llm-grep: Match lines using both classic and semantic regular expressions (github.com)

A new plugin called llm-grep has been introduced for matching text using both classic and semantic regular expressions, significantly enhancing text processing capabilities for the AI and ML community. This innovative tool employs a two-pass matching technique: it first identifies potential matches using traditional regular expressions and then refines this selection through a large language model (LLM) to filter based on semantic relevance. This approach not only aligns with established regex syntax but also introduces semantic tags to elevate the relevance of matches. The plug-in is designed for easy installation and integration within existing LLM environments, aiming to enhance applications ranging from data analysis to text management. Users can leverage intuitive command syntax to perform operations reminiscent of the well-known GNU grep, with added semantic flexibility. This capability allows users to match more complex concepts that go beyond mere string identification, making it a valuable addition for developers and researchers focusing on natural language processing and data understanding. While some traditional grep functionalities are not yet implemented, the current features promise to open new avenues for semantic text search and analysis.