A company is building a chatbot that uses private documents. They need to convert those documents into vector representations before storing them. Which type of foundation model should they use?

A text embeddings model is the correct choice because its primary function is to convert text, such as private documents, into numerical vector representations (embeddings). These embeddings capture the semantic meaning of the text, allowing for efficient storage, retrieval, and comparison of documents based on their content. This is crucial for tasks like building a chatbot that needs to understand and respond based on document content. Text completion models are designed to predict the next word or phrase in a sequence. Instruction following models are trained to execute commands or answer questions based on given instructions. Image generation models create images from text descriptions. None of these models are designed for the core task of converting text into vector representations for semantic search and retrieval.