# Getting Started

# Getting Started

Curiosity Workspace is where you bring your data, model it as a graph, make it searchable, and build AI-assisted experiences on top.

This section is intentionally practical: by the end, you should be able to:

• Install a Workspace and access the Admin/Management UI
• Configure a workspace (languages, tokens, basic settings)
• Ingest data (via a connector or integration)
• Validate data in the graph and search layers

# Mental model: what a “workspace” contains

At a high level, a Curiosity Workspace environment includes:

• Graph storage: your node/edge schemas and the resulting knowledge graph
• Search indexes: text indexes and vector (embedding) indexes over selected fields
• NLP/AI configuration: pipelines/models used to parse text and enable AI features
• Extensibility: custom endpoints and custom interfaces (apps) that run against your workspace
• Administration: identity, permissions, security settings, and observability hooks

# Developer vs Admin paths

• Developers typically start with:

  • Quickstart
  • Data Integration → Connectors
  • APIs & Extensibility → API Overview

• Admins typically start with:

  • Installation
  • Workspace Configuration
  • Administration → Security

# Conventions used in this documentation

• Node / edge schemas: define the types of graph objects you store (e.g., Device, Part, SupportCase) and how they relate.
• Properties: fields stored on nodes (and possibly edges) used for filtering, search, and display.
• Indexes: search structures built on top of node fields (text and/or embeddings).
• Pipelines: NLP processing configurations that transform text into structured signals (entities, links, etc.).

# Next steps

• Install Curiosity Workspace: Installation
• Or jump straight into an end-to-end walk-through: Quickstart