Continuous-Eval: A Comprehensive Tool for Evaluating LLM-Powered Applications
Overview
Continuous-eval is an open-source package designed to evaluate applications powered by Large Language Models (LLMs) using data-driven approaches. It's crafted to enable developers to assess the performance of their LLM applications by measuring each part of their pipeline with tailored metrics.
Unique Features of Continuous-Eval
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Modularized Evaluation: Continuous-eval allows users to measure each component of their application pipeline individually with specific metrics, ensuring a detailed evaluation process.
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Comprehensive Metric Library: The package includes a wide array of metrics for different LLM use cases, such as Retrieval-Augmented Generation (RAG), Code Generation, and others. Users can mix and match between deterministic, semantic, and LLM-based metrics to suit their needs.
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Incorporating User Feedback: The tool integrates user feedback into the evaluation processes, providing a more human-like assessment with mathematical backing.
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Synthetic Dataset Generation: Users can create large-scale synthetic datasets to rigorously test their pipelines before deploying them.
Getting Started
The continuous-eval package is available on PyPi, making it easy to install through a simple command. For those interested in exploring the source code, it can be cloned from the GitHub repository and installed using Poetry with all extras included.
To run metrics that rely on LLM, users must configure the package with an API key from supported LLM services.
Running a Single Metric
Continuous-eval allows developers to run a metric on individual data points with ease. For example, to evaluate retrieval precision with respect to retrieving correct information, users can apply metrics like PrecisionRecallF1.
Available Metrics
The package offers a robust selection of metrics divided into categories such as Retrieval, Text Generation, Classification, Code Generation, and Agent Tools. These metrics cover a range of methods and aspects to thoroughly evaluate various model facets.
Pipeline Evaluation
Developers can define multiple modules within their application pipeline, each susceptible to different types of evaluations. This modular approach enables a granular analysis of each component, such as a Retriever or Generator, ensuring a thorough assessment of the application’s performance.
Synthetic Data Generation
Creating a "golden" dataset manually can be cost-prohibitive. Continuous-eval addresses this by offering tools to generate synthetic data, helping developers create comprehensive datasets for testing purposes swiftly.
Contributing to Continuous-Eval
Those interested in contributing to the continuous-eval project can refer to the available contribution guide to get started.
Resources and Support
Continuous-eval is equipped with extensive documentation, resources including example repositories, and various blog posts that guide users through common use cases and advanced features. Additionally, a Discord community is available for users to engage with other LLM developers.
License and Analytics
The project is licensed under Apache 2.0. Anonymous usage statistics are collected to help the developers understand user patterns and improve features. Users have the option to disable this tracking if they prefer.
Continuous-eval is a versatile tool that empowers developers to meticulously evaluate and refine their LLM-powered applications, ultimately enhancing overall application performance and reliability.