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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.","intents":["measure whether my RAG system retrieves relevant context for user queries","verify that generated answers are grounded in retrieved documents and not hallucinating","quantify answer quality relative to expected outputs before deploying to production","benchmark different retrieval or generation strategies to identify performance regressions"],"best_for":["ML engineers building RAG systems who need automated evaluation without manual annotation","teams evaluating multiple LLM providers or retrieval backends for RAG applications","researchers comparing RAG architectures and publishing benchmarks"],"limitations":["LLM-based metrics depend on judge model quality and consistency — scoring can vary with model temperature and version changes","requires ground truth labels (expected answers) for supervised metrics; unsupervised evaluation limited to retrieval-only metrics","metric computation scales linearly with number of samples and LLM API calls, creating cost and latency bottlenecks for large datasets","no built-in statistical significance testing or confidence intervals — requires external analysis for small sample sizes"],"requires":["Python 3.8+","API key for LLM provider (OpenAI, Anthropic, Cohere, or local Ollama instance)","dataset with query-context-answer triples and optional ground truth labels","pandas for data handling"],"input_types":["structured data (query, retrieved context, generated answer)","ground truth labels (optional, for supervised metrics)","JSON/CSV datasets"],"output_types":["numerical scores (0-1 range per metric)","aggregated statistics (mean, std dev per metric)","detailed evaluation reports with per-sample scores"],"categories":["data-processing-analysis","evaluation-benchmarking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ragas__cap_1","uri":"capability://tool.use.integration.pluggable.llm.provider.abstraction.for.metric.computation","name":"pluggable llm provider abstraction for metric computation","description":"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.","intents":["use different LLM models (GPT-4, Claude, Llama) as judges without rewriting metric code","reduce API costs by switching to cheaper models for evaluation","run evaluation on local models for data privacy or offline scenarios","compare evaluation results across different judge models to validate metric robustness"],"best_for":["teams with multi-cloud or multi-provider LLM strategies","organizations with data privacy requirements preferring local model evaluation","cost-conscious teams optimizing evaluation spend across different model tiers"],"limitations":["metric scores are not directly comparable across different judge models due to inherent model bias and capability differences","local model evaluation (Ollama) requires sufficient GPU memory and adds latency compared to API-based providers","no built-in load balancing or rate limiting across multiple provider instances — requires external orchestration for high-volume evaluation"],"requires":["Python 3.8+","API credentials for chosen provider(s) (OpenAI, Anthropic, Cohere, Azure) OR local Ollama instance","network connectivity to provider endpoints or local Ollama server"],"input_types":["provider configuration (API keys, model names, endpoints)","evaluation samples (query-context-answer tuples)"],"output_types":["numerical metric scores","provider-specific metadata (token usage, latency)"],"categories":["tool-use-integration","evaluation-benchmarking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ragas__cap_2","uri":"capability://data.processing.analysis.batch.evaluation.with.distributed.metric.computation","name":"batch evaluation with distributed metric computation","description":"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.","intents":["evaluate large RAG datasets (1000+ samples) in reasonable time without sequential bottlenecks","parallelize metric computation to reduce wall-clock evaluation time","handle API rate limits gracefully by batching requests and retrying failed samples","monitor evaluation progress and identify failing samples for debugging"],"best_for":["teams evaluating production RAG systems with large test datasets","researchers running comprehensive benchmarks across multiple configurations","CI/CD pipelines requiring fast evaluation feedback for model updates"],"limitations":["parallelization overhead (process spawning, IPC) can exceed benefits for small datasets (<100 samples)","memory usage scales with batch size and number of workers — requires tuning for resource-constrained environments","API rate limits still apply per provider; batching reduces requests but doesn't eliminate throttling for high-volume evaluation","error handling per batch may mask systematic issues affecting entire evaluation run"],"requires":["Python 3.8+","sufficient system memory for parallel worker processes (typically 2-4GB per worker)","dataset with 100+ samples for meaningful parallelization benefits"],"input_types":["structured dataset (query-context-answer tuples)","batch size configuration","worker count specification"],"output_types":["aggregated metric scores across all samples","per-sample scores with error tracking","progress logs and performance metrics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ragas__cap_3","uri":"capability://data.processing.analysis.ground.truth.comparison.and.supervised.metric.computation","name":"ground truth comparison and supervised metric computation","description":"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.","intents":["measure answer correctness by comparing generated output to expected ground truth","detect when RAG system starts generating incorrect or hallucinated answers","validate that answer quality meets minimum thresholds before production deployment","identify specific samples where generation quality degrades for root cause analysis"],"best_for":["teams with labeled evaluation datasets containing expected answers","quality assurance workflows requiring objective answer correctness metrics","regression testing for RAG systems to catch answer quality degradation"],"limitations":["requires ground truth labels for all evaluation samples — expensive to create at scale","ground truth may be incomplete or subjective for open-ended questions with multiple valid answers","string/embedding-based similarity metrics miss semantic equivalence (e.g., 'USA' vs 'United States')","LLM-based comparison adds latency and cost compared to string-based metrics"],"requires":["Python 3.8+","evaluation dataset with ground truth labels for each query","optional: embedding model for semantic similarity (local or API-based)"],"input_types":["generated answer (string)","ground truth answer (string or list of valid answers)","optional: embedding model configuration"],"output_types":["similarity score (0-1 range)","match type (exact, fuzzy, semantic)","detailed comparison explanation (for LLM-based metrics)"],"categories":["data-processing-analysis","evaluation-benchmarking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ragas__cap_4","uri":"capability://data.processing.analysis.context.retrieval.quality.assessment.without.ground.truth","name":"context retrieval quality assessment without ground truth","description":"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.","intents":["measure retrieval quality without labeled ground truth documents","identify whether retrieval failures are causing downstream answer quality issues","optimize retrieval parameters (top-k, similarity threshold) based on context relevance scores","debug retrieval pipelines by analyzing which documents are retrieved for failing queries"],"best_for":["teams optimizing retrieval components of RAG systems","scenarios where ground truth document labels are unavailable or expensive","debugging retrieval failures to distinguish from generation failures"],"limitations":["unsupervised metrics cannot detect when relevant documents exist but weren't retrieved (recall is estimated, not measured)","LLM-based relevance scoring depends on judge model quality and may miss domain-specific relevance","metrics assume retrieved documents are independent; cannot detect redundancy or information overlap","no ground truth means no absolute quality baseline — only relative comparison across configurations"],"requires":["Python 3.8+","API key for LLM provider (for relevance scoring)","query-context pairs (ground truth documents not required)"],"input_types":["query (string)","retrieved context documents (list of strings)","optional: expected answer (for relevance scoring)"],"output_types":["context precision score (0-1)","context recall estimate (0-1)","context relevance score (0-1)","per-document relevance scores"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ragas__cap_5","uri":"capability://data.processing.analysis.hallucination.detection.via.faithfulness.scoring","name":"hallucination detection via faithfulness scoring","description":"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.","intents":["detect when RAG system generates answers not supported by retrieved documents","identify specific hallucinated claims within longer answers for debugging","measure hallucination rate across evaluation dataset to track system reliability","filter out low-faithfulness answers before returning to users in production"],"best_for":["teams deploying RAG systems where hallucination is a critical failure mode","quality assurance workflows requiring hallucination detection before user exposure","research on RAG system reliability and grounding"],"limitations":["faithfulness scoring depends on judge model's ability to extract and verify claims — may miss subtle hallucinations","per-claim scoring adds latency compared to single-pass hallucination detection","cannot distinguish between hallucinations and legitimate inferences from context","requires well-formatted context; fragmented or poorly structured documents reduce scoring accuracy"],"requires":["Python 3.8+","API key for LLM provider (for claim extraction and verification)","retrieved context documents (required for grounding verification)"],"input_types":["generated answer (string)","retrieved context (list of documents)","optional: query (for context)"],"output_types":["overall faithfulness score (0-1)","per-claim faithfulness scores","list of hallucinated claims","supporting context for each claim"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ragas__cap_6","uri":"capability://code.generation.editing.custom.metric.definition.and.composition.framework","name":"custom metric definition and composition framework","description":"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.","intents":["define domain-specific evaluation metrics tailored to my RAG application","combine built-in and custom metrics into evaluation suites for comprehensive assessment","configure metric behavior (model choice, thresholds) without forking the framework","share custom metrics across teams or publish as reusable components"],"best_for":["teams with domain-specific evaluation requirements not covered by built-in metrics","researchers implementing novel RAG evaluation approaches","organizations building internal evaluation frameworks on top of ragas"],"limitations":["custom metric implementation requires Python coding — not accessible to non-technical users","no built-in validation of metric implementations; poorly written metrics can introduce evaluation bias","metric composition doesn't handle dependencies between metrics — requires manual ordering","no standardized way to share custom metrics across projects; requires manual code copying or package publishing"],"requires":["Python 3.8+","understanding of ragas Metric base class and interface","optional: LLM provider API key if custom metric uses LLM-based scoring"],"input_types":["query (string)","context (string or list of documents)","answer (string)","optional: ground truth labels"],"output_types":["numerical score (0-1 range recommended)","optional: detailed scoring explanation or per-component scores"],"categories":["code-generation-editing","evaluation-benchmarking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ragas__cap_7","uri":"capability://data.processing.analysis.evaluation.dataset.management.and.versioning","name":"evaluation dataset management and versioning","description":"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.","intents":["load evaluation datasets from multiple formats without custom parsing code","validate dataset structure before running evaluation to catch configuration errors early","version evaluation datasets to ensure reproducible evaluation across team members","split datasets for cross-validation or separate test set evaluation"],"best_for":["teams managing multiple evaluation datasets across different RAG systems","research projects requiring reproducible evaluation with versioned datasets","CI/CD pipelines needing automated dataset validation before evaluation"],"limitations":["limited format support — primarily CSV/JSON; requires custom loaders for proprietary formats","no built-in data quality checks beyond schema validation (e.g., duplicate detection, outlier identification)","versioning is metadata-only; no automatic diff or change tracking between dataset versions","dataset size limits depend on available memory; very large datasets (>10GB) may require external storage"],"requires":["Python 3.8+","pandas for CSV/JSON handling","optional: huggingface_datasets for Hugging Face dataset loading"],"input_types":["CSV files with query, context, answer columns","JSON files with structured evaluation samples","Hugging Face dataset identifiers"],"output_types":["validated Dataset object with metadata","train/test splits","dataset statistics (sample count, column info)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ragas__cap_8","uri":"capability://data.processing.analysis.evaluation.results.aggregation.and.reporting","name":"evaluation results aggregation and reporting","description":"Aggregates metric scores across evaluation samples and generates summary statistics (mean, std dev, percentiles) with optional visualization. Implements result export to multiple formats (JSON, CSV, HTML reports) with configurable detail levels. Supports comparison across multiple evaluation runs enabling identification of performance changes between system versions.","intents":["summarize evaluation results across hundreds of samples into actionable metrics","generate evaluation reports for stakeholder communication and decision making","compare evaluation results across system versions to identify regressions or improvements","export evaluation data for further analysis in external tools (Excel, Jupyter, etc.)"],"best_for":["teams presenting evaluation results to non-technical stakeholders","CI/CD pipelines requiring automated evaluation reporting","research projects publishing evaluation results and benchmarks"],"limitations":["aggregation assumes metric scores are comparable across samples — may not hold for heterogeneous datasets","visualization is basic (matplotlib/plotly) — complex analysis requires external tools","no built-in statistical significance testing — requires external libraries for hypothesis testing","report generation is template-based; customization requires code modification"],"requires":["Python 3.8+","pandas for aggregation","optional: matplotlib/plotly for visualization"],"input_types":["evaluation results (metric scores per sample)","optional: multiple evaluation runs for comparison"],"output_types":["summary statistics (mean, std dev, percentiles)","JSON/CSV export files","HTML reports with tables and charts","comparison reports showing deltas between runs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ragas__cap_9","uri":"capability://tool.use.integration.llm.agnostic.metric.scoring.with.configurable.judge.models","name":"llm-agnostic metric scoring with configurable judge models","description":"Abstracts metric implementation from specific LLM models by parameterizing judge model selection at evaluation time. Metrics define scoring logic using a generic LLM interface (prompt + parsing) rather than hardcoding specific model APIs. Enables users to swap judge models (GPT-4 to Claude to Llama) without metric code changes, supporting cost optimization and model experimentation.","intents":["use different LLM models as judges for the same metric to validate metric robustness","reduce evaluation costs by using cheaper models for non-critical metrics","experiment with new judge models without rewriting metric implementations","ensure evaluation reproducibility by pinning judge model versions"],"best_for":["teams experimenting with different judge models for evaluation","cost-conscious organizations optimizing evaluation spend","research projects validating metric robustness across models"],"limitations":["metric scores are not directly comparable across different judge models due to model-specific biases","cheaper models may produce lower-quality scores, introducing evaluation noise","no automatic validation that judge model is suitable for metric task","requires careful prompt engineering to ensure consistent scoring across models"],"requires":["Python 3.8+","API credentials for chosen judge model(s)","understanding of metric-specific prompting requirements"],"input_types":["metric configuration with judge model specification","evaluation samples (query-context-answer tuples)"],"output_types":["metric scores","model-specific metadata (token usage, latency)"],"categories":["tool-use-integration","evaluation-benchmarking"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","API key for LLM provider (OpenAI, Anthropic, Cohere, or local Ollama instance)","dataset with query-context-answer triples and optional ground truth labels","pandas for data handling","API credentials for chosen provider(s) (OpenAI, Anthropic, Cohere, Azure) OR local Ollama instance","network connectivity to provider endpoints or local Ollama server","sufficient system memory for parallel worker processes (typically 2-4GB per worker)","dataset with 100+ samples for meaningful parallelization benefits","evaluation dataset with ground truth labels for each query","optional: embedding model for semantic similarity (local or API-based)"],"failure_modes":["LLM-based metrics depend on judge model quality and consistency — scoring can vary with model temperature and version changes","requires ground truth labels (expected answers) for supervised metrics; unsupervised evaluation limited to retrieval-only metrics","metric computation scales linearly with number of samples and LLM API calls, creating cost and latency bottlenecks for large datasets","no built-in statistical significance testing or confidence intervals — requires external analysis for small sample sizes","metric scores are not directly comparable across different judge models due to inherent model bias and capability differences","local model evaluation (Ollama) requires sufficient GPU memory and adds latency compared to API-based providers","no built-in load balancing or rate limiting across multiple provider instances — requires external orchestration for high-volume evaluation","parallelization overhead (process spawning, IPC) can exceed benefits for small datasets (<100 samples)","memory usage scales with batch size and number of workers — requires tuning for resource-constrained environments","API rate limits still apply per provider; batching reduces requests but doesn't eliminate throttling for high-volume evaluation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:18.279Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-ragas","compare_url":"https://unfragile.ai/compare?artifact=pypi-ragas"}},"signature":"iQgH3kpyJIDD7ZMLDwdTh7ieHH2jnAbcM9JO7pBmnX2f3mbPjIAof/GWetqrxl3Qbuf2HYxLD0WV2tbV0p77Cw==","signedAt":"2026-06-21T11:36:19.731Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-ragas","artifact":"https://unfragile.ai/pypi-ragas","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-ragas","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}