Flattening everything into a vector DB breaks retrieval: a typed-surfaces architecture (KB/DB/Graph/Skills/Memory)
Puzzleheaded_Box2842 · reddit · 2026-10-16
Most retrieval agents flatten all context into text chunks in a vector DB, but real data is heterogeneous: docs, SQL tables, dependency graphs, SOPs, and scoped user decisions. Flattening predictably breaks filtering/aggregation, isolates relationships, mixes procedures with facts, and desyncs representations.
The author proposes exposing typed data surfaces — KB (hybrid semantic/keyword docs), DB (filters, joins, aggregations), Graph (entities, bounded traversal), Skills (reusable procedures), Memory (scoped facts and decisions). Retrieval should pick the smallest sufficient set of surfaces per request, while a separate store agent routes inputs by structure, preserves metadata, and returns receipts with surface, revision, and write result. One source can project into several representations.
Implemented in OpenDCAI/DataMind; the author is unsure whether real demand exists for this architecture.
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