comprehension. It works well for common concepts and relationships but can fail on nuanced, domain-specific, or recently coined terms that the embedding model has not encountered.
Bigger embedding models are always better.
Larger models produce higher-quality embeddings but at higher cost and latency. For many use cases, a 768-dimension model performs within 2-3% of a 3,072-dimension model while cutting storage costs by 75%. Always benchmark your specific data before defaulting to the largest model.
Why Semantic Search Matters for Your Business
Semantic search is the foundation of modern AI-powered information retrieval. It enables users to find information using natural language rather than guessing the right keywords, fundamentally improving the search experience. Businesses deploying AI chatbots, knowledge bases, or search features must implement semantic search to meet user expectations. The technology has matured to the point where implementation costs are modest and performance is production-ready.
How Salt Technologies AI Uses Semantic Search
Salt Technologies AI integrates semantic search into every RAG and search project we build. We evaluate embedding models (OpenAI, Cohere, and open-source options) against client data to select the optimal quality/cost balance. Our semantic search implementations include query preprocessing, embedding caching for frequently repeated queries, and fallback to keyword search when semantic confidence is low. We typically pair semantic search with BM25 in a hybrid configuration for production deployments.
Further Reading
- Vector Database Performance Benchmark 2026
Salt Technologies AI Datasets
- LLM Model Comparison 2026
Salt Technologies AI Datasets
- Text Embeddings Guide
OpenAI
Related Terms
Embeddings
Embeddings are numerical vector representations of text, images, or other data that capture semantic meaning in a high-dimensional space. Similar concepts produce similar vectors, enabling machines to measure meaning-based similarity between documents, sentences, or words. Embeddings are the mathematical backbone of semantic search, RAG systems, recommendation engines, and clustering applications.
Vector Database
A vector database is a specialized data store designed to index, store, and query high-dimensional vector embeddings at scale. Unlike traditional databases that search by exact keyword matches, vector databases perform similarity search to find the most semantically relevant results. They are the critical infrastructure component in RAG systems, semantic search engines, and recommendation systems.
Hybrid Search
Hybrid search combines vector (semantic) search with keyword (BM25/sparse) search to retrieve documents that match both the meaning and specific terms of a query. By fusing results from both approaches, hybrid search captures conceptual relevance and exact keyword matches that either method alone would miss. It is the recommended retrieval strategy for production RAG systems.
Vector Indexing
Vector indexing is the process of organizing high-dimensional vectors in data structures optimized for fast approximate nearest neighbor (ANN) search. Algorithms like HNSW, IVF, and Product Quantization enable sub-millisecond similarity searches across millions of vectors. The choice of index type directly affects search speed, memory usage, and recall accuracy.
RAG Pipeline
A RAG pipeline is an architecture that augments large language model responses by retrieving relevant documents from an external knowledge base before generating answers. It combines retrieval (typically vector search) with generation, grounding LLM output in verified, up-to-date information. This pattern dramatically reduces hallucinations and enables domain-specific accuracy without retraining the model.
Retrieval Pipeline
A retrieval pipeline is the sequence of steps that finds and ranks the most relevant documents or data chunks in response to a user query. It typically includes query processing, embedding generation, vector search, optional keyword search, reranking, and filtering. The quality of your retrieval pipeline directly determines the quality of your RAG system's answers.