Models Retrieval
Vector Database
A specialized database that stores and retrieves data as mathematical vectors, enabling semantic search and similarity matching between content.
A vector database is a specialized data storage system designed to store and efficiently query high-dimensional vectors (mathematical representations of data). In AI applications, these vectors are typically embeddings that capture semantic meaning of text, images, or other content.
Vector databases enable similarity search, where queries find items that are semantically similar rather than just matching keywords. This is crucial for AI applications like semantic search, recommendation systems, and retrieval-augmented generation (RAG).
Popular vector databases include Pinecone, Weaviate, Milvus, and Chroma. They differ from traditional databases in their indexing methods, which are optimized for high-dimensional similarity calculations rather than exact matches.
Example: An AI chatbot stores embeddings of your documentation in a vector database. When users ask questions, it searches for semantically similar content to provide accurate, contextual answers.
In SeCoFlow: SeCoFlow uses vector databases internally for features like semantic content analysis and topic clustering, helping identify related content and coverage gaps across your site.