Glossary
Embedding
A numeric vector representing text, images or audio, positioned so similar things sit close together.
Embeddings turn meaning into geometry. Two passages about the same topic land near each other, which is what makes semantic search and retrieval possible.
Embedding choice matters more than teams expect, the model must be strong in your domain and, for Indian use cases, in your languages.
Embeddings also drift. Change the embedding model and every stored vector becomes incomparable with the new ones, which means a full re-index of the corpus. That is survivable when planned and painful when discovered, so the model choice deserves more deliberation than it usually gets.
Domain matters more than leaderboard position. An embedding model trained largely on general web text will happily place two unrelated clinical terms close together, so for specialised corpora it is worth benchmarking two or three candidates on your own retrieval questions before committing.
Related terms, in context
The concepts you almost always meet alongside embedding.
- Vector database
- A store for embeddings that answers 'what is semantically closest to this?' quickly.
- RAG
- Retrieving relevant passages from your own documents and giving them to the model, so answers are grounded and citable.
- Chunking
- Splitting documents into passages small enough to retrieve precisely but large enough to stay meaningful.
Where this shows up in our work
Embedding is not an abstraction for us. It is a decision we make on live projects. It shows up most directly in rag & knowledge retrieval, ai search implementation, where getting it wrong has a cost someone can measure.
If you are evaluating a vendor on this, the useful question is not whether they can define the term. It is what they measure, what they would refuse to do, and what happens in their system when the assumption behind embedding stops holding.
Questions
What is Embedding?
A numeric vector representing text, images or audio, positioned so similar things sit close together.
Does Orqent Labs build this?
Yes, RAG & Knowledge Retrieval Systems and AI Search Implementation. We work across India, covering all 19,238 PIN codes remotely.
Building something that involves embedding?
We will tell you honestly whether it is the right approach for your problem.
Or email bd@dtrasglobal.com · call +91 74118 77878