Embedding Model converts texts, words, images into numerical form known as vectors, Vectors are used for Context and Relationships between texts, words, they are stored in Vector Database. RAG systems rely on Vector Databases to retrieve and store data before generating responses. This allows the RAG system to easily access relevant content and identify semantic connections by converting queries and documents into vectors.
How RAG is powered by Embeddings
When a user submits a query to a RAG system, the System uses embedding models to convert user query into vectors. Then it uses those Vectors to retrieve from a Vector database for contextually relevant information using similarity search, semantic search. This collected data is transformed by a generator/ transformer to produce accurate, contextual responses.
- Word Embeddings - Word embeddings convert individual words into fixed-size numerical vectors, capturing semantic relationships between words based on their usage in large text corpora.
- Sentence Embeddings - Sentence embeddings represent entire sentences as fixed-size vectors, capturing semantic meaning beyond individual words. Unlike word embeddings (e.g., Word2Vec), which generate separate vectors for words, sentence embeddings account for context and meaning across a full sentence.
- Transformer-based Embeddings - This leverage deep learning models like BERT, GPT, and OpenAI’s Ada to create contextualized representations of words, phrases, and entire documents. These embeddings dynamically adjust based on context, offering deeper semantic understanding than traditional methods.
- Multimodal Embeddings - Multimodal embeddings integrate multiple types of data—such as text, images, and audio—into a shared vector space, allowing models to understand cross-modal relationships. These embeddings enable AI to process and relate different forms of data.
Conclusion
Embeddings are the Backbones of RAG systems because they enable efficient retrieval and contextual understanding of data for LLMs. By converting text, images, and other inputs into numerical vectors, embeddings allow AI models to find meaningful connections and generate more accurate responses. As RAG systems advance, embeddings will remain at the core, bridging the gap between raw data and meaningful data.
