Semantic Search
Semantic search goes beyond keyword matching to understand the intent and contextual meaning behind a query. By leveraging natural language processing and machine learning, semantic search systems deliver more relevant results by analyzing relationships between words, user context, and document semantics.1
Keyword Search vs. Semantic Search
| Aspect | Keyword Search | Semantic Search |
|---|---|---|
| Matching | Exact term matching | Meaning-based matching |
| Synonyms | Requires manual handling | Automatically understood |
| Context | Ignored | Considered |
| Query: “car” | Only matches “car” | Also matches “automobile”, “vehicle” |
How Semantic Search Works
1. Query Understanding
The system analyzes the query to identify:
- Keywords and phrases - Core terms in the query
- Named entities - People, places, organizations
- Intent - What the user is trying to accomplish
- Context - Previous searches, user preferences
2. Document Representation
Documents are converted into dense vector representations (embeddings) that capture semantic meaning. Similar concepts cluster together in the vector space, enabling similarity-based retrieval.2
3. Retrieval and Ranking
The system computes similarity between the query embedding and document embeddings, typically using cosine similarity or dot product. Results are ranked by semantic relevance rather than keyword frequency.
Key Components
Vector Embeddings
Dense numerical representations that capture semantic meaning. Models like BERT, Sentence-BERT, and OpenAI’s embedding models convert text into vectors where similar meanings are geometrically close.3
Vector Databases
Specialized databases optimized for storing and querying high-dimensional vectors:
- Pinecone - Managed vector database service
- Weaviate - Open-source vector search engine
- ChromaDB - Lightweight embedding database
- FAISS - Facebook’s similarity search library
Hybrid Search
Combines keyword-based (sparse) and semantic (dense) retrieval to leverage the strengths of both approaches. Useful when exact matches are important alongside semantic understanding.
Example
Query: “best laptops for graphic design students”
| Approach | How It Works |
|---|---|
| Keyword Search | Matches pages containing “laptops”, “graphic”, “design”, “students” |
| Semantic Search | Understands the user wants laptops with powerful GPUs, high RAM, quality displays, at student-friendly prices |
Applications
- Enterprise search - Finding relevant documents across organizational knowledge bases
- E-commerce - Product discovery beyond exact product names
- Customer support - Matching queries to relevant help articles
- RAG systems - Retrieving context for LLM-based applications
Related Topics
- Vector Embeddings - How text is converted to vectors
- BERT - Model commonly used for generating embeddings
- RAG Systems - Using semantic search with LLMs
- Chunk Engineering - Preparing documents for semantic search
References
Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP. https://arxiv.org/abs/1908.10084 ↩︎
Karpukhin, V., et al. (2020). Dense Passage Retrieval for Open-Domain Question Answering. EMNLP. https://arxiv.org/abs/2004.04906 ↩︎
Devlin, J., et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL. https://arxiv.org/abs/1810.04805 ↩︎