multi-metric rag evaluation with llm-as-judge scoring
Evaluates RAG pipeline quality by computing multiple metrics (faithfulness, answer relevance, context relevance, context precision) using LLM-based judges that score retrieved context and generated answers against ground truth. Implements a modular metric architecture where each metric is a callable class that accepts query-context-answer tuples and returns numerical scores, enabling composition of custom evaluation suites without modifying core framework code.
Unique: Implements domain-specific metrics (faithfulness, answer relevance, context precision) designed for RAG evaluation rather than generic NLG metrics; uses LLM-as-judge pattern with configurable judge models, enabling evaluation without human annotation while maintaining interpretability through metric-specific prompting strategies
vs alternatives: More specialized for RAG than generic LLM evaluation frameworks (like DeepEval or LangSmith), with metrics specifically designed to catch retrieval failures and hallucinations in context-grounded generation tasks
pluggable llm provider abstraction for metric computation
Abstracts LLM provider selection through a provider registry pattern, allowing metrics to run against OpenAI, Anthropic, Cohere, Azure, or local Ollama without code changes. Implements a standardized LLM interface that metrics call to score samples, with automatic fallback and retry logic, enabling users to swap providers or run distributed evaluation across multiple LLM backends.
Unique: Implements a provider registry pattern with standardized LLM interface that decouples metrics from specific provider implementations, enabling runtime provider swapping and distributed evaluation across heterogeneous LLM backends without metric code modification
vs alternatives: More flexible provider abstraction than frameworks tied to single providers (like LangChain's evaluation tools which default to OpenAI); enables cost optimization and privacy-first evaluation strategies unavailable in provider-locked alternatives
batch evaluation with distributed metric computation
Processes large evaluation datasets by parallelizing metric computation across multiple samples using Python's multiprocessing or async patterns. Implements batching logic that groups samples for efficient LLM API calls, reducing total API requests and latency compared to sequential evaluation. Supports progress tracking and error handling per batch, enabling evaluation of datasets with thousands of samples without memory exhaustion.
Unique: Implements intelligent batching that groups samples for efficient LLM API calls while maintaining parallelization across batches, reducing total API requests and latency; includes per-batch error handling and progress tracking for transparent evaluation of large datasets
vs alternatives: More efficient than naive sequential evaluation or simple multiprocessing; batching strategy reduces API costs while parallelization maintains throughput, making it practical for production-scale evaluation
ground truth comparison and supervised metric computation
Computes metrics that compare generated answers against ground truth labels using string similarity, semantic similarity, or LLM-based comparison. Implements supervised evaluation where metrics score answer quality relative to expected outputs, enabling detection of answer degradation or hallucination. Supports multiple comparison strategies (exact match, fuzzy matching, embedding-based similarity) configurable per metric.
Unique: Implements multiple comparison strategies (exact, fuzzy, semantic, LLM-based) in a unified interface, allowing users to choose trade-offs between speed and accuracy; supports multiple valid answers per query for flexible ground truth specification
vs alternatives: More flexible than single-strategy evaluation; enables cost-conscious teams to use fast string matching for obvious cases while reserving LLM-based comparison for ambiguous answers
context retrieval quality assessment without ground truth
Evaluates retrieval quality using unsupervised metrics (context precision, context recall, context relevance) that measure whether retrieved documents are relevant to the query without requiring ground truth labels. Uses LLM-as-judge to score context relevance and implements statistical measures for precision/recall based on query-context similarity. Enables evaluation of retrieval pipelines independently from answer generation.
Unique: Implements unsupervised retrieval metrics that work without ground truth labels, using LLM-as-judge for relevance scoring and statistical measures for precision/recall; enables independent evaluation of retrieval quality separate from answer generation
vs alternatives: Unique advantage over supervised-only frameworks in enabling retrieval evaluation without expensive ground truth labeling; allows teams to optimize retrieval independently from generation quality
hallucination detection via faithfulness scoring
Detects hallucinations in generated answers by scoring faithfulness — whether the answer is grounded in retrieved context using LLM-as-judge evaluation. Implements a two-stage scoring process: first extracting factual claims from the answer, then verifying each claim against context. Returns per-claim faithfulness scores enabling identification of specific hallucinated statements rather than binary hallucination detection.
Unique: Implements fine-grained per-claim faithfulness scoring rather than binary hallucination detection, enabling identification of specific hallucinated statements and their severity; uses two-stage LLM-as-judge approach (claim extraction then verification) for interpretable scoring
vs alternatives: More granular than simple hallucination classifiers; per-claim scoring enables debugging and targeted improvement of generation quality, while two-stage approach provides interpretability unavailable in end-to-end hallucination detectors
custom metric definition and composition framework
Enables users to define custom evaluation metrics by extending a base Metric class and implementing a score method that accepts query-context-answer tuples. Implements a metric composition pattern allowing users to combine multiple metrics into evaluation suites, with automatic aggregation and reporting. Supports metric-specific configuration (e.g., LLM model choice, similarity threshold) without modifying core framework code.
Unique: Implements a simple base class extension pattern for custom metrics with automatic integration into evaluation pipelines, enabling users to define domain-specific metrics without understanding internal framework architecture; supports metric-specific configuration through constructor parameters
vs alternatives: Lower barrier to entry than building evaluation frameworks from scratch; provides scaffolding and integration points while remaining flexible enough for novel metric implementations
evaluation dataset management and versioning
Provides utilities for loading, storing, and versioning evaluation datasets in standard formats (CSV, JSON, Hugging Face datasets). Implements dataset validation to ensure required columns (query, context, answer) are present and properly formatted. Supports dataset splitting for train/test evaluation and metadata tracking (dataset version, creation date, source) for reproducible evaluation runs.
Unique: Implements dataset abstraction with validation and metadata tracking, enabling reproducible evaluation across team members; supports multiple formats (CSV, JSON, Hugging Face) through unified interface
vs alternatives: Simpler than full data versioning systems (like DVC) while providing sufficient structure for evaluation reproducibility; unified format handling reduces boilerplate compared to format-specific loaders
+2 more capabilities