What is Semantic Search?
Semantic Search is an information retrieval technique that understands the meaning and intent behind search queries rather than just matching keywords, using vector embeddings and natural language understanding to find conceptually relevant results.
Quick Facts
| Created | Concept from 2000s, transformer-based from 2019 |
|---|---|
| Specification | Official Specification |
How It Works
Semantic search represents a paradigm shift from traditional keyword-based search. By converting text into dense vector representations (embeddings) that capture semantic meaning, it can find relevant documents even when they don't share exact words with the query. Modern semantic search systems typically use transformer-based embedding models and vector databases to enable similarity search at scale. This technology powers advanced search features in products from Google to enterprise knowledge bases.
Key Characteristics
- Understands meaning and context, not just keywords
- Uses vector embeddings to represent text semantically
- Finds conceptually similar content across different phrasings
- Supports multilingual and cross-lingual search
- Combines with keyword search for hybrid approaches
- Enables question-answering over document collections
Common Use Cases
- Enterprise knowledge base and documentation search
- E-commerce product discovery
- Customer support ticket routing
- Legal and medical document retrieval
- RAG systems for LLM applications
Example
Loading code...Frequently Asked Questions
What is the difference between semantic search and keyword search?
Keyword search matches exact words or phrases in documents, while semantic search understands the meaning and context of queries. Semantic search can find relevant results even when documents use different words than the query, by converting text into vector embeddings that capture semantic meaning.
How do vector embeddings work in semantic search?
Vector embeddings are dense numerical representations of text generated by transformer-based models. Each piece of text is converted into a high-dimensional vector where similar meanings are positioned close together in the vector space. Search is performed by computing similarity (like cosine similarity) between query and document vectors.
What is hybrid search and when should I use it?
Hybrid search combines semantic search with traditional keyword search to get the best of both approaches. Use hybrid search when you need both exact match capability (for specific terms, product codes, names) and semantic understanding. Most production search systems use hybrid approaches for optimal results.
Which embedding models are best for semantic search?
Popular embedding models include OpenAI's text-embedding-ada-002, Sentence Transformers (all-MiniLM-L6-v2, all-mpnet-base-v2), Cohere Embed, and BGE models. The best choice depends on your language requirements, latency needs, and whether you need multilingual support. Benchmark against your specific use case.
How does semantic search enable RAG (Retrieval-Augmented Generation)?
RAG uses semantic search to retrieve relevant documents or passages from a knowledge base, then provides these as context to an LLM for generating accurate, grounded responses. Semantic search is crucial for finding the most contextually relevant information, even when user queries don't match document keywords exactly.