---
license: mit
language:
  - en
  - yue
task_categories:
  - text-retrieval
tags:
  - code-search
  - information-retrieval
  - multilingual
  - postgres
  - yutabase
pretty_name: YUTABASE RepoSearch MiniEval
size_categories:
  - n<1K
configs:
  - config_name: corpus
    data_files:
      - split: corpus
        path: data/corpus.jsonl
  - config_name: queries
    data_files:
      - split: validation
        path: data/validation.jsonl
      - split: test
        path: data/test.jsonl
---

# YUTABASE RepoSearch MiniEval

YUTABASE RepoSearch MiniEval is a tiny, project-specific retrieval check over
one immutable public revision of
[`cambridgetcg/yutabase`](https://github.com/cambridgetcg/yutabase). It asks 27
English, Cantonese Traditional Chinese, and code-mixed questions about the
candidate specification, integration boundaries, optional SDK, and
non-normative serving-shape research.

This is an engineering fixture, not a universal code-search benchmark. Its
queries are synthetic and its public labels make memorization easy.

## Configs

- `corpus` contains exact, unmodified line spans from the allowlisted public
  source files. Its only split is also named `corpus`; it is not training data.
- `queries` contains 9 `validation` and 18 `test` rows with graded relevance
  judgments and curated hard negatives. Both label sets are public.

```python
from datasets import load_dataset

corpus = load_dataset(
    "Yu-and-Ai/yutabase-reposearch-minieval",
    "corpus",
    split="corpus",
)
validation = load_dataset(
    "Yu-and-Ai/yutabase-reposearch-minieval",
    "queries",
    split="validation",
)
```

Every corpus row carries the repository, commit, Git blob, whole-file SHA-256,
inclusive line range, chunk SHA-256, authority class, and a commit-pinned
source URL. `source-manifest.json` is the allowlist and reproducibility
boundary.

## Immutable source boundary

| Coordinate | Value |
|---|---|
| repository | `cambridgetcg/yutabase` |
| commit | `d72605d05061b7853a8e9bf3eb73417e3a4457dc` |
| tree | `592f1b15b14fbe0ee29ef4e4769a43aace7e1e65` |
| commit time | `2026-07-29T23:10:22+01:00` |
| source license | MIT |

The corpus is generated only from 12 technical files named in the manifest.
It excludes repository agent instructions, deployment metadata, heartbeat
state, generated catalogs, applications, play notebooks, creative/archival
writings, package locks, local repositories, account data, credentials, logs,
and conversations. Links appearing in source documents are retained as source
text; the linked pages are not copied into this dataset.

Authority classes matter. `SPEC.md` and `docs/CONFORMANCE.md` are candidate
contract material, while `docs/SERVING-SHAPE.md` is explicitly non-normative
research. Semantic similarity does not make those layers interchangeable.

## Query and judgment shape

Each query row contains:

- a stable `query_id`, split, language label, and query;
- `query_type` and coarse difficulty;
- an `expected_facts` audit aid;
- graded `relevance` entries with a chunk ID, grade, and rationale; and
- explicit `hard_negative_chunk_ids`.

Grades mean:

- `3`: directly answers the information need;
- `2`: materially corroborates or implements the answer;
- `1`: supplies an important caveat or motivating constraint;
- omitted: judged non-relevant inside this curated corpus.

The seed judgments were reviewed against the pinned corpus, but repeated facts
across README, specification, code, and design notes can still make relevance
subjective. Treat per-query output as evidence and inspect disagreements.

Language totals are 13 English (`en`), 11 Cantonese Traditional Chinese
(`yue-Hant`), and 3 mixed (`mul`) queries. The source corpus itself is
predominantly English, so the Cantonese rows measure cross-lingual retrieval,
not Cantonese source coverage or answer generation.

## Validate and evaluate

Everything below uses the Python standard library:

```bash
python3 scripts/validate_dataset.py
python3 -m unittest discover -s tests -v
python3 scripts/evaluate.py --split validation
python3 scripts/evaluate.py --split test
```

The evaluator's built-in lexical ranker is a deterministic smoke baseline, not
a competitive multilingual model. A custom run is JSON Lines with one row per
query:

```json
{"query_id":"Q01","ranked_chunk_ids":["readme.profile","spec.profile"]}
```

```bash
python3 scripts/evaluate.py --split test --run path/to/run.jsonl
```

The primary metric is graded `nDCG@10` with gain `2^grade - 1`. Secondary
binary metrics treat grades 2–3 as relevant: recall at 5, success at 1, and MRR
at 10. A hard-negative inversion is 1 for a query when its highest-ranked
curated hard negative appears above every grade-3 target, then the evaluator
averages that value across queries. It also reports per-language macro scores
and per-query results. With only 27 public questions, tiny score changes and
language gaps are descriptive rather than statistically robust.

The checked-in standard-library lexical smoke baseline produced:

| Split | nDCG@10 | Recall@5 | Success@1 | MRR@10 |
|---|---:|---:|---:|---:|
| validation | 0.8797 | 0.8519 | 0.8889 | 0.9444 |
| test | 0.8451 | 0.8148 | 0.8333 | 0.8889 |

On the public test split its English nDCG@10 was 0.8818 and Cantonese
Traditional nDCG@10 was 0.7726. That gap is a diagnostic for this lexical
baseline, not an estimate of all multilingual retrievers or users.

## Known limitations

- The questions are manually authored from project documentation, not sampled
  from real users or production logs.
- One repository and one revision cannot establish general retrieval quality.
- The public test labels support regression testing, not a protected
  leaderboard.
- Cantonese wording and code-switching cover only a small slice of Hong Kong
  usage and orthographic variation.
- Hard negatives are curated rather than exhaustive.
- A later YUTABASE revision requires a new dataset version; scores over
  different corpora are not directly comparable.
- Retrieving a security statement does not prove a deployment secure, a claim
  true, or generated advice correct.
- MIT permits redistribution of the selected source, but external material
  linked by that source retains its own terms and is not included here.

## Privacy and safety

All source chunks come from the exact public Git commit above. No private
AgentTool implementation, local KINGDOM catalog, local path, MCP/OAuth data,
secret, user log, or conversation is included. The validator rejects several
obvious local-path and credential markers as a regression guard; that check is
not a universal secret scanner.

## License and attribution

Dataset annotations and tooling are MIT licensed. The copied YUTABASE source
spans remain under YUTABASE's MIT notice, reproduced in
`SOURCE_LICENSES/YUTABASE-MIT.txt`.