Show HN: Nexa-gauge – Cache/cost-aware graph-based eval for LLM and RAG (github.com)

Nexa-gauge has been unveiled as a powerful new Python package and command-line toolkit for the AI/ML community, aimed at enhancing the evaluation of outputs generated by Large Language Models (LLMs) and Retrieval-Augmented Generations (RAG). This tool provides a structured, cache-aware evaluation process that not only streamlines metrics collection but also offers accurate cost estimates for running evaluations. By replacing labor-intensive manual evaluation with a graph-based approach, nexa-gauge ensures a more reliable and repeatable assessment of output quality across various applications, including prompt iterations, benchmarking, and risk assessments. The significance of nexa-gauge lies in its comprehensive evaluation features that include relevance scoring, grounding checks, safety assessments via red team scoring, and benchmark comparisons against reference metrics. Its design allows users to estimate costs prior to execution and utilize caching to minimize redundant computations, thereby optimizing resource allocation. The tool also emits structured JSON reports, making it a suitable fit for production environments and continuous integration systems. With these capabilities, nexa-gauge positions itself as a critical asset for teams striving to maintain high standards of output quality and safety in increasingly complex AI workflows.