Beyond RAG: Why You Need a Vault, Not Just a Vector DB
Vector similarity is fuzzy. Truth is precise. The case for graph-based memory.
Retrieval-Augmented Generation (RAG) is the current gold standard for giving AI memory. You chunk your documents, embed them into vectors, and perform a cosine similarity search.
It works for broad queries. It fails for precise knowledge.
The "Vector Soup" Problem
If you ask a RAG system "Who is the CEO?", it might retrieve a document from 2020 and a document from 2025. It sees them as semantically similar ("documents about CEOs"). It doesn't inherently understand time or currency.
Vectors are probabilistic. They tell you what things "feel" alike. They don't tell you what things are.
Enter the Knowledge Graph (The Vault)
Active Mirror uses a hybrid memory architecture we call "The Vault." It combines:
- Vector Store: For fuzzy semantic search ("Find concepts related to privacy").
- Lineage Graph: For strict relationships ("Document A supersedes Document B").
- Truth-State Ledger: For fact verification ("This entity was verified as CEO on 2025-12-01").
When you query Active Mirror, it doesn't just do a similarity search. It traverses the graph. It checks the timestamps. It verifies the cryptographic signature of the source file. This is how we move from "hallucination-resistant" to "truth-enforced."