proofrag

proofrag

Turn "did my change make the RAG better or worse?" into one reproducible command. You (the agent) wire the user's app to the kit; the kit does dataset generation, judging, and reporting.

When to use

• User changed a prompt, model, chunker, embedder, or retriever and wants to know if quality moved.
• User has docs/a knowledge base but no evaluation set.
• User wants a hallucination/groundedness number, or a CI gate on answer quality.

Install the engine

This skill drives the proofrag CLI. Make sure it's on PATH (install once), or run it ad-hoc with uvx:

uv tool install "proofrag[anthropic]"     # or: pipx install "proofrag[anthropic]"
# no install needed: uvx "proofrag[anthropic]" demo

Use [openai] instead of [anthropic] for an OpenAI-compatible/local backend. Credentials: ANTHROPIC_API_KEY (default, cheap Haiku judge) or OPENAI_API_KEY (OPENAI_BASE_URL for local/Ollama). No key? proofrag demo renders a sample scorecard.

The loop

1. Inspect and generate from the user's corpus.

   proofrag corpus ./docs
   

   Use --include, --exclude, and --no-gitignore when the docs tree is noisy. PDF loading needs the proofrag[pdf] extra; HTML is supported by default.

   Then generate:

   proofrag generate --corpus ./docs --out goldenset.jsonl --n 20
   

   Produces JSONL: {id, question, gold_answer, gold_contexts[], difficulty, sources[]} with tiers single_doc / multi_doc / unanswerable, plus context_metadata for each gold context. Commit this file — it is versioned.

2. Validate the golden set before committing it.

   proofrag validate --goldenset goldenset.jsonl --corpus ./docs --out validation.json
   

   This checks the JSONL contract, duplicate ids/questions, answerable cases without gold contexts, unanswerable cases that still cite context, source coverage, and a stable fingerprint. It exits non-zero on hard errors; add --strict to fail on warnings too.

3. Run the user's RAG over every question to produce predictions. Prefer proofrag run when the app exposes a local HTTP endpoint or Python callable:

   proofrag run --goldenset goldenset.jsonl \
     --endpoint http://localhost:8000/ask \
     --out predictions.jsonl
   
   proofrag run --goldenset goldenset.jsonl \
     --callable myapp.rag:answer \
     --out predictions.jsonl
   

   HTTP mode POSTs {"id": "...", "question": "..."}. Callable mode calls answer(question) by default; add --call-style record to pass the full golden record. The adapter may return an answer string, (answer, contexts), or:

   {"id": "q000", "answer": "<system answer>", "retrieved_contexts": ["<chunk>", "..."]}
   

   retrieved_contexts are the chunks their retriever returned (used for retrieval metrics). If neither adapter fits, write a small driver script that emits the same JSONL shape. If you can't find their entrypoint, ask the user where their "ask a question" function lives.

4. Judge.

   proofrag evaluate --goldenset goldenset.jsonl --predictions predictions.jsonl --out results.json
   

   Scores groundedness, correctness, completeness, citation_quality (LLM-as-judge, pinned + fingerprinted) and rank-aware retrieval metrics — Recall@k, Precision@k, NDCG@k, MRR (--k sets the cutoff; lexical by default, --semantic for embeddings). To score generation with DeepEval instead, add --backend deepeval (needs the proofrag[deepeval] extra; metrics become faithfulness / answer_relevancy / correctness). To score with Ragas instead, add --backend ragas (needs the proofrag[ragas] extra; metrics become faithfulness / factual_correctness, plus answer_relevancy when OpenAI-compatible embeddings are configured). Retrieval metrics and everything downstream stay the same. DeepEval metric reasons, when available, are preserved in the scorecard's weakest-case notes.

5. Report.

   proofrag report --results results.json --out scorecard.html
   proofrag summary --results results.json   # optional markdown for CI/logs
   

   Self-contained HTML — open it, attach it to a PR, screenshot it. Surfaces overall score, per-metric bars, and the weakest cases with the judge's rationale. The markdown summary gives CI systems a compact score table without opening the HTML.

CI gate

Absolute floor:

proofrag evaluate --goldenset goldenset.jsonl --predictions predictions.jsonl \
  --out results.json --fail-under 0.7      # exits 1 if overall generation score < 0.7

Regression vs a committed baseline (a known-good results.json):

proofrag diff --baseline baseline.json --candidate results.json --tolerance 0.02

To wire this into GitHub Actions, use the bundled composite action uses: unshDee/proofrag@v0 (see the repo README / examples/ci/). Tell the user to commit a baseline results.json from a good run, then diff every PR against it. The action writes a GitHub Actions job summary and uploads the scorecard/results artifact by default, including when a gate fails.

A/B comparison (blind)

To compare two variants (vector vs GraphRAG, two prompts, two models), run each over the same golden set to produce two prediction files, then:

proofrag compare --goldenset goldenset.jsonl \
  --a vector_preds.jsonl --a-name vector \
  --b graphrag_preds.jsonl --b-name graphrag \
  --out comparison.json --html comparison.html

The same pinned judge picks the better answer per question, blind — answers are shown in randomized order so it never knows which variant is which. Output: win counts + per-variant retrieval metrics + an HTML report. Render later with proofrag report --results comparison.json (it auto-detects the comparison format).

Credibility rules (state these to the user)

• Judge model is pinned; mixing judges makes scores non-comparable.
• LLM-as-judge has variance — treat single-point differences cautiously; the retrieval metrics are deterministic and separate retriever from generator faults.
• A low score on unanswerable cases means the system hallucinates instead of refusing.

Reference

• Engine + source: https://github.com/unshDee/proofrag (src/proofrag/).
• Runnable end-to-end example: examples/docs-rag/ in that repo (corpus + naive RAG driver).
• proofrag --help lists all commands and flags.