Vector Embedding

A numerical representation of text that captures its meaning, allowing AI systems to find semantically similar content even when the exact words don't match.

Technical definition

A vector embedding is a dense, fixed-dimensional numerical representation of text produced by an embedding model (e.g. OpenAI's text-embedding-3-small). Semantically similar text produces vectors that are close together in the embedding space, as measured by cosine similarity. This enables semantic search: finding relevant documents based on meaning rather than keyword overlap.

How Verabase uses Vector Embedding

When you add a source to your Verabase knowledge base, the content is split into logical chunks and each chunk is converted into a vector embedding using your LLM provider's embedding model. These embeddings are stored in a vector index. When a user asks a question, the query is also embedded and the system finds the closest matching chunks using cosine similarity — even if the user's phrasing doesn't match the exact words in your documents.

Why it matters for support teams

Traditional keyword search fails when customers phrase questions differently from how your documentation is written. Vector embeddings solve this by matching on meaning. A customer asking 'How do I cancel my plan?' will find an article titled 'Subscription management' even though no keywords overlap. This dramatically improves AI answer accuracy.

Related terms

How Verabase Handles Self-Healing Knowledge

The standard industry approach relies on support agents manually flagging stale content and technical writers updating documentation weeks later. Verabase completely upends this model through an autonomous AI platform that identifies knowledge gaps the moment an AI agent encounters a question it cannot answer.

Rather than requiring manual intervention, our visual RAG engine drafts a proposed fix. It analyzes the context of the user's question, reviews your existing knowledge base for conflicts, and creates a clear, structured article or snippet. All you need to do is click 'Approve'. This ensures your AI agents continually get smarter over time without adding to your support team's workload.

Security First Architecture

Many legacy platforms bolt AI onto their existing infrastructure, creating potential data leaks between tenants or exposing sensitive internal documents to end-users. Verabase is built from the ground up with a Bring Your Own Key (BYOK) architecture, enterprise-grade access controls, and strict semantic boundaries. This means your private engineering documents never accidentally leak into customer-facing agent responses.