RAG- Understanding of Embedding
The article discusses the concept of embedding in the context of RAG (Retrieval-Augmented Generation) systems. It explains how text chunks are converted into vectors for efficient semantic search, highlighting the importance of semantic similarity and cosine similarity in this process. Additionally, it outlines different retrieval methodologies and types of embedding models based on query and retrieval types.
- ▪Embedding involves converting text chunks into vectors for semantic search.
- ▪Cosine similarity is used to measure the closeness of vectors, providing a reliable method for semantic comparison.
- ▪Different embedding models are chosen based on the application scenario, including symmetric and asymmetric models.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3900955) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ramya Perumal Posted on May 17 RAG- Understanding of Embedding #rag #ai #python #beginners What is Embedding? After text is split into chunks, the next process is called embedding. In this step, each chunk is converted into vectors (points in vector space). In vector-based RAG systems, chunks are converted into vectors so that semantic search can be performed efficiently. Why Do We Need to Convert Chunks into Vectors? The main goal of a RAG application is to achieve semantic search.
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