What is Embeddings?
Numerical vector representations of text (or images, or audio) where semantically similar inputs land in similar regions of vector space — the substrate of semantic search and RAG.
Also known as
Embeddings — explained.
Embeddings are numerical vector representations of text (or images, or audio) produced by a neural network so that semantically similar inputs land in similar regions of the vector space. A modern text embedding model maps a sentence or paragraph to a dense vector of typically 384, 768, 1024, or 1536 dimensions. The cosine distance (or dot product) between two embedding vectors is then a usable measure of semantic similarity — much better than keyword overlap. Embeddings are the substrate for: semantic search (find the document whose embedding is closest to the query's), retrieval-augmented generation (find the top-K most relevant document chunks to feed an LLM), clustering, deduplication, and classification. The choice of embedding model matters — popular options include OpenAI's text-embedding-3, Cohere's Embed v3, BGE (open-weight), E5 (open-weight, multilingual), and Voyage. For on-prem deployments BGE and E5 give competitive quality without an external API dependency.
Zeour solutions that operate on this layer.
Verticals where embeddings is operationally critical.
Blog posts that go deeper on embeddings.
Adjacent definitions to read next.
Retrieval-Augmented Generation (RAG)
AI & ModelsA pattern where the LLM is given relevant excerpts from a knowledge base at query time — so answers come from authoritative source documents, not the model's memory.
Vector Database
AI & ModelsA database optimised for storing and querying high-dimensional embedding vectors — the storage layer behind semantic search and RAG.
Semantic Search
AI & ModelsSearching by meaning rather than keyword — uses embeddings + a vector database to surface documents that match the query's intent even when no terms overlap.
On-Premises AI
AI & ModelsOpen-weight large language models running on the operator's own hardware — no prompt, completion, or embedding ever leaves the perimeter.
Arabic Language Model
AI & ModelsAn open-weight or fine-tuned LLM that handles Modern Standard Arabic and major dialects with appropriate tokenisation efficiency and right-to-left rendering at the application layer.
Context Window
AI & ModelsThe maximum amount of text an LLM can process in a single request, measured in tokens — caps how much document context can be fed for RAG and long-form analysis.
Fine-Tuning
AI & ModelsAdapting a pre-trained LLM to your domain or task by continuing its training on a small, high-quality dataset — typically via LoRA or full SFT.
Large Language Model
AI & ModelsA neural network trained on internet-scale text that produces fluent generative output and powers most of what people call "AI" in 2026 — including on-premises sovereign deployments.
Talk to a Zeour engineer.
A 30-minute scoping call to walk your operational profile against where embeddings actually sits in your stack, then a fixed-fee Discovery price by the end of the call.