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Natural Language Processing Systems • Semantic Search (Dense Embeddings, ANN)Easy⏱️ ~3 min

What is Semantic Search and How Do Dense Embeddings Work?

Definition
Semantic Search finds content based on meaning rather than exact keyword matches. It converts text into numerical vectors (embeddings) that capture semantic similarity, then retrieves items whose vectors are closest to the query vector.
Why Keyword Search Fails
Traditional keyword search matches exact terms. A search for "laptop battery life" returns documents containing those exact words. But what about "MacBook power duration" or "how long does my computer last on a charge"? These mean the same thing but share zero keywords. Keyword search returns nothing useful.
Users do not know your terminology. They describe what they want in their own words. A system that only matches keywords frustrates users and misses relevant content.
How Embeddings Capture Meaning
Embedding models convert text into dense vectors, typically 384 to 1536 dimensions. These vectors position similar meanings close together in geometric space. "Laptop battery life" and "MacBook power duration" produce nearly identical vectors despite sharing no words. The model learned from billions of examples that these phrases describe the same concept.
💡 Key Insight: Embedding quality determines search quality. Domain-specific embedding models trained on your data type significantly outperform generic models - sometimes by 20-30% in relevance.
The Search Process
At query time, convert query text to a vector using the same embedding model used for documents. Find documents whose vectors are closest using cosine similarity or dot product. Return top K matches. The entire process takes 10-50ms for millions of documents with optimized vector indices.

💡 Key Takeaways

✓Semantic search matches meaning, not keywords: 'laptop battery life' finds 'MacBook power duration' despite zero word overlap

✓Embedding models convert text to 384-1536 dimension vectors where similar meanings cluster together in geometric space

✓Embedding quality determines search quality - domain-specific models outperform generic models significantly

✓Query-time process: convert query to vector, find closest document vectors, return top K matches in 10-50ms

📌 Interview Tips

1Start by explaining keyword search failure: user searches 'laptop battery' but your docs say 'notebook power' - zero matches despite identical meaning.

2Describe embeddings as learned from billions of examples, not hand-crafted rules. The model discovers that phrases mean the same thing.

3Mention the 10-50ms latency for semantic search on millions of documents - shows you understand production constraints.

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