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Architecture
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Architecture
Curiosity Workspace is a single product that brings together three layers that are often separate in modern data stacks:
- Graph layer: stores a knowledge graph (nodes + edges) with schemas, properties, and traversals.
- Search layer: provides text retrieval, ranking, filtering, and query-time constraints.
- AI layer: embeddings, NLP extraction, and LLM-driven features that use graph + search as grounding.
The platform is designed so these layers reinforce each other:
- Graph relationships improve navigation, filtering, and context building
- Search provides fast retrieval and ranking at scale
- AI adds semantic recall (embeddings) and reasoning/synthesis (LLMs) where appropriate
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Core building blocks
- Workspace
- an environment that contains data, configuration, and extensibility artifacts
- Schemas
- node schemas and edge schemas define the types and constraints of the graph
- Indexes
- text indexes and embedding indexes over selected fields
- Pipelines
- NLP processing that turns text into structured signals and graph links
- Extensibility
- custom endpoints (server-side logic)
- custom interfaces (front-end apps)
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Typical request flows
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Search and discovery flow
- A user searches for a term (keywords and/or semantic query).
- The search engine retrieves candidates (text and/or vector).
- The system applies filters (properties and/or graph-related facets).
- Results are ranked and returned; graph neighbors can be fetched for previews and navigation.
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AI-assisted flow (retrieval + reasoning)
- A user asks a question or starts a workflow.
- The system retrieves grounding context from search/graph.
- The LLM generates a response using that context.
- Optional: the result is saved back into the workspace (notes, links, tags) via endpoints/tasks.
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Design goals
- Schema-first clarity: you control what types exist and how they relate.
- Configurable retrieval: tune relevance without rewriting your app.
- Safe extensibility: move business logic into versionable endpoints and controlled interfaces.
- Operational control: deployments can be monitored, secured, and promoted across environments.
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Next steps
- Understand how data moves through the system: Data Flow
- Learn the foundational data structure: Graph Model