Vector Embeddings
Vector embeddings convert text into high-dimensional numerical lists that capture semantic meaning, enabling computers to compare concepts rather than keywords.
A vector embedding is a high-dimensional numerical representation of a piece of text where semantically similar concepts are placed close together in mathematical vector space.
How Embeddings works in practice
An embedding model processes text and outputs a coordinate array (e.g., 768 floating-point numbers). Sentences with identical meanings—such as “What is your return policy?” and “How do I send an item back?”—yield vectors with high mathematical proximity, despite sharing zero common words.
This geometric alignment allows search systems to understand user intent, synonyms, and natural phrasing rather than relying on brittle exact-string matching.
How SiteMind implements Embeddings
SiteMind generates 768-dimensional vector embeddings for every chunk of your website content and indexes them directly inside PostgreSQL using the `pgvector` extension for sub-45ms similarity lookups.
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Related Technical Concepts
Vector Database & pgvector
A vector database is specialized storage optimized to index high-dimensional embeddings and execute sub-second approximate nearest neighbor (ANN) searches.
Dense Semantic Search
Semantic search finds relevant information based on conceptual meaning and intent rather than matching exact string keywords.
Cosine Similarity Thresholds
Cosine similarity measures the angle between two embedding vectors in multidimensional space to determine how closely their meanings match.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a technique where an AI fetches verified passages from a knowledge base before answering, ensuring grounded, factual replies.
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