Config reference: evaluation

• Enterprise tuning surface

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  Defaults + constraints are rendered directly from Pydantic.

• Env keys when available

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  Many fields have an env-style alias (from TriBridConfig.to_flat_dict()).

• Tooltip-level guidance

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  If a matching glossary entry exists, you’ll see deeper tuning notes.

Config reference Config API & workflow Glossary

Total parameters: 8

Group index

• (root)

(root)

JSON key	Env key(s)	Type	Default	Constraints	Summary
evaluation.baseline_path	BASELINE_PATH	str	"data/evals/eval_baseline.json"	—	Baseline results path
evaluation.eval_dataset_path	EVAL_DATASET_PATH	str	"data/evaluation_dataset.json"	—	Evaluation dataset path
evaluation.eval_multi_m	EVAL_MULTI_M	int	10	≥ 1, ≤ 20	Multi-query variants for evaluation
evaluation.ndcg_at_10_k	—	int	10	≥ 1, ≤ 200	K used for ndcg_at_10 metric (default 10).
evaluation.precision_at_5_k	—	int	5	≥ 1, ≤ 200	K used for precision_at_5 metric (default 5).
evaluation.recall_at_10_k	—	int	10	≥ 1, ≤ 200	K used for recall_at_10 metric (default 10).
evaluation.recall_at_20_k	—	int	20	≥ 1, ≤ 200	K used for recall_at_20 metric (default 20).
evaluation.recall_at_5_k	—	int	5	≥ 1, ≤ 200	K used for recall_at_5 metric (default 5).

Details (glossary)

evaluation.baseline_path (BASELINE_PATH) — Baseline Path

Category: general

Directory where evaluation loop saves baseline results for regression testing and A/B comparison. Each eval run's metrics (Hit@K, MRR, latency) are stored here with timestamps. Use this to ensure retrieval quality doesn't regress after configuration changes, reindexing, or model upgrades. Compare current run against baseline to detect improvements or degradations.

Links: - Regression Prevention