Kura v0.5.0 Released

We're excited to announce the release of Kura v0.5.0! This release brings significant improvements to documentation, introduces a new procedural API for maximum flexibility, and includes numerous enhancements to make Kura even better for analyzing conversation data.

What's New in v0.5.0

New Procedural API (v1)

The headline feature of this release is the introduction of a functional, procedural API that gives you fine-grained control over the analysis pipeline:

from kura import (
    summarise_conversations,
    generate_base_clusters_from_conversation_summaries,
    reduce_clusters_from_base_clusters,
    reduce_dimensionality_from_clusters,
)

# Run each step independently
summaries = await summarise_conversations(conversations, model=summary_model)
clusters = await generate_base_clusters_from_conversation_summaries(summaries, model=cluster_model)
meta_clusters = await reduce_clusters_from_base_clusters(clusters, model=meta_cluster_model)
projected = await reduce_dimensionality_from_clusters(meta_clusters, model=dim_reduction_model)

This new API offers: - Complete control over each pipeline step - Easy integration with heterogeneous models (OpenAI, vLLM, Hugging Face) - Functional programming style with no hidden state - Keyword-only arguments for clarity

New Documentation Release

We're excited to announce a comprehensive overhaul of the Kura documentation! The new documentation is designed to help users get started quickly and make the most of Kura's powerful features for analyzing conversation data.

What's New

Our documentation has been completely reorganized and expanded to provide a better experience:

• Clear Structure: New organization with dedicated sections for Getting Started, Core Concepts, and API Reference
• Comprehensive Installation Guide: Detailed instructions for different installation methods, including both uv and pip
• In-depth Tutorials: Step-by-step guides in our Getting Started section
• Enriched API Reference: Better organized and more detailed API documentation
• Core Concepts Explained: Detailed explanations of Kura's architecture and components
• Improved Code Examples: Concise, practical examples throughout

Evaluating Kura's Clustering ability using Synthetic Analysis

Over the weekend, I spent some time to evaluate Kura's clustering ability using synthetic data. When tested against synthetically generated technical conversations, Kura is able to identify base clusters that align with our original category distribution with over 95% accuracy and also discover more nuanced groupings that align with real-world technical divisions and use cases.

In this article, we'll walk through the process of how we generated a diverse dataset of ~190 user conversations and then evaluated Kura's clustering ability against this dataset. These findings demonstrate that language model-assisted clustering can identify natural conversation patterns while validating synthetic data generation approaches.

Generating Synthetic Data

I carefully constructed a dataset of 190 user conversations by using a multi-step process. You can access the dataset of these conversations on hugging face here. To do so, we introduced controlle variation at each level through a systematic approach that involved 3 steps.