glossary terms
Semantic Search
- Category
- RAG, Embeddings & Search
- Difficulty
- Intermediate
Definition
A search technique that uses vector embeddings and natural language processing to retrieve information based on the conceptual meaning and intent of a query rather than exact keyword matches.
How It Works and Context
Instead, it converts both the user's query and the documents in a database into high-dimensional numerical representations called vector embeddings. These vectors capture the semantic relationships and context of the data. When a user performs a search, the system calculates the mathematical distance between the query vector and the document vectors to find the most conceptually similar content. This approach allows the system to handle synonyms, polysemy, and complex natural language queries effectively. However, semantic search can sometimes struggle with highly specific technical jargon or exact-match requirements, such as searching for a specific product serial number, where traditional keyword-based indexing remains superior.
Why It Matters
By enabling AI models to retrieve contextually relevant information from vast knowledge bases, it allows LLMs to provide accurate, grounded answers.
Real-world Example
Imagine a user searches for 'how to fix a leaky faucet' on a home improvement site. A traditional keyword search might fail if the article uses the term 'dripping tap' instead of 'leaky faucet.' A semantic search system, however, recognizes that 'leaky' and 'dripping' are conceptually similar and that 'faucet' and 'tap' are synonyms, successfully retrieving the correct repair guide despite the lack of exact keyword overlap.
Common Mistakes
- Assuming semantic search is always better than keyword search; it often performs poorly for exact-match queries like IDs or specific part numbers.
- Neglecting to re-index or update vector embeddings when the underlying source data changes.
- Failing to implement a hybrid search approach that combines both semantic and keyword-based retrieval for optimal performance.
- Overlooking the importance of embedding model selection, which can significantly impact the quality of search results.
Frequently Asked Questions
How does semantic search differ from keyword search?
Keyword search looks for exact matches of words or phrases in a document. Semantic search looks for the conceptual meaning behind the words, allowing it to find relevant results even if the specific words used in the query are not present in the document.
What is a vector embedding in the context of search?
A vector embedding is a numerical representation of text that captures its meaning. Words or documents with similar meanings are placed closer together in a multi-dimensional space, allowing the search engine to calculate similarity mathematically.
Can semantic search be used for non-text data?
Yes, semantic search can be applied to images, audio, and video by generating embeddings for those media types, allowing users to search for content based on visual or auditory similarity.