Language Intelligence
Language systems should grow with knowledge, not duplication.
Morphos reduces repeated representations of the same meaning before storage, so large, changing collections can remain compact and current as new information arrives.
Bring a dataset, retrieval system, or information problem worth testing.
The language burden
Every document leaves another footprint.
A policy appears in an onboarding guide, a support answer, a quarterly report, and several translations. A specification reappears in requirements, test plans, release notes, and later revisions. The wording changes. Much of the meaning does not.
To retrieve information by meaning, AI systems use embedding models to convert each piece of text into a numerical representation called a vector. Similar vectors help the system find related passages even when they do not use the same words.
Most systems store a new vector for every passage, version, and update. Across a large organization, the index grows with the amount of text produced, including the repetition inside it. More vectors require more storage, more search work, and more effort to keep changing source material synchronized with the index.
The collection grows. The amount of distinct meaning often grows much more slowly.
What this enables
Fit more distinct knowledge into the same system.
When repeated meaning stops consuming another representation every time it appears, more of the knowledge layer can be devoted to information that is actually new.
- 01
More useful knowledge within fixed storage
Use available capacity for distinct information instead of repeated versions of substantially the same meaning.
Distinct meaning - 02
Slower index growth from repetition
Keep documents, revisions, and translations from expanding the searchable layer in direct proportion to the amount of text produced.
Repetition absorbed - 03
A knowledge layer that stays current
Update what is already represented as related information changes, rather than treating every revision as another independent entry by default.
Continuous update - 04
Existing infrastructure stays in place
Use Green Vectors alongside the embedding model, vector database, and retrieval framework already selected.
No migration required
The index should grow with new meaning, not every new sentence.
Scope: the exact result depends on the collection, the amount of meaningful overlap, how the information changes, and the retrieval quality the application must preserve.
Before the database
Change what enters the index.
Embedding models convert source material into vectors. Green Vectors receives those vectors before storage, reduces redundant representations, and sends the resulting searchable layer to the existing vector database.
Compression and quantization make each vector smaller. Green Vectors addresses how many representations the system needs to store and search. The approaches can be used together.
Green Vectors performs its consolidation and update work during ingestion. It does not require an additional generative-model call to perform that work when a query arrives.
Green Vectors changes the searchable representation, not the original source material.
Read the researchConventional flow
Green Vectors flow
Project Gutenberg benchmark
260GB to 1.3GB on Project Gutenberg.
The benchmark used approximately 50,000 public-domain books from Project Gutenberg. The conventional vectorized index occupied 260GB. The Green Vectors configuration for the same source material occupied 1.3GB.
Stored vector-index size
- Source material
- Approximately 50,000 books
- Traditional vector count
- More than 15 million
- Green Vectors vector count
- Approximately 76,000
- Measured outcome
- Stored vector count and index size
- Language
- English
- Date
- June 2025
This result comes from one public literary collection under one configuration. Results on other collections or on sensor, perception, autonomous-system, and constrained-hardware workloads require separate tests.
How the reduction is possible
How Green Vectors builds the smaller searchable layer.
Reducing vector count is only useful if the system can still find the information a question needs. Green Vectors addresses that tradeoff through four connected actions.
- Action 1
Organize information by what it means.
New information is evaluated against the ideas already represented in the system. Content that belongs to an existing topic can contribute to that topic instead of automatically becoming another isolated vector.
Exact deduplication can remove identical copies. Green Vectors addresses the harder case: passages that use different words to express substantially the same idea.
- Action 2
Decide how much the new information should matter.
Not every mention deserves equal influence. A section devoted to remote-work policy should affect that topic more than a passing reference in an office-supplies document.
Green Vectors evaluates the relevance of incoming information before it updates the stored representation. This helps important content shape the topic while reducing the effect of generic, weakly related, or repeated language.
- Action 3
Preserve context at more than one level.
Large documents contain meaning at the document, section, and paragraph levels. Breaking everything into fixed, disconnected pieces can lose the relationships between them. Treating the whole document as one block can return too much unrelated material.
Green Vectors preserves those relationships so the system can work with a focused passage, its surrounding section, or the larger document when the question requires it.
- Action 4
Keep the representation current.
When related information arrives or an existing source changes, Green Vectors can update the relevant compact representation. The knowledge layer can evolve without appending another independent vector for every repeated or revised passage by default.
Green Vectors changes the searchable representation, not the source documents.
The actions depend on one another: relevance control keeps organized meaning from blurring a topic, document structure preserves context, and continuous updates keep the index from becoming stale. Green Vectors combines all four before storage.
Scoped beta
Use Green Vectors with the systems already in place.
Kitana is the Morphos SDK and API for integrating Green Vectors into language, search, and retrieval systems.
Kitana fits between the embedding model and storage, alongside the vector database and retrieval framework already in place.
A controlled evaluation can test whether this representation approach improves results on the workload. A database migration is a separate decision.
Kitana is in scoped beta and is not generally available.
Ask about Kitana access or a controlled evaluation.
Evaluation
Compare both approaches on the same work.
The result depends on the source material, how much meaningful overlap it contains, how it changes, and what successful retrieval means for the application. Green Vectors does not reduce the initial cost of generating embeddings, and it will not produce the same reduction on every collection. A useful evaluation holds the source material, queries, conditions, and success criteria constant.
Language results do not establish performance on perception, sensor, telemetry, or autonomous-system workloads. Those applications require separate tests on their own data and hardware.
Select
Choose a representative subset of source material and a query set drawn from real use.
JointEstablish
Measure the current system for storage, retrieval quality, latency, and update behavior.
PartnerRun
Apply a Green Vectors configuration to the same source material and evaluate it with the same queries and conditions.
MorphosCompare
Review the result against the success criteria agreed at the start.
JointBring the collection that has become difficult to store, search, or keep current.
We can help determine whether enough redundant information exists to justify a controlled evaluation.
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