Definition
Semantic search is a search approach that understands the meaning and intent behind queries rather than simply matching keywords. Using techniques like embeddings and neural networks, semantic search captures conceptual relationships between terms. When you search for “how to fix a leaky faucet,” semantic search understands you want plumbing repair guidance even if documents don’t contain those exact words—finding results about “repairing dripping taps” or “stopping water waste from fixtures.”
Why it matters
Semantic search transforms how we find information:
- Intent understanding — “cheap flights to Paris” finds budget travel options, not documents containing those literal words
- Synonym handling — “car” matches “automobile,” “vehicle,” “auto”
- Context awareness — “Apple” in tech context versus fruit context
- Natural language queries — search conversationally, not with keyword syntax
- Multilingual search — find relevant documents regardless of language
Semantic search powers modern search engines, e-commerce, enterprise search, and RAG systems.
How it works
┌────────────────────────────────────────────────────────────┐
│ SEMANTIC SEARCH │
├────────────────────────────────────────────────────────────┤
│ │
│ KEYWORD SEARCH vs SEMANTIC SEARCH: │
│ ────────────────────────────────── │
│ │
│ Query: "How do I make my laptop faster?" │
│ │
│ KEYWORD SEARCH: │
│ ┌────────────────────────────────────────────────┐ │
│ │ Looks for documents containing: │ │
│ │ "make" AND "laptop" AND "faster" │ │
│ │ │ │
│ │ ❌ Misses: "Speed up your notebook" │ │
│ │ ❌ Misses: "Improve PC performance" │ │
│ │ ❌ Misses: "Boost computer speed" │ │
│ └────────────────────────────────────────────────┘ │
│ │
│ SEMANTIC SEARCH: │
│ ┌────────────────────────────────────────────────┐ │
│ │ Understands the MEANING: │ │
│ │ "Improve computer performance" │ │
│ │ │ │
│ │ ✓ Finds: "Speed up your notebook" │ │
│ │ ✓ Finds: "Improve PC performance" │ │
│ │ ✓ Finds: "Boost computer speed" │ │
│ │ ✓ Finds: "Optimize Windows startup" │ │
│ └────────────────────────────────────────────────┘ │
│ │
│ │
│ HOW SEMANTIC SEARCH WORKS: │
│ ────────────────────────── │
│ │
│ Step 1: Encode everything into vectors │
│ │
│ ┌─────────────┐ ┌─────────────────────┐ │
│ │ Query │ │ Documents │ │
│ │ "laptop │ │ "Speed up your │ │
│ │ faster" │ │ notebook" │ │
│ └──────┬──────┘ └──────────┬──────────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌─────────────┐ ┌─────────────────────┐ │
│ │ Embedding │ │ Embedding │ │
│ │ Model │ │ Model │ │
│ │ (BERT etc) │ │ (same model) │ │
│ └──────┬──────┘ └──────────┬──────────┘ │
│ │ │ │
│ ▼ ▼ │
│ [0.2, 0.8, ...] [0.21, 0.79, ...] │
│ Query Vector Document Vectors │
│ │
│ │
│ Step 2: Find similar vectors │
│ │
│ Vector Space │
│ ┌────────────────────────────┐ │
│ │ ● │ │
│ │ Query ● Doc A │ │
│ │ (near) │ │
│ │ ● Doc B │ │
│ │ (near) │ │
│ │ │ │
│ │ ● Doc C │ │
│ │ (far) │ │
│ │ │ │
│ └────────────────────────────┘ │
│ │
│ Similar meaning = Nearby in vector space │
│ │
│ │
│ Step 3: Rank by similarity score │
│ │
│ Results: │
│ 1. Doc A: "Speed up notebook" [score: 0.94] │
│ 2. Doc B: "Optimize PC speed" [score: 0.89] │
│ 3. Doc C: "Buy a new computer" [score: 0.45] │
│ │
│ │
│ SEMANTIC SEARCH ARCHITECTURE: │
│ ───────────────────────────── │
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ INDEXING PHASE │ │
│ │ (done once, offline) │ │
│ │ │ │
│ │ Documents ──► Embedding ──► Vector ──► │ │
│ │ [D1,D2,D3] Model Vectors DB │ │
│ │ [V1,V2,V3] Index │ │
│ │ │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ QUERY PHASE │ │
│ │ (real-time) │ │
│ │ │ │
│ │ User Query ──► Embedding ──► Vector ──► Similarity │ │
│ │ Model Search Search │ │
│ │ │ │ │
│ │ ▼ │ │
│ │ Top K Results │ │
│ │ [D2, D1, D5] │ │
│ │ │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ │
│ HYBRID SEARCH (Best Practice): │
│ ────────────────────────────── │
│ │
│ Combine keyword + semantic for best results: │
│ │
│ Query: "tesla model 3 range" │
│ │
│ ┌──────────────┐ ┌──────────────┐ │
│ │ Keyword │ │ Semantic │ │
│ │ Search │ │ Search │ │
│ │ (BM25) │ │ (Vectors) │ │
│ └──────┬───────┘ └──────┬───────┘ │
│ │ │ │
│ └───────┬───────────┘ │
│ ▼ │
│ ┌──────────────┐ │
│ │ Fusion/ │ │
│ │ Re-ranking │ │
│ └──────┬───────┘ │
│ │ │
│ ▼ │
│ Final Results │
│ │
│ Why hybrid? Keywords catch exact matches (product SKUs, │
│ names), semantics catch meaning │
│ │
└────────────────────────────────────────────────────────────┘Semantic search vs keyword search:
| Aspect | Keyword Search | Semantic Search |
|---|---|---|
| Matching | Exact terms | Meaning/concepts |
| Synonyms | Requires expansion | Automatic |
| Misspellings | Usually fails | Often handles |
| Query understanding | Literal | Contextual |
| Setup complexity | Simple | Requires embeddings |
| Latency | Very fast | Slightly slower |
Common questions
Q: What embedding models should I use for semantic search?
A: For English, start with OpenAI text-embedding-3-small or sentence-transformers/all-MiniLM-L6-v2 (open source). For multilingual, use multilingual-e5-large or mBERT variants. For domain-specific search (legal, medical), consider fine-tuning or domain-specific models. Balance quality vs speed—larger models are better but slower.
Q: How much does semantic search improve results?
A: Heavily depends on use case. For queries with clear keyword matches (“iPhone 15 specs”), improvement is marginal. For natural language queries (“What phone has the best camera under $500”), improvement can be 30-50% in relevance. Semantic search shines with varied vocabularies and conceptual queries.
Q: Should I use semantic search or keyword search?
A: Use hybrid search—combine both. Keyword search (BM25) excels at exact matches: product codes, names, technical terms. Semantic search excels at conceptual understanding. Modern systems use reciprocal rank fusion to combine results from both approaches, getting the best of each.
Q: How do I handle the cold start problem?
A: Semantic search requires generating embeddings for all documents before queries work. For large datasets, this can take hours/days. Solutions: pre-compute embeddings during off-peak hours, use incremental indexing for new documents, start with a simpler keyword system and add semantic search gradually.
Related terms
- Embedding — vectors that power semantic search
- Dense retrieval — the underlying technique
- RAG — using semantic search for LLM context
- Knowledge graph — structured knowledge for search
References
Reimers & Gurevych (2019), “Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks”, EMNLP. [Foundational sentence embeddings]
Karpukhin et al. (2020), “Dense Passage Retrieval for Open-Domain Question Answering”, EMNLP. [DPR for semantic search]
Neelakantan et al. (2022), “Text and Code Embeddings by Contrastive Pre-Training”, arXiv. [OpenAI embedding models]
Muennighoff et al. (2022), “SGPT: GPT Sentence Embeddings for Semantic Search”, arXiv. [GPT-based semantic search]