Semantic Search in Python: Embeddings and Vector Search
Building a search system on vector similarity is easier than it looks. Learn how to create a vector search engine in Python using embeddings, cosine similarity calculations and basic retrieval logic. We walk through the components, the implementation and how to scale it.
AI-processed from KDnuggets; edited by Hamidun News
Semantic search (vector search) works differently from keyword matching: data is converted into embeddings, and the system looks for similar vectors. It underpins RAG systems, recommendation engines and modern AI applications.
What is vector search for?
Vector search is used in RAG systems, semantic search, recommendation engines and modern AI applications.
What are embeddings?
Embeddings are numerical representations of text, stored as an array of 300 to 1500+ numbers. Texts with similar meaning have embeddings that sit close to each other in vector space.
Where is semantic search used?
In RAG systems, semantic search, recommendation engines and modern AI applications.
Key Questions Answered
What is vector search for?
Vector search is used in RAG systems, semantic search, recommendation engines and modern AI applications.
What are embeddings?
Embeddings are numerical representations of text, stored as an array of 300 to 1500+ numbers. Texts with similar meaning have embeddings that sit close to each other in vector space.
Where is semantic search used?
In RAG systems, semantic search, recommendation engines and modern AI applications.
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