Αρχιτεκτονική

Vector Database

Ορισμός

A specialised database designed to store and efficiently search high-dimensional embedding vectors using approximate nearest-neighbour algorithms. Vector databases are the infrastructure backbone of RAG systems and semantic search applications.

Παραδείγματα

Pinecone
Weaviate
Milvus
pgvector

Σχετικοί Όροι

Embedding

A numerical representation of data (text, images, etc.) in a high-dimensional vector space where semantically similar items are mathematically close together. Embeddings are the foundational building block for semantic search, RAG pipelines, and recommendation systems.

Retrieval-Augmented Generation (RAG)

A technique that enhances LLM responses by retrieving relevant information from external knowledge sources before generating a response. RAG improves accuracy, reduces hallucinations, and enables LLMs to access up-to-date information without retraining.

Semantic Search

A search approach that retrieves results based on the meaning and intent of a query rather than exact keyword matches. Semantic search is powered by embeddings and vector databases, enabling far more relevant enterprise knowledge retrieval.

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