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> ML_LIBRARY // RAGAS_v1.0

Ragas

Exploding Gradients — Automated evaluation framework for Retrieval Augmented Generation (RAG) pipelines.

evaluation-observabilityv0.1.18Apache-2.0qualified
Official DocumentationSource Repository

Model Training

Not Supported

This library is a dedicated runtime engine for inference serving and does not train models.

Model Inference

Supported
Inference Accelerators:
CPU
Deployment Targets:server

What It Does

  • +Component-wise RAG metrics: Faithfulness (measuring hallucinations), Answer Relevance, Context Precision, and Context Recall
  • +End-to-end metrics: Answer Semantic Similarity and Answer Correctness
  • +Automated synthetic test set generator creating diverse question-answer pairs directly from raw corporate documents
  • +Works with local LLMs (Ollama, vLLM) and commercial APIs (OpenAI, Anthropic, Bedrock)

What It Does Not Do

  • -Host live vector databases or execute search queries directly
  • -Train neural weights or compile C++ kernels
  • -Serve real-time sub-10ms user traffic

>Suitable Work Types

  • Continuous benchmarking of enterprise RAG pipelines across chunking and embedding experiments
  • Generating 500+ diverse synthetic test questions from internal documentation to validate a new vector search index
  • Evaluating RAG hallucination rates in CI/CD before releasing updates to production

>Unsuitable Work Types

  • Classical tabular machine learning (use Evidently AI or Deepchecks)
  • Real-time per-token generation loops
Data Residency Implications

Can be configured with completely local Ollama/vLLM endpoints for strict air-gapped data residency compliance.

Security Considerations

Apache-2.0 license. Permissive for enterprise evaluation pipelines.

Operational Profile & Known Limitations

Maturity:mature
Learning Curve:low
Ops Complexity:low
Cost Tier:free-oss
> Known Limitations:
  • Evaluating large test suites using commercial LLMs (e.g. GPT-4 as judge) incurs API token costs; local vLLM evaluators eliminate this expense.

Associated Incident Patterns (Incidentpedia)

Enforce safeguards and monitoring to guard against these documented real-world failure modes:

training-serving-skew

> Primary Evidence & Benchmark Citations

Ragas Documentationofficial-docs • >=0.1.15, <=0.1.x
2026-09-25HIGH