ragpill 0.2.1

This library allows for granular testing of llm-applications based on expert input.

Stop believing your chatbot. Take the ragpill.

---

ragpill is an evaluation framework for LLM agents and RAG pipelines. Define facts, sources, and tool call expectations — and find out what your AI actually does.

What is RAGPill?

RAGPill helps you:

• Create test datasets from CSV files - Easy collaboration with domain experts
• Define custom evaluators - Add domain-specific knowledge to evaluations
• Track results in MLflow - Full experiment tracking and tracing
• Follow best practices - Opinionated design guides you to robust testing

It specializes in "offline" evaluation of LLM-based systems, meaning it's supposed to be part of your CI/CD pipeline or run as scheduled tests, not real-time monitoring.

Core Philosophy

Here we focus a lot on the LLM Judge evaluator, although it's the last evaluator you should use - prefer deterministic evaluators (regex, exact match) whenever possible. However, for deterministic tests, there's already a lot of tooling available, like pytest for example (yes, we like the 'code-first' approach).

Expert-Defined Attributes

LLM judges usually lack context awareness to judge which discrepancies between chatbot answers and expected answers are relevant - especially in specialized fields like law, engineering, and science where words have precise definitions.

Domain experts should define specific attributes and criteria for evaluation.

Binary Evaluations

We use boolean pass/fail values only, not scoring scales (1-10), because:

• Scales are arbitrary and often decided by LLMs
• Binary decisions are more stable and reproducible (although LLMs of course remain probabilistic)
• Easier to track and reason about over time

Tags and Attributes for Organization

Evaluators can have:

• Tags: Categorical labels for filtering (e.g., retrieval, time-aware-rag, basic_logic)
• Attributes: Key-value metadata for categorization (e.g., importance: high, scope: Phase1)

Metrics are automatically calculated per tag and attribute.

Quick Navigation

Getting Started:

• Installation
• Quick Start

Evaluators:

Key Concepts

As this library is built on pydantic-ai evals, please have a look here

Key Components

• Dataset: From pydantic-ai, contains test cases with inputs, evaluators, and metadata
• Evaluators: Check if outputs meet criteria (LLMJudge, regex matchers, custom evaluators)
• MLflow Integration: Wraps execution, traces runs, evaluates outputs, uploads results

Features

• Great MLflow Integration: Traces your agent/function execution to MLflow with evaluations in the native format
• CSV/Excel Adapter: Load test cases from CSV files with evaluator configurations
• Flexible Evaluators: Built-in LLM judges, regex matchers, and easy custom evaluator creation
• Metrics per Tags/Attributes: Automatic metric calculation for each tag and attribute combination
• Type Safety: Built on pydantic-ai with full type safety throughout

Built-in Evaluators

• LLMJudge: Uses an LLM to judge correctness based on a rubric
• RegexInSourcesEvaluator: Checks if regex patterns appear in retrieved sources
• RegexInDocumentMetadataEvaluator: Checks regex in document metadata
• Custom Evaluators: Inherit from BaseEvaluator and implement your logic

Best Practices

[!TIP] TDD Mindset — Begin with defining a Test-Set with potential users before even starting to develop the solution. This enables clear expectation management and progress tracking.

[!TIP] Create Multiple Testsets — It might make sense for you to have some core tests that run relatively quickly and inexpensive - use these for development. Before deploying to prod, you can run an exhaustive dataset that is integrated in your CI/CD.

[!TIP] Separate Evaluation Experiments — Create dedicated MLflow experiments for evaluations. Don't mix evaluation traces with production traces.

[!TIP] Use Domain Experts — Have domain experts define evaluation criteria rather than relying solely on generic LLM judges.

[!TIP] Version Your Tests — Keep test datasets in version control alongside your code.

Documentation

Full documentation is available at joelgotsch.github.io/ragpill/latest including:

• Installation Guide: Setup instructions
• Quickstart Tutorial: Run your first evaluation
• CSV Adapter Guide: Learn the CSV format and column meanings
• Evaluators Guide: Create custom evaluators
• MLflow Integration: Advanced MLflow usage
• API Reference: Complete API documentation

Roadmap

• Adapter for testset from CSV
• Documentation via mkdocs
• Evaluators for sources and regex
• Repeat Task Evaluations (run task multiple times and evaluate with threshold)
• Adapter for task from CSV (upload to mlflow)
• Create demo video
• CI/CD (tests, build package, publish docs)
• Global evaluators from CSV (empty input)
• Track git-commit hash in experiment
• Tests with mlflow server
• Dependency injection for llm, input_to_key functions
• pytest integration

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.