Technical Support

This demo showcases how to develop and deploy a Curiosity Workspace instance using a realistic technical support dataset.

The example implementation demonstrates Curiosity's knowledge graph capabilities, natural language processing features, and AI-powered search functionality.

https://github.com/curiosity-ai/technical-support

What's Included

The demo provides three interconnected datasets:

• Devices - A catalog of product names

• Parts - Components and their relationships to products, including manufacturer information

• Support Cases - AI-generated customer support scenarios with summaries, conversations, device associations, and resolution tracking

All data is fictional but structured to reflect real-world technical support operations.

Learning Path

1

Setting up your Curiosity Workspace environment

Guide to provision and configure a Curiosity Workspace for the demo datasets.

2

Writing data connectors to ingest the sample datasets

Instructions for creating connectors to import Devices, Parts, and Support Cases into the workspace.

3

Configuring natural language processing pipelines

Steps to configure NLP pipelines for extracting entities, intents, and relationships from support conversations.

4

Making data searchable with filters and queries

How to index data and expose search functionality with filters and advanced queries.

5

Creating custom API endpoints

Examples of custom endpoints to surface graph queries and search results.

6

Building custom user interfaces

Guidance on creating UIs that integrate search, conversation playback, and knowledge graph visualization.

Use Cases

This demo is designed for:

• Learning Curiosity Workspace fundamentals

• Experimenting with knowledge graph modeling

• Testing natural language processing configurations

• Prototyping search and discovery interfaces

• Understanding data relationships in support systems

This is a sample dataset for educational and demonstration purposes.