Which data structure is MOST suitable for storing embeddings generated from enterprise documents?

Embeddings are high-dimensional numerical vectors that represent semantic meaning. A vector index is a specialized data structure designed for efficiently storing and searching these vectors based on similarity (e.g., cosine similarity or Euclidean distance). This is the foundational technology behind vector databases, which are essential for applications like Retrieval-Augmented Generation (RAG) that need to find relevant documents quickly from a large corpus. The index allows for fast nearest-neighbor searches, which is how relevant context is retrieved.

Raster image: This is a data structure for storing a grid of pixels and is completely unsuitable for numerical vectors.

Spreadsheet formula bar: This is a user interface component for entering formulas, not a data storage structure.

Compressed video file: This is a format for storing sequential image frames and audio, not for indexing and searching individual vectors.

1. NVIDIA. (2024). What Is Retrieval-Augmented Generation?. NVIDIA Blogs. Retrieved from https://blogs.nvidia.com/blog/what-is-retrieval-augmented-generation/. (The article explains that RAG involves "indexing a knowledge base of documents... into a vector database" for retrieval.)

2. Jia, Z., et al. (2024). NVIDIA RAFT: A Library for Vector Search. NVIDIA On-Demand. GTC Session S62472. (This session details the RAFT library for GPU-accelerated vector search algorithms, which are used to build and query vector indexes.)

3. Stanford University. (Winter 2021). CS224N: NLP with Deep Learning, Lecture 11: Practical Tips for Projects, and Information Retrieval. (This lecture covers vector representations and the use of Approximate Nearest Neighbor (ANN) indexes for efficient retrieval in information retrieval systems.)