What is Vector Database?
A database optimised for storing and querying high-dimensional embedding vectors — the storage layer behind semantic search and RAG.
Also known as
Vector Database — explained.
A vector database is a database optimised for storing and querying high-dimensional embedding vectors. The core operation is approximate nearest-neighbour (ANN) search: given a query vector, return the top-K most similar vectors from millions or billions of stored vectors in single-digit milliseconds. The dominant index algorithms are HNSW (Hierarchical Navigable Small World), IVF (Inverted File), and product-quantised variants. Popular options include: dedicated vector databases (Pinecone, Weaviate, Qdrant, Milvus, Chroma) and vector extensions on existing databases (pgvector for PostgreSQL, Redis with RediSearch, Elasticsearch dense vectors). For on-prem deployments pgvector on PostgreSQL is often the right choice because it adds vector search to an existing operational database without introducing a new system to operate. The trade-off is throughput — dedicated vector databases scale further on the same hardware. The hybrid pattern (vector + keyword search combined, often called hybrid search) is increasingly the default because it catches what pure vector search misses for rare-term queries.
Zeour solutions that operate on this layer.
Verticals where vector database is operationally critical.
Blog posts that go deeper on vector database.
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.
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.
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.
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.
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A 30-minute scoping call to walk your operational profile against where vector database actually sits in your stack, then a fixed-fee Discovery price by the end of the call.