Embedding
Also known as: vector embedding, text embedding.
In plain English
An embedding model turns 'How do I reset my password?' and 'I forgot my login' into vectors that sit close together in a high-dimensional space, even though they share almost no words. That's the point: embeddings capture meaning, not keywords. Systems store millions of these vectors in a vector database; to answer a query, they embed the query and retrieve the nearest stored vectors — the most semantically relevant chunks — in milliseconds. Embeddings power semantic search, recommendation, clustering, deduplication, and the retrieval half of RAG. Typical embeddings have hundreds to a few thousand dimensions; more dimensions capture more nuance at higher storage cost. They're cheap and fast to generate compared with full LLM calls, which is why retrieval pipelines embed once and reuse the vectors. Different embedding models aren't interchangeable — you must query with the same model you indexed with.
Example
A company embeds all 4,000 of its help articles into vectors and stores them. When a customer types 'the app keeps logging me out,' the system embeds that phrase and finds the closest article vector — a piece titled 'Session timeout settings' that shares no keywords with the query. Keyword search would have missed it entirely; the embedding matched on meaning, and RAG then feeds that article to the model to answer.
Embedding in Vincony
Vincony's Semantic Search and Knowledge Graph turn your uploaded documents into embeddings so you can search and chat over them by meaning, not keywords — the retrieval layer behind grounded, cited answers.
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