Embedding

In Retrieval-Augmented Generation (RAG), embeddings are essential for linking information retrieval with natural language generation. Embeddings represent both the user query and documents as dense vectors in a shared space, enabling the system to retrieve relevant information based on similarity. This retrieved information is then fed into a generative model, such as GPT, to produce contextually informed and accurate responses. By using embeddings, RAG enhances the model's ability to generate content grounded in external knowledge, making it effective for tasks like question answering and summarization.

Visit the following resources to learn more:

• @article@Understanding the role of embeddings in RAG LLMs
• @article@Mastering RAG: How to Select an Embedding Model