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Traditional keyword-based search often falls short in delivering relevant results, especially as websites grow in size and complexity. Users expect smarter, more intuitive search experiences—ones that understand intent, not just exact words. That’s where semantic search comes in.
In this post, we’ll explore what semantic search is, why it matters, and how you can quickly add it to your website, whether you're running a personal blog, an e-commerce platform, or a documentation site.
What is Semantic Search?
Semantic search uses natural language processing (NLP) and machine learning to understand the meaning behind a user’s query. Instead of matching keywords literally, it looks for contextual relevance.
For example:
Keyword Search: “Apple” — might return pages with “Apple” the fruit, and “Apple Inc.” mixed together.
Semantic Search: “Apple stock performance 2024” — returns financial articles about Apple Inc., understanding you're referring to the company, not the fruit.
Why You Should Care
Better Results: Semantic search improves accuracy and relevance.
Natural Language Friendly: Users can type how they talk—no need for perfect keywords.
Improved UX: Fewer clicks and faster answers mean happier users.
Competitive Advantage: Stand out with smarter site features.
How to Add Semantic Search to Your Website
At ZeroEntropy.dev, we believe in developer-first, low-friction tools. Here’s how to integrate semantic search without rebuilding your stack from scratch.
1. Choose an Embedding Model
First, you need to convert your content and queries into vector embeddings—numerical representations that capture semantic meaning.
Popular options include:
OpenAI's
text-embedding-3-smallCohere's multilingual models
Hugging Face models (e.g.,
sentence-transformers)
2. Index Your Content
Convert your pages, articles, or product descriptions into embeddings and store them in a vector database, such as:
3. Handle User Queries
When a user submits a search, convert their query into an embedding using the same model, then run a vector similarity search to find semantically related content.
You can re-rank results with metadata filters, page popularity, or hybrid a
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