{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "jsdhwfmax-evalforge--compare-rag-configs-offline",
 "repo": "jsdhwfmax/EvalForge",
 "repo_url": "https://github.com/jsdhwfmax/EvalForge",
 "repo_slug": "jsdhwfmax-evalforge",
 "title": "Compare two RAG retrieval setups without a model key",
 "page_title": "How to compare two RAG retrieval setups without a model key with Eval Forge",
 "summary": "Run baseline and candidate RAG configurations offline and review the deltas in a dashboard.",
 "who": "A 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.",
 "problem": "Changes to retrieval settings look better or worse by feel, and comparing them on different test sets gives misleading results.",
 "outcome": "Two exported experiment summaries with matching dataset fingerprints, a gate verdict on the deltas, and a dashboard comparison.",
 "steps": [
  "Run `docker compose up --build` from a clone of the repo, then open the dashboard at the address shown in the README.",
  "Run `evalforge run baseline_top1 --name \"Baseline\" --output build/baseline.json`.",
  "Run `evalforge run hybrid_top3 --name \"Candidate\" --output build/candidate.json`.",
  "Run `evalforge check hybrid_top3 --name \"PR candidate\" --report-dir artifacts` to apply the release gate and write reports."
 ],
 "agent_prompt": "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.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "45 minutes",
 "basis": "readme",
 "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.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "python",
 "requirements": [
  "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"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.7,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/jsdhwfmax-evalforge/compare-rag-configs-offline/",
 "md": "https://dedupedai.com/use-cases/jsdhwfmax-evalforge/compare-rag-configs-offline/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/jsdhwfmax-evalforge--compare-rag-configs-offline.json",
 "api_version": "v1"
}