# Technical Support Use-Case

# Technical Support Use-Case

This tutorial is a product-first, end-to-end workflow for building a Curiosity Workspace application. It track uses the technical-support repository as a concrete example of how to build an application using Curiosity Workspace. It is not required for learning Curiosity Workspace, but it is useful for teams that want a shared sandbox with realistic entities, relationships, and unstructured text.

# What you’ll learn (by example)

By the end, you will have:

• a workspace with a well-defined graph schema
• a connector that maps JSON records into nodes/edges
• search configured (text + optional embeddings)
• one or more custom endpoints implementing domain logic (similarity, search wrappers, chat orchestration)
• a custom interface tailored to your users

# Step 1: Define the domain model

• Identify 3–7 core entity types (nodes).
• Identify the relationships users navigate (edges).
• Choose stable keys for each node type.

See Data Integration → Schema Design.

# Step 2: Implement ingestion

• Choose ingestion approach (connector vs configured integration).
• Build the mapping into nodes/edges.
• Ensure idempotency (reruns do not duplicate).

See Data Integration → Connectors and Data Integration → Ingestion Pipelines.

# Step 3: Validate graph correctness

Validate:

• node counts by type
• edge completeness
• correct keys (no duplicates)

See Reference → Graph Query Language.

# Step 4: Configure text search and facets

• Choose searchable fields (titles, summaries, identifiers).
• Configure facets that match user refinement behavior.
• Tune ranking via boosts and scoping.

See Search → Text Search and Search → Ranking Tuning.

# Step 5: Add embeddings and hybrid retrieval (optional)

• Enable embeddings on long, descriptive fields.
• Enable chunking for long text.
• Use hybrid search for balanced precision/recall.

See Search → Vector Search and Search → Hybrid Search.

# Step 6: Add endpoints for domain logic

Create endpoints for:

• aggregates and analytics
• similarity and recommendations
• AI orchestration (retrieve → generate → store)

See APIs & Extensibility → Custom Endpoints.

# Step 7: Build a user experience (optional)

Start with the default UI, then add a custom interface if you need domain-specific workflows:

• entity-centric pages (hub entities)
• curated search experiences (type-scoped + facets)
• workflow views (triage, investigation, review)

# Next steps

• Operationalize the environment: Administration → Deployment and Monitoring
• Apply design patterns: Best Practices → Graph Design Patterns