RoseglassDB is an experimental datastore framework designed to unify heterogeneous modes of knowledge storage, retrieval, and transformation.
It combines vector embedding retrieval with structured, immutable records, enabling both semantic approximation and explicit symbolic reference within a single substrate.
Contemporary language model systems require more than ephemeral context windows or opaque black-box memory modules.
RoseglassDB addresses this by providing:
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Immutable Knowledge Ledger
All stored entries (textual, numeric, audiovisual, or hybrid) are versioned and preserved as canonical artifacts, ensuring persistence and reproducibility. -
Dual Retrieval Paradigm
- Semantic similarity search via vector embeddings
- Deterministic lookup via structured relational schemas
This duality enables models to reason both approximately and exactly over the same corpus.
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Composable Transformations
Data is not only stored but also parameterized by transformations (e.g., color/contrast sequences, vector operators, narrative annotations).
This design allows complex workflows — analogous to node-based composition systems — to be represented and re-applied reproducibly. -
Agent-Mediated Interaction
LLMs and other autonomous systems can both read from and write to RoseglassDB, treating it as a shared substrate for communication, coordination, and memory consolidation.
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Long-Term Memory for Agents
Persistent, queryable store for agent state, narrative context, and evolving biography. -
Cultural and Personal Archives
Integration of heterogeneous data (inventory, images, diaries, annotations) within a rigorously queryable system. -
Experimental Datastore Design
Exploration of architectures that merge approximate semantic retrieval with strict relational structure.
RoseglassDB is under early development. Current prototypes emphasize schema design, integration with vector search backends (e.g., pgvector), and the formalization of a compositional transformation layer.
Future work will extend to agent-facing APIs, visualization interfaces, and distributed deployments.
Released under the MIT License.
Attribution is required in all redistributions and derivative works.