Vector Search, Embeddings & RAG: The Technical Foundation of AI Search

What Is Vector Search?

Vector search is a search method where content is represented as mathematical vectors (embeddings) in a high-dimensional space. Instead of searching for exact keywords, vector search finds semantically similar content — even when no common words appear.

How Do Embeddings Work?

• Text is converted into a numerical vector by an AI model (e.g., 1536 dimensions)
• Semantically similar texts are close together in vector space
• Search queries are also encoded as vectors and compared with stored vectors
• Cosine similarity or Euclidean distance measures similarity

What Is RAG (Retrieval-Augmented Generation)?

RAG is an architecture where AI models retrieve relevant documents from a knowledge base before generating responses:

1. User asks a question
2. The system searches a vector database for relevant documents
3. Found documents are passed as context to the AI model
4. The model generates a response based on these sources

Why Is This Relevant for Brands?

• Perplexity and similar systems use RAG for source-based answers
• Enterprise chatbots use RAG for precise customer information
• The quality of your content determines whether it is selected as a source in the RAG process
• Well-structured, semantically rich content is better represented as embeddings

In the CAFE Framework:

We use pgvector embeddings in our own knowledge base and understand the technical foundation on which AI systems select sources — so we can specifically optimize your content for vector search.