Intro to vector embeddings

Vector embeddings are numerical representations of data, such as text, images, or events, that capture their meaning in a format that computers can efficiently compare and analyze.

In practice, an embedding is a list of floating-point numbers (a vector) that encodes the semantic relationships between inputs. Similar inputs produce similar vectors.

For example:

The first two messages have similar vectors because they describe related concepts — both are connection errors — while the third one is far apart.

Why embeddings matter

Embeddings allow you to perform semantic search, clustering, anomaly detection, and AI-powered analytics on your data without needing to write explicit rules or string matches.

In Better Stack Warehouse, you can use embeddings to:

• Search events by meaning, not just keywords
• Implement Retrieval Augumented Generation (RAG) systems with just two HTTP requests
• Detect outliers or unusual patterns in high-volume data
• Power AI-driven summaries and recommendations