Morphos AI Glossary
Definitions of the core concepts behind Green Vectors and the Morphos AI semantic infrastructure stack.
Green Vectors
Patent-pending semantic transformation that eliminates redundant vector storage at ingestion.
Semantic Faceting
The first stage of Green Vectors: identifies meaning-bearing concepts and groups related semantic signal into facets.
Continuous Vectorization
Stores and updates semantic meaning in real time, incorporating new content incrementally without batch reindexing.
Megachunking
Represents documents as a hierarchy of semantically coherent chunks so retrieval picks the right level of context per query.
Auto Weighting
Scores how strongly new content should update the existing semantic representation at the moment of ingestion.
Vector Reduction
Reduces the number of vectors in a database by eliminating semantic redundancy, not precision.
Vector Redundancy
Accumulation of near-duplicate vectors that drives up storage cost and adds search noise.
Semantic Redundancy Elimination
Removes semantically duplicate vectors while preserving underlying meaning.
Ingestion-Time Optimization
Improves vector search at the point data enters the system, rather than at query time.
See Green Vectors in action
Kitana is in closed beta. Drop Green Vectors into your existing vector database and benchmark against your own workload.
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