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Evaluation Models

  • Eval Dataset

    EvalDatasetItem defines questions and expected paths.

  • Metrics

    EvalMetrics, EvalRun, EvalResult capture performance.

  • Comparisons

    EvalComparisonResult compares two runs.

Get started Configuration API

Match Production

Tune eval_final_k and eval_multi to reflect real usage; misaligned evals mislead.

Config Snapshots

EvalRun stores both nested and flat config snapshots for reproducibility.

AI analyses are persisted, not re-charged

POST /api/eval/analyze_comparison saves the generated analysis as an EvalAnalysisArtifact (under data/eval_runs/analysis/). GET /api/eval/analysis/{run_id}?compare_run_id=... serves it back without touching the gateway; it answers 404 when nothing is cached or when the cached analysis was generated against a different baseline — a stale pair is never served. Deleting a run deletes its cached analysis. See Evaluation Guide.

Latency Budget

Track latency_p95_ms across runs to guard against regressions.

Model Purpose
EvalDatasetItem Single question + expected file paths
EvalMetrics Aggregated metrics (MRR, Recall@K, NDCG@10, latency percentiles)
EvalRun Complete run with config snapshot and results
EvalComparisonResult Delta between baseline and current runs
EvalAnalysisArtifact Persisted AI comparison analysis, keyed by (run_id, compare_run_id) 
flowchart TB
    Dataset["Eval Dataset"] --> Run["Eval Run"]
    Run --> Metrics["Eval Metrics"]
    Run --> Results["Per-Entry Results"]
    Metrics --> Compare["Compare Runs"]
import httpx
base = "http://localhost:8000"
print(httpx.post(f"{base}/reranker/evaluate", json={"corpus_id": "tribrid"}).json())

BASE=http://localhost:8000
curl -sS -X POST "$BASE/reranker/evaluate" -H 'Content-Type: application/json' -d '{"corpus_id":"tribrid"}' | jq .

const report = await (await fetch('/reranker/evaluate', { method:'POST', headers:{'Content-Type':'application/json'}, body: JSON.stringify({ corpus_id: 'tribrid' }) })).json();
Top-K alignment

Ensure eval_final_k >= retrieval.final_k when you want strict hit@K parity with production.

Figure eval dataset models

Page-grounded figure evaluation uses its own dataset shapes in server/models/eval_figures.py (FigureEvalDataset / FigureEvalItem with question, expected_pages (1-based), figure_ref, kind (locate/content) and tags). They are serialized to data/eval_datasets/*.json and consumed by scripts/eval_figure_grounding.py; no frontend consumes them, so they are deliberately not registered for TypeScript generation. See Figure grounding eval.

These complement — they do not replace — the EvalDatasetItem path-based shapes above.