[Tech Series] kt cloud AI Retrieval-Augmented Generation (RAG) #4: Embedding and Vector Indexing Technology

The completeness of a RAG system depends on the balance between embedding, which converts text into sophisticated numerical values, and indexing, which quickly finds them within large-scale data. Beyond simply using a high-performance model, it is necessary to optimize the trade-off between the model's representational power and the infrastructure's search efficiency.

Embedding is the process of mapping text to a point in a high-dimensional vector space. It is designed so that data with similar meanings are positioned close together in that space, and similarity is measured mainly through cosine similarity, inner product, and Euclidean distance.

To efficiently process high-dimensional data, the Manifold hypothesis is utilized. This is the theory that high-dimensional data is actually densely concentrated on a much lower-dimensional smooth surface, which enables dimensionality reduction and efficient search.

For successful RAG implementation, an engineering approach that reduces memory costs and increases search speed is essential, utilizing Chunking strategies together with the latest indexing optimization techniques such as HNSW, DiskANN, GPU acceleration, and Quantization.