Vector Embedding

Vector Embedding is the process of translating raw data—text, images, audio—into fixed-length numerical vectors where geometric distance encodes semantic similarity. A neural model such as Sentence-BERT, CLIP, or OpenAI’s text-embedding-3-large consumes the input and outputs a high-dimensional array (e.g., 768 floats). Cosine or dot-product scores between vectors let systems perform fast semantic search, clustering, or Retrieval-Augmented Generation (RAG) without keyword overlap. Embeddings are stored in vector databases like Chroma, Pinecone, or FAISS and queried at millisecond latency. Fine-tuning on domain data sharpens nuance, while dimensionality reduction (PCA, UMAP) aids visualization. Quality is judged by information-retrieval metrics such as recall@k and mean-average-precision. By turning human language and perception into machine-friendly math, Vector Embedding underpins recommendation engines, anomaly detection, and every modern LLM pipeline.