Embedding

Embedding converts complex data (text, images, URLs) into numerical vectors, capturing essential meanings for efficient processing, comparison, and retrieval by ML models.

Embedding refers to transforming data into a numerical format (typically a vector) that captures its semantic meaning in a way that can be understood and processed by machine learning models or databases. In simpler terms, embedding is a way of representing complex data, such as words, images, or URLs, in a structured numerical form.

Context and Usage

Embeddings are numerical representations of data widely used in data processing and machine learning, particularly in natural language processing (NLP) for tasks like sentiment analysis, translation, and information retrieval. In ChromaDB, embedding converts browser history entries (e.g., URLs and metadata) into vectors stored in a database, enabling efficient, sophisticated searches and comparisons based on these numerical representations.