AiVibeEmbeddings (Vector Embeddings)
An embedding is a list of numbers, a vector, that represents text, an image or other data so that items with similar meaning lie close together in vector space, enabling semantic search, clustering and recommendation.
Embedding models are neural networks trained so that related inputs produce nearby vectors, for example sentences with similar meaning or images of similar objects. Vectors typically have hundreds to a few thousand dimensions, and similarity is measured with cosine similarity, dot product or Euclidean distance. Inside language models, each token is also mapped to an embedding before processing.
Embeddings power semantic search over documents, retrieval-augmented generation, deduplication, clustering of tickets or alarms, recommendation and anomaly detection. Multimodal embeddings place images and text in the same space, enabling searches such as finding inspection images that match a written defect description.
Embeddings from different models are not compatible, so changing the model requires re-embedding the corpus. Domain-specific vocabulary may be poorly represented by general models, which fine-tuned or domain-specific models can address. Quality is best measured with test queries from the actual application. Research has shown that embeddings can reveal information about the source text, so they need the same protection as the data itself.
Key points
- Vectors that place semantically similar items close together
- Similarity is measured with cosine similarity, dot product or distance
- The foundation of semantic search and retrieval-augmented generation
- Changing the embedding model requires re-embedding the data
Where AiVibe comes in
AiVibe delivers AI and machine learning services, chatbots and virtual assistants with RAG, MCP tools and voice, AI quality management including bias detection and model validation, and the AIMURUGA AI agent, and builds Intel-based edge AI devices using the Intel Distribution of OpenVINO toolkit.
Related terms
- Vector DatabaseAI & Machine Learning
- Retrieval-Augmented Generation (RAG)AI & Machine Learning
- Tokens and Tokenisation (LLMs)AI & Machine Learning
- Large Language Model (LLM)AI & Machine Learning
- Anomaly DetectionAI & Machine Learning