Understanding Vector Embeddings: The Mathematical Heart of RAG Systems

Author(s): MahendraMedapati

Originally published on Towards AI.

Converting words into mathematical vectors to unlock the power of semantic search

What if I told you that the key to making AI truly understand the meaning of text lies in converting words into mathematical vectors? That every word, sentence, and document can be represented as points in a multi-dimensional space where similar meanings cluster together?

In this article, we delve into vector embeddings, the backbone of Retrieval Augmented Generation (RAG) systems, explaining how they mathematically bridge human language with machine understanding. We explore the process of creating embeddings using various models, compare OpenAI embeddings with open-source alternatives, and discuss the importance of techniques like batch processing and text preprocessing in optimizing performance. Additionally, the article highlights biases inherent in embeddings, emphasizing the need for careful model selection and the ethical implications of these technologies. Ultimately, these concepts are pivotal for deploying AI systems that require accurate semantic understanding.

Read the full blog for free on Medium.

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Published via Towards AI