Semantic Search Explained: Beyond Keyword Matching

Traditional search relies on keyword matching - if you search for "automobile", you won't find documents about "cars". Semantic search changes this fundamentally.

How It Works

1. Text to Vectors

When you store a memory, we convert it into a high-dimensional vector (embedding) that captures its meaning:

"The patient has high blood pressure"

→ [0.23, -0.45, 0.12, ..., 0.89] (1536 dimensions)

2. Similarity Matching

When you search, we convert your query to a vector and find the closest matches:

// These will all match "cardiovascular issues"

"heart problems"

"cardiac conditions"

"blood pressure concerns"

3. Ranking by Relevance

Results are ranked by cosine similarity - measuring the angle between vectors in high-dimensional space.

Why It Matters for AI

RAG (Retrieval-Augmented Generation) systems depend on finding relevant context. Keyword matching fails when:

Semantic search solves all of these.

PiyAPI Implementation

We use state-of-the-art embedding models with: