Why vector-only embeddings fail at enterprise deterministic truth, and how KùzuDB property graphs paired with SQLite FTS5 achieve sub-2.4ms verifiable recall.
Standard Retrieval-Augmented Generation (RAG) relies on cosine similarity over floating-point vector embeddings. While effective for semantic gist, vector distance frequently hallucinates when faced with exact corporate identifiers, clause cross-references, contract versioning, or multi-step entity relationships.
If a corporate lawyer searches for Termination provisions in Master Services Agreement Amendment 4, vector similarity often returns Amendment 1 or 2 because their vocabulary is nearly identical. Deterministic truth requires exact structural awareness.
Compas Cortex implements a dual-engine architecture: an in-process columnar graph database (KùzuDB) for relationship traversal and an optimized SQLite FTS5 engine for exact lexical match with BM25 ranking. Queries execute in sub-2.4ms at 99th percentile across 100,000+ documents.
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