rag-ci

Stop shipping RAG regressions.

rag-ci measures every change to your RAG pipeline against a baseline and fails the pull request when retrieval actually got worse — not when it merely looks worse.

See it catch one, in one command and under a second — no API key, nothing to download:

$ uvx rag-ci demo
Built a synthetic corpus: 144 documents, 144 questions anchored to character offsets.

1. Baseline — chunk_size=240
┏━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ metric     ┃  mean ┃         95% CI ┃
┡━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ recall@3 * │ 0.951 │ [0.917, 0.986] │
└────────────┴───────┴────────────────┘

2. Someone halves the chunk size to fit more of them in context — chunk_size=120
┏━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ metric     ┃  mean ┃         95% CI ┃
┡━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ recall@3 * │ 0.736 │ [0.667, 0.806] │
└────────────┴───────┴────────────────┘

3. The gate compares them over the same questions
REGRESSION — the gate fails — recall@3 dropped by 0.215
(95% CI [-0.292, -0.139], p=0.0000) over 144 paired cases.

Exit code 1. In CI, that is a blocked pull request.

Nothing there is a recording. The corpus is generated on your machine and measured on the spot, through the same code paths run and gate use — a canned transcript would contradict the one thing this project argues for. Reproduce it yourself; the numbers will match, because they are deterministic, not because they were typed in.

Why the interval is the point

The demo above blocks because 144 questions are enough to be sure. The opposite case matters just as much. In examples/reference/ — six questions — cutting top_k to 1 drops recall@3 from 0.833 to 0.667, and rag-ci does not block:

Gate: Pass — recall@3 moved by -0.167 (95% CI [-0.500, 0.000]) over 6 paired cases.

A drop that large is probably real, and rag-ci still refuses to call it — because across six paired cases the paired bootstrap cannot separate it from noise. The interval, spanning -0.500 to 0.000, is the tool telling you how little evidence six questions buy. Widen the golden set and that interval tightens until the same change trips the gate. A tool that fails builds on noise gets switched off within a week, so this one errs toward silence and shows its evidence either way.

The same logic runs in reverse, which is where the money is: Beyond the Reranker finds that many retrieval enhancements stop contributing anything once a strong reranker is present. Teams are stacking techniques that do nothing and paying for them in latency and tokens, because a bare score moving from 0.71 to 0.74 reads like progress.

The academic side settled this years ago — paired bootstrap tests with 10,000 resamples and 95% confidence intervals are standard in T2-RAGBench and HetDocQA. That rigor had not reached the tools practitioners actually run. rag-ci is that rigor behind a CLI and a GitHub Action.

To be precise about the gap: promptfoo evaluates RAG and runs in CI, and ragas and DeepEval both compute retrieval metrics. What none of them do is condition the build verdict on a significance test. They compare a score to a threshold; rag-ci compares two runs over the same questions and reports how confident the difference is.

Commands

Every command below works today; see the design document for how they fit together.

uvx rag-ci demo            # watch a regression get caught, in one second ✅
uvx rag-ci init            # scaffold an adapter for your pipeline   ✅
uvx rag-ci golden anchor   # anchor a Q/A set you already have        ✅
uvx rag-ci golden gen      # generate candidate questions            ✅
uvx rag-ci golden review   # accept / edit / reject, then commit     ✅
uvx rag-ci run             # measure, with confidence intervals      ✅
uvx rag-ci gate            # fail the PR only on a real regression   ✅
uvx rag-ci sweep --report  # a shareable HTML page of the result       ✅
uvx rag-ci sweep --cache   # find the configuration that provably wins ✅
uvx rag-ci run --judge     # score answer grounding, not just recall ✅
uvx rag-ci judge calibrate # check the judge before trusting it      ✅

Generating questions needs the optional extra and an ANTHROPIC_API_KEY (uvx "rag-ci[generate]" golden gen …). Everything else installs and runs with no model dependency at all. See docs/golden-sets.md and docs/sweeps.md, and docs/judging.md.

Use it in CI

name: rag-ci
on: pull_request

permissions:
  contents: read
  pull-requests: write

jobs:
  retrieval:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v7
      - uses: Nokimalos/rag-ci@v0.7.1
        with:
          adapter: ragci_adapter.py
          golden: tests/golden.jsonl

The gate exits 1 on a regression and 2 when it cannot make a trustworthy comparison — an invalid run, or a baseline recorded against a different golden set. See docs/github-action.md for recording your first baseline and tuning min-effect.

Not on GitHub? The gate is a CLI, so any runner works. GitLab gets a one-job setup in docs/gitlab-ci.md, where allow_failure: exit_codes maps the 1 / 2 split onto the pipeline itself — a distinction GitHub Actions cannot express.

Two worked examples. examples/reference/ is dependency-free and gates every pull request to this repository. examples/langchain-chroma/ is the real thing — LangChain chunking, Chroma storage, ONNX embeddings — and shows the one flag (add_start_index=True) that decides whether rag-ci matches passages exactly or falls back to token overlap.

What makes it different

• Ground truth anchored to document passages, never to chunks. Change the chunk size and your golden set still works. This is what makes sweeping possible at all.
• A winner has to beat the field. sweep does not just rank means: it carries the finalists to the last rung, tests the winner against each with a paired bootstrap, and says plainly when the field is indistinguishable instead of naming a coin toss.
• A two-condition gate. A regression blocks the build only when it is both statistically significant and larger than min-effect. Significance alone would block on trivia; effect size alone would block on noise.
• Built for large corpora. Question generation streams the corpus and holds only identifiers while it decides what to sample, never the text. Sweeps use successive halving instead of grid search, separate index-time from query-time parameters so indexes are rebuilt as rarely as possible, and --extrapolate-to projects a sub-corpus result to full scale — refusing to project when the measured points do not support it.
• Agnostic by design. No RAG framework as a dependency. You write one adapter file; your stack stays yours. The adapter contract captures multi-step retrieval trajectories, so agentic and multi-hop pipelines are first-class.

License

MIT