How Do AI Agents Actually “Understand”? Discover Embeddings and Vector Search
Blog post from Epsilla
Embeddings and vector search are transformative technologies that enable AI agents to understand data in a human-like manner by translating it into numerical representations, or vectors, which machines can process. These vectors reside in a high-dimensional space where proximity indicates semantic similarity, allowing AI to measure the relatedness of data by calculating distances between vectors. Vector search further enhances this capability by finding vectors close to a query vector, thereby retrieving semantically similar results, regardless of exact word matches. This symbiotic relationship between embeddings and vector search enables AI systems to grasp context and nuance, improve information retrieval, handle multimodal data, and enhance search engines, recommendations, and language processing. These technologies pave the way for advanced concepts like Retrieval Augmented Generation (RAG), which integrates external knowledge into AI responses, offering a more comprehensive and up-to-date understanding.
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