Kura is an open-source library for analyzing unstructured chat data to uncover user behavior patterns, pain points, and feature requests without compromising PII. It is designed for product teams, customer success, and AI/ML teams dealing with large volumes of conversational data, offering automated intent discovery and semantic clustering.

How It Works

Kura processes conversations through a pipeline: first, it summarizes each conversation using a language model, then clusters these summaries semantically using K-means, recursively refines these clusters into a hierarchy, and finally reduces dimensionality using HDBUMAP for visualization. This approach allows for a privacy-first analysis of user needs and issues, moving beyond keyword matching to understand the underlying meaning.

Quick Start & Requirements

• Install via pip: pip install kura
• Requires Python 3.7+
• Example uses Hugging Face datasets library and rich for terminal output.
• Official quick-start and code walkthrough available.

Highlighted Details

• Processes 10,000 conversations into ~20 clear patterns.
• Achieved 40% reduction in support volume for an e-commerce bot.
• Led to 60% reduction in queries for confusing APIs in a developer documentation assistant.
• Increased trial conversion by 18% for a SaaS onboarding bot by identifying missing integrations.

Maintenance & Community

• Under active development; contributions and suggestions welcome via issues or PRs.
• No specific community links (Discord/Slack) or notable contributors mentioned.

Licensing & Compatibility

• MIT License.
• Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

Kura is designed for batch processing and is not suitable for real-time analysis. It focuses on pattern analysis rather than conversation-level sentiment. The project is under active development, and users are encouraged to report issues.