The original title is about pairing a knowledge graph with a vector DB for RAG systems, and why chunking destroys relational context.

Short summary

Enterprise RAG systems fail because chunking documents for vector databases destroys relational context—decisions sprawl across Slack, Jira, and contracts, but AI sees only isolated fragments. A dual-store architecture pairs vector DBs for semantic search with knowledge graphs for entity lineage, exposed via MCP so agents choose between fuzzy matches and precise queries. This preserves decision context, eliminates hallucinations, and reduces token costs while creating a reusable memory layer for any agent.

• •Vector DB-only RAG loses relational context; hybrid knowledge graph + vector DB preserves entity relationships and decision timelines
• •Dual-store exposed via MCP lets agents choose between semantic search and precise lineage queries
• •Reduces hallucinations, cuts token costs, and creates reusable memory layer for any AI agent

Generated with AI, which can make mistakes.