RAG Eval Suite

I have mapped out a production-grade evaluation suite for your LangChain application. The implementation focuses on isolated component testing, a wired RAG Triad, and a noise-aware regression harness to ensure that only statistically significant quality drops flag a failure.

RAG Evaluation Suite Implementation
Layer Metrics Scripts
Component Contextual Recall, Contextual Precision (retriever) · Faithfulness, Answer Relevancy (generator) eval_retriever.py, eval_generator.py
Pipeline RAG Triad — Contextual Relevancy, Faithfulness, Answer Relevancy eval_pipeline.py
Application Correctness, Completeness, Style (G-Eval) eval_application.py
Safety Scope Adherence, System Prompt Leakage, PII Leakage, Toxicity eval_safety.py
Operations P95 Latency, TTFT, Cost per Query, Reliability (success/error rate) eval_operations.py
Regression Noise-floor adjusted delta monitoring noise_baseline.py, compare.py
Noise-Aware Regression Strategy

To prevent false positives, we will not perform naive diffing. Instead:

Run noise_baseline.py on your current stable version 10 times to establish the standard deviation for each metric.
compare.py sets the regression threshold at 2 standard deviations above that noise band, per metric.
Only deltas exceeding this noise band will trigger a regression alert in your CI/CD pipeline — everything inside the band is reported as noise, not a failure.
yaml

config.yaml — point this at your LangChain retriever/generator/pipeline functions

judge_model: gpt-4o-mini
functions:
retriever_fn: my_app.retriever:retrieve
generator_fn: my_app.generator:generate
pipeline_fn: my_app.pipeline:run
golden_datasets:
retriever: examples/retriever_golden.json
generator: examples/generator_golden.json
pipeline: examples/pipeline_golden.json
bash

Establish the noise band once (or whenever the judge model changes)

python scripts/noise_baseline.py --config config.yaml --runs 10 --out results/noise_band.json

Run the full offline suite to get your baseline

python scripts/run_suite.py --config config.yaml --out results/baseline.json

After a change (chunking, prompt, model, reranker), rerun and compare

python scripts/run_suite.py --config config.yaml --out results/candidate.json
python scripts/compare.py --baseline results/baseline.json
--candidate results/candidate.json --noise-band results/noise_band.json

Next steps

Fill in config.yaml with dotted paths to your LangChain retriever, generator, and pipeline functions, and point it at your golden dataset files (see config.example.yaml and examples/sample_golden_dataset.json for the expected shape).
Run python scripts/noise_baseline.py --config config.yaml once to establish your judge model's variance floor — this is what makes compare.py's regression calls trustworthy instead of noisy.
Run python scripts/run_suite.py --config config.yaml to get your baseline, then wire compare.py into CI so it runs on every PR that touches retrieval, prompts, or the model.