What is Semantic Search?
Searching by meaning rather than keyword — uses embeddings + a vector database to surface documents that match the query's intent even when no terms overlap.
Also known as
Semantic Search — explained.
Semantic search retrieves documents by meaning rather than keyword match. The mechanic: the query and the corpus are each embedded into the same vector space, then nearest-neighbour search returns the documents whose embeddings are closest to the query's. The user benefit is that a query like 'how do I reset my password' finds a document titled 'forgotten credentials recovery procedure' even though no terms overlap. The trade-off versus keyword search: semantic search can miss documents that match on rare specialist terms (drug names, error codes, model numbers) because the embedding model abstracts them. The standard fix is hybrid search — run both keyword (BM25) and semantic (vector) search in parallel and merge the results with a learned ranker. Semantic search is the retrieval substrate for RAG and the powering technology behind modern enterprise search tools. Implementation needs three pieces: an embedding model, a vector database, and (ideally) a hybrid-search merging layer.
Zeour solutions that operate on this layer.
Verticals where semantic search is operationally critical.
Blog posts that go deeper on semantic search.
Adjacent definitions to read next.
Embeddings
AI & ModelsNumerical 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.
Vector Database
AI & ModelsA database optimised for storing and querying high-dimensional embedding vectors — the storage layer behind semantic search and RAG.
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.
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.
Llama (Meta)
AI & ModelsMeta's open-weight LLM family — Llama 3.x is the dominant open-weight base for enterprise on-prem deployments through 2025-2026.
Talk to a Zeour engineer.
A 30-minute scoping call to walk your operational profile against where semantic search actually sits in your stack, then a fixed-fee Discovery price by the end of the call.