Glossary of Terms

Retrieval-Augmented Generation(RAG)

RAG (Retrieval-Augmented Generation) is a language model approach that combines retrieval and generation techniques. Within this framework, a retrieval system first obtains relevant information from an external database based on a query or question. This retrieved data is then integrated with a generative model to produce more accurate and timely responses.

The key advantage of RAG lies in its ability to enhance the model's outputs by leveraging real-time external information, rather than relying solely on the model's pre-existing knowledge. This makes it particularly well-suited for rapidly changing or dynamically updated domains, improving the model's accuracy and flexibility when addressing unknown or the latest information.

Generative Language Model

A Generative Language Model is an AI model based on deep learning designed to generate coherent and logical natural language responses based on given input text. These models are typically trained on large corpora, learning the grammar, semantics, and contextual relationships within a language.

Generative language models can be applied to various tasks, such as text generation, question answering, translation, and summarization. They are also capable of creatively generating new content, mimicking human writing styles and modes of expression.

Embedding Model

An Embedding model is a technique used to transform text, words, or other types of data into vector representations. These vectors encode the semantic structure of language or other data within a high-dimensional space. Embedding models are a foundational technology in Natural Language Processing (NLP), enabling text data to be converted into numerical formats for efficient processing, computation, and analysis by machines.

The core idea of Embedding models is to convert words or sentences into fixed-length vectors (typically high-dimensional numerical vectors). This allows computational systems to interpret language data and capture semantic similarities. For instance, word Embedding models like Word2Vec and GloVe map words into a high-dimensional space where their semantic relationships are reflected in the relative distances between their corresponding vectors.

Vector Database

A Vector Database is a specialized database system designed to store and manage high-dimensional vector data. These vectors are typically generated by converting text, images, or other data into numerical representations. Vectors capture the semantic features of the data, enabling similar data points to be located closer together in the vector space.

Vector databases excel at efficiently performing similarity searches and are widely used in fields such as natural language processing, image retrieval, and recommendation systems. In these applications, data is transformed into vectors, allowing for rapid identification of the data points most similar to a given query vector, thereby enabling efficient retrieval and matching of relevant information.