Text similarity search via normalized compression distance (discourse.haskell.org)

A new library for text similarity search based on normalized compression distance (NCD) has been developed, leveraging insights from an older clustering algorithm rediscovered within the NLP community. This approach signifies a shift away from traditional neural network-based embedding methods, as it allows for efficient similarity comparisons without extensive model training. While various existing implementations utilize slow all-to-all distance calculations, this new library employs tree indexing, which optimizes performance by significantly reducing computational overhead. The development process revealed challenges in integrating large language models (LLMs) like Gemini into the coding workflow. Early attempts at generating an implementation faced issues such as type inconsistencies and incorrect function signatures. However, by incorporating property tests using the QuickCheck suite, the project improved, demonstrating the importance of grounding LLM outputs with rigorous testing protocols. These findings highlight ongoing challenges in AI model understanding of programming languages, particularly in capturing nuances such as type behavior in Haskell. Overall, this work not only advocates for innovative approaches to text comparison but also emphasizes the necessity for robust validation methods when leveraging AI in software development.