Semantic Search
Definition
Semantic search finds results based on meaning and intent rather than exact keyword matches, using embeddings to understand the conceptual similarity between queries and documents.
Why It Matters
Traditional keyword search fails when users don’t know the exact terminology in your documents. Someone searching “how to fix slow website” won’t find your article titled “Performance Optimization Techniques” unless those exact words appear. Semantic search solves this by understanding that “slow website” and “performance optimization” mean the same thing.
For AI engineers, semantic search is the foundation of modern retrieval systems. Every RAG pipeline relies on semantic search to find relevant context. Every chatbot that answers questions about your documentation uses semantic search. The quality of your semantic search directly determines the quality of your AI application’s responses.
The shift from keyword to semantic search represents a fundamental change in how machines understand language. Instead of matching strings, you’re matching concepts, and that unlocks use cases that were impossible with traditional search.
Implementation Basics
Semantic search works in three steps:
1. Embed Your Documents Convert each document (or chunk) into a dense vector using an embedding model like OpenAI’s text-embedding-3-small or open-source alternatives like BGE or E5. These vectors capture semantic meaning in numerical form.
2. Embed the Query When a user searches, convert their query into a vector using the same embedding model. Consistency matters. The query and documents must use identical embedding models.
3. Find Similar Vectors Compare the query vector against all document vectors using cosine similarity or dot product. The documents with the highest similarity scores are your search results.
The key implementation decisions: which embedding model to use (tradeoff between quality, speed, and cost), how to chunk documents (too large loses precision, too small loses context), and whether to combine with keyword search for hybrid approaches.
Start with a hosted embedding API for prototyping, then consider self-hosted models if you need lower latency, cost optimization, or data privacy. Most production systems benefit from adding a reranker on top of semantic search to improve precision.
Source
Word2Vec demonstrated that neural networks can learn distributed representations of words that capture semantic relationships, enabling similarity-based search.
https://arxiv.org/abs/1301.3781