Embedding models are a class of machine learning models used to convert discrete information into continuous vector representations. Their core goal is to map high-dimensional data such as text, images, audio, and code to a low-dimensional, dense vector space, enabling computers to measure semantic relationships between data through the distance between vectors. Unlike traditional keyword-based matching methods, embedding models can learn latent features in the data, making data with similar meanings or structural relationships closer together in the vector space.

The development of embedding technology has evolved from traditional statistical methods to deep learning representation learning. Early word representation methods mainly relied on statistical methods, such as TF-IDF based on word frequency statistics, and Latent Semantic Analysis (LSA) based on word-document co-occurrence matrices for dimensionality reduction. In 2013, Google researchers Tomas Mikolov et al. published a paper... Efficient Estimation of Word Representations in Vector Space The paper proposes the Word2Vec method, which learns distributed representations of words using two neural network structures: Skip-gram and CBOW. This allows words to be mapped to a continuous vector space and demonstrates the semantic relationships between word vectors. This work has advanced the development of neural network embedding methods in the field of natural language processing.

With the development of deep learning and large-scale pre-trained models, embedding models have gradually expanded from single-word representations to sentence, document, image, audio, and multimodal data representations. Modern embedding models typically utilize neural network architectures such as Transformer, and obtain richer semantic representation capabilities through large-scale data training. They are widely used in tasks such as semantic search, recommender systems, information retrieval, knowledge base construction, image matching, and retrieval-augmented generation (RAG).

Embedding models primarily address the challenge of traditional symbolic representation methods in expressing semantic relationships. By transforming data into a unified vector space, machine learning systems can discover connections between data based on vector similarity, thereby enhancing their ability to retrieve, classify, and understand unstructured information. Currently, embedding models have become a fundamental component of modern artificial intelligence systems, providing crucial support for large language model applications, intelligent search, and cross-modal artificial intelligence.