# FAQ

# What is Curiosity Workspace?

Curiosity Workspace is a product that combines a knowledge graph, a search engine, and AI capabilities (NLP, embeddings, LLM-driven workflows) into a single environment for building data-driven applications.

# Is Curiosity Workspace a graph database or a search engine?

Both—plus AI. Curiosity Workspace includes a graph layer (nodes/edges), a search layer (text + vector retrieval), and AI features that integrate with both.

# How do I ingest data?

Most teams start with connectors:

map source records into node/edge schemas
commit nodes and edges
re-run safely using stable keys

See Data Integration → Connectors.

# How do I make data searchable?

Search is explicit: you choose which types and fields to index for text search and which to index for embeddings.

See:

# What’s the difference between vector search and hybrid search?

Vector search uses embeddings to retrieve by meaning.
Hybrid search combines keyword and vector retrieval to improve overall relevance.

See Hybrid Search.

# How do I add custom business logic?

Use custom endpoints to implement business logic close to the data (graph + search), then call those endpoints from UIs and integrations.

See Custom Endpoints.

# How should I think about permissions?

Permissions should be designed early in production projects. Search and AI workflows must be permission-aware so users only retrieve authorized content.

See Administration → Permissions.