Glossary

Embedding vector

What is Embedding vector?

An embedding vector (embedding) is a list of numbers that represents the meaning of a word, sentence or document so that texts with similar content lie close together. This makes it possible to recognise, for example, that 'cancellation' and 'ending the contract' are related even though they share no words. Embeddings underpin many semantic search systems, recommendation services and RAG applications; they are computed by specially trained models.

How KI.ordo helps

KI.ordo does not use embeddings for its knowledge search but the BM25 keyword search, run directly on the server.

Topics

vector
semantic search
NLP

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Related terms

Word Embeddings

Word embeddings represent words as numerical vectors so that words with similar meanings lie close together in vector space, such as “car” and “vehicle”. Methods such as Word2Vec or GloVe learn these vectors from large amounts of text. They were an important step in language processing, for example for search or classification; today's language models use contextual embeddings in which a word is represented differently depending on the sentence.

NLP
Vectors
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Retrieval-augmented generation (RAG)

Retrieval-augmented generation combines search with text generation: before answering, the system searches a knowledge collection – such as manuals, policies or FAQs – for relevant passages and passes them to the language model as context. The answer is therefore based on current, company-specific content rather than only on the model's training knowledge. This reduces invented statements, makes sources traceable and allows updates without retraining.

RAG
knowledge search
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Information Retrieval

Information retrieval covers methods that find the documents or passages in a large body of text that match a query and rank them by relevance. Classic approaches such as BM25 weight search terms by how often and how rarely they occur; newer ones add semantic similarity. It underpins search engines, enterprise search and retrieval-augmented generation, in which a language model uses the retrieved passages as the basis for its answer.

Search
BM25
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Transformer

The transformer is a neural network architecture introduced in 2017 in the research paper “Attention Is All You Need”. At its core is the attention mechanism: each word is weighted in relation to all other words in a text, and the computation can be parallelised efficiently. Transformers underpin today's language models such as GPT, Claude and BERT and are also used for images and audio.

Architecture
Deep learning
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