Embeddings & Vector Databases

Embeddings transform text into numerical vectors that capture semantic meaning. Combined with vector databases, they enable powerful features like semantic search, recommendation systems, and retrieval-augmented generation (RAG).

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What Are Embeddings?

An embedding is a dense vector (list of floating-point numbers) that represents the meaning of a piece of text. Texts with similar meanings have vectors that are close together in the embedding space.

"The cat sat on the mat"   → [0.023, -0.114, 0.891, ...]   (1536 dimensions)
"A kitten was on the rug"  → [0.025, -0.109, 0.887, ...]   (very similar!)
"Stock prices rose today"  → [-0.412, 0.067, 0.203, ...]   (very different)

Why Embeddings Matter

Use Case	How Embeddings Help
Semantic search	Find results by meaning, not just keywords
Recommendations	Suggest similar items based on vector proximity
Clustering	Group similar documents automatically
RAG	Retrieve relevant context for LLM prompts
Anomaly detection	Identify outliers in vector space

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Generating Embeddings

Using OpenAI

from openai import OpenAI

client = OpenAI()

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="The cat sat on the mat",
)