We apply latent space cartography — the systematic mapping of structure in pre-trained embedding spaces (Liu et al., 2019) — to three general-purpose text embedding models using Wikidata knowledge graph triples as probes.
Current Retrieval-Augmented Generation (RAG) systems face a fundamental completeness-precision dilemma: vector-based approaches optimize for precise needle-in-haystack retrieval but sacrifice comprehensive context through isolated chunk retrieval, while knowledge graph systems aim for completeness but suffer from query specificity challenges and complex traversal overhead. We present **Topological RAG**, a graph-based architecture that reconstructs semantic "small worlds" through weighted multi-hop traversal, prioritizing comprehensive corpus coverage over retrieval speed.