Glossary · Modern AI
Vector Database
Vector databases store mathematical representations (embeddings) of meaning, enabling AI-powered semantic search, RAG systems, and recommendation engines at millisecond speed.
In short
Vector Database enables search that truly understands meaning and context, not just keywords. Common applications include semantic document search and rag knowledge retrieval. BespokeWorks deploys Vector Database solutions for UK businesses, typically live within 7 days.
Definition
What is Vector Database?
Vector Databases store mathematical representations (embeddings) of meaning in high-dimensional space, enabling AI to understand semantic similarity between concepts, documents, and queries. They use specialised algorithms like HNSW and IVF to rapidly search billions of high-dimensional vectors in milliseconds, powering RAG systems, semantic search, recommendation engines, and anomaly detection.
The vector database market has grown rapidly with the AI boom, with platforms like Pinecone, Weaviate, Chroma, and Milvus raising significant funding. Vector databases are essential infrastructure for any organisation building RAG applications, as they provide the retrieval layer that connects LLMs to your proprietary knowledge.
BespokeWorks architects vector database solutions as part of RAG and semantic search implementations. Our deployments include embedding model selection, chunking strategy design, index optimisation, and hybrid search configurations to ensure your AI applications retrieve the most relevant information at production speed.
Where it earns its keep
Real-world applications.
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Semantic Document Search
Finds relevant documents by meaning, context, and conceptual similarity, not just keyword matches, enabling natural language queries across your entire knowledge base.
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RAG Knowledge Retrieval
Rapidly retrieves the most relevant document chunks from massive knowledge bases to provide context for LLM responses, enabling accurate, grounded AI answers.
Why it matters
Key benefits.
- Enables search that truly understands meaning and context, not just keywords
- Searches billions of items in single-digit milliseconds with sub-linear scaling
- Provides essential infrastructure for production RAG and semantic search systems
See how Vector Database fits your business.
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