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How to compare two RAG retrieval setups without a model key with Eval Forge

Run baseline and candidate RAG configurations offline and review the deltas in a dashboard.

Who it is forA team tuning retrieval for a RAG app that wants to know whether a hybrid setup beats a baseline before choosing it, without a hosted model account.

The problemChanges to retrieval settings look better or worse by feel, and comparing them on different test sets gives misleading results.

The outcomeTwo exported experiment summaries with matching dataset fingerprints, a gate verdict on the deltas, and a dashboard comparison.

What you need first

  • Python 3 with pip and a virtual environment
  • A committed evaluation artifact, or results from promptfoo, Ragas, or DeepEval
  • Git, and a GitHub repository if you want the composite Action
  • Docker, only if you use the Compose path

Steps

  1. Run docker compose up --build from a clone of the repo, then open the dashboard at the address shown in the README.
  2. Run evalforge run baseline_top1 --name "Baseline" --output build/baseline.json.
  3. Run evalforge run hybrid_top3 --name "Candidate" --output build/candidate.json.
  4. Run evalforge check hybrid_top3 --name "PR candidate" --report-dir artifacts to apply the release gate and write reports.

Prompt for your agent

In the EvalForge repo (https://github.com/jsdhwfmax/EvalForge), start the stack with `docker compose up --build`, then run `evalforge run baseline_top1 --name "Baseline" --output build/baseline.json` and `evalforge run hybrid_top3 --name "Candidate" --output build/candidate.json`. Expected output is two experiment JSON files; verify both share the same dataset fingerprint before comparing them.

Caveat. The offline evaluator is an extractive baseline with hashing embeddings, so it demonstrates the workflow but does not measure how a hosted model would perform.

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