Voyage AI's Approach to RAG: A Novel Solution

Voyage AI's approach to RAG involves training AI models to convert text, documents, PDFs, and other forms of data into numerical representations called vector embeddings. These embeddings capture the meaning and relationships between different data points in a compact format, making them useful for search-related applications like RAG. Voyage AI uses a particular type of embedding called contextual embedding, which captures not only the semantic meaning of data but also the context in which the data appears. For example, given the word "bank" in the sentences "I sat on the bank of the river" and "I deposited money in the bank," Voyage's embedding models would generate different vectors for each instance of "bank" – reflecting the different meanings implied by the context.