diff --git a/.gitignore b/.gitignore index dde1b02..4ae3b95 100644 --- a/.gitignore +++ b/.gitignore @@ -12,6 +12,7 @@ benchmark-results/live/*.json benchmark-results/mteb-reranking/ benchmark-results/*original-check.json confidence-runs/ +runs/ artifacts/*confidence*.joblib dist/ build/ diff --git a/README.ko.md b/README.ko.md index 7aa6517..71e728b 100644 --- a/README.ko.md +++ b/README.ko.md @@ -14,7 +14,7 @@ Azure OpenAI 기반 zero-shot candidate reranking에 집중합니다. 주요 특징: - listwise RankGPT, pairwise PRP, tournament 방식 TourRank-r, - uncertainty-aware AcuRank built-in Strategy + uncertainty-aware AcuRank, confidence-gain built-in Strategy - 커스텀 reranking 메소드를 위한 public Strategy contract - vendor 독립 LLM 호출을 위한 `ModelClient` / `ModelProvider` 경계 - 엄격한 JSON parsing과 fast-fail 오류 정책 @@ -68,6 +68,8 @@ for result in results: | `tourrank_r`, `rounds=2` | `TourRankStrategy` | 중간 수준 호출 예산에서 listwise보다 강한 품질을 원할 때 | RankGPT보다 호출 수가 많지만 TourRank-10보다 훨씬 가벼움 | | `tourrank_r`, `rounds=10` | `TourRankStrategy` | 품질 중심 offline reranking, 논문식 평가, 최종 reranking처럼 latency를 감수할 수 있을 때 | 일반 사용 기준 built-in 중 호출 비용이 가장 큼 | | `acurank` | `AcuRankStrategy` | top-k 경계 근처의 불확실한 후보에 listwise 호출을 집중하고 싶을 때 | TrueSkill 상태를 사용하며, cap을 두지 않으면 기본 listwise보다 호출 수가 늘 수 있음 | +| `confidence_gain` | `ConfidenceGainStrategy` | query-only 및 query+context confidence scorer를 학습했고 `Conf(Q+C)-Conf(Q)`로 문서를 정렬하고 싶을 때 | scorer artifact와 answer generator hook이 필요함. 문서 수가 `N`이면 runtime에서 answer generation `N+1`회, confidence scoring `N+1`회를 수행함 | +| `cbdr` | `CBDRStrategy` | answerability confidence scorer를 학습했고 `Conf(Q)`가 충분히 높으면 context reranking을 건너뛰고, 낮으면 confidence gain으로 정렬하고 싶을 때 | scorer artifact와 answer generator hook이 필요함. skip path는 answer generation 1회와 confidence scoring 1회, rerank path는 각각 `N+1`회를 수행함 | | Custom strategy | `RerankStrategy` / `AsyncRerankStrategy` | deterministic business logic, proprietary ranking, 새 research method가 필요할 때 | ranking contract와 validation을 직접 책임져야 함 | ### 전략 적용 방법 @@ -308,8 +310,74 @@ worker thread들이 공유하므로 thread-safe backend에서만 `max_workers>1` 합니다. 첫 worker error에서 pending work를 취소하지만, 이미 시작된 Python thread는 background에서 완료될 수 있습니다. -`ranksmith.confidence_generation`은 raw answer/relevance 예시에 대해 closed -model을 호출해 confidence training용 supervised canonical JSONL을 생성할 수 +`ConfidenceGainStrategy`는 별도 sync reranking Strategy입니다. 두 개의 compatible +confidence estimator와 answer generator hook을 받아 사용합니다. + +```python +from ranksmith.confidence import StructuralConfidenceEstimator +from ranksmith.strategies import ConfidenceGainStrategy + +base_estimator = StructuralConfidenceEstimator.from_artifact( + "query-answerability.joblib" +) +context_estimator = StructuralConfidenceEstimator.from_artifact( + "query-context-answerability.joblib" +) + +strategy = ConfidenceGainStrategy( + base_estimator=base_estimator, + context_estimator=context_estimator, + answer_generator=my_answer_generator, +) +``` + +이 Strategy는 `Conf(Q+C)-Conf(Q)` 기준으로 정렬합니다. CBDR retrieval skip, async +reranking, scorer 학습은 구현하지 않습니다. + +`CBDRStrategy`는 sync reranking-side router입니다. retriever와 통합하거나 upstream +retrieval 호출 자체를 멈추지는 않습니다. 이미 `rerank(...)`에 documents가 전달된 +뒤 context reranking을 건너뛸지 결정합니다. + +```python +from ranksmith.integrations import AzureAnswerGenerator +from ranksmith.strategies import CBDRStrategy + +answer_generator = AzureAnswerGenerator.from_env() + +strategy = CBDRStrategy.from_artifacts( + base_artifact_path="query-answerability.joblib", + context_artifact_path="query-context-answerability.joblib", + answer_generator=answer_generator, + skip_threshold=0.8, +) + +results = strategy.rerank(query=query, documents=documents) +``` + +`Conf(Q) >= skip_threshold`이면 original document order를 보존하고 +`metadata["cbdr_skipped"] == True`를 남깁니다. `Conf(Q) < skip_threshold`이면 +모든 문서를 scoring한 뒤 `top_k`를 적용합니다. +`AzureAnswerGenerator`는 `ranksmith.confidence_generation`과 같은 no-answer +sentinel 계약을 사용하며, 답할 수 없으면 `{"answer":"__NO_ANSWER__"}`를 +반환하게 합니다. + +compatible scorer artifact가 있으면 benchmark runner에서도 CBDR을 명시적으로 실행할 +수 있습니다. + +```bash +uv run python scripts/compare_reranking.py \ + --dataset benchmark-cache \ + --cache-dir .benchmark-cache/askubuntu-bm25 \ + --candidates benchmark-results/pyserini/askubuntu-bm25-top20.trec \ + --algorithm cbdr \ + --cbdr-base-artifact query-answerability.joblib \ + --cbdr-context-artifact query-context-answerability.joblib \ + --cbdr-max-document-chars 4000 \ + --allow-live +``` + +`ranksmith.confidence_generation`은 raw answer/relevance/answerability 예시에 대해 +closed model을 호출해 confidence training용 supervised canonical JSONL을 생성할 수 있습니다. 이 모듈은 reranking Strategy가 아니라 데이터 생성 utility입니다. ### compatible confidence scorer 학습 @@ -341,6 +409,102 @@ result = train_confidence_scorer( print(result.export_path) ``` +### LM Studio 로컬 confidence pipeline + +CBDR에는 두 answerability scorer가 필요합니다. query-only 예시에서 `Conf(Q)`를, +query+context 예시에서 `Conf(Q+C)`를 학습합니다. LM Studio는 supervised label +생성에만 사용하며, scorer artifact는 그대로 `ranksmith.confidence_training`이 +학습합니다. + +로컬 OpenAI-compatible server를 시작하고 loaded model을 지정합니다. + +```bash +lms server start +export LMSTUDIO_MODEL=google/gemma-4-12b +``` + +canonical JSONL dataset을 생성합니다. + +```bash +uv run python scripts/generate_confidence_dataset.py \ + --task query_answerability_confidence \ + --provider lmstudio \ + --input runs/confidence/local/raw/query_answerability.jsonl \ + --output runs/confidence/local/canonical/query_answerability_confidence.jsonl \ + --resume + +uv run python scripts/generate_confidence_dataset.py \ + --task query_context_answerability_confidence \ + --provider lmstudio \ + --input runs/confidence/local/raw/query_context_answerability.jsonl \ + --output runs/confidence/local/canonical/query_context_answerability_confidence.jsonl \ + --max-context-chars 8000 \ + --resume +``` + +scorer를 범용적으로 다루기 전에 source/group balance를 확인합니다. + +```bash +uv run python scripts/report_confidence_dataset.py \ + --task query_answerability_confidence \ + --dataset runs/confidence/local/canonical/query_answerability_confidence.jsonl +``` + +CBDR-compatible scorer artifact를 학습합니다. + +```bash +uv run python scripts/train_confidence_scorer.py \ + --task query_answerability_confidence \ + --dataset runs/confidence/local/canonical/query_answerability_confidence.jsonl \ + --output-dir runs/confidence/local/training/query_answerability \ + --export-path runs/confidence/local/artifacts/query_answerability.joblib \ + --encoder-name bert-base-uncased \ + --max-length 256 + +uv run python scripts/train_confidence_scorer.py \ + --task query_context_answerability_confidence \ + --dataset runs/confidence/local/canonical/query_context_answerability_confidence.jsonl \ + --output-dir runs/confidence/local/training/query_context_answerability \ + --export-path runs/confidence/local/artifacts/query_context_answerability.joblib \ + --encoder-name bert-base-uncased \ + --max-length 256 +``` + +학습된 artifact를 LM Studio runtime과 함께 사용합니다. + +```python +from ranksmith.integrations import LMStudioModelProvider, ProviderAnswerGenerator +from ranksmith.strategies import CBDRStrategy + +answer_generator = ProviderAnswerGenerator( + provider=LMStudioModelProvider(model="google/gemma-4-12b") +) + +strategy = CBDRStrategy.from_artifacts( + base_artifact_path="runs/confidence/local/artifacts/query_answerability.joblib", + context_artifact_path="runs/confidence/local/artifacts/query_context_answerability.joblib", + answer_generator=answer_generator, + skip_threshold=0.8, +) +``` + +benchmark runner도 같은 provider를 사용할 수 있습니다. 이 명령은 live 실행이므로 +`--allow-live`가 필요합니다. summary artifact를 만들고 커밋하기 전까지는 benchmark +품질 수치로 주장하지 않습니다. + +```bash +uv run python scripts/compare_reranking.py \ + --dataset benchmark-cache \ + --cache-dir .benchmark-cache/askubuntu-bm25 \ + --candidates benchmark-results/pyserini/askubuntu-bm25-top20.trec \ + --algorithm cbdr \ + --cbdr-answer-provider lmstudio \ + --cbdr-base-artifact runs/confidence/local/artifacts/query_answerability.joblib \ + --cbdr-context-artifact runs/confidence/local/artifacts/query_context_answerability.joblib \ + --lmstudio-model google/gemma-4-12b \ + --allow-live +``` + ## 실전 가이드 (Examples) 실행 가능한 예제는 `examples/` 폴더에 있습니다. @@ -392,6 +556,7 @@ top-5만 출력할 수 있습니다. Live LLM 호출에는 Azure OpenAI deployme - [`benchmark-results/live/askubuntu-bm25-top20-default-live.v3.merged.json`](https://github.com/pko89403/ranksmith/blob/main/benchmark-results/live/askubuntu-bm25-top20-default-live.v3.merged.json) - [`benchmark-results/pyserini/askubuntu-bm25-top20.trec`](https://github.com/pko89403/ranksmith/blob/main/benchmark-results/pyserini/askubuntu-bm25-top20.trec) +- [`benchmark-results/askubuntu-bm25-top20-cbdr-live.json`](https://github.com/pko89403/ranksmith/blob/main/benchmark-results/askubuntu-bm25-top20-cbdr-live.json) (optional `cbdr` method, 별도 run) | Method | NDCG@5 | MRR@5 | Recall@5 | Valid rows | Invalid rate | Nominal LLM calls/query | LLM row attempts/query incl. retries | | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | @@ -402,13 +567,26 @@ top-5만 출력할 수 있습니다. Live LLM 호출에는 Azure OpenAI deployme | `tourrank_r2` | 0.4236 | 0.5725 | 0.3601 | 361/361 | 0.000 | 8 | 1.03 | | `setwise_hs_s10` | 0.3653 | 0.5059 | 0.3005 | 361/361 | 0.000 | 12 | 1.00 | | `prp_sliding_p1` | 0.4065 | 0.5818 | 0.3277 | 361/361 | 0.000 | 38 | 1.00 | +| `cbdr` *(scorer: TriviaQA, 도메인 밖)* | 0.2259 | 0.3458 | 0.1867 | 361/361 | 0.000 | 21 | 1.00 | `tourrank_r2`는 NDCG@5와 Recall@5가 가장 높았고, `prp_sliding_p1`은 MRR@5가 가장 높았습니다. `single_call_listwise@20`은 one-shot listwise baseline입니다. `rankgpt_sw_w5`는 이 top-20 설정의 실제 sliding-window listwise baseline입니다. `acurank_k5_b1`은 AcuRank uncertainty boundary를 `@5` 평가 cutoff와 맞춘 설정입니다. `setwise_hs_s10`은 20개 후보에서 평가 대상 top-5만 추출하는 실용적인 -Setwise Heapsort 설정입니다. +Setwise Heapsort 설정입니다. `cbdr`은 [LM Studio 로컬 confidence +pipeline](#lm-studio-로컬-confidence-pipeline)에 문서화된 `Conf(Q)`/`Conf(Q+C)` +스코어러 두 개를 사용하며, TriviaQA로 학습해 AskUbuntu 기준 도메인 밖입니다. +이 벤치마크에서는 BM25 baseline보다 낮은 점수를 기록했고, 이기도록 튜닝하지 +않고 측정된 그대로 보고합니다. + +왜 이기게 튜닝하지 않는가: 스코어러를 이 벤치마크 분포에 맞추면 알고리즘의 +일반적인 품질이 아니라 AskUbuntu에 대한 과적합을 측정하게 됩니다. 이는 +smoke/partial run이나 cherry-pick된 수치를 벤치마크 품질로 보고하지 않는다는 +이 프로젝트의 보고 규칙([`docs/benchmarks/bm25_top20_reranking.md`](docs/benchmarks/bm25_top20_reranking.md#reporting-rules))과도 +어긋납니다. 더 강한 domain-in 스코어러를 만드는 건 정당한 후속 작업이지만, +그건 더 나은 artifact를 학습해서 같은 커맨드를 다시 돌리는 것이지 보고 +방식을 바꾸는 게 아닙니다. 재시도 후에도 `single_call_listwise@20` 2개 row와 `acurank_k5_b1` 5개 row가 invalid로 남았습니다. 이 row는 보정하지 않고 invalid rate에 반영했습니다. diff --git a/README.md b/README.md index 6abbf86..0530bf8 100644 --- a/README.md +++ b/README.md @@ -14,8 +14,8 @@ documents. Highlights: -- Built-in listwise RankGPT, pairwise PRP, tournament-style TourRank-r, and - uncertainty-aware AcuRank strategies +- Built-in listwise RankGPT, pairwise PRP, tournament-style TourRank-r, + uncertainty-aware AcuRank, and confidence-gain strategies - Public strategy contracts for custom reranking methods - `ModelClient` / `ModelProvider` boundary for vendor-independent LLM calls - Strict JSON parsing and fast-fail error behavior @@ -70,6 +70,8 @@ logic (Algorithm). | `tourrank_r`, `rounds=2` | `TourRankStrategy` | You want stronger quality than listwise on a moderate call budget. | More calls than RankGPT, much fewer than TourRank-10. | | `tourrank_r`, `rounds=10` | `TourRankStrategy` | You are doing quality-focused offline reranking, paper-style evaluation, or final reranking where latency is acceptable. | Highest call cost among built-in methods in normal use. | | `acurank` | `AcuRankStrategy` | You want adaptive listwise reranking that spends calls on uncertain candidates near the top-k boundary. | Uses TrueSkill state and may issue more calls than basic listwise reranking unless capped. | +| `confidence_gain` | `ConfidenceGainStrategy` | You have trained query-only and query+context confidence scorers and want to rank documents by `Conf(Q+C)-Conf(Q)`. | Requires scorer artifacts and an answer generator hook. Runtime calls answer generation `N+1` times and confidence scoring `N+1` times for `N` documents. | +| `cbdr` | `CBDRStrategy` | You have trained answerability confidence scorers and want to skip context reranking when `Conf(Q)` is already high, otherwise rerank by confidence gain. | Requires scorer artifacts and an answer generator hook. Skip path uses 1 answer generation call and 1 confidence score; rerank path uses `N+1` answer generations and `N+1` confidence scores. | | Custom strategy | `RerankStrategy` / `AsyncRerankStrategy` | You need deterministic business logic, a proprietary ranking process, or a new research method. | You own the ranking contract and validation behavior. | ### Applying a Strategy @@ -312,9 +314,76 @@ encoder and scorer instances across worker threads, so use `max_workers>1` only with thread-safe backends. It cancels pending work on the first worker error, but Python threads that have already started may finish in the background. +`ConfidenceGainStrategy` is a separate sync reranking Strategy that consumes +two compatible confidence estimators and an answer generator hook: + +```python +from ranksmith.confidence import StructuralConfidenceEstimator +from ranksmith.strategies import ConfidenceGainStrategy + +base_estimator = StructuralConfidenceEstimator.from_artifact( + "query-answerability.joblib" +) +context_estimator = StructuralConfidenceEstimator.from_artifact( + "query-context-answerability.joblib" +) + +strategy = ConfidenceGainStrategy( + base_estimator=base_estimator, + context_estimator=context_estimator, + answer_generator=my_answer_generator, +) +``` + +It ranks by `Conf(Q+C)-Conf(Q)`. It does not implement CBDR retrieval skipping, +async reranking, or scorer training. + +`CBDRStrategy` is a sync reranking-side router. It does not integrate with a +retriever or stop upstream retrieval calls; it only skips context reranking once +documents have already been passed to `rerank(...)`. + +```python +from ranksmith.integrations import AzureAnswerGenerator +from ranksmith.strategies import CBDRStrategy + +answer_generator = AzureAnswerGenerator.from_env() + +strategy = CBDRStrategy.from_artifacts( + base_artifact_path="query-answerability.joblib", + context_artifact_path="query-context-answerability.joblib", + answer_generator=answer_generator, + skip_threshold=0.8, +) + +results = strategy.rerank(query=query, documents=documents) +``` + +When `Conf(Q) >= skip_threshold`, results preserve original document order and +include `metadata["cbdr_skipped"] == True`. When `Conf(Q) < skip_threshold`, all +documents are scored before `top_k` slicing. +`AzureAnswerGenerator` uses the same no-answer sentinel contract as +`ranksmith.confidence_generation` and returns `{"answer":"__NO_ANSWER__"}` when +the model cannot answer. + +The benchmark runner can execute CBDR explicitly when compatible scorer +artifacts are available: + +```bash +uv run python scripts/compare_reranking.py \ + --dataset benchmark-cache \ + --cache-dir .benchmark-cache/askubuntu-bm25 \ + --candidates benchmark-results/pyserini/askubuntu-bm25-top20.trec \ + --algorithm cbdr \ + --cbdr-base-artifact query-answerability.joblib \ + --cbdr-context-artifact query-context-answerability.joblib \ + --cbdr-max-document-chars 4000 \ + --allow-live +``` + `ranksmith.confidence_generation` can create supervised canonical JSONL for -confidence training by calling a closed model over raw answer or relevance -examples. It is a data-generation utility, not a reranking Strategy. +confidence training by calling a closed model over raw answer, relevance, or +answerability examples. It is a data-generation utility, not a reranking +Strategy. ### Training a compatible confidence scorer @@ -345,6 +414,102 @@ result = train_confidence_scorer( print(result.export_path) ``` +### Local LM Studio confidence pipeline + +For CBDR, train two answerability scorers: `Conf(Q)` from query-only examples +and `Conf(Q+C)` from query+context examples. LM Studio is used only to generate +supervised labels; the scorer artifact is still trained by +`ranksmith.confidence_training`. + +Start the local OpenAI-compatible server and select the loaded model: + +```bash +lms server start +export LMSTUDIO_MODEL=google/gemma-4-12b +``` + +Generate canonical JSONL datasets: + +```bash +uv run python scripts/generate_confidence_dataset.py \ + --task query_answerability_confidence \ + --provider lmstudio \ + --input runs/confidence/local/raw/query_answerability.jsonl \ + --output runs/confidence/local/canonical/query_answerability_confidence.jsonl \ + --resume + +uv run python scripts/generate_confidence_dataset.py \ + --task query_context_answerability_confidence \ + --provider lmstudio \ + --input runs/confidence/local/raw/query_context_answerability.jsonl \ + --output runs/confidence/local/canonical/query_context_answerability_confidence.jsonl \ + --max-context-chars 8000 \ + --resume +``` + +Review source and group balance before treating the scorer as general: + +```bash +uv run python scripts/report_confidence_dataset.py \ + --task query_answerability_confidence \ + --dataset runs/confidence/local/canonical/query_answerability_confidence.jsonl +``` + +Train CBDR-compatible scorer artifacts: + +```bash +uv run python scripts/train_confidence_scorer.py \ + --task query_answerability_confidence \ + --dataset runs/confidence/local/canonical/query_answerability_confidence.jsonl \ + --output-dir runs/confidence/local/training/query_answerability \ + --export-path runs/confidence/local/artifacts/query_answerability.joblib \ + --encoder-name bert-base-uncased \ + --max-length 256 + +uv run python scripts/train_confidence_scorer.py \ + --task query_context_answerability_confidence \ + --dataset runs/confidence/local/canonical/query_context_answerability_confidence.jsonl \ + --output-dir runs/confidence/local/training/query_context_answerability \ + --export-path runs/confidence/local/artifacts/query_context_answerability.joblib \ + --encoder-name bert-base-uncased \ + --max-length 256 +``` + +Use the artifacts with LM Studio at runtime: + +```python +from ranksmith.integrations import LMStudioModelProvider, ProviderAnswerGenerator +from ranksmith.strategies import CBDRStrategy + +answer_generator = ProviderAnswerGenerator( + provider=LMStudioModelProvider(model="google/gemma-4-12b") +) + +strategy = CBDRStrategy.from_artifacts( + base_artifact_path="runs/confidence/local/artifacts/query_answerability.joblib", + context_artifact_path="runs/confidence/local/artifacts/query_context_answerability.joblib", + answer_generator=answer_generator, + skip_threshold=0.8, +) +``` + +The benchmark runner can use the same provider. This command is live and +requires `--allow-live`; it does not imply any benchmark quality number unless +summary artifacts are produced and committed. + +```bash +uv run python scripts/compare_reranking.py \ + --dataset benchmark-cache \ + --cache-dir .benchmark-cache/askubuntu-bm25 \ + --candidates benchmark-results/pyserini/askubuntu-bm25-top20.trec \ + --algorithm cbdr \ + --cbdr-answer-provider lmstudio \ + --cbdr-base-artifact runs/confidence/local/artifacts/query_answerability.joblib \ + --cbdr-context-artifact runs/confidence/local/artifacts/query_context_answerability.joblib \ + --lmstudio-model google/gemma-4-12b \ + --allow-live +``` + ## Examples Runnable examples live in the `examples/` directory. @@ -396,6 +561,7 @@ algorithm run. The committed evidence artifacts are: - [`benchmark-results/live/askubuntu-bm25-top20-default-live.v3.merged.json`](https://github.com/pko89403/ranksmith/blob/main/benchmark-results/live/askubuntu-bm25-top20-default-live.v3.merged.json) - [`benchmark-results/pyserini/askubuntu-bm25-top20.trec`](https://github.com/pko89403/ranksmith/blob/main/benchmark-results/pyserini/askubuntu-bm25-top20.trec) +- [`benchmark-results/askubuntu-bm25-top20-cbdr-live.json`](https://github.com/pko89403/ranksmith/blob/main/benchmark-results/askubuntu-bm25-top20-cbdr-live.json) (optional `cbdr` method, run separately) | Method | NDCG@5 | MRR@5 | Recall@5 | Valid rows | Invalid rate | Nominal LLM calls/query | LLM row attempts/query incl. retries | | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | @@ -406,13 +572,27 @@ algorithm run. The committed evidence artifacts are: | `tourrank_r2` | 0.4236 | 0.5725 | 0.3601 | 361/361 | 0.000 | 8 | 1.03 | | `setwise_hs_s10` | 0.3653 | 0.5059 | 0.3005 | 361/361 | 0.000 | 12 | 1.00 | | `prp_sliding_p1` | 0.4065 | 0.5818 | 0.3277 | 361/361 | 0.000 | 38 | 1.00 | +| `cbdr` *(scorers: TriviaQA, out-of-domain)* | 0.2259 | 0.3458 | 0.1867 | 361/361 | 0.000 | 21 | 1.00 | `tourrank_r2` had the best NDCG@5 and Recall@5, while `prp_sliding_p1` had the best MRR@5. `single_call_listwise@20` is the one-shot listwise baseline. `rankgpt_sw_w5` is the true sliding-window listwise baseline for this top-20 setup. `acurank_k5_b1` aligns AcuRank's uncertainty boundary with the `@5` evaluation cutoff. `setwise_hs_s10` is a practical Setwise Heapsort setting -that extracts only the evaluated top-5 from 20 candidates. +that extracts only the evaluated top-5 from 20 candidates. `cbdr` uses the two +`Conf(Q)`/`Conf(Q+C)` scorers documented under [Local LM Studio confidence +pipeline](#local-lm-studio-confidence-pipeline), trained on TriviaQA (out-of-domain +for AskUbuntu); it scores below the BM25 baseline here and is reported as +measured, not tuned to win. + +Why not tune it to win: fitting the scorer to this benchmark's distribution +would measure overfitting to AskUbuntu, not the algorithm's general quality, +which conflicts with this project's reporting rule that smoke/partial runs +and cherry-picked numbers are never reported as benchmark quality (see +[`docs/benchmarks/bm25_top20_reranking.md`](docs/benchmarks/bm25_top20_reranking.md#reporting-rules)). +A stronger in-domain scorer is a legitimate follow-up, but that means +training a better artifact and re-running this exact command, not adjusting +the report. After retries, 2 `single_call_listwise@20` rows and 5 `acurank_k5_b1` rows remained invalid. They are included in the invalid-rate accounting instead of diff --git a/benchmark-results/askubuntu-bm25-top20-cbdr-live.checkpoint.jsonl b/benchmark-results/askubuntu-bm25-top20-cbdr-live.checkpoint.jsonl new file mode 100644 index 0000000..a86116d --- /dev/null +++ b/benchmark-results/askubuntu-bm25-top20-cbdr-live.checkpoint.jsonl @@ -0,0 +1,361 @@ +{"algorithm": "cbdr", "fixture_id": "askubuntu-bm25-test-test_query0", "metrics": {"mrr@5": 0.2, "ndcg@5": 0.13120507751234178, "recall@5": 0.08333333333333333}, "query_id": "test_query0", "ranked_ids": ["negative_test_query36_00011", "apositive_test_query130_00000", "negative_test_query9_00017", "negative_test_query221_00006", "apositive_test_query0_00003"], "valid": true} +{"algorithm": "cbdr", "fixture_id": "askubuntu-bm25-test-test_query1", "metrics": {"mrr@5": 0.0, "ndcg@5": 0.0, "recall@5": 0.0}, "query_id": "test_query1", "ranked_ids": ["negative_test_query1_00005", "negative_test_query1_00009", "negative_test_query331_00002", 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"apositive_test_query193_00010", + "apositive_test_query99_00001" + ], + "valid": true + } + ] +} \ No newline at end of file diff --git a/docs/specs/spec_cbdr_strategy.md b/docs/specs/spec_cbdr_strategy.md new file mode 100644 index 0000000..d46365c --- /dev/null +++ b/docs/specs/spec_cbdr_strategy.md @@ -0,0 +1,402 @@ +# Spec: CBDR Strategy + +## 1. 개요 (Overview) +- **작업 목적**: query-only confidence가 충분히 높으면 context reranking을 건너뛰고, 낮으면 `Conf(Q+C)-Conf(Q)` confidence gain으로 documents를 rerank하는 CBDR 본체 Strategy를 추가한다. +- **Reference**: + - `docs/wiki/references/parametric_post_retrieval_confidence.md` + - `docs/specs/spec_confidence_runtime_readiness.md` + - `docs/specs/spec_confidence_gain_reranking.md` +- **상태**: `[ ] Draft` | `[ ] In Progress` | `[x] Completed` + +이 스펙은 논문 원형을 그대로 재현하지 않는다. + +논문 원형의 CBDR은 query-only confidence가 높으면 retrieval 자체를 skip한다. +ranksmith는 retriever, indexer, vector search를 소유하지 않으므로 true pre-retrieval skip은 구현하지 않는다. +이번 스펙은 **reranking-side CBDR router**만 구현한다. + +즉, 이미 documents가 전달된 `rerank()` 호출 안에서 context reranking을 skip할 수 있다. +외부 retriever가 retrieval 호출 전에 사용할 query-only decision API는 이번 범위에 포함하지 않는다. + +핵심 목표: + +```text +base = Conf(Q) + +if base >= skip_threshold: + context scoring과 reranking을 수행하지 않음 + original order를 보존하고 cbdr_skipped metadata를 남김 +else: + after_i = Conf(Q + C_i) + gain_i = after_i - base + documents sorted by gain_i desc +``` + +## 2. 요구 사항 및 제약 (Requirements & Constraints) + +### 포함 범위 +- sync `CBDRStrategy` 추가. +- query-only confidence 기반 skip decision 추가. +- low-confidence query에서 confidence gain reranking 수행. +- `ConfidenceGainStrategy`와 공유 가능한 계산/검증 로직 정리. +- `AzureOpenAIReranker` sync facade에서 built-in strategy로 처리. +- `ranksmith.strategies` submodule export 추가. +- README/README.ko 및 architecture wiki 문서 반영. + +### 제외 범위 +- retriever integration. +- vector search/indexing. +- upstream retrieval 호출 자체를 중단하는 orchestration API. +- query-only `should_retrieve()` helper. +- async `CBDRStrategy`. +- closed model provider 병렬 호출. +- answer generation cache. +- confidence scorer 학습 또는 artifact 생성. +- benchmark 수치 README 반영. +- CBDR threshold 자동 튜닝. +- reranker fine-tuning. + +### 입력 (Inputs) + +```python +CBDRStrategy( + base_estimator: ConfidenceEstimator, + context_estimator: ConfidenceEstimator, + answer_generator: AnswerGenerator, + skip_threshold: float = 0.8, + max_document_chars: int = 4000, + algorithm: Literal["cbdr"] = "cbdr", +) +``` + +필수 task type: + +```text +base_estimator.task_type == "query_answerability_confidence" +context_estimator.task_type == "query_context_answerability_confidence" +``` + +`answer_generator`는 기존 `ConfidenceGainStrategy`와 같은 contract를 사용한다. + +```python +class AnswerGenerator(Protocol): + def answer_query(self, query: str) -> str: ... + def answer_with_context(self, query: str, context: str) -> str: ... +``` + +`model_client`는 `RerankStrategy` protocol 때문에 `rerank()` 인자로 받지만 사용하지 않는다. +answer generation은 `answer_generator` 책임이다. +CBDRStrategy는 `model_client` capability를 검사하지 않는다. + +### 출력 (Outputs) + +#### skip path + +`Conf(Q) >= skip_threshold`이면 context answer generation과 context confidence scoring을 수행하지 않는다. +입력 documents의 original order를 보존해 `RerankResult`를 반환한다. +rank는 반환 결과 기준 1-based로 부여한다. +original_index는 입력 documents 기준 0-based를 보존한다. + +```python +{ + "strategy": "cbdr", + "algorithm": "cbdr", + "cbdr_skipped": True, + "base_confidence": 0.91, + "skip_threshold": 0.8, + "context_confidence": None, + "confidence_gain": None, +} +``` + +#### rerank path + +`Conf(Q) < skip_threshold`이면 confidence gain으로 정렬한다. +rank는 반환 결과 기준 1-based로 부여한다. +original_index는 입력 documents 기준 0-based를 보존한다. + +```python +{ + "strategy": "cbdr", + "algorithm": "cbdr", + "cbdr_skipped": False, + "base_confidence": 0.42, + "skip_threshold": 0.8, + "context_confidence": 0.81, + "confidence_gain": 0.39, +} +``` + +### 제약 사항 (Constraints) +- hidden state, logits, attention에 직접 접근하지 않는다. +- runtime Strategy는 scorer artifact를 학습하지 않는다. +- `Conf(Q)`는 query당 한 번만 계산한다. +- 빈 documents 또는 `top_k == 0`이면 base answer generation과 confidence scoring을 수행하지 않고 `[]`를 반환한다. +- skip path에서는 `answer_with_context()`와 `context_estimator.score()`를 호출하지 않는다. +- skip path에서는 document text를 context로 읽지 않으므로 `max_document_chars` 검증도 수행하지 않는다. +- rerank path에서는 문서별로 `Conf(Q+C_i)`를 계산한다. +- skip 여부를 metadata에 반드시 남긴다. +- skip path에서 original order를 조용히 수정하지 않는다. +- rerank path에서 gain 동점은 original index 오름차순으로 안정 정렬한다. +- `top_k > 0`은 skip/rerank path 모두 최종 결과에 slicing으로만 적용한다. +- score는 finite float이며 `[0, 1]` 범위여야 한다. +- `skip_threshold`는 finite float이며 `[0, 1]` 범위여야 한다. +- bool은 numeric score 또는 `skip_threshold`로 인정하지 않는다. +- `skip_threshold=0.0`이면 non-empty documents에서 항상 skip된다. +- `skip_threshold=1.0`이면 `base_confidence == 1.0`일 때만 skip된다. +- skip path는 answer generation 1회와 confidence scoring 1회를 수행한다. +- rerank path는 문서 수가 `N`이면 answer generation `N + 1`회와 confidence scoring `N + 1`회를 수행한다. +- rerank path에서는 `top_k`가 작아도 모든 문서를 scoring한 뒤 slicing한다. +- confidence scoring 실패 시 기존 ranking으로 fallback하지 않는다. +- rerank path에서 `max_document_chars`를 초과하면 `DocumentTooLongError`로 실패한다. +- metadata에 answer text, scorer artifact path, HuggingFace token, raw hidden state, raw feature vector를 넣지 않는다. +- root import 확장은 사용자 승인 없이는 하지 않는다. + +## 3. 상세 설계 (Architecture & Design) + +### 동작 메커니즘 +1. query와 `top_k`를 검증한다. +2. `answer_generator.answer_query(query)`로 base answer를 생성한다. +3. `base_estimator.score(QueryAnswerabilityConfidenceInput(...))`로 `Conf(Q)`를 계산한다. +4. `base_confidence >= skip_threshold`이면 skip path로 간다. +5. skip path는 document length 검증과 context 호출 없이 original order를 보존해 결과를 만든다. +6. `base_confidence < skip_threshold`이면 rerank path로 간다. +7. rerank path에서 document length를 검증한다. +8. 문서별 `answer_with_context(query, document.text)`를 호출한다. +9. 문서별 `Conf(Q+C_i)`를 계산한다. +10. `gain_i = context_confidence_i - base_confidence`를 계산한다. +11. gain 내림차순, original index 오름차순으로 정렬한다. + +### 의사 알고리즘 (Pseudo-algorithm) + +```text +cbdr_rerank(query, documents, top_k): + validate query, top_k + + if documents is empty or top_k == 0: + return [] + + base_answer = answer_generator.answer_query(query) + base = score_base(query, base_answer) + + if base >= skip_threshold: + results = original_order_results( + cbdr_skipped=True, + base_confidence=base, + context_confidence=None, + confidence_gain=None, + ) + if top_k is not None: + results = results[:top_k] + assign rank from 1 over returned results + return results + + validate document lengths + scored = [] + for each document with original_index: + context_answer = answer_generator.answer_with_context(query, document.text) + context = score_context(query, document.text, context_answer) + gain = context - base + scored.append(original_index, context, gain) + + scored = sort scored by (-gain, original_index) + results = scored_results(scored) + if top_k is not None: + results = results[:top_k] + assign rank from 1 over returned results + return results +``` + +### 의사 코드 (Pseudo-code) + +```python +from ranksmith.strategies import CBDRStrategy + +strategy = CBDRStrategy( + base_estimator=query_estimator, + context_estimator=query_context_estimator, + answer_generator=answer_generator, + skip_threshold=0.8, +) + +reranker = AzureOpenAIReranker( + model_client=model_client, + strategy=strategy, +) + +results = reranker.rerank(query, documents, top_k=10) +``` + +### 통합 지점 (Integration Points) + +Strategy: +- `src/ranksmith/strategies/_cbdr.py` +- `src/ranksmith/strategies/_confidence_gain.py` +- `src/ranksmith/strategies/__init__.py` + +Facade: +- `src/ranksmith/azure.py` + +Tests: +- `tests/test_cbdr_strategy.py` + +Docs: +- `docs/wiki/02_architecture.md` +- `docs/specs/spec_cbdr_strategy.md` +- `README.md` +- `README.ko.md` + +## 4. 재사용 및 모듈화 (Reusability & Modularization) + +### Metadata contract +공통 confidence key: + +```python +{ + "base_confidence": float, + "context_confidence": float | None, + "confidence_gain": float | None, +} +``` + +CBDR 전용 key: + +```python +{ + "strategy": "cbdr", + "algorithm": "cbdr", + "cbdr_skipped": bool, + "skip_threshold": float, +} +``` + +metadata에는 다음 값을 넣지 않는다. +- generated answer text +- scorer artifact path +- HuggingFace token +- raw hidden state +- raw structural feature vector + +### 공통 컴포넌트 식별 (Shared Components) +- `AnswerGenerator` protocol은 `ConfidenceGainStrategy`와 공유한다. +- `ConfidenceEstimator` protocol은 `ConfidenceGainStrategy`와 공유한다. +- task type 상수는 중복 정의하지 않는다. +- answer validation과 confidence score validation은 공유 helper로 분리한다. +- context gain 계산은 CBDR과 confidence gain strategy가 같은 helper를 사용한다. + +### 추상화 방안 (Abstraction Plan) +- `src/ranksmith/strategies/_confidence_gain.py`에 이미 있는 공통 요소를 private helper로 정리한다. +- `CBDRStrategy`는 `ConfidenceGainStrategy`를 상속하지 않는다. + - 이유: skip path가 있는 router와 pure confidence gain reranker는 의미가 다르다. +- 대신 shared helper를 사용해 검증, answer 호출, confidence gain 계산의 중복을 줄인다. + +권장 구조: + +```text +_confidence_gain.py + AnswerGenerator + ConfidenceEstimator + ConfidenceGainResult + validate_answer + validate_confidence_score + score_base_answerability + score_context_answerability + +_cbdr.py + CBDRStrategy +``` + +## 5. 에러 핸들링 (Error Handling) +- 빈 query: `RerankInputError` +- 빈 documents: `[]` +- `top_k == 0`: `[]` +- `top_k < 0`: `RerankInputError` +- `skip_threshold`가 finite probability가 아니거나 bool이면 `ValueError` +- `max_document_chars < 1`: `ValueError` +- low-confidence rerank path에서 document length 초과: `DocumentTooLongError` +- base/context estimator task mismatch: `RerankInputError` +- answer generator가 빈 answer 반환: `RerankProviderError` +- answer generator가 예외 발생: `RerankProviderError`로 래핑 +- direct `CBDRStrategy.rerank()`에서 confidence estimator의 기존 `RerankError`는 그대로 전파 +- direct `CBDRStrategy.rerank()`에서 confidence estimator의 unexpected exception은 그대로 전파 +- `AzureOpenAIReranker` facade에서 built-in strategy의 unexpected exception은 `RerankProviderError`로 래핑 +- confidence score가 finite probability가 아님: `RerankStrategyError` +- gain이 finite float가 아님: `RerankStrategyError` +- output ranking 보정 필요 상황: 조용히 보정하지 않고 실패 + +## 6. 테스트 계획 (Test Plan) + +### 성공 케이스 (Happy Paths) +- 빈 documents이면 `[]` 반환. +- 빈 documents에서 `answer_query()`와 estimator가 호출되지 않음. +- `top_k == 0`이면 `[]` 반환. +- `top_k == 0`에서 `answer_query()`와 estimator가 호출되지 않음. +- `base_confidence >= skip_threshold`이면 original order 보존. +- skip path rank는 반환 결과 기준 1-based. +- skip path original_index는 입력 documents 기준 0-based. +- skip path에서 `answer_with_context()`가 호출되지 않음. +- skip path에서 `context_estimator.score()`가 호출되지 않음. +- skip path metadata에 `cbdr_skipped=True` 기록. +- skip path에서 `top_k` slicing 적용. +- skip path에서 긴 document가 있어도 `DocumentTooLongError`가 발생하지 않음. +- `base_confidence < skip_threshold`이면 confidence gain으로 rerank. +- rerank path metadata에 `cbdr_skipped=False`, base/context/gain 기록. +- gain 동점 시 original index 유지. +- rerank path에서 `top_k`가 작아도 모든 문서를 scoring한 뒤 slicing. +- `skip_threshold=0.0`이면 non-empty documents에서 skip. +- `skip_threshold=1.0`이면 `base_confidence == 1.0`일 때만 skip. +- `AzureOpenAIReranker` facade에서 `CBDRStrategy` 예외 surface가 built-in strategy와 동일함. + +### 엣지/실패 케이스 (Edge & Failure Cases) +- 잘못된 `skip_threshold` 실패. +- bool `skip_threshold` 실패. +- 잘못된 estimator task type 실패. +- 빈 query 실패. +- low-confidence rerank path에서만 긴 document 실패. +- answer generator empty output 실패. +- base confidence score 범위 밖 실패. +- context confidence score 범위 밖 실패. +- base scoring 실패가 skip fallback으로 숨겨지지 않음. +- context scoring 실패가 original order fallback으로 숨겨지지 않음. + +### 공통 Reranking Smoke/Benchmark +- fake answer generator와 fake confidence estimator로 deterministic smoke test를 추가한다. +- artifact load 기반 skip path E2E smoke를 추가해 `from_artifact()` -> `CBDRStrategy` -> `AzureOpenAIReranker` 경로를 검증한다. +- artifact load 기반 rerank path E2E smoke를 추가해 `from_artifact()` -> `CBDRStrategy` -> `AzureOpenAIReranker` 경로를 검증한다. +- 실제 closed model live test는 credential/cost 때문에 opt-in으로 분리한다. +- README benchmark 수치는 추가하지 않는다. + +검증 명령: + +```bash +uv run pytest tests/test_cbdr_strategy.py tests/test_confidence_gain_strategy.py -q +./scripts/verify.sh +``` + +--- + +## 7. 작업 태스크 추적 (Task Checklist) + +### Phase 1: 컨텍스트 및 설계 확인 +- [x] 관련 기존 코드베이스 및 Wiki 문서 확인 +- [x] CBDR을 ranksmith에서 reranking-side router로 정의 +- [x] 스펙 문서 작성 + +### Phase 2: 로직 구현 (Implementation) +- [x] `src/ranksmith/strategies/_confidence_gain.py`: 공통 protocol/helper 정리 +- [x] `src/ranksmith/strategies/_cbdr.py`: `CBDRStrategy` 구현 +- [x] `src/ranksmith/strategies/__init__.py`: strategy export 추가 +- [x] `src/ranksmith/azure.py`: built-in sync strategy 처리 추가 + +### Phase 3: 검증 (Verification) +- [x] `tests/test_cbdr_strategy.py`: skip path 정상 케이스 추가 +- [x] `tests/test_cbdr_strategy.py`: rerank path 정상 케이스 추가 +- [x] `tests/test_cbdr_strategy.py`: 엣지/실패 케이스 추가 +- [x] `tests/test_cbdr_strategy.py`: Azure facade smoke 추가 +- [x] `tests/test_cbdr_strategy.py`: artifact load 기반 skip path E2E smoke 추가 +- [x] `tests/test_cbdr_strategy.py`: artifact load 기반 rerank path E2E smoke 추가 +- [x] `./scripts/verify.sh` 스크립트를 통한 린트/타입/전체 테스트 통과 확인 + +### Phase 4: 완료 및 정리 +- [x] `docs/wiki/02_architecture.md`: CBDR strategy 위치 추가 +- [x] `README.md` / `README.ko.md`: benchmark 없는 usage 문서 추가 +- [x] 본 문서 최상단의 **상태**를 `Completed`로 변경 diff --git a/docs/specs/spec_cbdr_user_facing_integration.md b/docs/specs/spec_cbdr_user_facing_integration.md new file mode 100644 index 0000000..a44b9e3 --- /dev/null +++ b/docs/specs/spec_cbdr_user_facing_integration.md @@ -0,0 +1,105 @@ +# Spec: CBDR User-Facing Integration Layer + +## 1. 개요 (Overview) +- **작업 목적**: scorer artifact와 Azure 설정만으로 `CBDRStrategy`를 쉽게 생성하고, 기존 benchmark runner에서 `--algorithm cbdr`로 실행할 수 있게 한다. +- **Reference**: + - `docs/specs/spec_cbdr_strategy.md` + - `docs/specs/spec_confidence_gain_reranking.md` + - `docs/wiki/references/parametric_post_retrieval_confidence.md` +- **상태**: `[ ] Draft` | `[ ] In Progress` | `[x] Completed` + +## 2. 요구 사항 및 제약 (Requirements & Constraints) +- **입력 (Inputs)**: + - `CBDRStrategy.from_artifacts(...)`: base/context scorer artifact path, optional metadata path, answer generator, threshold, HF 로딩 옵션. + - `AzureAnswerGenerator`: Azure OpenAI credential/config 또는 기존 환경 변수, no-answer sentinel. + - `compare_reranking.py`: `--algorithm cbdr`, CBDR artifact flags, HF/runtime flags. +- **출력 (Outputs)**: + - 기존 `CBDRStrategy.rerank(...)`와 동일한 `list[RerankResult]`. + - benchmark report에는 CBDR method settings와 provider call upper bound estimate를 기록한다. +- **제약 사항 (Constraints)**: + - CBDR은 sync만 지원한다. + - root import는 확장하지 않는다. + - scorer training, retriever integration, async CBDR은 제외한다. + - 잘못된 설정과 malformed closed-model output은 fast fail한다. + +## 3. 상세 설계 (Architecture & Design) +- `CBDRStrategy.from_artifacts(...)`는 `StructuralConfidenceEstimator.from_artifact(...)`를 두 번 호출해 base/context estimator를 만든다. +- `ranksmith.integrations.AzureAnswerGenerator`는 query-only와 query+context answer 생성을 담당한다. +- `AzureAnswerGenerator`는 generation pipeline과 같은 no-answer sentinel 계약을 prompt에 포함하고, JSON object 응답에서 `answer` 문자열만 파싱한다. +- `compare_reranking.py`는 CBDR일 때 factory와 Azure answer generator를 조립한 뒤 `CBDRStrategy.rerank(...)`를 직접 호출한다. + +### Pseudo-algorithm +```text +create CBDR: + base_estimator = StructuralConfidenceEstimator.from_artifact(base_artifact, task=query_answerability_confidence, ...) + context_estimator = StructuralConfidenceEstimator.from_artifact(context_artifact, task=query_context_answerability_confidence, ...) + return CBDRStrategy(base_estimator, context_estimator, answer_generator, threshold) + +benchmark cbdr: + validate live opt-in + validate artifact flags + answer_generator = AzureAnswerGenerator.from_env(...) + strategy = CBDRStrategy.from_artifacts(...) + results = strategy.rerank(query=query, documents=documents) +``` + +### Integration Points +- `src/ranksmith/strategies/_cbdr.py` +- `src/ranksmith/integrations/` +- `scripts/compare_reranking.py` +- `tests/test_cbdr_strategy.py` +- `tests/test_azure_answer_generator.py` +- `tests/test_compare_reranking.py` + +## 4. 재사용 및 모듈화 (Reusability & Modularization) +- `CBDRStrategy.from_artifacts(...)`는 estimator 조립만 담당하고 reranking logic은 기존 `CBDRStrategy.rerank(...)`를 재사용한다. +- `AzureAnswerGenerator`는 Strategy가 아니라 integration helper다. +- benchmark runner는 CBDR 전용 설정만 추가하고 기존 algorithm selection/report 구조를 유지한다. + +## 5. 에러 핸들링 (Error Handling) +- artifact path 누락: `SystemExit` 또는 기존 artifact error. +- 잘못된 confidence task type: 기존 `ConfidenceArtifactError`. +- malformed answer JSON: `RerankParseError`. +- missing/empty answer: `RerankParseError`. +- Azure env 누락: `RerankInputError`. +- invalid no-answer sentinel: `ValueError`. + +## 6. 테스트 계획 (Test Plan) +- **성공 케이스**: + - `CBDRStrategy.from_artifacts(...)`가 base/context artifact를 올바른 task type으로 로드한다. + - `AzureAnswerGenerator`가 `{"answer": "..."}`를 파싱한다. + - `AzureAnswerGenerator` prompt가 no-answer sentinel 계약을 포함한다. + - `compare_reranking.py --algorithm cbdr`가 CBDR strategy를 생성한다. +- **엣지/실패 케이스**: + - artifact flag 누락 시 실패. + - `--allow-live` 없으면 CBDR 실행 차단. + - `--cbdr-max-document-chars`가 strategy factory로 전달됨. + - malformed/missing/empty answer는 실패. + - HF token env 이름은 실제 env 값으로 resolve한다. +- **공통 Reranking Smoke/Benchmark**: + - live provider는 opt-in으로만 실행한다. + - CBDR provider call estimate는 upper bound로 기록한다. + +--- + +## 7. 작업 태스크 추적 (Task Checklist) + +### Phase 1: 컨텍스트 및 설계 확인 +- [x] 관련 기존 코드베이스 및 Wiki 문서 확인 +- [x] 스펙 문서(본 문서) 상의 의사 코드 설계 검토 및 확정 + +### Phase 2: 로직 구현 (Implementation) +- [x] `src/ranksmith/strategies/_cbdr.py`: `from_artifacts(...)` factory 추가 +- [x] `src/ranksmith/integrations/`: `AzureAnswerGenerator` 추가 +- [x] `scripts/compare_reranking.py`: CBDR algorithm/flags/estimate 연결 + +### Phase 3: 검증 (Verification) +- [x] `tests/test_cbdr_strategy.py`: factory 테스트 추가 +- [x] `tests/test_azure_answer_generator.py`: answer generator 테스트 추가 +- [x] `tests/test_compare_reranking.py`: benchmark 연결 테스트 추가 +- [x] `./scripts/verify.sh` 스크립트를 통한 린트/타입/전체 테스트 통과 확인 + +### Phase 4: 완료 및 정리 +- [x] `docs/wiki/02_architecture.md` 업데이트 +- [x] `README.md`, `README.ko.md` 업데이트 +- [x] 본 문서 최상단의 **상태**를 `Completed`로 변경 diff --git a/docs/specs/spec_confidence_gain_reranking.md b/docs/specs/spec_confidence_gain_reranking.md new file mode 100644 index 0000000..e84445d --- /dev/null +++ b/docs/specs/spec_confidence_gain_reranking.md @@ -0,0 +1,415 @@ +# Spec: Confidence Gain Reranking + +## 1. 개요 (Overview) +- **작업 목적**: post-retrieval context가 closed model의 answerability confidence를 얼마나 올리는지 계산하고, 그 변화량으로 documents를 rerank한다. +- **Reference**: + - `docs/wiki/references/parametric_post_retrieval_confidence.md` + - `docs/wiki/references/structural_confidence.md` + - `docs/specs/spec_structural_confidence.md` + - `docs/specs/spec_confidence_runtime_readiness.md` + - `docs/specs/spec_confidence_training_pipeline.md` + - `docs/specs/spec_confidence_generation_pipeline.md` +- **상태**: `[ ] Draft` | `[ ] In Progress` | `[x] Completed` + +이 스펙은 논문 원형을 그대로 재현하지 않는다. + +논문 원형은 target LLM의 hidden state 기반 confidence detector와 reranker fine-tuning을 사용한다. +ranksmith는 closed model API와 runtime training-free reranking을 지향하므로, 기존 structural confidence module을 confidence proxy로 사용한다. +runtime reranking은 training-free다. +단, confidence scorer artifact는 사전에 generation/training pipeline으로 준비되어 있어야 한다. + +핵심 목표: + +```text +base = Conf(Q) +after_i = Conf(Q + C_i) +gain_i = after_i - base +documents sorted by gain_i desc +``` + +## 2. 요구 사항 및 제약 (Requirements & Constraints) + +### 포함 범위 +- query-only answerability confidence task 추가. +- query+context answerability confidence task 추가. +- confidence generation pipeline에서 두 task의 canonical JSONL 생성 지원. +- confidence training pipeline에서 두 task 학습 지원. +- confidence runtime에서 두 task inference 지원. +- confidence gain 계산 utility 추가. +- confidence gain 기반 sync reranking Strategy 추가. + +### 제외 범위 +- CBDR retrieval skip 구현. +- retriever integration. +- vector search/indexing. +- async confidence gain strategy. +- closed model provider 병렬 호출. +- answer generation cache. +- reranker fine-tuning. +- benchmark 수치 README 반영. +- semantic feature fusion. +- hidden state, logits, attention 직접 사용. + +CBDR은 이 스펙의 직접 범위가 아니다. +다만 `Conf(Q)`와 `gain_i`를 metadata로 남겨 후속 CBDR spec에서 재사용 가능하게 한다. + +### 입력 (Inputs) + +#### 새 confidence task type +기존: + +```python +TaskType = Literal["answer_confidence", "judgment_confidence"] +``` + +확장: + +```python +TaskType = Literal[ + "answer_confidence", + "judgment_confidence", + "query_answerability_confidence", + "query_context_answerability_confidence", +] +``` + +#### 새 runtime input type + +```python +@dataclass(frozen=True) +class QueryAnswerabilityConfidenceInput: + query: str + answer: str + +@dataclass(frozen=True) +class QueryContextAnswerabilityConfidenceInput: + query: str + context: str + answer: str +``` + +`answer`는 closed model이 생성한 답변이다. +confidence scorer는 “이 입력 조건에서 생성된 answer가 맞을 가능성”을 추정한다. + +#### generation raw JSONL + +Query-only answerability input: + +```json +{ + "id": "sample-1::base", + "query": "who played karen in married to the mob?", + "gold_answer": "Nancy Travis", + "source": "nq", + "group_id": "sample-1", + "metadata": {} +} +``` + +Query+context answerability input: + +```json +{ + "id": "sample-1::doc-1", + "query": "who played karen in married to the mob?", + "context": "Angela de Marco ... Karen (Nancy Travis).", + "gold_answer": "Nancy Travis", + "source": "nq", + "group_id": "sample-1", + "metadata": {} +} +``` + +### 출력 (Outputs) + +#### canonical JSONL + +`query_answerability_confidence`: + +```json +{ + "id": "sample-1::base", + "task_type": "query_answerability_confidence", + "query": "...", + "answer": "Nancy Travis", + "gold_answer": "Nancy Travis", + "label": 1, + "source": "nq", + "group_id": "sample-1", + "metadata": { + "generation": {}, + "input_metadata": {} + } +} +``` + +`query_context_answerability_confidence`: + +```json +{ + "id": "sample-1::doc-1", + "task_type": "query_context_answerability_confidence", + "query": "...", + "context": "...", + "answer": "Nancy Travis", + "gold_answer": "Nancy Travis", + "label": 1, + "source": "nq", + "group_id": "sample-1", + "metadata": { + "generation": {}, + "input_metadata": {} + } +} +``` + +#### runtime confidence gain result + +```python +@dataclass(frozen=True) +class ConfidenceGainResult: + base_score: float + context_score: float + gain: float + base_result: StructuralConfidenceResult + context_result: StructuralConfidenceResult +``` + +#### reranking result metadata + +`ConfidenceGainStrategy`가 반환하는 `RerankResult.metadata`: + +```python +{ + "strategy": "confidence_gain", + "algorithm": "confidence_gain", + "base_confidence": 0.42, + "context_confidence": 0.81, + "confidence_gain": 0.39, +} +``` + +### 제약 사항 (Constraints) +- hidden state, logits, attention에 직접 접근하지 않는다. +- runtime Strategy는 scorer artifact를 학습하지 않는다. +- scorer artifact는 task type별로 별도 사용한다. +- `Conf(Q)`는 query당 한 번만 계산한다. +- `Conf(Q+C_i)`는 문서별로 계산한다. +- runtime reranking은 기본적으로 answer generation `N + 1`회와 confidence scoring `N + 1`회를 수행한다. +- 호출 수를 조용히 줄이기 위해 일부 문서를 skip하지 않는다. +- score는 finite float이며 `[0, 1]` 범위여야 한다. +- gain은 `[-1, 1]` 범위의 finite float이어야 한다. +- gain 동점은 original index 오름차순으로 안정 정렬한다. +- confidence scoring 실패 시 기존 ranking으로 fallback하지 않는다. +- `top_k`는 정렬 후 slicing만 수행한다. +- `max_document_chars`를 초과하면 `DocumentTooLongError`로 실패한다. +- root import 확장은 사용자 승인 없이는 하지 않는다. + +## 3. 상세 설계 (Architecture & Design) + +### 동작 메커니즘 +1. closed model generation pipeline이 query-only 답변을 생성한다. +2. 같은 pipeline이 query+context 답변을 문서별로 생성한다. +3. gold answer와 normalized exact match로 canonical label을 만든다. +4. training pipeline이 task type별 scorer artifact를 학습한다. +5. runtime에서 base estimator와 context estimator를 로드한다. +6. Strategy 호출 시 base answer를 생성하거나 caller가 제공한다. +7. Strategy 호출 시 각 context answer를 생성하거나 caller가 제공한다. +8. base confidence와 context confidence를 계산한다. +9. `gain = context_confidence - base_confidence`를 계산한다. +10. gain 내림차순, original index 오름차순으로 정렬한다. + +### Runtime answer 생성 정책 +confidence gain에는 answer text가 필요하다. + +1차 구현은 Strategy 내부에서 closed model answer generation까지 수행하지 않는다. + +대신 Strategy는 answer generator hook을 받는다. + +```python +AnswerGenerator = Protocol: + def answer_query(self, query: str) -> str: ... + def answer_with_context(self, query: str, context: str) -> str: ... +``` + +이렇게 분리하면 confidence reranking과 answer generation 책임이 섞이지 않는다. +기존 `ModelClient.rank/compare/select` 계약도 변경하지 않는다. + +호출 비용: +- 문서 수가 `N`이면 `answer_query` 1회, `answer_with_context` `N`회를 호출한다. +- confidence scoring도 base 1회, context `N`회를 수행한다. +- 호출 수 절감을 위한 cache/batching은 이번 범위에서 제외한다. + +### 의사 알고리즘 (Pseudo-algorithm) + +```text +confidence_gain_rerank(query, documents): + validate inputs + base_answer = answer_generator.answer_query(query) + base_score = base_estimator.score(QueryAnswerabilityInput(query, base_answer)) + + scored = [] + for each document with original_index: + context_answer = answer_generator.answer_with_context(query, document.text) + context_score = context_estimator.score( + QueryContextAnswerabilityInput(query, document.text, context_answer) + ) + gain = context_score - base_score + scored.append(original_index, context_score, gain) + + order = sort scored by (-gain, original_index) + return RerankResult list +``` + +### 의사 코드 (Pseudo-code) + +```python +strategy = ConfidenceGainStrategy( + base_estimator=query_estimator, + context_estimator=query_context_estimator, + answer_generator=answer_generator, +) + +results = strategy.rerank( + query="who played karen in married to the mob?", + documents=documents, + model_client=model_client, + top_k=5, +) +``` + +`model_client`는 Strategy protocol 때문에 인자로 받지만, 1차 구현에서는 사용하지 않는다. +대신 `answer_generator`가 answer generation 책임을 가진다. + +### 통합 지점 (Integration Points) + +Confidence runtime: +- `src/ranksmith/confidence/_types.py` +- `src/ranksmith/confidence/_templates.py` +- `src/ranksmith/confidence/_scorer.py` +- `src/ranksmith/confidence/_structural.py` +- `src/ranksmith/confidence/__init__.py` + +Confidence generation: +- `src/ranksmith/confidence_generation/_types.py` +- `src/ranksmith/confidence_generation/_io.py` +- `src/ranksmith/confidence_generation/_prompts.py` +- `src/ranksmith/confidence_generation/_pipeline.py` +- `src/ranksmith/confidence_generation/__init__.py` + +Confidence training: +- `src/ranksmith/confidence_training/_types.py` +- `src/ranksmith/confidence_training/_dataset.py` +- `src/ranksmith/confidence_training/_features.py` +- `src/ranksmith/confidence_training/_artifact.py` + +Strategy: +- `src/ranksmith/strategies/_confidence_gain.py` +- `src/ranksmith/strategies/__init__.py` + +Docs: +- `docs/wiki/02_architecture.md` +- `docs/wiki/04_references_index.md` +- `README.md` +- `README.ko.md` + +## 4. 재사용 및 모듈화 (Reusability & Modularization) + +### 공통 컴포넌트 식별 (Shared Components) +- normalized exact match labeling은 기존 confidence generation labeling helper를 재사용한다. +- canonical JSONL validation은 기존 confidence training dataset validator 패턴을 확장한다. +- structural feature extraction은 기존 `structural-v1`을 그대로 재사용한다. +- batch scoring은 기존 `StructuralConfidenceEstimator.score_batch()`를 재사용한다. + +### 추상화 방안 (Abstraction Plan) +- answer generation은 Strategy 내부 private prompt로 직접 만들지 않는다. +- `AnswerGenerator` protocol로 분리한다. +- confidence gain 계산은 Strategy와 분리된 utility로 둔다. + +후속 CBDR spec은 같은 utility를 사용해 `Conf(Q) >= beta` retrieval skip을 구현할 수 있다. + +## 5. 에러 핸들링 (Error Handling) +- 빈 query: `RerankInputError` +- 빈 documents: `[]` +- `top_k < 0`: `RerankInputError` +- document length 초과: `DocumentTooLongError` +- base/context estimator task mismatch: `RerankInputError` +- answer generator가 빈 answer 반환: `RerankProviderError` +- answer generator가 예외 발생: `RerankProviderError`로 래핑 +- confidence estimator score 실패: 해당 confidence error를 감싸지 않고 전파 +- confidence score가 finite probability가 아님: `ConfidenceArtifactError` +- gain이 finite float가 아님: `RerankStrategyError` +- output ranking 보정 필요 상황: 조용히 보정하지 않고 실패 + +## 6. 테스트 계획 (Test Plan) + +### 성공 케이스 (Happy Paths) +- query-only input template 생성. +- query+context input template 생성. +- 새 task type scorer metadata validation 성공. +- canonical JSONL validation 성공. +- base/context confidence로 gain 계산. +- gain 내림차순 정렬. +- gain 동점 시 original index 유지. +- `top_k` slicing. +- metadata에 base/context/gain 기록. +- answer generator 호출 수가 `N + 1`인지 확인. + +### 엣지/실패 케이스 (Edge & Failure Cases) +- unsupported task type 실패. +- task type과 input dataclass mismatch 실패. +- scorer artifact task mismatch 실패. +- 빈 query/context/answer 실패. +- answer generator empty output 실패. +- context estimator를 query-only task로 주입하면 실패. +- base estimator를 query+context task로 주입하면 실패. +- score 범위 밖 실패. +- `max_document_chars` 초과 실패. + +### 공통 Reranking Smoke/Benchmark +- synthetic answer generator와 fake confidence estimator로 deterministic reranking smoke test를 추가한다. +- 실제 LLM live test는 credential/cost 때문에 opt-in으로 분리한다. +- README benchmark 수치는 추가하지 않는다. + +검증 명령: + +```bash +uv run pytest tests/test_confidence_*.py tests/test_confidence_generation_*.py tests/test_confidence_training_*.py tests/test_confidence_gain_strategy.py -q +uv run ruff check src/ranksmith tests +uv run mypy src/ranksmith tests +./scripts/verify.sh +``` + +--- + +## 7. 작업 태스크 추적 (Task Checklist) + +### Phase 1: 컨텍스트 및 설계 확인 +- [x] 관련 기존 코드베이스 및 Wiki 문서 확인 +- [x] 스펙 문서(본 문서) 상의 의사 코드 설계 검토 및 확정 + +### Phase 2: Confidence task 확장 +- [x] `src/ranksmith/confidence/_types.py`: query-only/contextual answerability input type 추가 +- [x] `src/ranksmith/confidence/_templates.py`: 새 input template 추가 +- [x] `src/ranksmith/confidence/_scorer.py`: metadata task validation 확장 +- [x] `src/ranksmith/confidence/__init__.py`: submodule export 추가 +- [x] `tests/test_confidence_*.py`: 새 task runtime tests 추가 + +### Phase 3: Generation/Training 확장 +- [x] `src/ranksmith/confidence_generation/*`: answerability generation config/pipeline 추가 +- [x] `src/ranksmith/confidence_training/*`: canonical schema/task validation 확장 +- [x] `tests/test_confidence_generation_*.py`: 새 generation tests 추가 +- [x] `tests/test_confidence_training_*.py`: 새 training tests 추가 + +### Phase 4: Confidence gain Strategy +- [x] `src/ranksmith/strategies/_confidence_gain.py`: `ConfidenceGainStrategy` 구현 +- [x] `src/ranksmith/strategies/__init__.py`: strategy export 추가 +- [x] `tests/test_confidence_gain_strategy.py`: deterministic unit tests 추가 +- [x] 필요 시 `tests/fixtures/reranking_smoke_fixture.jsonl` 기반 smoke test 추가/검토 + +### Phase 5: 문서 및 검증 +- [x] `docs/wiki/02_architecture.md`: confidence gain strategy 위치 추가 +- [x] `docs/wiki/04_references_index.md`: reference 상태 갱신 +- [x] `README.md` / `README.ko.md`: benchmark 없는 usage 문서 추가 +- [x] `./scripts/verify.sh` 스크립트를 통한 린트/타입/전체 테스트 통과 확인 +- [x] 본 문서 최상단의 **상태**를 `Completed`로 변경 diff --git a/docs/specs/spec_lmstudio_confidence_training_pipeline.md b/docs/specs/spec_lmstudio_confidence_training_pipeline.md new file mode 100644 index 0000000..db1ae16 --- /dev/null +++ b/docs/specs/spec_lmstudio_confidence_training_pipeline.md @@ -0,0 +1,513 @@ +# Spec: LM Studio Confidence Training Pipeline + +## 1. 개요 (Overview) +- **작업 목적**: MacBook에서 LM Studio로 로컬 LLM을 배포해 answerability confidence 학습 데이터를 생성하고, CBDR이 사용할 `Conf(Q)` / `Conf(Q+C)` scorer artifact를 재현 가능하게 만든다. +- **Reference**: + - `docs/wiki/references/structural_confidence.md` + - `docs/wiki/references/parametric_post_retrieval_confidence.md` + - `docs/specs/spec_confidence_generation_pipeline.md` + - `docs/specs/spec_confidence_training_pipeline.md` + - `docs/specs/spec_confidence_gain_reranking.md` + - `docs/specs/spec_cbdr_strategy.md` + - `docs/specs/spec_cbdr_user_facing_integration.md` + - LM Studio model page: `https://lmstudio.ai/models/google/gemma-4-12b` +- **상태**: `[ ] Draft` | `[ ] In Progress` | `[x] Completed` + +이번 작업은 새 reranking algorithm이 아니다. +이미 구현된 `confidence_generation`, `confidence_training`, `CBDRStrategy`를 로컬 LM Studio 환경에서 실제로 운용하기 위한 integration / CLI / run artifact layer다. + +## 2. 요구 사항 및 제약 (Requirements & Constraints) + +### 2.1 목표 +- LM Studio OpenAI-compatible server를 `ModelProvider`로 사용할 수 있어야 한다. +- query-only answerability dataset과 query+context answerability dataset을 생성할 수 있어야 한다. +- 두 dataset에서 각각 scorer artifact를 학습할 수 있어야 한다. +- 학습 결과는 `CBDRStrategy.from_artifacts(...)`에 바로 연결 가능해야 한다. +- 단일 도메인 과적합을 피하기 위해 source / group 기반 리포트를 남겨야 한다. + +### 2.2 입력 (Inputs) +- LM Studio runtime: + - `base_url`: 기본값 `http://localhost:1234/v1` + - `model`: 명시 인자 또는 환경 변수 + - `api_key`: LM Studio 호환용 optional 값 + - `timeout` + - 권장 context length: `16384` + - 권장 max output tokens: `128` +- raw generation dataset: + - 권장 생성 경로: `scripts/build_qa_confidence_raw_dataset.py` + - TriviaQA-style QA examples를 입력으로 사용한다. + - positive context는 answer alias를 포함하는 evidence로 제한한다. + - negative context는 질문 기준 TF-IDF retrieved context 중 현재 answer alias를 포함하지 않는 context로 생성한다. + - synthetic fixed seed QA는 실제 학습 데이터로 사용하지 않는다. + - `query_answerability_confidence` + - `id` + - `query` + - `gold_answer` + - optional `source`, `group_id`, `metadata` + - `query_context_answerability_confidence` + - `id` + - `query` + - `context` + - `gold_answer` + - optional `source`, `group_id`, `metadata` +- training config: + - `task_type` + - `dataset_path` + - `output_dir` + - `export_path` + - HuggingFace encoder options + - split options + +### 2.3 출력 (Outputs) +권장 run directory: + +```text +runs/confidence// + raw/ + query_answerability.jsonl + query_context_answerability.jsonl + canonical/ + query_answerability_confidence.jsonl + query_context_answerability_confidence.jsonl + training/ + query_answerability/ + model.joblib + metadata.json + report.json + report.md + query_context_answerability/ + model.joblib + metadata.json + report.json + report.md + artifacts/ + query_answerability.joblib + query_context_answerability.joblib + reports/ + generation_summary.json + dataset_balance.json + generalization_report.json +``` + +### 2.4 제약 사항 (Constraints) +- Fast fail을 유지한다. +- JSON 응답을 조용히 보정하지 않는다. +- hidden state, logits, attention, logprobs에 의존하지 않는다. +- LM Studio 모델은 scorer가 아니라 label 생성을 위한 answer generator다. +- confidence scorer 학습은 기존 `FrozenAutoEncoder + structural-v1 features + LightGBM + sigmoid calibration`을 사용한다. +- public root import는 늘리지 않는다. +- `ranksmith.integrations` submodule export는 허용한다. +- scorer training 결과를 README benchmark 수치로 쓰려면 실제 benchmark summary artifact가 필요하다. + +## 3. 상세 설계 (Architecture & Design) + +### 3.1 전체 흐름 +```text +TriviaQA/NQ-style QA evidence examples + -> build_qa_confidence_raw_dataset.py + -> raw QA/context examples + -> LMStudioModelProvider + -> confidence_generation canonical JSONL + -> confidence_training scorer artifacts + -> CBDRStrategy.from_artifacts(...) + -> compare_reranking.py --algorithm cbdr +``` + +### 3.2 LM Studio Provider +새 provider는 `ModelProvider` protocol만 만족한다. + +통합 지점: + +```text +src/ranksmith/integrations/_lmstudio_provider.py +src/ranksmith/integrations/__init__.py +tests/test_lmstudio_provider.py +``` + +Public submodule API: + +```python +from ranksmith.integrations import LMStudioModelProvider + +provider = LMStudioModelProvider( + base_url="http://localhost:1234/v1", + model="google/gemma-4-12b", + api_key="lm-studio", + max_tokens=128, + timeout=60, +) +``` + +기본 정책: +- `base_url`: `LMSTUDIO_BASE_URL` 또는 `http://localhost:1234/v1` +- `model`: 명시 인자 또는 `LMSTUDIO_MODEL` +- `api_key`: `LMSTUDIO_API_KEY` 또는 `"lm-studio"` +- `temperature`: `ModelRequest.temperature` 그대로 사용. generation pipeline에서는 이미 `0`을 전달한다. +- `response_format`: ranksmith의 `json_object` 요청을 LM Studio의 `json_schema` 요청으로 변환한다. +- `max_tokens`: 기본값 `128`, 명시 인자로 조정 가능. + +LM Studio 0.4.16 기준 실제 확인 결과: + +```text +response_format={"type":"json_object"} -> 실패 +response_format={"type":"json_schema", ...} -> 성공 +``` + +따라서 `LMStudioModelProvider`는 `ModelRequest.response_format == "json_object"`일 때 아래 schema를 전달한다. + +```json +{ + "type": "json_schema", + "json_schema": { + "name": "ranksmith_json_response", + "schema": { + "type": "object" + } + } +} +``` + +answerability task의 최종 출력 형태 `{"answer":"..."}`는 기존 prompt와 strict parser가 검증한다. + +LM Studio / Gemma 4 12B 운영 권장값: + +```text +model: google/gemma-4-12b +context length: 16384 +max output tokens: 128 +temperature: 0 +thinking: disabled when supported by LM Studio runtime +response_format: json_schema +output contract: {"answer":"..."} +``` + +`thinking`은 OpenAI-compatible 표준 필드가 아니므로 provider public config에 넣지 않는다. +LM Studio 런타임 UI 또는 모델 설정에서 끄는 운영 조건으로 문서화한다. + +`query_context_answerability_confidence` 생성에서는 기본 `max_context_chars`를 `8000`으로 권장한다. +이는 LM Studio context length `16384`에서 system prompt, JSON schema, query 여유를 확보하기 위한 값이다. +단, ranksmith 원칙상 숨은 truncation은 하지 않는다. +`max_context_chars` 초과 입력은 기존 generation loader 정책대로 명시적으로 실패하거나, 사용자가 CLI 옵션으로 한도를 조정해야 한다. + +### 3.3 QA Raw Dataset Builder +LM Studio 호출 전에 원천 QA dataset을 ranksmith generation raw schema로 변환한다. + +통합 지점: + +```text +scripts/build_qa_confidence_raw_dataset.py +``` + +초기 공식 지원: +- `--source triviaqa` +- HuggingFace `mandarjoshi/trivia_qa`, 기본 config `rc` +- 기본 split `train[:20000]` +- 기본 출력: + - `query_answerability_raw.jsonl` + - `query_context_answerability_raw.jsonl` + - `dataset_manifest.json` + +정책: +- positive query-context row는 정답 alias가 들어 있는 evidence context만 사용한다. +- negative query-context row는 질문과 유사한 retrieved context를 쓰되, 현재 정답 alias가 포함되면 제외한다. +- context가 `--max-context-chars`를 넘으면 조용히 자르지 않고 제외한다. +- 요청한 row 수를 만들 수 없으면 fast-fail한다. + +예시: + +```bash +uv run --with datasets python scripts/build_qa_confidence_raw_dataset.py \ + --source triviaqa \ + --dataset-name mandarjoshi/trivia_qa \ + --dataset-config rc \ + --split 'train[:20000]' \ + --output-dir runs/confidence/local/raw \ + --max-source-items 20000 \ + --max-query-items 5000 \ + --max-query-context-items 5000 \ + --max-context-chars 8000 +``` + +### 3.4 Dataset Generation CLI +기존 `ranksmith.confidence_generation`은 library API만 제공한다. +로컬 실험을 재현 가능하게 하려면 얇은 CLI가 필요하다. + +통합 지점: + +```text +scripts/generate_confidence_dataset.py +tests/test_generate_confidence_dataset_script.py +``` + +지원 task: +- `query_answerability_confidence` +- `query_context_answerability_confidence` + +초기 범위에서는 `answer_confidence`, `judgment_confidence` CLI는 추가하지 않는다. +CBDR에 필요한 두 task만 먼저 고정한다. + +예시: + +```bash +uv run python scripts/generate_confidence_dataset.py \ + --task query_answerability_confidence \ + --provider lmstudio \ + --lmstudio-base-url http://localhost:1234/v1 \ + --lmstudio-model google/gemma-4-12b \ + --input runs/confidence/local/raw/query_answerability.jsonl \ + --output runs/confidence/local/canonical/query_answerability_confidence.jsonl \ + --resume +``` + +```bash +uv run python scripts/generate_confidence_dataset.py \ + --task query_context_answerability_confidence \ + --provider lmstudio \ + --lmstudio-base-url http://localhost:1234/v1 \ + --lmstudio-model google/gemma-4-12b \ + --input runs/confidence/local/raw/query_context_answerability.jsonl \ + --output runs/confidence/local/canonical/query_context_answerability_confidence.jsonl \ + --max-context-chars 8000 \ + --resume +``` + +### 3.5 Training CLI +기존 `ranksmith.confidence_training`도 library API만 제공한다. +학습 재현성을 위해 CLI를 추가한다. + +통합 지점: + +```text +scripts/train_confidence_scorer.py +tests/test_train_confidence_scorer_script.py +``` + +예시: + +```bash +uv run python scripts/train_confidence_scorer.py \ + --task query_answerability_confidence \ + --dataset runs/confidence/local/canonical/query_answerability_confidence.jsonl \ + --output-dir runs/confidence/local/training/query_answerability \ + --export-path runs/confidence/local/artifacts/query_answerability.joblib \ + --encoder-name bert-base-uncased \ + --max-length 256 +``` + +```bash +uv run python scripts/train_confidence_scorer.py \ + --task query_context_answerability_confidence \ + --dataset runs/confidence/local/canonical/query_context_answerability_confidence.jsonl \ + --output-dir runs/confidence/local/training/query_context_answerability \ + --export-path runs/confidence/local/artifacts/query_context_answerability.joblib \ + --encoder-name bert-base-uncased \ + --max-length 256 +``` + +### 3.6 Dataset Balance / Generalization Report +범용성을 주장하려면 단순 train/test metric만으로 부족하다. +source별, group별, held-out source별 리포트가 필요하다. + +초기 구현은 새 학습 알고리즘을 만들지 않고 report utility만 추가한다. + +통합 지점: + +```text +src/ranksmith/confidence_training/_dataset_report.py +scripts/report_confidence_dataset.py +tests/test_confidence_training_dataset_report.py +``` + +리포트 항목: +- total sample count +- positive / negative count +- positive rate +- source별 count / positive rate +- group_id count +- duplicate id 여부 +- missing source 비율 +- missing group_id 비율 + +held-out generalization metric은 학습 pipeline 내부의 split 방식만으로는 충분하지 않다. +이번 범위에서는 다음을 명시한다. + +```text +generalization claim = source-balanced dataset report + source-heldout manual run evidence +``` + +즉, CLI는 source별 분포를 보여주고, benchmark claim은 별도 실행 artifact가 있을 때만 문서화한다. + +### 3.7 CBDR 연결 +기존 `compare_reranking.py --algorithm cbdr`를 그대로 사용한다. +이번 작업은 CBDR algorithm을 변경하지 않는다. + +예시: + +```bash +uv run python scripts/compare_reranking.py \ + --dataset fixture \ + --algorithm cbdr \ + --cbdr-base-artifact runs/confidence/local/artifacts/query_answerability.joblib \ + --cbdr-context-artifact runs/confidence/local/artifacts/query_context_answerability.joblib \ + --allow-live +``` + +## 4. 재사용 및 모듈화 (Reusability & Modularization) + +### 4.1 재사용 컴포넌트 +- `ModelProvider` +- `ModelRequest` +- `ModelResponse` +- `confidence_generation` prompt / parser / labeling +- `confidence_training` dataset loader / feature extraction / trainer / artifact export +- `CBDRStrategy.from_artifacts(...)` + +### 4.2 새 모듈 책임 +- `LMStudioModelProvider` + - LM Studio OpenAI-compatible API 호출만 담당한다. + - answerability prompt 의미를 알지 않는다. + - ranksmith의 `json_object` 요청을 LM Studio의 `json_schema` 요청으로 변환한다. +- `generate_confidence_dataset.py` + - provider 조립과 generation config 생성을 담당한다. + - label 계산은 기존 generation pipeline에 위임한다. +- `train_confidence_scorer.py` + - CLI arg를 `ConfidenceTrainingConfig`로 변환한다. + - 학습 본체는 기존 training pipeline에 위임한다. +- `dataset_report` + - dataset 품질/분포 리포트만 담당한다. + - scorer 학습이나 ranking correction을 하지 않는다. +- `build_qa_confidence_raw_dataset.py` + - QA 원천 dataset을 ranksmith raw generation schema로 변환한다. + - retrieved negative context 생성까지만 담당한다. + - LM Studio 호출, canonical label 생성, scorer 학습은 하지 않는다. + +## 5. 에러 핸들링 (Error Handling) + +### 5.1 LM Studio Provider +- LM Studio 서버 연결 실패: `RerankProviderError` +- HTTP non-2xx: `RerankProviderError` +- 응답 JSON 파싱 실패: `RerankProviderError` +- `choices[0].message.content` 누락: `RerankProviderError` +- 빈 content: `RerankProviderError` +- `model` 누락: `RerankInputError` + +### 5.2 Generation CLI +- 지원하지 않는 task: CLI parser error +- provider가 `lmstudio`가 아님: CLI parser error +- input schema 오류: 기존 `ConfidenceGenerationInputError` +- output 존재 + `--overwrite/--resume` 없음: 기존 fast fail +- malformed model output: 기존 `ConfidenceGenerationParseError` + +### 5.2.1 QA Raw Dataset Builder +- HuggingFace `datasets` 미설치: 명시적 실행 방법과 함께 fast-fail +- usable evidence 부족: 요청 row 수를 채울 수 없으면 fast-fail +- context 길이 초과: 숨은 truncation 없이 제외 +- retrieved negative 부족: 요청 row 수를 채울 수 없으면 fast-fail + +### 5.3 Training CLI +- unsupported task: 기존 `ConfidenceTrainingConfigError` +- label이 한쪽 클래스만 존재: 기존 training error +- HF model load 실패: 기존 training error 또는 원 예외를 wrapped error로 유지 +- export path 쓰기 실패: fast fail + +### 5.4 Dataset Report +- canonical JSONL schema 오류: 기존 dataset loader error +- source/group 누락은 실패가 아니라 warning count로 기록한다. +- duplicate id는 실패로 처리한다. + +## 6. 테스트 계획 (Test Plan) + +### 6.1 Unit Tests +- `LMStudioModelProvider` + - 정상 OpenAI-compatible response를 `ModelResponse`로 변환한다. + - `LMSTUDIO_MODEL` env fallback을 사용한다. + - `json_object` 요청을 `json_schema`로 변환한다. + - `max_tokens` 기본값 `128`을 전달한다. + - model 누락 시 `RerankInputError`. + - HTTP error / malformed response / empty content는 `RerankProviderError`. +- `generate_confidence_dataset.py` + - query-only task가 올바른 generation function을 호출한다. + - query+context task가 올바른 generation function을 호출한다. + - LM Studio provider 옵션이 전달된다. + - `--resume`, `--overwrite`, `--max-items`, `--max-context-chars`가 config에 반영된다. +- `train_confidence_scorer.py` + - CLI args가 `ConfidenceTrainingConfig`로 변환된다. + - HF options가 전달된다. + - task mismatch / ratio 오류는 fast fail. +- dataset report + - source별 count와 positive rate를 계산한다. + - duplicate id를 실패 처리한다. + - missing source/group count를 기록한다. +- QA raw dataset builder + - TriviaQA-style local JSONL fixture에서 query raw와 query-context raw를 생성한다. + - query-context row는 positive/negative 균형을 유지한다. + - negative row metadata에 `negative_retrieved_tfidf`를 기록한다. + +### 6.2 Smoke Tests +- fake LM Studio provider로 query-only canonical JSONL 생성. +- fake LM Studio provider로 query+context canonical JSONL 생성. +- small synthetic canonical dataset으로 training CLI가 artifact를 생성하는지 확인. +- 생성된 artifact가 `CBDRStrategy.from_artifacts(...)`에서 task type 검증을 통과하는지 확인. + +### 6.3 Live Verification +LM Studio 서버가 떠 있을 때만 수동 실행한다. +CI 기본 경로에는 포함하지 않는다. + +```bash +uv run python scripts/generate_confidence_dataset.py \ + --task query_answerability_confidence \ + --provider lmstudio \ + --lmstudio-base-url http://localhost:1234/v1 \ + --lmstudio-model google/gemma-4-12b \ + --input tests/fixtures/confidence_query_answerability_raw.jsonl \ + --output /tmp/ranksmith-lmstudio-query-answerability.jsonl \ + --overwrite \ + --max-items 3 +``` + +검증 기준: +- 응답이 strict JSON이다. +- `answer`가 비어 있지 않다. +- canonical row의 `label`이 0 또는 1이다. +- positive/negative count가 리포트된다. +- LM Studio usage에서 reasoning tokens가 `0`이거나 낮게 유지된다. + +### 6.4 최종 검증 +```bash +uv run pytest tests/test_lmstudio_provider.py tests/test_generate_confidence_dataset_script.py tests/test_train_confidence_scorer_script.py tests/test_confidence_training_dataset_report.py -q +./scripts/verify.sh +``` + +## 7. 작업 태스크 추적 (Task Checklist) + +### Phase 1: 컨텍스트 및 설계 확인 +- [x] 관련 기존 코드베이스 및 Wiki 문서 확인 +- [x] LM Studio / Gemma 4 12B 운영 조건 확인 +- [x] 스펙 문서(본 문서) 사용자 검토 및 확정 + +### Phase 2: 로직 구현 (Implementation) +- [x] `src/ranksmith/integrations/_lmstudio_provider.py`: LM Studio OpenAI-compatible `ModelProvider` 구현 +- [x] `src/ranksmith/integrations/__init__.py`: `LMStudioModelProvider` submodule export 추가 +- [x] `scripts/generate_confidence_dataset.py`: CBDR용 answerability generation CLI 추가 +- [x] `scripts/train_confidence_scorer.py`: confidence training CLI 추가 +- [x] `src/ranksmith/confidence_training/_dataset_report.py`: dataset balance/generalization helper 추가 +- [x] `scripts/report_confidence_dataset.py`: dataset report CLI 추가 +- [x] `scripts/build_qa_confidence_raw_dataset.py`: TriviaQA 기반 raw dataset builder 추가 +- [x] `scripts/run_lmstudio_confidence_pipeline.py`: raw 생성 → LM Studio generation → training → optional benchmark 통합 runner 추가 + +### Phase 3: 검증 (Verification) +- [x] `tests/test_lmstudio_provider.py`: provider 정상/실패 케이스 테스트 추가 +- [x] `tests/test_generate_confidence_dataset_script.py`: generation CLI 테스트 추가 +- [x] `tests/test_train_confidence_scorer_script.py`: training CLI 테스트 추가 +- [x] `tests/test_confidence_training_dataset_report.py`: dataset report 테스트 추가 +- [x] fake provider 기반 canonical generation smoke 실행 +- [x] synthetic artifact 기반 CBDR loading smoke 실행 +- [x] LM Studio live smoke는 수동 opt-in으로 실행 +- [x] TriviaQA-style local fixture 기반 raw builder smoke 실행 +- [x] `./scripts/verify.sh` 스크립트를 통한 린트/타입/전체 테스트 통과 확인 + +### Phase 4: 완료 및 정리 +- [x] `README.md`: LM Studio confidence pipeline 최소 예시 추가 +- [x] `README.ko.md`: 동일 구조의 한국어 예시 추가 +- [x] `docs/wiki/02_architecture.md`: LM Studio integration과 confidence pipeline CLI 위치 반영 +- [x] 본 문서 최상단의 **상태**를 `Completed`로 변경 diff --git a/docs/superpowers/plans/2026-06-04-cbdr-strategy.md b/docs/superpowers/plans/2026-06-04-cbdr-strategy.md new file mode 100644 index 0000000..3db8507 --- /dev/null +++ b/docs/superpowers/plans/2026-06-04-cbdr-strategy.md @@ -0,0 +1,1307 @@ +# CBDR Strategy Implementation Plan + +> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. + +**Goal:** Add a sync `CBDRStrategy` that skips context reranking when `Conf(Q) >= skip_threshold`, otherwise reranks by `Conf(Q+C)-Conf(Q)`. + +**Architecture:** Reuse the existing query-only and query+context answerability confidence contracts. Keep CBDR as a separate Strategy, not a subclass of `ConfidenceGainStrategy`, while sharing private helper functions for answer calls, task validation, score validation, and confidence gain scoring. Export only through `ranksmith.strategies`, and register it as a built-in sync strategy in `AzureOpenAIReranker`. + +**Tech Stack:** Python 3.10+, dataclasses, pytest, ranksmith confidence runtime, existing `AzureOpenAIReranker` facade. + +--- + +## File Structure + +- Modify: `src/ranksmith/strategies/_confidence_gain.py` + - Expose private helper functions for task validation, answer generation calls, base scoring, context scoring, and confidence score validation. + - Keep public exports unchanged. +- Create: `src/ranksmith/strategies/_cbdr.py` + - Implement sync `CBDRStrategy`. + - Use shared helpers from `_confidence_gain.py`. +- Modify: `src/ranksmith/strategies/__init__.py` + - Export `CBDRStrategy` from the strategies submodule only. +- Modify: `src/ranksmith/azure.py` + - Import `CBDRStrategy`. + - Treat `CBDRStrategy` as built-in sync strategy for facade exception handling. +- Create: `tests/test_cbdr_strategy.py` + - Cover skip path, rerank path, no-call paths, invalid inputs, facade behavior, and artifact-load E2E smoke for both paths. +- Modify: `docs/wiki/02_architecture.md` + - Add CBDR strategy and algorithm placement. +- Modify: `README.md` + - Add benchmark-free CBDR usage docs. +- Modify: `README.ko.md` + - Mirror README structure and content in Korean. +- Modify: `docs/specs/spec_cbdr_strategy.md` + - Mark implementation checklist items completed only after exit code 0 with no test failures. + +--- + +### Task 1: Shared Confidence Gain Helpers + +**Files:** +- Modify: `src/ranksmith/strategies/_confidence_gain.py` +- Test: `tests/test_confidence_gain_strategy.py` + +- [ ] **Step 1: Run existing confidence gain tests before changing helpers** + +Run: + +```bash +uv run pytest tests/test_confidence_gain_strategy.py -q +``` + +Expected: + +```text +39 passed +``` + +- [ ] **Step 2: Refactor helper functions without behavior change** + +Modify `src/ranksmith/strategies/_confidence_gain.py` so shared private helpers have these signatures and behavior: + +```python +def _validate_estimator_tasks( + *, + base_estimator: ConfidenceEstimator, + context_estimator: ConfidenceEstimator, +) -> None: + if base_estimator.task_type != QUERY_ANSWERABILITY_TASK: + raise RerankInputError( + f"base_estimator task_type must be {QUERY_ANSWERABILITY_TASK!r}" + ) + if context_estimator.task_type != QUERY_CONTEXT_ANSWERABILITY_TASK: + raise RerankInputError( + "context_estimator task_type must be " + f"{QUERY_CONTEXT_ANSWERABILITY_TASK!r}" + ) + + +def _score_base_answerability( + *, + estimator: ConfidenceEstimator, + query: str, + answer: str, +) -> StructuralConfidenceResult: + return estimator.score( + QueryAnswerabilityConfidenceInput(query=query, answer=answer) + ) + + +def _score_context_answerability( + *, + estimator: ConfidenceEstimator, + query: str, + context: str, + answer: str, +) -> StructuralConfidenceResult: + return estimator.score( + QueryContextAnswerabilityConfidenceInput( + query=query, + context=context, + answer=answer, + ) + ) + + +def _confidence_gain( + *, + base_score: float, + context_score: float, +) -> float: + gain = context_score - base_score + if not math.isfinite(gain) or gain < -1.0 or gain > 1.0: + raise RerankStrategyError("confidence gain must be finite in [-1, 1]") + return gain +``` + +Keep `_call_answer_query`, `_call_answer_with_context`, `_validate_answer`, and `_validate_confidence_score` private and reusable from `_cbdr.py`. + +Update `ConfidenceGainStrategy.__post_init__()` to call `_validate_estimator_tasks(...)`. + +Update `ConfidenceGainStrategy.rerank()` to call `_score_base_answerability(...)`, `_score_context_answerability(...)`, and `_confidence_gain(...)`. + +- [ ] **Step 3: Run confidence gain regression tests** + +Run: + +```bash +uv run pytest tests/test_confidence_gain_strategy.py -q +``` + +Expected: + +```text +39 passed +``` + +- [ ] **Step 4: Commit helper refactor** + +Run: + +```bash +git add src/ranksmith/strategies/_confidence_gain.py tests/test_confidence_gain_strategy.py +git commit -m "refactor: share confidence gain helpers" +``` + +Expected: + +```text +commit succeeds +``` + +--- + +### Task 2: CBDR Unit Tests and Strategy + +**Files:** +- Create: `src/ranksmith/strategies/_cbdr.py` +- Modify: `src/ranksmith/strategies/__init__.py` +- Modify: `src/ranksmith/azure.py` +- Create: `tests/test_cbdr_strategy.py` + +- [ ] **Step 1: Write failing CBDR tests** + +Create `tests/test_cbdr_strategy.py` with this structure: + +```python +from __future__ import annotations + +import math +from dataclasses import dataclass +from typing import Any, cast + +import pytest + +from ranksmith import AzureOpenAIReranker +from ranksmith.confidence import StructuralConfidenceResult, TaskType +from ranksmith.errors import ( + DocumentTooLongError, + RerankInputError, + RerankProviderError, + RerankStrategyError, +) +from ranksmith.strategies import CBDRStrategy +from ranksmith.types import Document + + +@dataclass +class FakeEstimator: + task_type: TaskType + scores: list[Any] + calls: list[object] | None = None + + def score(self, item: object) -> StructuralConfidenceResult: + if self.calls is not None: + self.calls.append(item) + value = self.scores.pop(0) + if isinstance(value, BaseException): + raise value + return StructuralConfidenceResult( + score=value, + task_type=self.task_type, + feature_schema_version="structural-v1", + ) + + +class FakeGenerator: + def __init__( + self, + *, + base_answer: object = "base answer", + context_answers: list[object] | None = None, + ) -> None: + self.base_answer = base_answer + self.context_answers = context_answers or [] + self.query_calls: list[str] = [] + self.context_calls: list[tuple[str, str]] = [] + + def answer_query(self, query: str) -> Any: + self.query_calls.append(query) + if isinstance(self.base_answer, BaseException): + raise self.base_answer + return self.base_answer + + def answer_with_context(self, query: str, context: str) -> Any: + self.context_calls.append((query, context)) + answer = self.context_answers.pop(0) + if isinstance(answer, BaseException): + raise answer + return answer + + +def _strategy( + *, + base_scores: list[Any] | None = None, + context_scores: list[Any] | None = None, + generator: FakeGenerator | None = None, + skip_threshold: float = 0.8, + max_document_chars: int = 4000, +) -> CBDRStrategy: + return CBDRStrategy( + base_estimator=FakeEstimator( + task_type="query_answerability_confidence", + scores=base_scores or [0.2], + ), + context_estimator=FakeEstimator( + task_type="query_context_answerability_confidence", + scores=context_scores or [0.7], + ), + answer_generator=generator or FakeGenerator(context_answers=["context answer"]), + skip_threshold=skip_threshold, + max_document_chars=max_document_chars, + ) + + +def _unused_model_client() -> Any: + return object() +``` + +Add these tests in the same file: + +```python +def test_cbdr_exports_are_submodule_only() -> None: + import importlib + + strategies = importlib.import_module("ranksmith.strategies") + root = importlib.import_module("ranksmith") + + assert strategies.CBDRStrategy is not None + assert not hasattr(root, "CBDRStrategy") + + +def test_cbdr_empty_documents_returns_empty_without_calls() -> None: + generator = FakeGenerator(context_answers=[]) + strategy = _strategy(generator=generator) + + assert strategy.rerank( + query="Who?", + documents=[], + model_client=object(), + ) == [] + assert generator.query_calls == [] + assert generator.context_calls == [] + + +def test_cbdr_top_k_zero_returns_empty_without_calls() -> None: + generator = FakeGenerator(context_answers=["a"]) + strategy = _strategy(generator=generator) + + assert strategy.rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + top_k=0, + ) == [] + assert generator.query_calls == [] + assert generator.context_calls == [] + + +def test_cbdr_skip_path_preserves_original_order_and_metadata() -> None: + generator = FakeGenerator(context_answers=[]) + strategy = _strategy(base_scores=[0.91], generator=generator, skip_threshold=0.8) + documents = [ + Document(id="a", text="alpha"), + Document(id="b", text="beta"), + ] + + results = strategy.rerank(query="Who?", documents=documents, model_client=object()) + + assert [result.document.id for result in results] == ["a", "b"] + assert [result.rank for result in results] == [1, 2] + assert [result.original_index for result in results] == [0, 1] + assert [dict(result.metadata) for result in results] == [ + { + "strategy": "cbdr", + "algorithm": "cbdr", + "cbdr_skipped": True, + "base_confidence": 0.91, + "skip_threshold": 0.8, + "context_confidence": None, + "confidence_gain": None, + }, + { + "strategy": "cbdr", + "algorithm": "cbdr", + "cbdr_skipped": True, + "base_confidence": 0.91, + "skip_threshold": 0.8, + "context_confidence": None, + "confidence_gain": None, + }, + ] + assert generator.query_calls == ["Who?"] + assert generator.context_calls == [] + + +def test_cbdr_skip_path_applies_top_k_after_original_order() -> None: + strategy = _strategy(base_scores=[0.9], skip_threshold=0.8) + + results = strategy.rerank( + query="Who?", + documents=[ + Document(id="a", text="alpha"), + Document(id="b", text="beta"), + ], + model_client=object(), + top_k=1, + ) + + assert [result.document.id for result in results] == ["a"] + assert [result.rank for result in results] == [1] + assert [result.original_index for result in results] == [0] + + +def test_cbdr_skip_path_does_not_validate_long_documents() -> None: + strategy = _strategy( + base_scores=[0.9], + context_scores=[], + generator=FakeGenerator(context_answers=[]), + skip_threshold=0.8, + max_document_chars=3, + ) + + results = strategy.rerank( + query="Who?", + documents=[Document(text="abcdef")], + model_client=object(), + ) + + assert len(results) == 1 + assert results[0].metadata["cbdr_skipped"] is True + + +def test_cbdr_rerank_path_sorts_by_gain_and_preserves_ties() -> None: + generator = FakeGenerator(context_answers=["answer a", "answer b", "answer c"]) + strategy = _strategy( + base_scores=[0.4], + context_scores=[0.6, 0.8, 0.8], + generator=generator, + skip_threshold=0.9, + ) + + results = strategy.rerank( + query="Who?", + documents=[ + Document(id="a", text="alpha"), + Document(id="b", text="beta"), + Document(id="c", text="gamma"), + ], + model_client=object(), + top_k=2, + ) + + assert [result.document.id for result in results] == ["b", "c"] + assert [result.rank for result in results] == [1, 2] + assert [result.original_index for result in results] == [1, 2] + assert [result.metadata["cbdr_skipped"] for result in results] == [False, False] + assert [result.metadata["base_confidence"] for result in results] == [0.4, 0.4] + assert [result.metadata["context_confidence"] for result in results] == [0.8, 0.8] + assert [result.metadata["confidence_gain"] for result in results] == pytest.approx( + [0.4, 0.4] + ) + assert generator.context_calls == [ + ("Who?", "alpha"), + ("Who?", "beta"), + ("Who?", "gamma"), + ] + + +@pytest.mark.parametrize("skip_threshold", [math.nan, math.inf, -0.1, 1.1, True]) +def test_cbdr_invalid_skip_threshold_fails(skip_threshold: object) -> None: + with pytest.raises(ValueError, match="skip_threshold"): + CBDRStrategy( + base_estimator=FakeEstimator( + task_type="query_answerability_confidence", + scores=[0.1], + ), + context_estimator=FakeEstimator( + task_type="query_context_answerability_confidence", + scores=[0.2], + ), + answer_generator=FakeGenerator(), + skip_threshold=cast(float, skip_threshold), + ) + + +def test_cbdr_threshold_zero_always_skips_non_empty_documents() -> None: + strategy = _strategy(base_scores=[0.0], skip_threshold=0.0) + + results = strategy.rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + assert results[0].metadata["cbdr_skipped"] is True + + +def test_cbdr_threshold_one_skips_only_at_exact_one() -> None: + skipped = _strategy(base_scores=[1.0], skip_threshold=1.0).rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + reranked = _strategy( + base_scores=[0.999], + context_scores=[1.0], + generator=FakeGenerator(context_answers=["a"]), + skip_threshold=1.0, + ).rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + assert skipped[0].metadata["cbdr_skipped"] is True + assert reranked[0].metadata["cbdr_skipped"] is False + + +def test_cbdr_rerank_path_validates_long_documents() -> None: + strategy = _strategy(base_scores=[0.2], skip_threshold=0.8, max_document_chars=3) + + with pytest.raises(DocumentTooLongError): + strategy.rerank( + query="Who?", + documents=[Document(text="abcdef")], + model_client=object(), + ) + + +def test_cbdr_empty_query_fails() -> None: + with pytest.raises(RerankInputError, match="query"): + _strategy().rerank( + query=" ", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +def test_cbdr_negative_top_k_fails() -> None: + with pytest.raises(RerankInputError, match="top_k"): + _strategy().rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + top_k=-1, + ) + + +def test_cbdr_invalid_task_types_fail() -> None: + with pytest.raises(RerankInputError, match="base_estimator"): + CBDRStrategy( + base_estimator=FakeEstimator( + task_type="query_context_answerability_confidence", + scores=[0.1], + ), + context_estimator=FakeEstimator( + task_type="query_context_answerability_confidence", + scores=[0.2], + ), + answer_generator=FakeGenerator(), + ) + + with pytest.raises(RerankInputError, match="context_estimator"): + CBDRStrategy( + base_estimator=FakeEstimator( + task_type="query_answerability_confidence", + scores=[0.1], + ), + context_estimator=FakeEstimator( + task_type="query_answerability_confidence", + scores=[0.2], + ), + answer_generator=FakeGenerator(), + ) + + +@pytest.mark.parametrize("score", [math.nan, math.inf, -0.1, 1.1, "0.5", True]) +def test_cbdr_invalid_base_score_fails(score: object) -> None: + with pytest.raises(RerankStrategyError): + _strategy(base_scores=[score]).rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +@pytest.mark.parametrize("score", [math.nan, math.inf, -0.1, 1.1, "0.5", True]) +def test_cbdr_invalid_context_score_fails(score: object) -> None: + with pytest.raises(RerankStrategyError): + _strategy( + base_scores=[0.1], + context_scores=[score], + skip_threshold=0.8, + ).rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +def test_cbdr_answer_generator_empty_output_fails() -> None: + with pytest.raises(RerankProviderError): + _strategy(generator=FakeGenerator(base_answer="")).rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +def test_cbdr_generator_unexpected_exception_wraps_provider_error() -> None: + with pytest.raises(RerankProviderError) as exc_info: + _strategy(generator=FakeGenerator(base_answer=RuntimeError("boom"))).rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + assert isinstance(exc_info.value.__cause__, RuntimeError) + + +def test_cbdr_direct_estimator_unexpected_exception_propagates() -> None: + error = RuntimeError("confidence failed") + + with pytest.raises(RuntimeError, match="confidence failed"): + _strategy(base_scores=[error]).rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +def test_cbdr_facade_wraps_unexpected_estimator_error() -> None: + reranker = AzureOpenAIReranker( + model_client=_unused_model_client(), + strategy=_strategy(base_scores=[RuntimeError("confidence failed")]), + ) + + with pytest.raises(RerankProviderError) as exc_info: + reranker.rerank("Who?", [Document(text="alpha")]) + + assert isinstance(exc_info.value.__cause__, RuntimeError) +``` + +- [ ] **Step 2: Run tests and verify import failure** + +Run: + +```bash +uv run pytest tests/test_cbdr_strategy.py -q +``` + +Expected: + +```text +ImportError: cannot import name 'CBDRStrategy' +``` + +- [ ] **Step 3: Implement `CBDRStrategy`** + +Create `src/ranksmith/strategies/_cbdr.py`: + +```python +from __future__ import annotations + +import math +from collections.abc import Sequence +from dataclasses import dataclass +from numbers import Real +from typing import Literal + +from ranksmith.errors import RerankInputError, RerankStrategyError +from ranksmith.types import Document, RerankResult + +from ._common import validate_documents_max_chars, validate_top_k +from ._confidence_gain import ( + AnswerGenerator, + ConfidenceEstimator, + _call_answer_query, + _call_answer_with_context, + _confidence_gain, + _score_base_answerability, + _score_context_answerability, + _validate_confidence_score, + _validate_estimator_tasks, +) + +CBDRAlgorithm = Literal["cbdr"] + + +@dataclass(frozen=True) +class CBDRStrategy: + base_estimator: ConfidenceEstimator + context_estimator: ConfidenceEstimator + answer_generator: AnswerGenerator + skip_threshold: float = 0.8 + max_document_chars: int = 4000 + algorithm: CBDRAlgorithm = "cbdr" + + def __post_init__(self) -> None: + if self.algorithm != "cbdr": + raise ValueError('algorithm must be "cbdr"') + if self.max_document_chars < 1: + raise ValueError("max_document_chars must be greater than 0") + _validate_probability_config(self.skip_threshold, "skip_threshold") + _validate_estimator_tasks( + base_estimator=self.base_estimator, + context_estimator=self.context_estimator, + ) + + def rerank( + self, + *, + query: str, + documents: Sequence[Document], + model_client: object, + top_k: int | None = None, + ) -> list[RerankResult]: + del model_client + validate_top_k(top_k) + if query.strip() == "": + raise RerankInputError("query must not be empty") + if not documents or top_k == 0: + return [] + + base_answer = _call_answer_query(self.answer_generator, query) + base_result = _score_base_answerability( + estimator=self.base_estimator, + query=query, + answer=base_answer, + ) + base_score = _validate_confidence_score(base_result.score, "base") + + if base_score >= self.skip_threshold: + return _original_order_results( + documents=documents, + top_k=top_k, + algorithm=self.algorithm, + base_score=base_score, + skip_threshold=self.skip_threshold, + ) + + validate_documents_max_chars( + documents, + max_document_chars=self.max_document_chars, + ) + scored = [] + for original_index, document in enumerate(documents): + context_answer = _call_answer_with_context( + self.answer_generator, + query, + document.text, + ) + context_result = _score_context_answerability( + estimator=self.context_estimator, + query=query, + context=document.text, + answer=context_answer, + ) + context_score = _validate_confidence_score( + context_result.score, + "context", + ) + scored.append( + ( + original_index, + context_score, + _confidence_gain( + base_score=base_score, + context_score=context_score, + ), + ) + ) + + scored.sort(key=lambda item: (-item[2], item[0])) + if top_k is not None: + scored = scored[:top_k] + + return [ + RerankResult( + document=documents[original_index], + rank=rank, + original_index=original_index, + metadata={ + "strategy": "cbdr", + "algorithm": self.algorithm, + "cbdr_skipped": False, + "base_confidence": base_score, + "skip_threshold": self.skip_threshold, + "context_confidence": context_score, + "confidence_gain": gain, + }, + ) + for rank, (original_index, context_score, gain) in enumerate( + scored, + start=1, + ) + ] + + +def _validate_probability_config(value: object, name: str) -> float: + if isinstance(value, bool) or not isinstance(value, Real): + raise ValueError(f"{name} must be a finite probability in [0, 1]") + probability = float(value) + if not math.isfinite(probability) or probability < 0.0 or probability > 1.0: + raise ValueError(f"{name} must be a finite probability in [0, 1]") + return probability + + +def _original_order_results( + *, + documents: Sequence[Document], + top_k: int | None, + algorithm: CBDRAlgorithm, + base_score: float, + skip_threshold: float, +) -> list[RerankResult]: + indexed = list(enumerate(documents)) + if top_k is not None: + indexed = indexed[:top_k] + return [ + RerankResult( + document=document, + rank=rank, + original_index=original_index, + metadata={ + "strategy": "cbdr", + "algorithm": algorithm, + "cbdr_skipped": True, + "base_confidence": base_score, + "skip_threshold": skip_threshold, + "context_confidence": None, + "confidence_gain": None, + }, + ) + for rank, (original_index, document) in enumerate(indexed, start=1) + ] +``` + +- [ ] **Step 4: Export CBDR strategy and register facade behavior** + +Modify `src/ranksmith/strategies/__init__.py`: + +```python +from ranksmith.strategies._cbdr import CBDRStrategy +``` + +Add `"CBDRStrategy"` to `__all__`. + +Modify `src/ranksmith/azure.py`: + +```python +from ranksmith.strategies import ( + AcuRankStrategy, + AsyncAcuRankStrategy, + AsyncListwiseStrategy, + AsyncPairwiseStrategy, + AsyncSetwiseStrategy, + AsyncTourRankStrategy, + CBDRStrategy, + ConfidenceGainStrategy, + ListwiseStrategy, + PairwiseStrategy, + SetwiseStrategy, + TourRankStrategy, +) +``` + +Add `CBDRStrategy` to `_is_builtin_sync_strategy(...)`: + +```python +def _is_builtin_sync_strategy(strategy: object) -> bool: + return type(strategy) in { + AcuRankStrategy, + CBDRStrategy, + ConfidenceGainStrategy, + ListwiseStrategy, + PairwiseStrategy, + SetwiseStrategy, + TourRankStrategy, + } +``` + +- [ ] **Step 5: Run CBDR and confidence gain tests** + +Run: + +```bash +uv run pytest tests/test_cbdr_strategy.py tests/test_confidence_gain_strategy.py -q +``` + +Expected: + +```text +exit code 0 with no test failures +``` + +- [ ] **Step 6: Commit CBDR strategy** + +Run: + +```bash +git add src/ranksmith/strategies/_confidence_gain.py src/ranksmith/strategies/_cbdr.py src/ranksmith/strategies/__init__.py src/ranksmith/azure.py tests/test_cbdr_strategy.py +git commit -m "feat: add cbdr strategy" +``` + +Expected: + +```text +commit succeeds +``` + +--- + +### Task 3: Artifact-Load E2E Smoke Tests + +**Files:** +- Modify: `tests/test_cbdr_strategy.py` + +- [ ] **Step 1: Add artifact smoke helpers** + +Append these imports near the top of `tests/test_cbdr_strategy.py`: + +```python +import sys +from pathlib import Path +from types import ModuleType + +from ranksmith.confidence import StructuralConfidenceEstimator +from ranksmith.confidence._scorer import ARTIFACT_SCHEMA_VERSION +``` + +Add these helper classes and functions: + +```python +class ArtifactScorer: + def __init__(self, scores: list[float]) -> None: + self.scores = scores + + def predict_confidence(self, features: object) -> float: + del features + return self.scores.pop(0) + + +class ArtifactEncoder: + encoder_name = "bert-base-uncased" + encoder_revision = None + tokenizer_name = "bert-base-uncased" + tokenizer_revision = None + + def __init__(self, *, max_length: int) -> None: + self.max_length = max_length + + def encode(self, text: str) -> tuple[list[list[float]], list[int]]: + seed = float(len(text) % 7 + 1) + hidden = [[seed + row * 0.01, row * 0.02, seed * 0.03] for row in range(40)] + return hidden, [1] * len(hidden) + + +def _artifact_metadata(task_type: TaskType) -> dict[str, object]: + return { + "artifact_schema_version": ARTIFACT_SCHEMA_VERSION, + "scorer_type": "joblib-wrapper", + "task_type": task_type, + "encoder_name": "bert-base-uncased", + "encoder_revision": None, + "tokenizer_name": "bert-base-uncased", + "tokenizer_revision": None, + "input_template_version": "structural-template-v1", + "feature_schema_version": "structural-v1", + "feature_dim": 70, + "feature_dtype": "float64", + "max_length": 64, + "granularity": "two_scale", + "local_window_size": 5, + "local_stride": 2, + "score_output": "probability", + "positive_class_index": 1, + } + + +def _install_artifact_joblib( + monkeypatch: pytest.MonkeyPatch, + artifacts: dict[Path, object], +) -> None: + module = ModuleType("joblib") + + def load(path: str | Path) -> object: + return artifacts[Path(path)] + + module.load = load # type: ignore[attr-defined] + monkeypatch.setitem(sys.modules, "joblib", module) + + +def _artifact_strategy( + *, + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + base_scores: list[float], + context_scores: list[float], + generator: FakeGenerator, + skip_threshold: float, +) -> CBDRStrategy: + base_artifact_path = tmp_path / "query_answerability.joblib" + context_artifact_path = tmp_path / "query_context_answerability.joblib" + _install_artifact_joblib( + monkeypatch, + { + base_artifact_path: { + "metadata": _artifact_metadata("query_answerability_confidence"), + "scorer": ArtifactScorer(base_scores), + }, + context_artifact_path: { + "metadata": _artifact_metadata( + "query_context_answerability_confidence" + ), + "scorer": ArtifactScorer(context_scores), + }, + }, + ) + + def fake_from_pretrained(**kwargs: object) -> ArtifactEncoder: + return ArtifactEncoder(max_length=cast(int, kwargs["max_length"])) + + monkeypatch.setattr( + "ranksmith.confidence._structural.FrozenAutoEncoder.from_pretrained", + fake_from_pretrained, + ) + + return CBDRStrategy( + base_estimator=StructuralConfidenceEstimator.from_artifact(base_artifact_path), + context_estimator=StructuralConfidenceEstimator.from_artifact( + context_artifact_path + ), + answer_generator=generator, + skip_threshold=skip_threshold, + ) +``` + +- [ ] **Step 2: Add artifact-load skip path smoke** + +Append this test to `tests/test_cbdr_strategy.py`: + +```python +def test_cbdr_artifact_e2e_skip_path_through_azure_facade( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + strategy = _artifact_strategy( + monkeypatch=monkeypatch, + tmp_path=tmp_path, + base_scores=[0.91], + context_scores=[], + generator=FakeGenerator(context_answers=[]), + skip_threshold=0.8, + ) + reranker = AzureOpenAIReranker( + model_client=_unused_model_client(), + strategy=strategy, + ) + + results = reranker.rerank( + "who played karen in married to the mob?", + [ + Document( + id="similar-but-weak", + text="Michelle Pfeiffer appears in the film.", + ), + Document(id="direct-evidence", text="Nancy Travis played Karen."), + ], + ) + + assert [result.document.id for result in results] == [ + "similar-but-weak", + "direct-evidence", + ] + assert [result.metadata["cbdr_skipped"] for result in results] == [True, True] + assert [result.metadata["base_confidence"] for result in results] == [0.91, 0.91] + assert [result.metadata["context_confidence"] for result in results] == [None, None] + assert [result.metadata["confidence_gain"] for result in results] == [None, None] +``` + +- [ ] **Step 3: Add artifact-load rerank path smoke** + +Append this test to `tests/test_cbdr_strategy.py`: + +```python +def test_cbdr_artifact_e2e_rerank_path_through_azure_facade( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + strategy = _artifact_strategy( + monkeypatch=monkeypatch, + tmp_path=tmp_path, + base_scores=[0.3], + context_scores=[0.45, 0.9], + generator=FakeGenerator( + base_answer="base answer", + context_answers=["low answer", "high answer"], + ), + skip_threshold=0.8, + ) + reranker = AzureOpenAIReranker( + model_client=_unused_model_client(), + strategy=strategy, + ) + + results = reranker.rerank( + "who played karen in married to the mob?", + [ + Document( + id="similar-but-weak", + text="Michelle Pfeiffer appears in the film.", + ), + Document(id="direct-evidence", text="Nancy Travis played Karen."), + ], + ) + + assert [result.document.id for result in results] == [ + "direct-evidence", + "similar-but-weak", + ] + assert [result.metadata["cbdr_skipped"] for result in results] == [False, False] + assert [result.metadata["base_confidence"] for result in results] == [0.3, 0.3] + assert [result.metadata["context_confidence"] for result in results] == [0.9, 0.45] + assert [result.metadata["confidence_gain"] for result in results] == pytest.approx( + [0.6, 0.15] + ) +``` + +- [ ] **Step 4: Run CBDR tests** + +Run: + +```bash +uv run pytest tests/test_cbdr_strategy.py -q +``` + +Expected: + +```text +exit code 0 with no test failures +``` + +- [ ] **Step 5: Run targeted regression tests** + +Run: + +```bash +uv run pytest tests/test_cbdr_strategy.py tests/test_confidence_gain_strategy.py tests/test_ranksmith.py -q +``` + +Expected: + +```text +exit code 0 with no test failures +``` + +- [ ] **Step 6: Commit E2E smoke tests** + +Run: + +```bash +git add tests/test_cbdr_strategy.py +git commit -m "test: add cbdr artifact smoke tests" +``` + +Expected: + +```text +commit succeeds +``` + +--- + +### Task 4: Documentation, Spec Checklist, and Final Verification + +**Files:** +- Modify: `docs/wiki/02_architecture.md` +- Modify: `README.md` +- Modify: `README.ko.md` +- Modify: `docs/specs/spec_cbdr_strategy.md` + +- [ ] **Step 1: Update architecture wiki** + +Modify `docs/wiki/02_architecture.md`: + +Add `CBDRStrategy` to the v1 public strategy list after `ConfidenceGainStrategy`. + +Add `cbdr` to the v1 algorithm list after `confidence_gain`. + +Update the confidence section with: + +```markdown +`CBDRStrategy`는 `Conf(Q)`가 `skip_threshold` 이상이면 context reranking을 skip하고 original order를 보존한다. +`Conf(Q)`가 threshold보다 낮으면 `Conf(Q+C)-Conf(Q)` confidence gain으로 문서를 정렬한다. +true pre-retrieval skip, retriever integration, async CBDR은 구현하지 않는다. +``` + +- [ ] **Step 2: Update README method table and usage** + +Modify `README.md` method table by adding this row after `confidence_gain`: + +```markdown +| `cbdr` | `CBDRStrategy` | You have trained answerability confidence scorers and want to skip context reranking when `Conf(Q)` is already high, otherwise rerank by confidence gain. | Requires scorer artifacts and an answer generator hook. Skip path uses 1 answer generation call and 1 confidence score; rerank path uses `N+1` answer generations and `N+1` confidence scores. | +``` + +Add a short usage block near the existing Confidence Gain section: + +```markdown +`CBDRStrategy` is a sync reranking-side router. It does not integrate with a +retriever or stop upstream retrieval calls; it only skips context reranking once +documents have already been passed to `rerank(...)`. + +```python +from ranksmith import AzureOpenAIReranker +from ranksmith.strategies import CBDRStrategy + +strategy = CBDRStrategy( + base_estimator=query_estimator, + context_estimator=query_context_estimator, + answer_generator=answer_generator, + skip_threshold=0.8, +) + +reranker = AzureOpenAIReranker( + model_client=model_client, + strategy=strategy, +) + +results = reranker.rerank(query, documents) +``` + +When `Conf(Q) >= skip_threshold`, results preserve original document order and +include `metadata["cbdr_skipped"] == True`. When `Conf(Q) < skip_threshold`, all +documents are scored before `top_k` slicing. +``` + +Ensure Markdown fences are balanced. + +- [ ] **Step 3: Mirror README.ko** + +Modify `README.ko.md` with the same section structure and table position. + +Use this table row: + +```markdown +| `cbdr` | `CBDRStrategy` | answerability confidence scorer를 학습했고 `Conf(Q)`가 충분히 높으면 context reranking을 건너뛰고, 낮으면 confidence gain으로 정렬하고 싶을 때 | scorer artifact와 answer generator hook이 필요함. skip path는 answer generation 1회와 confidence scoring 1회, rerank path는 각각 `N+1`회를 수행함 | +``` + +Use this usage text: + +```markdown +`CBDRStrategy`는 sync reranking-side router입니다. retriever와 통합하거나 upstream +retrieval 호출 자체를 멈추지는 않습니다. 이미 `rerank(...)`에 documents가 전달된 +뒤 context reranking을 건너뛸지 결정합니다. + +```python +from ranksmith import AzureOpenAIReranker +from ranksmith.strategies import CBDRStrategy + +strategy = CBDRStrategy( + base_estimator=query_estimator, + context_estimator=query_context_estimator, + answer_generator=answer_generator, + skip_threshold=0.8, +) + +reranker = AzureOpenAIReranker( + model_client=model_client, + strategy=strategy, +) + +results = reranker.rerank(query, documents) +``` + +`Conf(Q) >= skip_threshold`이면 original document order를 보존하고 +`metadata["cbdr_skipped"] == True`를 남깁니다. `Conf(Q) < skip_threshold`이면 +모든 문서를 scoring한 뒤 `top_k`를 적용합니다. +``` + +Ensure README.md and README.ko.md keep the same section and table structure. + +- [ ] **Step 4: Update spec checklist** + +Modify `docs/specs/spec_cbdr_strategy.md`: + +Set status: + +```markdown +- **상태**: `[ ] Draft` | `[ ] In Progress` | `[x] Completed` +``` + +Mark all implementation, verification, and docs checklist items complete: + +```markdown +- [x] `src/ranksmith/strategies/_confidence_gain.py`: 공통 protocol/helper 정리 +- [x] `src/ranksmith/strategies/_cbdr.py`: `CBDRStrategy` 구현 +- [x] `src/ranksmith/strategies/__init__.py`: strategy export 추가 +- [x] `src/ranksmith/azure.py`: built-in sync strategy 처리 추가 +- [x] `tests/test_cbdr_strategy.py`: skip path 정상 케이스 추가 +- [x] `tests/test_cbdr_strategy.py`: rerank path 정상 케이스 추가 +- [x] `tests/test_cbdr_strategy.py`: 엣지/실패 케이스 추가 +- [x] `tests/test_cbdr_strategy.py`: Azure facade smoke 추가 +- [x] `tests/test_cbdr_strategy.py`: artifact load 기반 skip path E2E smoke 추가 +- [x] `tests/test_cbdr_strategy.py`: artifact load 기반 rerank path E2E smoke 추가 +- [x] `./scripts/verify.sh` 스크립트를 통한 린트/타입/전체 테스트 통과 확인 +- [x] `docs/wiki/02_architecture.md`: CBDR strategy 위치 추가 +- [x] `README.md` / `README.ko.md`: benchmark 없는 usage 문서 추가 +- [x] 본 문서 최상단의 **상태**를 `Completed`로 변경 +``` + +- [ ] **Step 5: Run final verification** + +Run: + +```bash +./scripts/verify.sh +``` + +Expected: + +```text +pytest passes +ruff passes +format check passes +mypy passes +sdist and wheel build +``` + +- [ ] **Step 6: Commit docs and final spec** + +Run: + +```bash +git add docs/wiki/02_architecture.md README.md README.ko.md docs/specs/spec_cbdr_strategy.md +git commit -m "docs: document cbdr strategy" +``` + +Expected: + +```text +commit succeeds +``` + +--- + +## Self-Review + +- Spec coverage: + - sync `CBDRStrategy`: Task 2. + - skip decision: Task 2 skip tests and implementation. + - low-confidence confidence gain reranking: Task 2 rerank tests and implementation. + - shared helper refactor: Task 1. + - `AzureOpenAIReranker` built-in behavior: Task 2 facade import and tests. + - submodule-only export: Task 2 export test. + - docs and README: Task 4. + - no upstream retrieval skip, no `should_retrieve()`, no async: preserved by not adding those APIs. + - no scorer training or artifact creation: preserved by using existing `from_artifact()` only in smoke tests. + - no benchmark numbers: Task 4 explicitly avoids benchmark metrics. +- Placeholder scan: + - No placeholder implementation steps remain. + - Every new test and code path has concrete snippets. +- Type consistency: + - `CBDRStrategy.rerank(...)` matches `RerankStrategy`. + - `AnswerGenerator` and `ConfidenceEstimator` reuse existing strategy contracts. + - Metadata keys match `docs/specs/spec_cbdr_strategy.md`. diff --git a/docs/superpowers/plans/2026-06-04-confidence-gain-reranking.md b/docs/superpowers/plans/2026-06-04-confidence-gain-reranking.md new file mode 100644 index 0000000..a16a6ed --- /dev/null +++ b/docs/superpowers/plans/2026-06-04-confidence-gain-reranking.md @@ -0,0 +1,1256 @@ +# Confidence Gain Reranking Implementation Plan + +> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. + +**Goal:** Add `Inc(Q, C) = Conf(Q+C) - Conf(Q)` support by extending confidence tasks and adding a sync `ConfidenceGainStrategy`. + +**Architecture:** Reuse the existing `StructuralConfidenceEstimator.from_artifact()`, `score()`, and `score_batch()` runtime. Add two answerability task types, extend generation/training validation, then add a small Strategy that consumes two estimators plus an answer generator hook and sorts by confidence gain. + +**Tech Stack:** Python 3.10+, dataclasses, existing `ranksmith.confidence`, existing `ranksmith.confidence_generation`, existing `ranksmith.confidence_training`, existing Strategy protocol, pytest, ruff, mypy. + +--- + +## File Map + +- Modify `src/ranksmith/confidence/_types.py` + - Add `QueryAnswerabilityConfidenceInput` and `QueryContextAnswerabilityConfidenceInput`. + - Extend `TaskType` and `StructuralConfidenceInput`. +- Modify `src/ranksmith/confidence/_templates.py` + - Add templates for query-only and query+context answerability confidence. +- Modify `src/ranksmith/confidence/_scorer.py` + - Accept the new task types in scorer metadata. +- Modify `src/ranksmith/confidence/__init__.py` + - Export the new input types from the confidence submodule. +- Modify `src/ranksmith/confidence_training/_types.py` + - Extend training config and canonical sample fields. +- Modify `src/ranksmith/confidence_training/_dataset.py` + - Validate and parse the new canonical JSONL schemas. +- Modify `src/ranksmith/confidence_training/_features.py` + - Convert new canonical samples into new runtime input dataclasses. +- Modify `src/ranksmith/confidence_training/_artifact.py` + - Ensure metadata export accepts new task types through existing `ScorerMetadata`. +- Modify `src/ranksmith/confidence_generation/_types.py` + - Add configs and raw sample dataclasses for query-only and query+context answerability generation. +- Modify `src/ranksmith/confidence_generation/_io.py` + - Load the new raw JSONL schemas and validate resume output rows. +- Modify `src/ranksmith/confidence_generation/_prompts.py` + - Add prompts for base answer and context-conditioned answer. +- Modify `src/ranksmith/confidence_generation/_pipeline.py` + - Add `generate_query_answerability_confidence_dataset()` and `generate_query_context_answerability_confidence_dataset()`. +- Modify `src/ranksmith/confidence_generation/__init__.py` + - Export the new generation configs/functions. +- Create `src/ranksmith/strategies/_confidence_gain.py` + - Implement `AnswerGenerator`, `ConfidenceGainResult`, and `ConfidenceGainStrategy`. +- Modify `src/ranksmith/strategies/__init__.py` + - Export `ConfidenceGainStrategy`. +- Modify docs: + - `docs/wiki/02_architecture.md` + - `docs/wiki/04_references_index.md` + - `README.md` + - `README.ko.md` + - `docs/specs/spec_confidence_gain_reranking.md` +- Tests: + - `tests/test_confidence_answerability_tasks.py` + - `tests/test_confidence_training_answerability.py` + - `tests/test_confidence_generation_answerability.py` + - `tests/test_confidence_gain_strategy.py` + +--- + +### Task 1: Confidence Runtime Answerability Tasks + +**Files:** +- Modify: `src/ranksmith/confidence/_types.py` +- Modify: `src/ranksmith/confidence/_templates.py` +- Modify: `src/ranksmith/confidence/_scorer.py` +- Modify: `src/ranksmith/confidence/__init__.py` +- Test: `tests/test_confidence_answerability_tasks.py` + +- [ ] **Step 1: Write failing runtime task tests** + +Create `tests/test_confidence_answerability_tasks.py`: + +```python +from __future__ import annotations + +import importlib +from collections.abc import Sequence + +import pytest + +from ranksmith.confidence import ( + ConfidenceInputError, + QueryAnswerabilityConfidenceInput, + QueryContextAnswerabilityConfidenceInput, + ScorerMetadata, + StructuralConfidenceEstimator, +) + + +class FakeEncoder: + encoder_name = "bert-base-uncased" + encoder_revision = None + tokenizer_name = "bert-base-uncased" + tokenizer_revision = None + max_length = 256 + + def __init__(self) -> None: + self.texts: list[str] = [] + + def encode(self, text: str) -> tuple[list[list[float]], list[int]]: + self.texts.append(text) + return [[0.1, 0.2], [0.3, 0.4]], [1, 1] + + +class FakeScorer: + def __init__(self, task_type: str) -> None: + self.metadata = ScorerMetadata( + artifact_schema_version="structural-artifact-v1", + scorer_type="fake", + task_type=task_type, # type: ignore[arg-type] + encoder_name="bert-base-uncased", + encoder_revision=None, + tokenizer_name="bert-base-uncased", + tokenizer_revision=None, + input_template_version="structural-template-v1", + feature_schema_version="structural-v1", + feature_dim=70, + feature_dtype="float64", + max_length=256, + granularity="token", + local_window_size=5, + local_stride=2, + score_output="probability", + positive_class_index=1, + ) + + def predict_confidence(self, features: Sequence[float]) -> float: + return 0.75 + + +def test_confidence_submodule_exports_answerability_inputs() -> None: + confidence = importlib.import_module("ranksmith.confidence") + + assert hasattr(confidence, "QueryAnswerabilityConfidenceInput") + assert hasattr(confidence, "QueryContextAnswerabilityConfidenceInput") + + +def test_query_answerability_template_is_scored(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setattr( + "ranksmith.confidence._structural.extract_structural_features", + lambda hidden, mask, *, max_length: [0.0] * 70, + ) + encoder = FakeEncoder() + estimator = StructuralConfidenceEstimator( + encoder=encoder, + scorer=FakeScorer("query_answerability_confidence"), + task_type="query_answerability_confidence", + ) + + result = estimator.score( + QueryAnswerabilityConfidenceInput(query="Who?", answer="Nancy Travis") + ) + + assert result.score == 0.75 + assert encoder.texts == ["Query:\nWho?\n\nAnswer:\nNancy Travis"] + + +def test_query_context_answerability_template_is_scored( + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setattr( + "ranksmith.confidence._structural.extract_structural_features", + lambda hidden, mask, *, max_length: [0.0] * 70, + ) + encoder = FakeEncoder() + estimator = StructuralConfidenceEstimator( + encoder=encoder, + scorer=FakeScorer("query_context_answerability_confidence"), + task_type="query_context_answerability_confidence", + ) + + result = estimator.score( + QueryContextAnswerabilityConfidenceInput( + query="Who?", + context="Karen was played by Nancy Travis.", + answer="Nancy Travis", + ) + ) + + assert result.score == 0.75 + assert encoder.texts == [ + "Query:\nWho?\n\nContext:\nKaren was played by Nancy Travis.\n\nAnswer:\nNancy Travis" + ] + + +def test_answerability_task_rejects_wrong_input_type() -> None: + estimator = StructuralConfidenceEstimator( + encoder=FakeEncoder(), + scorer=FakeScorer("query_answerability_confidence"), + task_type="query_answerability_confidence", + ) + + with pytest.raises(ConfidenceInputError): + estimator.score( + QueryContextAnswerabilityConfidenceInput( + query="Who?", + context="Context", + answer="Answer", + ) + ) +``` + +- [ ] **Step 2: Run runtime task tests and verify failure** + +Run: + +```bash +uv run pytest tests/test_confidence_answerability_tasks.py -q +``` + +Expected: FAIL because `QueryAnswerabilityConfidenceInput` is not exported. + +- [ ] **Step 3: Implement runtime task types and templates** + +Modify `src/ranksmith/confidence/_types.py`: + +```python +TaskType: TypeAlias = Literal[ + "answer_confidence", + "judgment_confidence", + "query_answerability_confidence", + "query_context_answerability_confidence", +] + + +@dataclass(frozen=True) +class QueryAnswerabilityConfidenceInput: + query: str + answer: str + + +@dataclass(frozen=True) +class QueryContextAnswerabilityConfidenceInput: + query: str + context: str + answer: str + + +StructuralConfidenceInput: TypeAlias = ( + AnswerConfidenceInput + | JudgmentConfidenceInput + | QueryAnswerabilityConfidenceInput + | QueryContextAnswerabilityConfidenceInput +) +``` + +Modify `src/ranksmith/confidence/_templates.py`: + +```python +from ranksmith.confidence._types import ( + AnswerConfidenceInput, + JudgmentConfidenceInput, + QueryAnswerabilityConfidenceInput, + QueryContextAnswerabilityConfidenceInput, + StructuralConfidenceInput, + TaskType, +) +``` + +Add branches in `format_confidence_input(...)`: + +```python + if task_type == "query_answerability_confidence": + if not isinstance(item, QueryAnswerabilityConfidenceInput): + raise ConfidenceInputError( + "query_answerability_confidence requires " + "QueryAnswerabilityConfidenceInput" + ) + query = _require_non_empty(item.query, field_name="query") + answer = _require_non_empty(item.answer, field_name="answer") + return f"Query:\n{query}\n\nAnswer:\n{answer}" + + if task_type == "query_context_answerability_confidence": + if not isinstance(item, QueryContextAnswerabilityConfidenceInput): + raise ConfidenceInputError( + "query_context_answerability_confidence requires " + "QueryContextAnswerabilityConfidenceInput" + ) + query = _require_non_empty(item.query, field_name="query") + context = _require_non_empty(item.context, field_name="context") + answer = _require_non_empty(item.answer, field_name="answer") + return f"Query:\n{query}\n\nContext:\n{context}\n\nAnswer:\n{answer}" +``` + +Modify `_task_type(...)` in `src/ranksmith/confidence/_scorer.py` to accept the two new literals. + +Modify `src/ranksmith/confidence/__init__.py` to import/export both new dataclasses. + +- [ ] **Step 4: Run runtime task tests and targeted existing tests** + +Run: + +```bash +uv run pytest tests/test_confidence_answerability_tasks.py tests/test_confidence_estimator.py tests/test_confidence_scorer.py -q +``` + +Expected: PASS. + +- [ ] **Step 5: Commit Task 1** + +```bash +git add src/ranksmith/confidence tests/test_confidence_answerability_tasks.py +git commit -m "feat: add answerability confidence tasks" +``` + +--- + +### Task 2: Training Dataset And Feature Support + +**Files:** +- Modify: `src/ranksmith/confidence_training/_types.py` +- Modify: `src/ranksmith/confidence_training/_dataset.py` +- Modify: `src/ranksmith/confidence_training/_features.py` +- Test: `tests/test_confidence_training_answerability.py` + +- [ ] **Step 1: Write failing training tests** + +Create `tests/test_confidence_training_answerability.py`: + +```python +from __future__ import annotations + +import json +from pathlib import Path + +import pytest + +from ranksmith.confidence import ( + QueryAnswerabilityConfidenceInput, + QueryContextAnswerabilityConfidenceInput, +) +from ranksmith.confidence_training import ConfidenceTrainingConfig +from ranksmith.confidence_training._dataset import load_canonical_dataset +from ranksmith.confidence_training._errors import ConfidenceDatasetError +from ranksmith.confidence_training._features import sample_to_confidence_input + + +def _write_jsonl(path: Path, rows: list[dict[str, object]]) -> None: + path.write_text( + "".join(json.dumps(row) + "\n" for row in rows), + encoding="utf-8", + ) + + +def test_training_config_accepts_query_answerability_task(tmp_path: Path) -> None: + config = ConfidenceTrainingConfig( + task_type="query_answerability_confidence", + dataset_path=tmp_path / "data.jsonl", + output_dir=tmp_path / "run", + export_path=tmp_path / "scorer.joblib", + ) + + assert config.task_type == "query_answerability_confidence" + + +def test_loads_query_answerability_canonical_dataset(tmp_path: Path) -> None: + path = tmp_path / "query.jsonl" + _write_jsonl( + path, + [ + { + "id": "q1::base", + "task_type": "query_answerability_confidence", + "query": "Who?", + "answer": "Nancy Travis", + "gold_answer": "Nancy Travis", + "label": 1, + "metadata": {"input_metadata": {}}, + } + ], + ) + + samples = load_canonical_dataset( + path, + task_type="query_answerability_confidence", + ) + + assert samples[0].query == "Who?" + assert samples[0].answer == "Nancy Travis" + assert isinstance(sample_to_confidence_input(samples[0]), QueryAnswerabilityConfidenceInput) + + +def test_loads_query_context_answerability_canonical_dataset(tmp_path: Path) -> None: + path = tmp_path / "query_context.jsonl" + _write_jsonl( + path, + [ + { + "id": "q1::doc1", + "task_type": "query_context_answerability_confidence", + "query": "Who?", + "context": "Karen was played by Nancy Travis.", + "answer": "Nancy Travis", + "gold_answer": "Nancy Travis", + "label": 1, + "metadata": {"input_metadata": {}}, + } + ], + ) + + samples = load_canonical_dataset( + path, + task_type="query_context_answerability_confidence", + ) + + assert samples[0].context == "Karen was played by Nancy Travis." + assert isinstance( + sample_to_confidence_input(samples[0]), + QueryContextAnswerabilityConfidenceInput, + ) + + +def test_query_context_dataset_rejects_missing_context(tmp_path: Path) -> None: + path = tmp_path / "bad.jsonl" + _write_jsonl( + path, + [ + { + "id": "q1::doc1", + "task_type": "query_context_answerability_confidence", + "query": "Who?", + "answer": "Nancy Travis", + "label": 1, + } + ], + ) + + with pytest.raises(ConfidenceDatasetError, match="missing required field: context"): + load_canonical_dataset( + path, + task_type="query_context_answerability_confidence", + ) +``` + +- [ ] **Step 2: Run training tests and verify failure** + +Run: + +```bash +uv run pytest tests/test_confidence_training_answerability.py -q +``` + +Expected: FAIL because `ConfidenceTrainingConfig` rejects the new task type or dataset schemas are missing. + +- [ ] **Step 3: Implement training schema support** + +Modify `ConfidenceTrainingConfig.__post_init__` in `src/ranksmith/confidence_training/_types.py`: + +```python +if self.task_type not in { + "answer_confidence", + "judgment_confidence", + "query_answerability_confidence", + "query_context_answerability_confidence", +}: + raise ConfidenceTrainingConfigError("unsupported task_type") +``` + +Extend `CanonicalConfidenceSample` only if `query`, `context`, and `answer` fields already cannot represent the new tasks. Prefer reusing existing fields. + +Modify `src/ranksmith/confidence_training/_dataset.py`: + +```python +_QUERY_ANSWERABILITY_REQUIRED = ("id", "query", "answer", "label") +_QUERY_CONTEXT_ANSWERABILITY_REQUIRED = ("id", "query", "context", "answer", "label") +_QUERY_ANSWERABILITY_ALLOWED = { + *_QUERY_ANSWERABILITY_REQUIRED, + "task_type", + "gold_answer", + "source", + "group_id", + "metadata", +} +_QUERY_CONTEXT_ANSWERABILITY_ALLOWED = { + *_QUERY_CONTEXT_ANSWERABILITY_REQUIRED, + "task_type", + "gold_answer", + "source", + "group_id", + "metadata", +} +``` + +Add `_TASK_SCHEMAS` entries that construct `CanonicalConfidenceSample` with the matching `task_type`, `query`, `context`, `answer`, `gold_answer`, `source`, `group_id`, and `metadata`. + +Modify `src/ranksmith/confidence_training/_features.py` to map: + +```python +if sample.task_type == "query_answerability_confidence": + return QueryAnswerabilityConfidenceInput( + query=_required(sample.query, "query"), + answer=_required(sample.answer, "answer"), + ) +if sample.task_type == "query_context_answerability_confidence": + return QueryContextAnswerabilityConfidenceInput( + query=_required(sample.query, "query"), + context=_required(sample.context, "context"), + answer=_required(sample.answer, "answer"), + ) +``` + +- [ ] **Step 4: Run training tests and existing confidence training tests** + +Run: + +```bash +uv run pytest tests/test_confidence_training_answerability.py tests/test_confidence_training_*.py -q +``` + +Expected: PASS. + +- [ ] **Step 5: Commit Task 2** + +```bash +git add src/ranksmith/confidence_training tests/test_confidence_training_answerability.py +git commit -m "feat: support answerability confidence training" +``` + +--- + +### Task 3: Generation Pipeline Answerability Datasets + +**Files:** +- Modify: `src/ranksmith/confidence_generation/_types.py` +- Modify: `src/ranksmith/confidence_generation/_io.py` +- Modify: `src/ranksmith/confidence_generation/_prompts.py` +- Modify: `src/ranksmith/confidence_generation/_pipeline.py` +- Modify: `src/ranksmith/confidence_generation/__init__.py` +- Test: `tests/test_confidence_generation_answerability.py` + +- [ ] **Step 1: Write failing generation tests** + +Create `tests/test_confidence_generation_answerability.py`: + +```python +from __future__ import annotations + +import json +from pathlib import Path + +from ranksmith.confidence_generation import ( + QueryAnswerabilityGenerationConfig, + QueryContextAnswerabilityGenerationConfig, + generate_query_answerability_confidence_dataset, + generate_query_context_answerability_confidence_dataset, +) +from ranksmith.model import ModelResponse + + +class RecordingProvider: + def __init__(self, answer: str = "Nancy Travis") -> None: + self.answer = answer + self.requests = [] + + def complete(self, request): # noqa: ANN001 + self.requests.append(request) + return ModelResponse(content=json.dumps({"answer": self.answer})) + + +def _write_jsonl(path: Path, rows: list[dict[str, object]]) -> None: + path.write_text( + "".join(json.dumps(row) + "\n" for row in rows), + encoding="utf-8", + ) + + +def _read_jsonl(path: Path) -> list[dict[str, object]]: + return [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines()] + + +def test_generates_query_answerability_dataset(tmp_path: Path) -> None: + input_path = tmp_path / "input.jsonl" + output_path = tmp_path / "out.jsonl" + _write_jsonl( + input_path, + [ + { + "id": "q1::base", + "query": "Who played Karen?", + "gold_answer": "Nancy Travis", + "metadata": {"split": "tiny"}, + } + ], + ) + provider = RecordingProvider() + + result = generate_query_answerability_confidence_dataset( + QueryAnswerabilityGenerationConfig( + input_path=input_path, + output_path=output_path, + provider=provider, + ) + ) + + rows = _read_jsonl(output_path) + assert result.generated_count == 1 + assert rows[0]["task_type"] == "query_answerability_confidence" + assert rows[0]["query"] == "Who played Karen?" + assert rows[0]["answer"] == "Nancy Travis" + assert rows[0]["label"] == 1 + + +def test_generates_query_context_answerability_dataset(tmp_path: Path) -> None: + input_path = tmp_path / "input.jsonl" + output_path = tmp_path / "out.jsonl" + _write_jsonl( + input_path, + [ + { + "id": "q1::doc1", + "query": "Who played Karen?", + "context": "Karen was played by Nancy Travis.", + "gold_answer": "Nancy Travis", + "group_id": "q1", + } + ], + ) + provider = RecordingProvider() + + result = generate_query_context_answerability_confidence_dataset( + QueryContextAnswerabilityGenerationConfig( + input_path=input_path, + output_path=output_path, + provider=provider, + ) + ) + + rows = _read_jsonl(output_path) + assert result.generated_count == 1 + assert rows[0]["task_type"] == "query_context_answerability_confidence" + assert rows[0]["context"] == "Karen was played by Nancy Travis." + assert rows[0]["answer"] == "Nancy Travis" + assert rows[0]["label"] == 1 +``` + +- [ ] **Step 2: Run generation tests and verify failure** + +Run: + +```bash +uv run pytest tests/test_confidence_generation_answerability.py -q +``` + +Expected: FAIL because new generation configs/functions are not exported. + +- [ ] **Step 3: Implement generation types, IO, prompts, and pipelines** + +Add config/sample dataclasses to `src/ranksmith/confidence_generation/_types.py`: + +```python +@dataclass(frozen=True) +class QueryAnswerabilityGenerationConfig: + input_path: str | Path + output_path: str | Path + provider: ModelProvider + overwrite: bool = False + resume: bool = False + max_items: int | None = None + include_raw_model_output: bool = True + on_usage: UsageCallback | None = None + source: str | None = None + no_answer_value: str = "__NO_ANSWER__" + + +@dataclass(frozen=True) +class QueryContextAnswerabilityGenerationConfig: + input_path: str | Path + output_path: str | Path + provider: ModelProvider + overwrite: bool = False + resume: bool = False + max_items: int | None = None + max_context_chars: int = 4000 + include_raw_model_output: bool = True + on_usage: UsageCallback | None = None + source: str | None = None + no_answer_value: str = "__NO_ANSWER__" +``` + +Reuse existing `_validate_common_config`, `no_answer_value`, and positive integer validation. + +Add raw sample dataclasses: + +```python +@dataclass(frozen=True) +class QueryAnswerabilityGenerationSample: + id: str + query: str + gold_answer: str | list[str] + source: str | None = None + group_id: str | None = None + metadata: Mapping[str, Any] = field(default_factory=dict) + + +@dataclass(frozen=True) +class QueryContextAnswerabilityGenerationSample: + id: str + query: str + context: str + gold_answer: str | list[str] + source: str | None = None + group_id: str | None = None + metadata: Mapping[str, Any] = field(default_factory=dict) +``` + +In `_io.py`, add loaders mirroring existing answer sample loading: +- query-only requires `id`, `query`, `gold_answer`. +- query+context requires `id`, `query`, `context`, `gold_answer`. +- unexpected fields fail. +- whitespace-only text fails. +- context length uses raw string length and `max_context_chars`. + +In `_prompts.py`, add: +- `QUERY_ANSWERABILITY_SYSTEM_PROMPT` +- `QUERY_CONTEXT_ANSWERABILITY_SYSTEM_PROMPT` +- `build_query_answerability_prompt(sample, no_answer_value=...)` +- `build_query_context_answerability_prompt(sample, no_answer_value=...)` + +Both prompts require JSON response `{"answer": "..."}`. + +In `_pipeline.py`, add: +- `generate_query_answerability_confidence_dataset(config)` +- `generate_query_context_answerability_confidence_dataset(config)` + +Build canonical rows with task types: +- `query_answerability_confidence` +- `query_context_answerability_confidence` + +Use existing `parse_answer_output()` and `normalized_exact_match()`. + +Update `__init__.py` exports. + +- [ ] **Step 4: Run generation tests and existing generation tests** + +Run: + +```bash +uv run pytest tests/test_confidence_generation_answerability.py tests/test_confidence_generation_*.py -q +``` + +Expected: PASS. + +- [ ] **Step 5: Commit Task 3** + +```bash +git add src/ranksmith/confidence_generation tests/test_confidence_generation_answerability.py +git commit -m "feat: generate answerability confidence datasets" +``` + +--- + +### Task 4: Confidence Gain Strategy + +**Files:** +- Create: `src/ranksmith/strategies/_confidence_gain.py` +- Modify: `src/ranksmith/strategies/__init__.py` +- Test: `tests/test_confidence_gain_strategy.py` + +- [ ] **Step 1: Write failing strategy tests** + +Create `tests/test_confidence_gain_strategy.py`: + +```python +from __future__ import annotations + +from dataclasses import dataclass + +import pytest + +from ranksmith.errors import DocumentTooLongError, RerankInputError, RerankProviderError +from ranksmith.strategies import ConfidenceGainStrategy +from ranksmith.types import Document + + +@dataclass +class FakeConfidenceResult: + score: float + task_type: str + feature_schema_version: str = "structural-v1" + metadata: dict[str, object] | None = None + + +class BaseEstimator: + task_type = "query_answerability_confidence" + + def __init__(self, score: float) -> None: + self.score_value = score + self.items = [] + + def score(self, item): # noqa: ANN001 + self.items.append(item) + return FakeConfidenceResult(score=self.score_value, task_type=self.task_type) + + +class ContextEstimator: + task_type = "query_context_answerability_confidence" + + def __init__(self, scores: list[float]) -> None: + self.scores = list(scores) + self.items = [] + + def score(self, item): # noqa: ANN001 + self.items.append(item) + return FakeConfidenceResult( + score=self.scores.pop(0), + task_type=self.task_type, + ) + + +class AnswerGenerator: + def __init__(self) -> None: + self.query_calls: list[str] = [] + self.context_calls: list[tuple[str, str]] = [] + + def answer_query(self, query: str) -> str: + self.query_calls.append(query) + return "base answer" + + def answer_with_context(self, query: str, context: str) -> str: + self.context_calls.append((query, context)) + return f"answer from {context}" + + +def test_confidence_gain_strategy_sorts_by_gain_and_preserves_ties() -> None: + generator = AnswerGenerator() + strategy = ConfidenceGainStrategy( + base_estimator=BaseEstimator(0.4), + context_estimator=ContextEstimator([0.9, 0.5, 0.5]), + answer_generator=generator, + ) + documents = [Document("doc-a"), Document("doc-b"), Document("doc-c")] + + results = strategy.rerank(query="Who?", documents=documents, model_client=object()) + + assert [result.original_index for result in results] == [0, 1, 2] + assert [result.metadata["confidence_gain"] for result in results] == [ + pytest.approx(0.5), + pytest.approx(0.1), + pytest.approx(0.1), + ] + assert len(generator.query_calls) == 1 + assert len(generator.context_calls) == 3 + + +def test_confidence_gain_strategy_applies_top_k() -> None: + strategy = ConfidenceGainStrategy( + base_estimator=BaseEstimator(0.2), + context_estimator=ContextEstimator([0.4, 0.9]), + answer_generator=AnswerGenerator(), + ) + + results = strategy.rerank( + query="Who?", + documents=[Document("a"), Document("b")], + model_client=object(), + top_k=1, + ) + + assert [result.original_index for result in results] == [1] + + +def test_confidence_gain_strategy_rejects_wrong_estimator_task() -> None: + bad_base = ContextEstimator([0.1]) + + with pytest.raises(RerankInputError, match="base_estimator"): + ConfidenceGainStrategy( + base_estimator=bad_base, + context_estimator=ContextEstimator([0.2]), + answer_generator=AnswerGenerator(), + ) + + +def test_confidence_gain_strategy_wraps_empty_answer() -> None: + class EmptyAnswerGenerator(AnswerGenerator): + def answer_query(self, query: str) -> str: + return " " + + strategy = ConfidenceGainStrategy( + base_estimator=BaseEstimator(0.2), + context_estimator=ContextEstimator([0.4]), + answer_generator=EmptyAnswerGenerator(), + ) + + with pytest.raises(RerankProviderError, match="answer_query returned empty"): + strategy.rerank(query="Who?", documents=[Document("a")], model_client=object()) + + +def test_confidence_gain_strategy_rejects_long_document() -> None: + strategy = ConfidenceGainStrategy( + base_estimator=BaseEstimator(0.2), + context_estimator=ContextEstimator([0.4]), + answer_generator=AnswerGenerator(), + max_document_chars=3, + ) + + with pytest.raises(DocumentTooLongError): + strategy.rerank(query="Who?", documents=[Document("abcd")], model_client=object()) +``` + +- [ ] **Step 2: Run strategy tests and verify failure** + +Run: + +```bash +uv run pytest tests/test_confidence_gain_strategy.py -q +``` + +Expected: FAIL because `ConfidenceGainStrategy` is not exported. + +- [ ] **Step 3: Implement strategy** + +Create `src/ranksmith/strategies/_confidence_gain.py`: + +```python +from __future__ import annotations + +import math +from collections.abc import Sequence +from dataclasses import dataclass +from typing import Protocol + +from ranksmith.confidence import ( + QueryAnswerabilityConfidenceInput, + QueryContextAnswerabilityConfidenceInput, + StructuralConfidenceResult, +) +from ranksmith.errors import ( + DocumentTooLongError, + RerankInputError, + RerankProviderError, + RerankStrategyError, +) +from ranksmith.types import Document, RerankResult + +from ._common import validate_top_k + + +class AnswerGenerator(Protocol): + def answer_query(self, query: str) -> str: ... + + def answer_with_context(self, query: str, context: str) -> str: ... + + +class ConfidenceEstimator(Protocol): + task_type: str + + def score(self, item: object) -> StructuralConfidenceResult: ... + + +@dataclass(frozen=True) +class ConfidenceGainResult: + base_score: float + context_score: float + gain: float + base_result: StructuralConfidenceResult + context_result: StructuralConfidenceResult + + +@dataclass(frozen=True) +class ConfidenceGainStrategy: + base_estimator: ConfidenceEstimator + context_estimator: ConfidenceEstimator + answer_generator: AnswerGenerator + max_document_chars: int = 4000 + algorithm: str = "confidence_gain" + + def __post_init__(self) -> None: + if self.algorithm != "confidence_gain": + raise ValueError('algorithm must be "confidence_gain"') + if self.max_document_chars < 1: + raise ValueError("max_document_chars must be greater than 0") + if self.base_estimator.task_type != "query_answerability_confidence": + raise RerankInputError( + "base_estimator must use query_answerability_confidence" + ) + if ( + self.context_estimator.task_type + != "query_context_answerability_confidence" + ): + raise RerankInputError( + "context_estimator must use " + "query_context_answerability_confidence" + ) + + def rerank( + self, + *, + query: str, + documents: Sequence[Document], + model_client: object, + top_k: int | None = None, + ) -> list[RerankResult]: + del model_client + validate_top_k(top_k) + _require_non_empty(query, "query") + self._validate_documents(documents) + if not documents: + return [] + + base_answer = _call_answer_query(self.answer_generator, query) + base_result = self.base_estimator.score( + QueryAnswerabilityConfidenceInput(query=query, answer=base_answer) + ) + base_score = _probability(base_result.score, "base confidence") + + scored: list[tuple[int, ConfidenceGainResult]] = [] + for original_index, document in enumerate(documents): + context_answer = _call_answer_with_context( + self.answer_generator, + query, + document.text, + ) + context_result = self.context_estimator.score( + QueryContextAnswerabilityConfidenceInput( + query=query, + context=document.text, + answer=context_answer, + ) + ) + context_score = _probability( + context_result.score, + f"context confidence at index {original_index}", + ) + gain = context_score - base_score + if not math.isfinite(gain) or gain < -1 or gain > 1: + raise RerankStrategyError("confidence gain must be finite in [-1, 1]") + scored.append( + ( + original_index, + ConfidenceGainResult( + base_score=base_score, + context_score=context_score, + gain=gain, + base_result=base_result, + context_result=context_result, + ), + ) + ) + + ordered = sorted(scored, key=lambda item: (-item[1].gain, item[0])) + if top_k is not None: + ordered = ordered[:top_k] + return [ + RerankResult( + document=documents[original_index], + rank=rank, + original_index=original_index, + metadata={ + "strategy": "confidence_gain", + "algorithm": self.algorithm, + "base_confidence": gain_result.base_score, + "context_confidence": gain_result.context_score, + "confidence_gain": gain_result.gain, + }, + ) + for rank, (original_index, gain_result) in enumerate(ordered, start=1) + ] + + def _validate_documents(self, documents: Sequence[Document]) -> None: + for index, document in enumerate(documents): + if len(document.text) > self.max_document_chars: + raise DocumentTooLongError( + f"Document at index {index} has {len(document.text)} " + f"characters, exceeding max_document_chars={self.max_document_chars}." + ) + + +def _require_non_empty(value: str, name: str) -> str: + if not isinstance(value, str) or not value.strip(): + raise RerankInputError(f"{name} must not be empty") + return value + + +def _call_answer_query(generator: AnswerGenerator, query: str) -> str: + try: + answer = generator.answer_query(query) + except RerankProviderError: + raise + except Exception as exc: + raise RerankProviderError(str(exc)) from exc + return _answer_text(answer, "answer_query") + + +def _call_answer_with_context( + generator: AnswerGenerator, + query: str, + context: str, +) -> str: + try: + answer = generator.answer_with_context(query, context) + except RerankProviderError: + raise + except Exception as exc: + raise RerankProviderError(str(exc)) from exc + return _answer_text(answer, "answer_with_context") + + +def _answer_text(value: object, caller: str) -> str: + if not isinstance(value, str) or not value.strip(): + raise RerankProviderError(f"{caller} returned empty answer.") + return value + + +def _probability(value: object, name: str) -> float: + if isinstance(value, bool) or not isinstance(value, int | float): + raise RerankStrategyError(f"{name} must be numeric") + score = float(value) + if not math.isfinite(score) or score < 0 or score > 1: + raise RerankStrategyError(f"{name} must be finite in [0, 1]") + return score +``` + +Modify `src/ranksmith/strategies/__init__.py` to export: + +```python +from ranksmith.strategies._confidence_gain import ( + AnswerGenerator, + ConfidenceGainResult, + ConfidenceGainStrategy, +) +``` + +- [ ] **Step 4: Run strategy tests and existing strategy tests** + +Run: + +```bash +uv run pytest tests/test_confidence_gain_strategy.py tests/test_*strategy*.py -q +``` + +Expected: PASS. + +- [ ] **Step 5: Commit Task 4** + +```bash +git add src/ranksmith/strategies tests/test_confidence_gain_strategy.py +git commit -m "feat: add confidence gain strategy" +``` + +--- + +### Task 5: Documentation And Verification + +**Files:** +- Modify: `docs/wiki/02_architecture.md` +- Modify: `docs/wiki/04_references_index.md` +- Modify: `README.md` +- Modify: `README.ko.md` +- Modify: `docs/specs/spec_confidence_gain_reranking.md` + +- [ ] **Step 1: Update architecture docs** + +In `docs/wiki/02_architecture.md`, update the Confidence section: + +```markdown +`ConfidenceGainStrategy`는 `ranksmith.confidence`의 answerability confidence scorer를 사용해 `Conf(Q+C)-Conf(Q)`를 계산하고 문서를 정렬하는 Strategy다. + +현재 범위: +- query-only answerability confidence +- query+context answerability confidence +- sync confidence gain reranking + +제외: +- CBDR retrieval skip +- async confidence gain reranking +- reranker fine-tuning +``` + +- [ ] **Step 2: Update README and Korean README with matching structure** + +Add a short usage section to both `README.md` and `README.ko.md`. + +English example: + +```python +from ranksmith.confidence import StructuralConfidenceEstimator +from ranksmith.strategies import ConfidenceGainStrategy + +base_estimator = StructuralConfidenceEstimator.from_artifact( + "query-answerability.joblib" +) +context_estimator = StructuralConfidenceEstimator.from_artifact( + "query-context-answerability.joblib" +) + +strategy = ConfidenceGainStrategy( + base_estimator=base_estimator, + context_estimator=context_estimator, + answer_generator=my_answer_generator, +) +``` + +Korean README must mirror the same section order, table shape, and no benchmark claims. + +- [ ] **Step 3: Mark spec checklist implementation items complete** + +In `docs/specs/spec_confidence_gain_reranking.md`, mark completed task checklist items as `[x]` only after the corresponding implementation and tests pass. + +- [ ] **Step 4: Run targeted checks** + +Run: + +```bash +uv run pytest tests/test_confidence_answerability_tasks.py tests/test_confidence_training_answerability.py tests/test_confidence_generation_answerability.py tests/test_confidence_gain_strategy.py -q +uv run ruff check src/ranksmith tests +uv run mypy src/ranksmith tests +``` + +Expected: +- pytest passes. +- ruff passes. +- mypy passes. + +- [ ] **Step 5: Run full verification** + +Run: + +```bash +./scripts/verify.sh +``` + +Expected: all tests, lint, format, type check, and build pass. + +- [ ] **Step 6: Commit docs and verification updates** + +```bash +git add docs README.md README.ko.md +git commit -m "docs: document confidence gain reranking" +``` + +--- + +## Self-Review + +### Spec Coverage +- Runtime query-only/contextual confidence tasks: Task 1. +- Training schema/task expansion: Task 2. +- Generation dataset expansion: Task 3. +- Confidence gain utility/Strategy: Task 4. +- Docs and verification: Task 5. +- CBDR retrieval skip: intentionally excluded by spec. +- Async strategy: intentionally excluded by spec. +- Benchmark claims: intentionally excluded by spec. + +### Placeholder Scan +- No placeholder markers or unspecified implementation steps remain. +- Each task has explicit files, tests, commands, and expected outcomes. + +### Type Consistency +- New task names are consistently: + - `query_answerability_confidence` + - `query_context_answerability_confidence` +- New runtime inputs are consistently: + - `QueryAnswerabilityConfidenceInput` + - `QueryContextAnswerabilityConfidenceInput` +- Strategy name is consistently `ConfidenceGainStrategy`. diff --git a/docs/superpowers/plans/2026-06-05-lmstudio-confidence-training-pipeline.md b/docs/superpowers/plans/2026-06-05-lmstudio-confidence-training-pipeline.md new file mode 100644 index 0000000..07304f3 --- /dev/null +++ b/docs/superpowers/plans/2026-06-05-lmstudio-confidence-training-pipeline.md @@ -0,0 +1,1313 @@ +# LM Studio Confidence Training Pipeline Implementation Plan + +> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. + +**Goal:** Build a local LM Studio path that can generate answerability confidence datasets, train CBDR-ready scorer artifacts, and run CBDR with LM Studio instead of Azure. + +**Architecture:** Add LM Studio as a `ModelProvider` under `ranksmith.integrations`, keep answer generation provider-agnostic, and expose thin scripts over the existing confidence generation/training APIs. CBDR remains unchanged as an algorithm; only its runtime answer generator assembly becomes provider-selectable. + +**Tech Stack:** Python 3.10+, OpenAI Python SDK, pytest, argparse, existing `ranksmith.confidence_generation`, existing `ranksmith.confidence_training`, existing `CBDRStrategy`. + +--- + +## File Map + +- Create `src/ranksmith/integrations/_lmstudio_provider.py` + LM Studio OpenAI-compatible `ModelProvider`. Converts ranksmith `json_object` requests to LM Studio `json_schema`. + +- Create `src/ranksmith/integrations/_answer_generator.py` + Provider-agnostic sync answer generator with `answer_query()` and `answer_with_context()`. + +- Modify `src/ranksmith/integrations/_azure_answer_generator.py` + Make `AzureAnswerGenerator` reuse `ProviderAnswerGenerator` instead of owning duplicate prompt/parse logic. + +- Modify `src/ranksmith/integrations/__init__.py` + Export `LMStudioModelProvider` and `ProviderAnswerGenerator` from submodule only. + +- Create `scripts/generate_confidence_dataset.py` + CLI for `query_answerability_confidence` and `query_context_answerability_confidence`. + +- Create `scripts/train_confidence_scorer.py` + CLI wrapper for `train_confidence_scorer()`. + +- Create `src/ranksmith/confidence_training/_dataset_report.py` + Dataset balance/source/group summary helper. + +- Create `scripts/report_confidence_dataset.py` + CLI wrapper for dataset report. + +- Modify `scripts/compare_reranking.py` + Add `--cbdr-answer-provider azure|lmstudio` and LM Studio flags for CBDR runtime. + +- Create fixtures: + - `tests/fixtures/confidence_query_answerability_raw.jsonl` + - `tests/fixtures/confidence_query_context_answerability_raw.jsonl` + +- Add tests: + - `tests/test_lmstudio_provider.py` + - `tests/test_provider_answer_generator.py` + - `tests/test_generate_confidence_dataset_script.py` + - `tests/test_train_confidence_scorer_script.py` + - `tests/test_confidence_training_dataset_report.py` + - extend `tests/test_compare_reranking.py` + +--- + +## Task 1: LM Studio Provider + +**Files:** +- Create: `src/ranksmith/integrations/_lmstudio_provider.py` +- Modify: `src/ranksmith/integrations/__init__.py` +- Test: `tests/test_lmstudio_provider.py` + +- [x] **Step 1: Write failing provider tests** + +Create `tests/test_lmstudio_provider.py`: + +```python +from __future__ import annotations + +import importlib + +import pytest + +from ranksmith.errors import RerankInputError, RerankProviderError +from ranksmith.model import ModelMessage, ModelRequest + + +class FakeChoiceMessage: + content = '{"answer":"Paris"}' + + +class FakeChoice: + message = FakeChoiceMessage() + + +class FakeUsage: + prompt_tokens = 3 + completion_tokens = 2 + total_tokens = 5 + + +class FakeResponse: + choices = [FakeChoice()] + usage = FakeUsage() + + +class FakeCompletions: + def __init__(self) -> None: + self.kwargs = None + + def create(self, **kwargs: object) -> FakeResponse: + self.kwargs = kwargs + return FakeResponse() + + +class FakeChat: + def __init__(self) -> None: + self.completions = FakeCompletions() + + +class FakeClient: + def __init__(self) -> None: + self.chat = FakeChat() + + +def test_lmstudio_provider_is_submodule_export_only() -> None: + integrations = importlib.import_module("ranksmith.integrations") + root = importlib.import_module("ranksmith") + + assert integrations.LMStudioModelProvider is not None + assert not hasattr(root, "LMStudioModelProvider") + + +def test_lmstudio_provider_converts_json_object_to_json_schema() -> None: + from ranksmith.integrations import LMStudioModelProvider + + client = FakeClient() + provider = LMStudioModelProvider(model="google/gemma-4-12b", client=client) + + response = provider.complete( + ModelRequest( + messages=[ModelMessage(role="user", content="Return JSON.")], + response_format="json_object", + temperature=0, + ) + ) + + kwargs = client.chat.completions.kwargs + assert response.content == '{"answer":"Paris"}' + assert kwargs["model"] == "google/gemma-4-12b" + assert kwargs["temperature"] == 0 + assert kwargs["max_tokens"] == 128 + assert kwargs["response_format"]["type"] == "json_schema" + assert kwargs["response_format"]["json_schema"]["name"] == "ranksmith_json_response" + + +def test_lmstudio_provider_uses_env_model(monkeypatch: pytest.MonkeyPatch) -> None: + from ranksmith.integrations import LMStudioModelProvider + + monkeypatch.setenv("LMSTUDIO_MODEL", "google/gemma-4-12b") + + provider = LMStudioModelProvider(client=FakeClient()) + + provider.complete(ModelRequest(messages=[ModelMessage(role="user", content="x")])) + + +def test_lmstudio_provider_requires_model(monkeypatch: pytest.MonkeyPatch) -> None: + from ranksmith.integrations import LMStudioModelProvider + + monkeypatch.delenv("LMSTUDIO_MODEL", raising=False) + + with pytest.raises(RerankInputError, match="LMSTUDIO_MODEL"): + LMStudioModelProvider(client=FakeClient()) + + +def test_lmstudio_provider_wraps_client_error() -> None: + from ranksmith.integrations import LMStudioModelProvider + + class FailingCompletions: + def create(self, **kwargs: object) -> object: + del kwargs + raise RuntimeError("server down") + + class FailingChat: + completions = FailingCompletions() + + class FailingClient: + chat = FailingChat() + + provider = LMStudioModelProvider(model="google/gemma-4-12b", client=FailingClient()) + + with pytest.raises(RerankProviderError, match="server down"): + provider.complete(ModelRequest(messages=[ModelMessage(role="user", content="x")])) +``` + +- [x] **Step 2: Run failing test** + +```bash +uv run pytest tests/test_lmstudio_provider.py -q +``` + +Expected: fails because `LMStudioModelProvider` does not exist. + +- [x] **Step 3: Implement provider** + +Create `src/ranksmith/integrations/_lmstudio_provider.py`: + +```python +from __future__ import annotations + +import os +from typing import Any, cast + +from openai import OpenAI + +from ranksmith.errors import RerankInputError, RerankProviderError +from ranksmith.model import ModelRequest, ModelResponse +from ranksmith.types import RerankUsage + + +class LMStudioModelProvider: + def __init__( + self, + *, + model: str | None = None, + base_url: str | None = None, + api_key: str | None = None, + timeout: float | None = None, + max_tokens: int = 128, + client: Any | None = None, + ) -> None: + resolved_model = model or _env_value("LMSTUDIO_MODEL") + if resolved_model is None or resolved_model == "": + raise RerankInputError("LMSTUDIO_MODEL is required") + if max_tokens < 1: + raise RerankInputError("max_tokens must be greater than 0") + self._model = resolved_model + self._max_tokens = max_tokens + self._client = client or OpenAI( + base_url=base_url or _env_value("LMSTUDIO_BASE_URL") or "http://localhost:1234/v1", + api_key=api_key or _env_value("LMSTUDIO_API_KEY") or "lm-studio", + timeout=timeout, + ) + + def complete(self, request: ModelRequest) -> ModelResponse: + try: + response = self._client.chat.completions.create( + model=self._model, + messages=_to_openai_messages(request), + response_format=_lmstudio_response_format(request), + temperature=request.temperature, + max_tokens=self._max_tokens, + ) + except Exception as exc: + raise RerankProviderError(str(exc)) from exc + + content = _extract_content(response) + if content is None or content == "": + raise RerankProviderError("LM Studio returned an empty response.") + return ModelResponse(content=content, usage=_extract_usage(response)) + + +def _lmstudio_response_format(request: ModelRequest) -> dict[str, object]: + if request.response_format != "json_object": + raise RerankProviderError("LM Studio provider only supports json_object requests.") + return { + "type": "json_schema", + "json_schema": { + "name": "ranksmith_json_response", + "schema": {"type": "object"}, + }, + } + + +def _to_openai_messages(request: ModelRequest) -> list[dict[str, str]]: + return [ + {"role": message.role, "content": message.content} + for message in request.messages + ] + + +def _extract_content(response: object) -> str | None: + choices = getattr(response, "choices", None) + if not choices: + raise RerankProviderError("LM Studio returned an invalid response.") + try: + return cast(str | None, choices[0].message.content) + except AttributeError as exc: + raise RerankProviderError("LM Studio returned an invalid response.") from exc + + +def _extract_usage(response: object) -> RerankUsage | None: + usage = getattr(response, "usage", None) + if usage is None: + return None + return RerankUsage( + prompt_tokens=int(getattr(usage, "prompt_tokens", 0) or 0), + completion_tokens=int(getattr(usage, "completion_tokens", 0) or 0), + total_tokens=int(getattr(usage, "total_tokens", 0) or 0), + ) + + +def _env_value(name: str) -> str | None: + value = os.environ.get(name) + if value is not None and value != "": + return value + return None +``` + +Modify `src/ranksmith/integrations/__init__.py`: + +```python +from ranksmith.integrations._azure_answer_generator import AzureAnswerGenerator +from ranksmith.integrations._lmstudio_provider import LMStudioModelProvider + +__all__ = ["AzureAnswerGenerator", "LMStudioModelProvider"] +``` + +- [x] **Step 4: Run provider tests** + +```bash +uv run pytest tests/test_lmstudio_provider.py -q +``` + +Expected: all tests pass. + +--- + +## Task 2: Provider-Agnostic Answer Generator + +**Files:** +- Create: `src/ranksmith/integrations/_answer_generator.py` +- Modify: `src/ranksmith/integrations/_azure_answer_generator.py` +- Modify: `src/ranksmith/integrations/__init__.py` +- Test: `tests/test_provider_answer_generator.py` +- Update: `tests/test_azure_answer_generator.py` + +- [x] **Step 1: Write failing tests** + +Create `tests/test_provider_answer_generator.py`: + +```python +from __future__ import annotations + +from dataclasses import dataclass + +import pytest + +from ranksmith.errors import RerankParseError, RerankProviderError +from ranksmith.model import ModelRequest, ModelResponse + + +@dataclass +class FakeProvider: + responses: list[str] + requests: list[ModelRequest] + + def complete(self, request: ModelRequest) -> ModelResponse: + self.requests.append(request) + return ModelResponse(content=self.responses.pop(0)) + + +def test_provider_answer_generator_parses_query_answer() -> None: + from ranksmith.integrations import ProviderAnswerGenerator + + provider = FakeProvider(responses=['{"answer":"Paris"}'], requests=[]) + generator = ProviderAnswerGenerator(provider=provider) + + assert generator.answer_query("capital of france?") == "Paris" + assert provider.requests[0].response_format == "json_object" + assert provider.requests[0].temperature == 0 + + +def test_provider_answer_generator_parses_context_answer() -> None: + from ranksmith.integrations import ProviderAnswerGenerator + + provider = FakeProvider(responses=['{"answer":"Nancy Travis"}'], requests=[]) + generator = ProviderAnswerGenerator(provider=provider) + + assert generator.answer_with_context("who?", "Nancy Travis played Karen.") == "Nancy Travis" + assert "Use only the context" in provider.requests[0].messages[1].content + + +@pytest.mark.parametrize("content", ["not json", "{}", '{"answer":""}', '{"answer":1}']) +def test_provider_answer_generator_rejects_invalid_json(content: str) -> None: + from ranksmith.integrations import ProviderAnswerGenerator + + generator = ProviderAnswerGenerator( + provider=FakeProvider(responses=[content], requests=[]), + ) + + with pytest.raises(RerankParseError): + generator.answer_query("x") + + +def test_provider_answer_generator_preserves_provider_error() -> None: + from ranksmith.integrations import ProviderAnswerGenerator + + class FailingProvider: + def complete(self, request: ModelRequest) -> ModelResponse: + del request + raise RerankProviderError("provider failed") + + generator = ProviderAnswerGenerator(provider=FailingProvider()) + + with pytest.raises(RerankProviderError, match="provider failed"): + generator.answer_query("x") +``` + +- [x] **Step 2: Run failing test** + +```bash +uv run pytest tests/test_provider_answer_generator.py -q +``` + +Expected: fails because `ProviderAnswerGenerator` does not exist. + +- [x] **Step 3: Implement generator** + +Create `src/ranksmith/integrations/_answer_generator.py`: + +```python +from __future__ import annotations + +import json +from dataclasses import dataclass + +from ranksmith.errors import RerankParseError, RerankProviderError +from ranksmith.model import ModelMessage, ModelProvider, ModelRequest + + +@dataclass(frozen=True) +class ProviderAnswerGenerator: + provider: ModelProvider + no_answer_value: str = "__NO_ANSWER__" + + def __post_init__(self) -> None: + _validate_no_answer_value(self.no_answer_value) + + def answer_query(self, query: str) -> str: + return self._complete( + system=( + "You answer questions for confidence estimation. Return only JSON " + 'with an "answer" string.' + ), + user=( + f"Question:\n{query}\n\n" + "Return JSON exactly like this shape:\n" + '{"answer":"..."}\n\n' + "Answer from your parametric knowledge. If you do not know the " + f"answer, return {_answer_contract(self.no_answer_value)}." + ), + ) + + def answer_with_context(self, query: str, context: str) -> str: + return self._complete( + system=( + "You answer questions using the provided context for confidence " + 'estimation. Return only JSON with an "answer" string.' + ), + user=( + f"Question:\n{query}\n\n" + f"Context:\n{context}\n\n" + "Return JSON exactly like this shape:\n" + '{"answer":"..."}\n\n' + "Use only the context. If the context does not contain the answer, " + f"return {_answer_contract(self.no_answer_value)}." + ), + ) + + def _complete(self, *, system: str, user: str) -> str: + try: + response = self.provider.complete( + ModelRequest( + messages=[ + ModelMessage(role="system", content=system), + ModelMessage(role="user", content=user), + ], + response_format="json_object", + temperature=0, + ) + ) + except RerankProviderError: + raise + except Exception as exc: + raise RerankProviderError(str(exc)) from exc + return parse_answer(response.content) + + +def parse_answer(content: str) -> str: + try: + parsed = json.loads(content) + except json.JSONDecodeError as exc: + raise RerankParseError("answer response must be valid JSON") from exc + if not isinstance(parsed, dict): + raise RerankParseError("answer response must be a JSON object") + answer = parsed.get("answer") + if not isinstance(answer, str) or answer.strip() == "": + raise RerankParseError('answer response must contain a non-empty "answer"') + return answer + + +def _answer_contract(no_answer_value: str) -> str: + return json.dumps( + {"answer": no_answer_value}, + ensure_ascii=False, + separators=(",", ":"), + ) + + +def _validate_no_answer_value(value: object) -> None: + if not isinstance(value, str) or value.strip() == "": + raise ValueError("no_answer_value must be a non-empty string") +``` + +Modify `src/ranksmith/integrations/_azure_answer_generator.py` so `AzureAnswerGenerator` delegates: + +```python +from ranksmith.integrations._answer_generator import ProviderAnswerGenerator + +# keep env helpers and Azure provider assembly +# replace answer_query/answer_with_context/_complete/_parse_answer with: + + def answer_query(self, query: str) -> str: + return ProviderAnswerGenerator( + provider=self.provider, + no_answer_value=self.no_answer_value, + ).answer_query(query) + + def answer_with_context(self, query: str, context: str) -> str: + return ProviderAnswerGenerator( + provider=self.provider, + no_answer_value=self.no_answer_value, + ).answer_with_context(query, context) +``` + +Modify `src/ranksmith/integrations/__init__.py`: + +```python +from ranksmith.integrations._answer_generator import ProviderAnswerGenerator +from ranksmith.integrations._azure_answer_generator import AzureAnswerGenerator +from ranksmith.integrations._lmstudio_provider import LMStudioModelProvider + +__all__ = [ + "AzureAnswerGenerator", + "LMStudioModelProvider", + "ProviderAnswerGenerator", +] +``` + +- [x] **Step 4: Run tests** + +```bash +uv run pytest tests/test_provider_answer_generator.py tests/test_azure_answer_generator.py -q +``` + +Expected: all tests pass. + +--- + +## Task 3: Generation CLI + +**Files:** +- Create: `scripts/generate_confidence_dataset.py` +- Create: `tests/fixtures/confidence_query_answerability_raw.jsonl` +- Create: `tests/fixtures/confidence_query_context_answerability_raw.jsonl` +- Test: `tests/test_generate_confidence_dataset_script.py` + +- [x] **Step 1: Add raw fixtures** + +Create `tests/fixtures/confidence_query_answerability_raw.jsonl`: + +```jsonl +{"id":"qa-1","query":"What is the capital of France?","gold_answer":"Paris","source":"fixture-general","group_id":"q1"} +{"id":"qa-2","query":"Who played Karen in Married to the Mob?","gold_answer":"Nancy Travis","source":"fixture-movie","group_id":"q2"} +``` + +Create `tests/fixtures/confidence_query_context_answerability_raw.jsonl`: + +```jsonl +{"id":"qc-1","query":"What is the capital of France?","context":"Paris is the capital and largest city of France.","gold_answer":"Paris","source":"fixture-general","group_id":"q1"} +{"id":"qc-2","query":"Who played Karen in Married to the Mob?","context":"Nancy Travis played Karen Lutnick in Married to the Mob.","gold_answer":"Nancy Travis","source":"fixture-movie","group_id":"q2"} +``` + +- [x] **Step 2: Write failing CLI tests** + +Create `tests/test_generate_confidence_dataset_script.py`: + +```python +from __future__ import annotations + +import importlib.util +import json +from pathlib import Path + + +SCRIPT = Path("scripts/generate_confidence_dataset.py") + + +def _load_script() -> object: + spec = importlib.util.spec_from_file_location("generate_confidence_dataset", SCRIPT) + assert spec is not None and spec.loader is not None + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def test_parse_args_accepts_query_answerability() -> None: + module = _load_script() + + args = module.parse_args( + [ + "--task", + "query_answerability_confidence", + "--provider", + "lmstudio", + "--lmstudio-model", + "google/gemma-4-12b", + "--input", + "in.jsonl", + "--output", + "out.jsonl", + ] + ) + + assert args.task == "query_answerability_confidence" + assert args.lmstudio_model == "google/gemma-4-12b" + + +def test_generation_cli_runs_with_fake_provider(tmp_path: Path, monkeypatch) -> None: + module = _load_script() + input_path = tmp_path / "raw.jsonl" + output_path = tmp_path / "canonical.jsonl" + input_path.write_text( + '{"id":"1","query":"What is the capital of France?","gold_answer":"Paris"}\n', + encoding="utf-8", + ) + + class FakeProvider: + def __init__(self, **kwargs: object) -> None: + del kwargs + + def complete(self, request): + del request + from ranksmith.model import ModelResponse + + return ModelResponse(content='{"answer":"Paris"}') + + monkeypatch.setattr(module, "LMStudioModelProvider", FakeProvider) + + result = module.run( + module.parse_args( + [ + "--task", + "query_answerability_confidence", + "--provider", + "lmstudio", + "--lmstudio-model", + "google/gemma-4-12b", + "--input", + str(input_path), + "--output", + str(output_path), + "--overwrite", + ] + ) + ) + + rows = [json.loads(line) for line in output_path.read_text(encoding="utf-8").splitlines()] + assert result.generated_count == 1 + assert rows[0]["task_type"] == "query_answerability_confidence" + assert rows[0]["label"] == 1 +``` + +- [x] **Step 3: Implement CLI** + +Create `scripts/generate_confidence_dataset.py`: + +```python +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from ranksmith.confidence_generation import ( + QueryAnswerabilityGenerationConfig, + QueryContextAnswerabilityGenerationConfig, + generate_query_answerability_confidence_dataset, + generate_query_context_answerability_confidence_dataset, +) +from ranksmith.integrations import LMStudioModelProvider + + +def main() -> None: + result = run(parse_args()) + print(json.dumps(result.__dict__ | {"output_path": str(result.output_path)}, sort_keys=True)) + + +def run(args: argparse.Namespace): + provider = LMStudioModelProvider( + base_url=args.lmstudio_base_url, + model=args.lmstudio_model, + api_key=args.lmstudio_api_key, + timeout=args.timeout, + max_tokens=args.lmstudio_max_tokens, + ) + if args.task == "query_answerability_confidence": + return generate_query_answerability_confidence_dataset( + QueryAnswerabilityGenerationConfig( + input_path=args.input, + output_path=args.output, + provider=provider, + overwrite=args.overwrite, + resume=args.resume, + max_items=args.max_items, + source=args.source, + ) + ) + if args.task == "query_context_answerability_confidence": + return generate_query_context_answerability_confidence_dataset( + QueryContextAnswerabilityGenerationConfig( + input_path=args.input, + output_path=args.output, + provider=provider, + overwrite=args.overwrite, + resume=args.resume, + max_items=args.max_items, + max_context_chars=args.max_context_chars, + source=args.source, + ) + ) + raise SystemExit(f"unsupported task: {args.task}") + + +def parse_args(argv: list[str] | None = None) -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--task", choices=("query_answerability_confidence", "query_context_answerability_confidence"), required=True) + parser.add_argument("--provider", choices=("lmstudio",), required=True) + parser.add_argument("--input", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--overwrite", action="store_true") + parser.add_argument("--resume", action="store_true") + parser.add_argument("--max-items", type=int) + parser.add_argument("--source") + parser.add_argument("--max-context-chars", type=int, default=8000) + parser.add_argument("--timeout", type=float) + parser.add_argument("--lmstudio-base-url") + parser.add_argument("--lmstudio-model") + parser.add_argument("--lmstudio-api-key") + parser.add_argument("--lmstudio-max-tokens", type=int, default=128) + return parser.parse_args(argv) + + +if __name__ == "__main__": + main() +``` + +- [x] **Step 4: Run CLI tests** + +```bash +uv run pytest tests/test_generate_confidence_dataset_script.py -q +``` + +Expected: all tests pass. + +--- + +## Task 4: Training CLI + +**Files:** +- Create: `scripts/train_confidence_scorer.py` +- Test: `tests/test_train_confidence_scorer_script.py` + +- [x] **Step 1: Write failing CLI test** + +Create `tests/test_train_confidence_scorer_script.py`: + +```python +from __future__ import annotations + +import importlib.util +from pathlib import Path + + +SCRIPT = Path("scripts/train_confidence_scorer.py") + + +def _load_script() -> object: + spec = importlib.util.spec_from_file_location("train_confidence_scorer_script", SCRIPT) + assert spec is not None and spec.loader is not None + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def test_training_cli_builds_config(monkeypatch, tmp_path: Path) -> None: + module = _load_script() + captured = {} + + def fake_train(config): + captured["config"] = config + from ranksmith.confidence_training import ConfidenceTrainingResult + + return ConfidenceTrainingResult( + output_dir=Path(config.output_dir), + export_path=Path(config.export_path), + report_path=Path(config.output_dir) / "report.json", + metadata_path=Path(config.output_dir) / "metadata.json", + ) + + monkeypatch.setattr(module, "train_confidence_scorer", fake_train) + + module.run( + module.parse_args( + [ + "--task", + "query_answerability_confidence", + "--dataset", + str(tmp_path / "data.jsonl"), + "--output-dir", + str(tmp_path / "out"), + "--export-path", + str(tmp_path / "artifact.joblib"), + "--encoder-name", + "bert-base-uncased", + "--max-length", + "256", + ] + ) + ) + + config = captured["config"] + assert config.task_type == "query_answerability_confidence" + assert config.encoder_name == "bert-base-uncased" + assert config.max_length == 256 +``` + +- [x] **Step 2: Implement CLI** + +Create `scripts/train_confidence_scorer.py`: + +```python +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from ranksmith.confidence_training import ( + ConfidenceTrainingConfig, + train_confidence_scorer, +) + + +def main() -> None: + result = run(parse_args()) + print( + json.dumps( + { + "output_dir": str(result.output_dir), + "export_path": str(result.export_path), + "report_path": str(result.report_path), + "metadata_path": str(result.metadata_path), + }, + sort_keys=True, + ) + ) + + +def run(args: argparse.Namespace): + return train_confidence_scorer( + ConfidenceTrainingConfig( + task_type=args.task, + dataset_path=args.dataset, + output_dir=args.output_dir, + export_path=args.export_path, + encoder_name=args.encoder_name, + encoder_revision=args.encoder_revision, + tokenizer_name=args.tokenizer_name, + tokenizer_revision=args.tokenizer_revision, + cache_dir=str(args.cache_dir) if args.cache_dir is not None else None, + local_files_only=args.local_files_only, + max_length=args.max_length, + allow_truncation=args.allow_truncation, + seed=args.seed, + train_ratio=args.train_ratio, + valid_ratio=args.valid_ratio, + test_ratio=args.test_ratio, + ) + ) + + +def parse_args(argv: list[str] | None = None) -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--task", choices=("query_answerability_confidence", "query_context_answerability_confidence"), required=True) + parser.add_argument("--dataset", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--export-path", type=Path, required=True) + parser.add_argument("--encoder-name", default="bert-base-uncased") + parser.add_argument("--encoder-revision") + parser.add_argument("--tokenizer-name") + parser.add_argument("--tokenizer-revision") + parser.add_argument("--cache-dir", type=Path) + parser.add_argument("--local-files-only", action="store_true") + parser.add_argument("--max-length", type=int, default=256) + parser.add_argument("--allow-truncation", action="store_true") + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--train-ratio", type=float, default=0.8) + parser.add_argument("--valid-ratio", type=float, default=0.1) + parser.add_argument("--test-ratio", type=float, default=0.1) + return parser.parse_args(argv) + + +if __name__ == "__main__": + main() +``` + +- [x] **Step 3: Run test** + +```bash +uv run pytest tests/test_train_confidence_scorer_script.py -q +``` + +Expected: all tests pass. + +--- + +## Task 5: Dataset Balance Report + +**Files:** +- Create: `src/ranksmith/confidence_training/_dataset_report.py` +- Create: `scripts/report_confidence_dataset.py` +- Test: `tests/test_confidence_training_dataset_report.py` + +- [x] **Step 1: Write failing report tests** + +Create `tests/test_confidence_training_dataset_report.py`: + +```python +from __future__ import annotations + +import json +from pathlib import Path + +from ranksmith.confidence_training._dataset_report import build_dataset_report + + +def test_build_dataset_report_counts_sources_and_groups(tmp_path: Path) -> None: + path = tmp_path / "data.jsonl" + path.write_text( + "\n".join( + [ + json.dumps({"id":"1","task_type":"query_answerability_confidence","query":"q","answer":"a","label":1,"source":"s1","group_id":"g1"}), + json.dumps({"id":"2","task_type":"query_answerability_confidence","query":"q","answer":"x","label":0,"source":"s1","group_id":"g1"}), + json.dumps({"id":"3","task_type":"query_answerability_confidence","query":"q","answer":"a","label":1,"source":"s2"}), + ] + ) + + "\n", + encoding="utf-8", + ) + + report = build_dataset_report(path, task_type="query_answerability_confidence") + + assert report["sample_count"] == 3 + assert report["positive_count"] == 2 + assert report["negative_count"] == 1 + assert report["missing_group_id_count"] == 1 + assert report["sources"]["s1"]["sample_count"] == 2 + assert report["sources"]["s1"]["positive_count"] == 1 +``` + +- [x] **Step 2: Implement report helper** + +Create `src/ranksmith/confidence_training/_dataset_report.py`: + +```python +from __future__ import annotations + +from collections import Counter, defaultdict +from pathlib import Path +from typing import Any + +from ranksmith.confidence import TaskType +from ranksmith.confidence_training._dataset import load_canonical_dataset + + +def build_dataset_report(path: str | Path, *, task_type: TaskType) -> dict[str, Any]: + samples = load_canonical_dataset(path, task_type=task_type) + positive_count = sum(sample.label for sample in samples) + source_counts: dict[str, Counter[str]] = defaultdict(Counter) + missing_source_count = 0 + missing_group_id_count = 0 + group_ids: set[str] = set() + + for sample in samples: + source = sample.source + if source is None: + missing_source_count += 1 + source = "__MISSING__" + if sample.group_id is None: + missing_group_id_count += 1 + else: + group_ids.add(sample.group_id) + source_counts[source]["sample_count"] += 1 + source_counts[source]["positive_count"] += sample.label + source_counts[source]["negative_count"] += 1 - sample.label + + return { + "task_type": task_type, + "sample_count": len(samples), + "positive_count": positive_count, + "negative_count": len(samples) - positive_count, + "positive_rate": _rate(positive_count, len(samples)), + "source_count": len(source_counts), + "group_count": len(group_ids), + "missing_source_count": missing_source_count, + "missing_group_id_count": missing_group_id_count, + "sources": { + source: { + "sample_count": counts["sample_count"], + "positive_count": counts["positive_count"], + "negative_count": counts["negative_count"], + "positive_rate": _rate(counts["positive_count"], counts["sample_count"]), + } + for source, counts in sorted(source_counts.items()) + }, + } + + +def _rate(count: int, total: int) -> float: + if total == 0: + return 0.0 + return count / total +``` + +- [x] **Step 3: Add CLI** + +Create `scripts/report_confidence_dataset.py`: + +```python +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from ranksmith.confidence_training._dataset_report import build_dataset_report + + +def main() -> None: + report = run(parse_args()) + print(json.dumps(report, ensure_ascii=False, indent=2, sort_keys=True)) + + +def run(args: argparse.Namespace) -> dict[str, object]: + return build_dataset_report(args.dataset, task_type=args.task) + + +def parse_args(argv: list[str] | None = None) -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--task", choices=("query_answerability_confidence", "query_context_answerability_confidence"), required=True) + parser.add_argument("--dataset", type=Path, required=True) + return parser.parse_args(argv) + + +if __name__ == "__main__": + main() +``` + +- [x] **Step 4: Run report tests** + +```bash +uv run pytest tests/test_confidence_training_dataset_report.py -q +``` + +Expected: all tests pass. + +--- + +## Task 6: CBDR Benchmark LM Studio Runtime + +**Files:** +- Modify: `scripts/compare_reranking.py` +- Test: extend `tests/test_compare_reranking.py` + +- [x] **Step 1: Add failing compare test** + +Add to `tests/test_compare_reranking.py`: + +```python +def test_compare_cbdr_can_use_lmstudio_answer_provider(monkeypatch, tmp_path): + import scripts.compare_reranking as compare_reranking + from ranksmith._benchmark import BenchmarkCase, BenchmarkDocument + + created = {} + + class FakeLMStudioProvider: + def __init__(self, **kwargs): + created["lmstudio"] = kwargs + + class FakeProviderAnswerGenerator: + def __init__(self, *, provider, no_answer_value="__NO_ANSWER__"): + created["generator"] = provider + self.no_answer_value = no_answer_value + + class FakeCBDRStrategy: + @classmethod + def from_artifacts(cls, **kwargs): + created["strategy"] = kwargs + return cls() + + def rerank(self, *, query, documents, top_k=None): + del query, top_k + return [ + type("Result", (), {"document": document}) + for document in documents + ] + + monkeypatch.setattr("ranksmith.integrations.LMStudioModelProvider", FakeLMStudioProvider) + monkeypatch.setattr("ranksmith.integrations.ProviderAnswerGenerator", FakeProviderAnswerGenerator) + monkeypatch.setattr("ranksmith.strategies.CBDRStrategy", FakeCBDRStrategy) + + case = BenchmarkCase( + fixture_id="fixture", + dataset="dataset", + source="source", + license="license", + query_id="q1", + query="query", + documents=(BenchmarkDocument(id="d1", text="doc", title=""),), + qrels={"d1": 1}, + ) + + ranking = compare_reranking._rank_case( + case=case, + algorithm="cbdr", + window_size=20, + stride=10, + passes=10, + tourrank_rounds=2, + set_size=3, + cbdr_base_artifact=tmp_path / "base.joblib", + cbdr_context_artifact=tmp_path / "context.joblib", + cbdr_answer_provider="lmstudio", + lmstudio_model="google/gemma-4-12b", + ) + + assert ranking == ("d1",) + assert created["lmstudio"]["model"] == "google/gemma-4-12b" + assert created["strategy"]["answer_generator"].no_answer_value == "__NO_ANSWER__" +``` + +- [x] **Step 2: Modify compare runner** + +In `scripts/compare_reranking.py`: + +Add parser flags: + +```python +parser.add_argument("--cbdr-answer-provider", choices=("azure", "lmstudio"), default="azure") +parser.add_argument("--lmstudio-base-url") +parser.add_argument("--lmstudio-model") +parser.add_argument("--lmstudio-api-key") +parser.add_argument("--lmstudio-max-tokens", type=int, default=128) +``` + +Add `_rank_case(...)` parameters: + +```python +cbdr_answer_provider: str = "azure", +lmstudio_base_url: str | None = None, +lmstudio_model: str | None = None, +lmstudio_api_key: str | None = None, +lmstudio_max_tokens: int = 128, +``` + +Replace hardcoded CBDR generator: + +```python +if cbdr_answer_provider == "azure": + answer_generator = AzureAnswerGenerator.from_env(timeout=timeout) +elif cbdr_answer_provider == "lmstudio": + from ranksmith.integrations import LMStudioModelProvider, ProviderAnswerGenerator + + answer_generator = ProviderAnswerGenerator( + provider=LMStudioModelProvider( + base_url=lmstudio_base_url, + model=lmstudio_model, + api_key=lmstudio_api_key, + timeout=timeout, + max_tokens=lmstudio_max_tokens, + ) + ) +else: + raise SystemExit("--cbdr-answer-provider must be azure or lmstudio.") +``` + +Pass new args from the caller to `_rank_case(...)`. + +- [x] **Step 3: Run compare tests** + +```bash +uv run pytest tests/test_compare_reranking.py -q +``` + +Expected: all tests pass. + +--- + +## Task 7: Docs And Spec Checklist + +**Files:** +- Modify: `docs/specs/spec_lmstudio_confidence_training_pipeline.md` +- Modify: `README.md` +- Modify: `README.ko.md` +- Modify: `docs/wiki/02_architecture.md` + +- [x] **Step 1: Update spec checklist** + +Mark implemented items as `[x]` only after the corresponding tests pass. + +- [x] **Step 2: Update architecture wiki** + +Add under `Integrations`: + +```markdown +- `LMStudioModelProvider`: LM Studio OpenAI-compatible local model provider for confidence dataset generation and CBDR answer generation. +- `ProviderAnswerGenerator`: provider-agnostic sync answer helper used by Azure and LM Studio runtime paths. +``` + +Add under `Confidence`: + +```markdown +Confidence generation/training can be operated through local CLI scripts for query-only and query+context answerability scorer artifacts. The scripts are utility entry points and do not add a new reranking algorithm. +``` + +- [x] **Step 3: Update README and README.ko** + +Add a minimal LM Studio confidence pipeline section with: + +```bash +lms server start + +uv run python scripts/generate_confidence_dataset.py \ + --task query_answerability_confidence \ + --provider lmstudio \ + --lmstudio-model google/gemma-4-12b \ + --input runs/confidence/local/raw/query_answerability.jsonl \ + --output runs/confidence/local/canonical/query_answerability_confidence.jsonl \ + --resume +``` + +Do not add benchmark quality numbers. + +--- + +## Task 8: Verification + +**Files:** all touched files. + +- [x] **Step 1: Run targeted tests** + +```bash +uv run pytest \ + tests/test_lmstudio_provider.py \ + tests/test_provider_answer_generator.py \ + tests/test_azure_answer_generator.py \ + tests/test_generate_confidence_dataset_script.py \ + tests/test_train_confidence_scorer_script.py \ + tests/test_confidence_training_dataset_report.py \ + tests/test_compare_reranking.py \ + -q +``` + +Expected: all selected tests pass. + +- [x] **Step 2: Run full verification** + +```bash +./scripts/verify.sh +``` + +Expected: ruff, format check, mypy, pytest, and build pass. + +- [x] **Step 3: Optional live LM Studio smoke** + +Requires LM Studio server running and `google/gemma-4-12b` loaded. + +```bash +lms server status + +uv run python scripts/generate_confidence_dataset.py \ + --task query_answerability_confidence \ + --provider lmstudio \ + --lmstudio-model google/gemma-4-12b \ + --input tests/fixtures/confidence_query_answerability_raw.jsonl \ + --output /tmp/ranksmith-lmstudio-query-answerability.jsonl \ + --overwrite \ + --max-items 2 +``` + +Expected: canonical JSONL is written with strict `answer` values and labels. + +--- + +## Self-Review + +- Spec coverage: + - LM Studio provider: Task 1. + - `json_object -> json_schema`: Task 1. + - provider-agnostic answer generation and CBDR LM Studio runtime: Tasks 2 and 6. + - generation CLI: Task 3. + - training CLI: Task 4. + - dataset balance report: Task 5. + - docs/spec/wiki/README: Task 7. + - verification/live smoke: Task 8. + +- Known intentional limits: + - No automatic external dataset download. + - No async generation. + - No model fine-tuning. + - No benchmark quality numbers without real summary artifacts. diff --git a/docs/wiki/02_architecture.md b/docs/wiki/02_architecture.md index e5d1b51..fd1ac5d 100644 --- a/docs/wiki/02_architecture.md +++ b/docs/wiki/02_architecture.md @@ -26,13 +26,21 @@ src/ranksmith/ setwise.py tourrank.py acurank.py + confidence_gain.py + cbdr.py providers/ __init__.py # public provider exports azure.py # Azure OpenAI implementation + integrations/ + __init__.py # public runtime helper exports + azure_answer_generator.py + answer_generator.py + lmstudio_provider.py + validation.py ``` -외부 사용자는 root import 또는 `ranksmith.strategies`, `ranksmith.providers`의 public export를 사용한다. -strategies/, providers/ 하위 개별 모듈은 내부 구현으로 취급한다. +외부 사용자는 root import 또는 `ranksmith.strategies`, `ranksmith.providers`, `ranksmith.integrations`의 public export를 사용한다. +strategies/, providers/, integrations/ 하위 개별 모듈은 내부 구현으로 취급한다. ## ModelProvider 실제 SDK 호출은 Azure OpenAI만 구현한다. @@ -41,6 +49,22 @@ strategies/, providers/ 하위 개별 모듈은 내부 구현으로 취급한다 Provider는 `ModelRequest`를 받아 `ModelResponse`를 반환한다. Provider는 ranking 도메인 prompt의 의미를 알지 않는다. +## Integrations +`ranksmith.integrations`는 closed model runtime helper layer다. +Strategy나 Algorithm을 추가하지 않고, 기존 Strategy가 필요로 하는 외부 hook을 공식 조립 경로로 제공한다. + +현재 범위: +- `AzureAnswerGenerator`: Azure OpenAI JSON answer generation helper +- `ProviderAnswerGenerator`: `ModelProvider` 기반 sync JSON answer generation helper +- `LMStudioModelProvider`: LM Studio OpenAI-compatible runtime helper +- confidence generation과 같은 no-answer sentinel prompt contract +- root import가 아닌 `ranksmith.integrations` submodule export + +제외: +- async answer generation +- non-Azure hosted provider implementation +- scorer training + ## ModelClient Listwise model client call은 1-based ranking permutation을 담은 JSON 문자열을 반환한다. @@ -61,6 +85,8 @@ v1 공개 strategy: - `AsyncTourRankStrategy` - `AcuRankStrategy` - `AsyncAcuRankStrategy` +- `ConfidenceGainStrategy` +- `CBDRStrategy` 공식 확장 지점: - 새 reranking 방법은 새 Strategy 클래스로 추가한다. @@ -79,9 +105,11 @@ v1 지원 algorithm: - `setwise_heapsort` - `tourrank_r` - `acurank` +- `confidence_gain` +- `cbdr` 향후 algorithm 후보: -- `confidence` +- `Pointwise` ## Confidence `ranksmith.confidence`는 reranking Strategy나 Algorithm이 아니라, closed model output confidence를 계산하는 utility layer다. @@ -91,6 +119,10 @@ v1 지원 algorithm: - `structural-v1` 70차원 feature extraction - 학습된 scorer artifact 기반 single-item sync confidence inference - bounded batch sync confidence inference +- `answer_confidence` +- `judgment_confidence` +- `query_answerability_confidence` +- `query_context_answerability_confidence` - root import가 아닌 `ranksmith.confidence` submodule export `score_batch(..., max_workers>1)`은 같은 encoder/scorer instance를 worker thread들이 공유하므로, concurrent call에 안전한 backend에서만 사용한다. 기본값은 안정성을 위해 `max_workers=1`이다. @@ -100,6 +132,13 @@ v1 지원 algorithm: - async inference - reranking Strategy +`ConfidenceGainStrategy`는 confidence utility layer 자체가 아니라, `ranksmith.confidence`의 query-only 및 query+context answerability scorer를 소비하는 별도 sync Strategy다. +`Conf(Q+C)-Conf(Q)`를 계산해 confidence gain 내림차순으로 문서를 정렬한다. + +`CBDRStrategy`는 `Conf(Q)`가 `skip_threshold` 이상이면 context reranking을 skip하고 original order를 보존한다. +`Conf(Q)`가 threshold보다 낮으면 `Conf(Q+C)-Conf(Q)` confidence gain으로 문서를 정렬한다. +true pre-retrieval skip, retriever integration, async CBDR은 구현하지 않는다. + `ranksmith.confidence_training`은 Phase 1 compatible scorer artifact를 만들기 위한 별도 training utility layer다. 현재 범위: @@ -109,11 +148,12 @@ v1 지원 algorithm: - LightGBM binary classifier training - validation split 기반 sigmoid calibration - Phase 1 `ScorerMetadata` compatible artifact export +- CBDR용 answerability task CLI wrapper +- source/group balance dataset report helper 제외: - 외부 benchmark/source adapter - label 생성 -- CLI - reranking Strategy 또는 Algorithm `ranksmith.confidence_generation`은 closed model output을 생성해 confidence training canonical JSONL로 저장하는 utility layer다. @@ -121,13 +161,15 @@ v1 지원 algorithm: 현재 범위: - answer-oriented raw JSONL -> `answer_confidence` canonical JSONL - relevance-oriented raw JSONL -> `judgment_confidence` canonical JSONL +- query-only answerability raw JSONL -> `query_answerability_confidence` canonical JSONL +- query+context answerability raw JSONL -> `query_context_answerability_confidence` canonical JSONL - sync closed model call - resume 가능한 JSONL output +- CBDR용 answerability task CLI wrapper 제외: - async generation - dataset adapter -- CLI - runtime reranking Strategy 또는 Algorithm ## LLM 응답 계약 diff --git a/docs/wiki/04_references_index.md b/docs/wiki/04_references_index.md index 368e4bd..9f9b6e7 100644 --- a/docs/wiki/04_references_index.md +++ b/docs/wiki/04_references_index.md @@ -7,6 +7,7 @@ - [AcuRank](references/acurank.md): Paper + Repo / Bayesian, Uncertainty-aware Adaptive Reranking / 요약 완료, 구현 완료 - [A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models](references/setwise_ranking_prompting.md): Paper / Setwise Reranking, Heapsort-based Selection Reranking / 요약 완료, 구현 완료 - [Trust in One Round: Confidence Estimation for Large Language Models via Structural Signals](references/structural_confidence.md): Paper / Black-box confidence, proxy hidden-state trajectory / 요약 완료, Phase 1 inference spec 작성 +- [Rethinking LLM Parametric Knowledge as Post-retrieval Confidence for Dynamic Retrieval and Reranking](references/parametric_post_retrieval_confidence.md): Paper / Confidence gain, post-retrieval reranking, CBDR / 요약 완료, spec 작성 ## 처리 대기 Reference - [Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers](references/Attention%20in%20Large%20Language%20Models%20Yields%20Efficient%20Zero-Shot%20Re-Rankers.pdf): Paper / Attention-based reranking, zero-shot reranking / 요약 대기 diff --git a/docs/wiki/references/Rethinking LLM Parametric Knowledge as Post-retrieval Confidence for Dynamic Retrieval and Reranking b/docs/wiki/references/Rethinking LLM Parametric Knowledge as Post-retrieval Confidence for Dynamic Retrieval and Reranking new 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w蔪WwėEɤ?&!o)n˓Bߞ>RM7W6L]O;ZOuHpS ><;,<%tzH238kE}ܻiDԈ'f畸-x<3O>fxW4sАΚ`VBӛ @({` +endstream +endobj +367 0 obj +<< /BitsPerComponent 8 /ColorSpace [ /ICCBased 545 0 R ] /Filter /FlateDecode /Height 918 /SMask 576 0 R /Subtype /Image /Type /XObject /Width 706 /Length 5191 >> +stream +x1YE4K.`!@t'I V" qUGF->^O?|Ͽ/_[[[~?O?~_[[[W;_Oooo?*+*hT|W*h&AE%@r+X˵rrߩqdJAE qhTЬT 8`"ܮ 4*hTШQ@"f *nWNq'@6ܮT8+FEJE8V*J8@"FE4*hV"~Pp|EMOШqdJAE qhTЬT 8`"ܮ 4*hTШQ@"f *nWNq'@6ܮT8+FEJE8V*J8@"FE4*hV"~Pp|EMOШqdJAE qhTЬT 8`"ܮ 4*hTШQ@"f *nWNq'@6ܮT8+FEJE8V*J8@"FE4*hV"~Pp|EMOШqdJAE qhTЬT 8`"ܮ 4*hTШQ@"f *nWNq'@6ܮT8+FEJE8V*J8@"FE4*hV"~Pp|EMOШqdJAE qhTЬT 8`"ܮ 4*hTШQ@"f *nWNq'@6ܮT8+FEJE8V*J8@"FE4*hV"~Pp|EMOШqdJAE qhTЬT 8`"ܮ 4*hTШQ@"f *nWNq'@6ܮT8+FEJE8V*J8@"FE4*hV"~Pp|EMOШqdJAE qhTЬT 8`"ܮ 4*hTШQ@"f *nWNq'@6ܮT8+FEJE8V*J8@"FE4*hV"~Pp|EMOШqdJAE qhTЬT 8`"ܮ 4*hTШQ@"f *nWNq'@6ܮT8+FEJE8V*J8@"FE4*hV"~Pp|EMOШqdJAE qhTЬT 8`"ܮ 4*hTШQ@"f *nWNq'@6ܮT8+FEJE8V*J8@"FE4*hV"~Pp|EMOШqdJAE qhTЬT 8`"ܮ 4*hTШQ@"f 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vkN{݂\2:@:B>$5B~ٴPRgtlGEm1eK_nz/kցoڶd2\"^*X$#z"X~2͛?--q\9-4 *>HDq !HD$>W0~8L{,Z"$bEâC(p(X l fj󳳳IJCo`sL)% <39d03f0197fbfb674edba2fb80b54380>] >> +startxref +1245342 +%%EOF diff --git a/docs/wiki/references/parametric_post_retrieval_confidence.md b/docs/wiki/references/parametric_post_retrieval_confidence.md new file mode 100644 index 0000000..0cff4ab --- /dev/null +++ b/docs/wiki/references/parametric_post_retrieval_confidence.md @@ -0,0 +1,64 @@ +# Reference: Rethinking LLM Parametric Knowledge as Post-retrieval Confidence for Dynamic Retrieval and Reranking + +## Source +- Paper: Rethinking LLM Parametric Knowledge as Post-retrieval Confidence for Dynamic Retrieval and Reranking +- Local PDF: `docs/wiki/references/Rethinking LLM Parametric Knowledge as Post-retrieval Confidence for Dynamic Retrieval and Reranking` +- arXiv: 2509.06472v2 +- Authors: Haoxiang Jin, Ronghan Li, Zixiang Lu, Qiguang Miao +- License: arXiv non-exclusive distribution license + +## 적용 영역 +- confidence-based reranking +- post-retrieval context filtering +- future CBDR-style retrieval routing + +## 핵심 메커니즘 +논문은 LLM 내부 hidden state로 confidence detector를 학습하고, retrieved context 주입 전후의 confidence 변화량을 preference signal로 사용한다. + +핵심 수식: + +```text +Inc(Q, C_i) = Conf(H_M,Q+C_i) - Conf(H_M,Q) +``` + +`Inc(Q, C_i) > 0`이면 해당 context는 target LLM의 answerability를 높이는 positive context로 보고, `Inc(Q, C_i) < 0`이면 negative context로 본다. + +논문 원형은 이 preference dataset으로 reranker를 fine-tune하고, 별도로 CBDR을 통해 query-only confidence가 높으면 retrieval/reranking을 skip한다. + +## ranksmith 매핑 +- Strategy: 새 `ConfidenceGainStrategy` 후보 +- Algorithm: `confidence_gain` +- Public API 영향: + - `ranksmith.confidence`에 query-only/contextual answerability input type 추가 필요 + - `ranksmith.confidence_generation`에 answerability confidence dataset generation 추가 필요 + - `ranksmith.confidence_training` task type 확장 필요 + - `ranksmith.strategies`에 confidence gain 기반 Strategy 추가 가능 +- Error 동작: + - confidence artifact/task mismatch는 fast fail + - confidence score가 finite probability가 아니면 fast fail + - 문서별 confidence scoring 실패는 조용히 fallback ranking하지 않고 실패 +- 추가할 테스트: + - base confidence와 context confidence 차이 계산 + - confidence gain 내림차순 정렬 + - 동점 시 original_index 유지 + - query-only/contextual task mismatch 실패 + - scorer score 범위 검증 + +## 현재 설계와 충돌 +- 논문 원형은 open-source LLM hidden state 접근을 전제한다. +- 논문 원형은 confidence detector 학습과 reranker fine-tuning을 포함한다. +- ranksmith는 closed LLM API, hidden state/logits/attention 비의존, runtime training-free reranking을 지향한다. + +따라서 논문 원형을 그대로 구현하지 않는다. +ranksmith에서는 이미 구현된 structural confidence layer를 closed model proxy confidence로 사용한다. +runtime reranking은 training-free지만, confidence scorer artifact는 사전에 생성/학습되어 있어야 한다. + +## Do Not Copy +- 논문 구현 코드가 제공되더라도 그대로 복사하지 않는다. +- ranksmith 구현은 public API, error policy, metadata validation, optional dependency 정책을 따른다. + +## 부족한 정보 +- query-only answerability confidence의 canonical input schema를 확정해야 한다. +- query+context answerability confidence의 canonical input schema를 확정해야 한다. +- generation label을 exact match로만 둘지, 별도 evaluator를 도입할지 결정해야 한다. +- CBDR retrieval skip은 이번 strategy 구현과 같은 범위에 넣을지 별도 spec으로 분리할지 결정해야 한다. diff --git a/pyproject.toml b/pyproject.toml index aa992d8..0bb20fc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -98,6 +98,7 @@ select = ["E", "F", "I", "UP", "B"] python_version = "3.10" strict = true files = ["src", "tests"] +mypy_path = "src" [tool.pytest.ini_options] pythonpath = ["src", "."] diff --git a/scripts/build_qa_confidence_raw_dataset.py b/scripts/build_qa_confidence_raw_dataset.py new file mode 100644 index 0000000..6d50710 --- /dev/null +++ b/scripts/build_qa_confidence_raw_dataset.py @@ -0,0 +1,498 @@ +#!/usr/bin/env python +from __future__ import annotations + +import argparse +import importlib +import json +import random +import re +import sys +from collections.abc import Iterable, Mapping, Sequence +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "src")) + +NO_ANSWER = "__NO_ANSWER__" + + +def main(argv: Sequence[str] | None = None) -> int: + args = _parse_args(argv) + rows = _load_rows(args) + examples = _build_examples( + rows, + max_query_items=args.max_query_items, + max_query_context_items=args.max_query_context_items, + max_context_chars=args.max_context_chars, + seed=args.seed, + ) + + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + query_path = output_dir / "query_answerability_raw.jsonl" + context_path = output_dir / "query_context_answerability_raw.jsonl" + manifest_path = output_dir / "dataset_manifest.json" + _ensure_outputs_writable( + (query_path, context_path, manifest_path), + overwrite=args.overwrite, + ) + query_rows = [example.query_row for example in examples if example.query_row] + context_rows = [ + context_row + for example in examples + for context_row in (example.positive_context_row, example.negative_context_row) + if context_row is not None + ] + _write_jsonl(query_path, query_rows) + _write_jsonl( + context_path, + context_rows, + ) + manifest = { + "source": args.source, + "dataset_name": args.dataset_name, + "dataset_config": args.dataset_config, + "split": args.split, + "max_source_items": args.max_source_items, + "max_query_items": args.max_query_items, + "max_query_context_items": args.max_query_context_items, + "max_context_chars": args.max_context_chars, + "seed": args.seed, + "query_answerability_count": len(query_rows), + "query_context_answerability_count": len(context_rows), + "query_answerability_raw": str(query_path), + "query_context_answerability_raw": str(context_path), + "label_policy": { + "query_answerability": "gold answer aliases from QA dataset", + "query_context_positive": "evidence context from same QA example", + "query_context_negative": ( + "TF-IDF retrieved evidence context from a different QA example " + "that does not contain the current answer aliases" + ), + }, + } + _write_json(manifest_path, manifest) + print(json.dumps(manifest, ensure_ascii=False, indent=2, sort_keys=True)) + return 0 + + +def _parse_args(argv: Sequence[str] | None) -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=( + "Build confidence raw datasets from TriviaQA-style QA examples with " + "positive evidence and retrieved negative contexts." + ) + ) + parser.add_argument("--source", choices=("triviaqa",), default="triviaqa") + parser.add_argument("--dataset-name", default="mandarjoshi/trivia_qa") + parser.add_argument("--dataset-config", default="rc") + parser.add_argument("--split", default="train[:20000]") + parser.add_argument("--input-jsonl", type=Path) + parser.add_argument("--output-dir", required=True, type=Path) + parser.add_argument("--max-source-items", type=int, default=20000) + parser.add_argument("--max-query-items", type=int, default=5000) + parser.add_argument("--max-query-context-items", type=int, default=5000) + parser.add_argument("--max-context-chars", type=int, default=8000) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--overwrite", action="store_true") + args = parser.parse_args(argv) + if args.max_query_items < 30: + parser.error("--max-query-items must be >= 30") + if args.max_query_context_items < 30: + parser.error("--max-query-context-items must be >= 30") + if args.max_source_items < 2: + parser.error("--max-source-items must be >= 2") + if args.max_query_context_items % 2 != 0: + parser.error("--max-query-context-items must be even for balanced labels") + if args.max_context_chars < 1: + parser.error("--max-context-chars must be >= 1") + return args + + +@dataclass(frozen=True) +class _RawExample: + id: str + question: str + answers: list[str] + positive_context: str + + +@dataclass(frozen=True) +class _BuiltExample: + query_row: Mapping[str, Any] + positive_context_row: Mapping[str, Any] | None + negative_context_row: Mapping[str, Any] | None + + +def _load_rows(args: argparse.Namespace) -> list[Mapping[str, Any]]: + if args.input_jsonl is not None: + return _read_jsonl(args.input_jsonl, max_items=args.max_source_items) + try: + datasets = __import__("datasets") + except ImportError as exc: + raise SystemExit( + "HuggingFace datasets is required for direct dataset loading. " + "Run with: uv run --with datasets python " + "scripts/build_qa_confidence_raw_dataset.py ..." + ) from exc + dataset = datasets.load_dataset( + args.dataset_name, + args.dataset_config, + split=args.split, + ) + rows: list[Mapping[str, Any]] = [] + for index, row in enumerate(dataset): + if index >= args.max_source_items: + break + rows.append(dict(row)) + return rows + + +def _build_examples( + rows: Sequence[Mapping[str, Any]], + *, + max_query_items: int, + max_query_context_items: int, + max_context_chars: int, + seed: int, +) -> list[_BuiltExample]: + raw_examples: list[_RawExample] = [] + for row in rows: + example = _parse_triviaqa_row(row) + if example is None: + continue + if len(example.positive_context) > max_context_chars: + continue + raw_examples.append(example) + if len(raw_examples) < 2: + raise SystemExit("Need at least two usable QA examples.") + + rng = random.Random(seed) + rng.shuffle(raw_examples) + required_context_pairs = max_query_context_items // 2 + required_examples = max(max_query_items, required_context_pairs) + selected = raw_examples[:required_examples] + if len(selected) < required_examples: + raise SystemExit( + f"Only {len(selected)} usable examples found; " + f"requested {required_examples}." + ) + + negative_by_id = _retrieve_negative_contexts( + selected, + max_pairs=required_context_pairs, + ) + built: list[_BuiltExample] = [] + for index, example in enumerate(selected): + include_query = index < max_query_items + include_context = index < required_context_pairs + negative = negative_by_id.get(example.id) + built.append( + _BuiltExample( + query_row=( + { + "id": f"triviaqa-q-{example.id}", + "query": example.question, + "gold_answer": example.answers, + "source": "triviaqa", + "group_id": f"triviaqa-{example.id}", + "metadata": {"qa_id": example.id}, + } + if include_query + else {} + ), + positive_context_row=( + { + "id": f"triviaqa-qc-pos-{example.id}", + "query": example.question, + "context": example.positive_context, + "gold_answer": example.answers, + "source": "triviaqa", + "group_id": f"triviaqa-{example.id}", + "metadata": { + "qa_id": example.id, + "context_label": "positive", + }, + } + if include_context + else None + ), + negative_context_row=( + { + "id": f"triviaqa-qc-neg-{example.id}", + "query": example.question, + "context": negative.positive_context, + "gold_answer": NO_ANSWER, + "source": "triviaqa", + "group_id": f"triviaqa-{example.id}", + "metadata": { + "qa_id": example.id, + "negative_context_qa_id": negative.id, + "context_label": "negative_retrieved_tfidf", + }, + } + if include_context and negative is not None + else None + ), + ) + ) + + query_count = sum(1 for example in built if example.query_row) + context_count = sum( + 1 + for example in built + for row in (example.positive_context_row, example.negative_context_row) + if row is not None + ) + if query_count != max_query_items: + raise SystemExit( + f"Built {query_count} query rows; requested {max_query_items}." + ) + if context_count != max_query_context_items: + raise SystemExit( + f"Built {context_count} query-context rows; " + f"requested {max_query_context_items}. " + "Increase source rows or lower the requested context count." + ) + return built + + +def _retrieve_negative_contexts( + examples: Sequence[_RawExample], + *, + max_pairs: int, +) -> dict[str, _RawExample]: + try: + sklearn_text = importlib.import_module("sklearn.feature_extraction.text") + sklearn_neighbors = importlib.import_module("sklearn.neighbors") + except ImportError as exc: + raise SystemExit( + "scikit-learn is required for retrieved negatives. " + "Install dev dependencies or run through `uv run`." + ) from exc + + tfidf_vectorizer = sklearn_text.TfidfVectorizer + nearest_neighbors = sklearn_neighbors.NearestNeighbors + contexts = [example.positive_context for example in examples] + vectorizer = tfidf_vectorizer( + lowercase=True, + stop_words="english", + max_features=100_000, + ngram_range=(1, 2), + ) + context_matrix = vectorizer.fit_transform(contexts) + query_matrix = vectorizer.transform([example.question for example in examples]) + neighbors = nearest_neighbors( + algorithm="brute", + metric="cosine", + n_neighbors=min(len(examples), 64), + ) + neighbors.fit(context_matrix) + _, neighbor_indices = neighbors.kneighbors(query_matrix[:max_pairs]) + + result: dict[str, _RawExample] = {} + for index, example in enumerate(examples[:max_pairs]): + for candidate_index in neighbor_indices[index]: + candidate = examples[int(candidate_index)] + if _is_valid_negative_candidate(example, candidate): + result[example.id] = candidate + break + if example.id in result: + continue + for candidate in examples: + if _is_valid_negative_candidate(example, candidate): + result[example.id] = candidate + break + return result + + +def _is_valid_negative_candidate( + example: _RawExample, + candidate: _RawExample, +) -> bool: + return candidate.id != example.id and not _contains_any_answer( + candidate.positive_context, + example.answers, + ) + + +def _contains_any_answer(context: str, answers: Sequence[str]) -> bool: + context_tokens = _tokens_for_match(context) + for answer in answers: + answer_tokens = _tokens_for_match(answer) + if answer_tokens and _contains_token_sequence(context_tokens, answer_tokens): + return True + return False + + +def _contains_token_sequence( + context_tokens: Sequence[str], + answer_tokens: Sequence[str], +) -> bool: + if len(answer_tokens) > len(context_tokens): + return False + window_size = len(answer_tokens) + for index in range(len(context_tokens) - window_size + 1): + if list(context_tokens[index : index + window_size]) == list(answer_tokens): + return True + return False + + +def _tokens_for_match(value: str) -> list[str]: + return re.findall(r"[a-z0-9]+", value.casefold()) + + +def _parse_triviaqa_row(row: Mapping[str, Any]) -> _RawExample | None: + question = _text(row.get("question")) + if question is None: + return None + row_id = _text(row.get("question_id")) or _text(row.get("id")) + if row_id is None: + return None + answers = _answer_aliases(row.get("answer")) + if not answers: + return None + context = _best_context(row, answers=answers) + if context is None: + return None + return _RawExample( + id=row_id, + question=question, + answers=answers, + positive_context=context, + ) + + +def _answer_aliases(answer: object) -> list[str]: + if not isinstance(answer, Mapping): + return [] + values: list[str] = [] + for key in ("value", "normalized_value"): + value = _text(answer.get(key)) + if value is not None: + values.append(value) + for key in ("aliases", "normalized_aliases"): + aliases = answer.get(key) + if isinstance(aliases, Sequence) and not isinstance(aliases, (str, bytes)): + for alias in aliases: + value = _text(alias) + if value is not None: + values.append(value) + return _dedupe(values) + + +def _best_context(row: Mapping[str, Any], *, answers: Sequence[str]) -> str | None: + candidates: list[str] = [] + for collection_name in ("entity_pages", "search_results"): + collection = row.get(collection_name) + if isinstance(collection, Mapping): + candidates.extend(_contexts_from_columnar_collection(collection)) + if isinstance(collection, Sequence) and not isinstance( + collection, (str, bytes) + ): + candidates.extend(_contexts_from_records(collection)) + for context in candidates: + if _contains_any_answer(context, answers): + return context + return None + + +def _contexts_from_columnar_collection(collection: Mapping[str, Any]) -> list[str]: + contexts = collection.get("wiki_context") or collection.get("search_context") + titles = collection.get("title") + if not isinstance(contexts, Sequence) or isinstance(contexts, (str, bytes)): + return [] + title_values = titles if isinstance(titles, Sequence) else [] + result: list[str] = [] + for index, context in enumerate(contexts): + text = _text(context) + if text is None: + continue + title = "" + if index < len(title_values): + raw_title = _text(title_values[index]) + if raw_title is not None: + title = raw_title + "\n\n" + result.append(title + text) + return result + + +def _contexts_from_records(records: Sequence[object]) -> list[str]: + result: list[str] = [] + for record in records: + if not isinstance(record, Mapping): + continue + text = _text(record.get("wiki_context")) or _text(record.get("search_context")) + if text is None: + continue + title = _text(record.get("title")) + result.append(f"{title}\n\n{text}" if title is not None else text) + return result + + +def _text(value: object) -> str | None: + if not isinstance(value, str): + return None + stripped = value.strip() + return stripped if stripped else None + + +def _dedupe(values: Iterable[str]) -> list[str]: + seen: set[str] = set() + result: list[str] = [] + for value in values: + key = value.strip().lower() + if key in seen: + continue + seen.add(key) + result.append(value) + return result + + +def _read_jsonl(path: Path, *, max_items: int) -> list[Mapping[str, Any]]: + rows: list[Mapping[str, Any]] = [] + for line_number, line in enumerate( + path.read_text(encoding="utf-8").splitlines(), 1 + ): + if len(rows) >= max_items: + break + if not line.strip(): + continue + value = json.loads(line) + if not isinstance(value, Mapping): + raise SystemExit(f"line {line_number} must be a JSON object") + rows.append(value) + return rows + + +def _write_jsonl(path: Path, rows: Sequence[Mapping[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text( + "".join( + json.dumps(row, ensure_ascii=True, sort_keys=True) + "\n" for row in rows + ), + encoding="utf-8", + ) + + +def _write_json(path: Path, value: Mapping[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text( + json.dumps(value, ensure_ascii=False, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + + +def _ensure_outputs_writable(paths: Sequence[Path], *, overwrite: bool) -> None: + existing = [path for path in paths if path.exists()] + if existing and not overwrite: + formatted = ", ".join(str(path) for path in existing) + raise SystemExit( + f"output already exists: {formatted}. Use --overwrite to replace it." + ) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/compare_reranking.py b/scripts/compare_reranking.py index 91c81d9..5ce1a9d 100644 --- a/scripts/compare_reranking.py +++ b/scripts/compare_reranking.py @@ -2,13 +2,14 @@ from __future__ import annotations import argparse +import functools import json import math import os import sys from collections.abc import Mapping, Sequence from pathlib import Path -from typing import Literal, cast +from typing import Any, Literal, cast ROOT = Path(__file__).resolve().parents[1] sys.path.insert(0, str(ROOT)) @@ -29,6 +30,7 @@ from benchmarks.mteb_eval import ( # noqa: E402 tourrank_stage_configs_for_candidate_count, ) +from ranksmith.types import Document # noqa: E402 Algorithm = Literal[ "original_bm25", @@ -49,6 +51,7 @@ "tourrank_r", "setwise_heapsort", "acurank", + "cbdr", ] Dataset = Literal["fixture", "benchmark-cache", "beir-scifact"] DEFAULT_FIXTURE = ROOT / "tests/fixtures/reranking_smoke_fixture.jsonl" @@ -66,6 +69,7 @@ "acurank_b4", "tourrank_r10", "prp_sliding_p3", + "cbdr", ) LEGACY_ALGORITHMS: tuple[Algorithm, ...] = ( "rankgpt_sliding_window", @@ -87,7 +91,7 @@ def main() -> None: needs_live = any(algorithm != "original_bm25" for algorithm in algorithms) if needs_live and not args.allow_live: raise SystemExit("Refusing live Azure calls without --allow-live.") - call_estimates = { + call_estimates: dict[str, int] = { algorithm: sum( _estimate_provider_calls( len(case.documents), @@ -105,13 +109,20 @@ def main() -> None: } if needs_live: print( - "Live Azure comparison will run " - f"{sum(call_estimates.values())} provider calls: {call_estimates}", + _call_estimate_message( + needs_live=True, + algorithms=algorithms, + call_estimates=call_estimates, + ), file=sys.stderr, ) else: print( - f"Offline comparison will run {call_estimates}", + _call_estimate_message( + needs_live=False, + algorithms=algorithms, + call_estimates=call_estimates, + ), file=sys.stderr, ) @@ -195,6 +206,25 @@ def _parse_args() -> argparse.Namespace: parser.add_argument("--tourrank-rounds", type=int, default=2) parser.add_argument("--set-size", type=int, default=3) parser.add_argument("--top-k", type=int, default=5) + parser.add_argument("--cbdr-base-artifact", type=Path) + parser.add_argument("--cbdr-context-artifact", type=Path) + parser.add_argument("--cbdr-skip-threshold", type=float, default=0.8) + parser.add_argument("--cbdr-device", default="cpu") + parser.add_argument("--cbdr-cache-dir", type=Path) + parser.add_argument("--cbdr-local-files-only", action="store_true") + parser.add_argument("--cbdr-hf-token-env") + parser.add_argument("--cbdr-max-length", type=int) + parser.add_argument("--cbdr-max-document-chars", type=int, default=4000) + parser.add_argument("--cbdr-allow-truncation", action="store_true") + parser.add_argument( + "--cbdr-answer-provider", + choices=("azure", "lmstudio"), + default="azure", + ) + parser.add_argument("--lmstudio-base-url") + parser.add_argument("--lmstudio-model") + parser.add_argument("--lmstudio-api-key") + parser.add_argument("--lmstudio-max-tokens", type=int, default=128) parser.add_argument( "--query-id", action="append", @@ -221,7 +251,7 @@ def _parse_args() -> argparse.Namespace: parser.add_argument( "--allow-live", action="store_true", - help="Required because this script sends live Azure OpenAI requests.", + help="Required because this script sends live model provider requests.", ) return parser.parse_args() @@ -242,6 +272,24 @@ def _validate_args(args: argparse.Namespace) -> None: raise SystemExit("--max-cases must be greater than 0.") if args.timeout is not None and args.timeout <= 0: raise SystemExit("--timeout must be greater than 0.") + cbdr_skip_threshold = getattr(args, "cbdr_skip_threshold", 0.8) + if cbdr_skip_threshold < 0.0 or cbdr_skip_threshold > 1.0: + raise SystemExit("--cbdr-skip-threshold must be in [0, 1].") + cbdr_max_length = getattr(args, "cbdr_max_length", None) + if cbdr_max_length is not None and cbdr_max_length < 1: + raise SystemExit("--cbdr-max-length must be greater than 0.") + cbdr_max_document_chars = getattr(args, "cbdr_max_document_chars", 4000) + if cbdr_max_document_chars < 1: + raise SystemExit("--cbdr-max-document-chars must be greater than 0.") + if getattr(args, "lmstudio_max_tokens", 128) < 1: + raise SystemExit("--lmstudio-max-tokens must be greater than 0.") + if args.algorithm == "cbdr": + if getattr(args, "cbdr_base_artifact", None) is None: + raise SystemExit("--cbdr-base-artifact is required with --algorithm cbdr.") + if getattr(args, "cbdr_context_artifact", None) is None: + raise SystemExit( + "--cbdr-context-artifact is required with --algorithm cbdr." + ) if args.checkpoint_output is not None and args.checkpoint_output == args.output: raise SystemExit("--checkpoint-output must differ from --output.") if args.dataset == "fixture": @@ -344,6 +392,41 @@ def _evaluate_cases( set_size=args.set_size, top_k=args.top_k, timeout=getattr(args, "timeout", None), + cbdr_base_artifact=getattr(args, "cbdr_base_artifact", None), + cbdr_context_artifact=getattr( + args, + "cbdr_context_artifact", + None, + ), + cbdr_skip_threshold=getattr(args, "cbdr_skip_threshold", 0.8), + cbdr_device=getattr(args, "cbdr_device", "cpu"), + cbdr_cache_dir=getattr(args, "cbdr_cache_dir", None), + cbdr_local_files_only=getattr( + args, + "cbdr_local_files_only", + False, + ), + cbdr_hf_token_env=getattr(args, "cbdr_hf_token_env", None), + cbdr_max_length=getattr(args, "cbdr_max_length", None), + cbdr_max_document_chars=getattr( + args, + "cbdr_max_document_chars", + 4000, + ), + cbdr_allow_truncation=getattr( + args, + "cbdr_allow_truncation", + False, + ), + cbdr_answer_provider=getattr( + args, + "cbdr_answer_provider", + "azure", + ), + lmstudio_base_url=getattr(args, "lmstudio_base_url", None), + lmstudio_model=getattr(args, "lmstudio_model", None), + lmstudio_api_key=getattr(args, "lmstudio_api_key", None), + lmstudio_max_tokens=getattr(args, "lmstudio_max_tokens", 128), ) evaluation = evaluate_ranked_ids( case=case, @@ -381,6 +464,43 @@ def _evaluate_cases( return evaluations, per_query +@functools.cache +def _cached_cbdr_estimators( + *, + base_artifact_path: Path, + context_artifact_path: Path, + hf_token: str | None, + cache_dir: str | None, + device: str, + local_files_only: bool, + max_length: int | None, + allow_truncation: bool, +) -> tuple[Any, Any]: + from ranksmith.confidence import StructuralConfidenceEstimator + + base_estimator = StructuralConfidenceEstimator.from_artifact( + base_artifact_path, + task_type="query_answerability_confidence", + hf_token=hf_token, + cache_dir=cache_dir, + device=device, + local_files_only=local_files_only, + max_length=max_length, + allow_truncation=allow_truncation, + ) + context_estimator = StructuralConfidenceEstimator.from_artifact( + context_artifact_path, + task_type="query_context_answerability_confidence", + hf_token=hf_token, + cache_dir=cache_dir, + device=device, + local_files_only=local_files_only, + max_length=max_length, + allow_truncation=allow_truncation, + ) + return base_estimator, context_estimator + + def _rank_case( *, case: BenchmarkCase, @@ -392,19 +512,42 @@ def _rank_case( set_size: int = 3, top_k: int | None = None, timeout: float | None = None, + cbdr_base_artifact: Path | None = None, + cbdr_context_artifact: Path | None = None, + cbdr_skip_threshold: float = 0.8, + cbdr_device: str = "cpu", + cbdr_cache_dir: Path | None = None, + cbdr_local_files_only: bool = False, + cbdr_hf_token_env: str | None = None, + cbdr_max_length: int | None = None, + cbdr_max_document_chars: int = 4000, + cbdr_allow_truncation: bool = False, + cbdr_answer_provider: str = "azure", + lmstudio_base_url: str | None = None, + lmstudio_model: str | None = None, + lmstudio_api_key: str | None = None, + lmstudio_max_tokens: int = 128, ) -> tuple[str, ...]: from ranksmith import ( AcuRankStrategy, AzureOpenAIReranker, - Document, ListwiseStrategy, PairwiseStrategy, SetwiseStrategy, TourRankStrategy, ) + from ranksmith.integrations import ( + AzureAnswerGenerator, + LMStudioModelProvider, + ProviderAnswerGenerator, + ) + from ranksmith.protocols import RerankStrategy + from ranksmith.strategies import CBDRStrategy if algorithm == "original_bm25": return tuple(document.id for document in case.documents) + documents = _case_documents(case) + strategy: RerankStrategy[Any] if algorithm in {"prp_sliding_k", "prp_sliding_p1", "prp_sliding_p3"}: strategy = PairwiseStrategy(passes=_prp_passes_for_algorithm(algorithm, passes)) elif algorithm in {"tourrank_r", "tourrank_r2", "tourrank_r10"}: @@ -434,6 +577,51 @@ def _rank_case( window_size=window_size, max_adaptive_reranker_calls=_acurank_budget_for_algorithm(algorithm), ) + elif algorithm == "cbdr": + if cbdr_base_artifact is None: + raise SystemExit("--cbdr-base-artifact is required with --algorithm cbdr.") + if cbdr_context_artifact is None: + raise SystemExit( + "--cbdr-context-artifact is required with --algorithm cbdr." + ) + answer_generator: Any + if cbdr_answer_provider == "azure": + answer_generator = AzureAnswerGenerator.from_env(timeout=timeout) + elif cbdr_answer_provider == "lmstudio": + answer_generator = ProviderAnswerGenerator( + provider=LMStudioModelProvider( + base_url=lmstudio_base_url, + model=lmstudio_model, + api_key=lmstudio_api_key, + timeout=timeout, + max_tokens=lmstudio_max_tokens, + ) + ) + else: + raise SystemExit("--cbdr-answer-provider must be azure or lmstudio.") + base_estimator, context_estimator = _cached_cbdr_estimators( + base_artifact_path=cbdr_base_artifact, + context_artifact_path=cbdr_context_artifact, + hf_token=_env_value_required(cbdr_hf_token_env), + cache_dir=str(cbdr_cache_dir) if cbdr_cache_dir is not None else None, + device=cbdr_device, + local_files_only=cbdr_local_files_only, + max_length=cbdr_max_length, + allow_truncation=cbdr_allow_truncation, + ) + strategy = CBDRStrategy( + base_estimator=base_estimator, + context_estimator=context_estimator, + answer_generator=answer_generator, + skip_threshold=cbdr_skip_threshold, + max_document_chars=cbdr_max_document_chars, + ) + results = strategy.rerank( + query=case.query, + documents=documents, + top_k=top_k, + ) + return tuple(result.document.id or "" for result in results) else: listwise_window_size, listwise_stride = _listwise_window_stride_for_algorithm( algorithm, @@ -456,11 +644,22 @@ def _rank_case( "AZURE_OPENAI_LLM_API_VERSION", fallback="AZURE_OPENAI_API_VERSION", default="2024-08-01-preview", - ), + ) + or "2024-08-01-preview", timeout=timeout or _env_float("AZURE_OPENAI_LLM_TIMEOUT"), strategy=strategy, ) - documents = [ + if top_k is None: + results = reranker.rerank(case.query, documents) + else: + results = reranker.rerank(case.query, documents, top_k=top_k) + return tuple(result.document.id or "" for result in results) + + +def _case_documents(case: BenchmarkCase) -> list[Document]: + from ranksmith import Document + + return [ Document( id=document.id, text=f"{document.title}\n\n{document.text}", @@ -468,11 +667,6 @@ def _rank_case( ) for document in case.documents ] - if top_k is None: - results = reranker.rerank(case.query, documents) - else: - results = reranker.rerank(case.query, documents, top_k=top_k) - return tuple(result.document.id or "" for result in results) def _build_report( @@ -582,6 +776,57 @@ def _method_setting( "passes": _prp_passes_for_algorithm(algorithm, args.passes), "top_k_early_stop": False, } + if algorithm == "cbdr": + base_artifact = getattr(args, "cbdr_base_artifact", None) + context_artifact = getattr(args, "cbdr_context_artifact", None) + cache_dir = getattr(args, "cbdr_cache_dir", None) + answer_provider = getattr(args, "cbdr_answer_provider", "azure") + lmstudio_base_url = None + lmstudio_model = None + lmstudio_api_key_configured = None + if answer_provider == "lmstudio": + lmstudio_base_url = _resolve_lmstudio_setting( + explicit=getattr(args, "lmstudio_base_url", None), + env_name="LMSTUDIO_BASE_URL", + default="http://localhost:1234/v1", + ) + lmstudio_model = _resolve_lmstudio_setting( + explicit=getattr(args, "lmstudio_model", None), + env_name="LMSTUDIO_MODEL", + default=None, + ) + lmstudio_api_key_configured = ( + _resolve_lmstudio_setting( + explicit=getattr(args, "lmstudio_api_key", None), + env_name="LMSTUDIO_API_KEY", + default="lm-studio", + ) + is not None + ) + return { + **base, + "base_artifact": ( + str(base_artifact) if base_artifact is not None else None + ), + "context_artifact": ( + str(context_artifact) if context_artifact is not None else None + ), + "answer_provider": answer_provider, + "skip_threshold": args.cbdr_skip_threshold, + "device": args.cbdr_device, + "cache_dir": str(cache_dir) if cache_dir is not None else None, + "local_files_only": args.cbdr_local_files_only, + "hf_token_env": args.cbdr_hf_token_env, + "max_length": args.cbdr_max_length, + "max_document_chars": args.cbdr_max_document_chars, + "allow_truncation": args.cbdr_allow_truncation, + "lmstudio_base_url": lmstudio_base_url, + "lmstudio_model": lmstudio_model, + "lmstudio_api_key": ("configured" if lmstudio_api_key_configured else None), + "lmstudio_max_tokens": getattr(args, "lmstudio_max_tokens", 128), + "provider_call_estimate": "upper_bound", + "top_k_early_stop": False, + } return { **base, "window_size": args.window_size, @@ -590,11 +835,44 @@ def _method_setting( } +def _resolve_lmstudio_setting( + *, + explicit: str | None, + env_name: str, + default: str | None, +) -> str | None: + if explicit is not None: + return explicit + env_value = os.environ.get(env_name) + if env_value is not None: + return env_value + return default + + def _append_checkpoint_row(path: Path, row: Mapping[str, object]) -> None: with path.open("a", encoding="utf-8") as handle: handle.write(json.dumps(row, sort_keys=True) + "\n") +def _call_estimate_message( + *, + needs_live: bool, + algorithms: Sequence[Algorithm], + call_estimates: Mapping[str, int], +) -> str: + total = sum(call_estimates.values()) + if not needs_live: + return f"Offline comparison will run {dict(call_estimates)}" + if "cbdr" in algorithms: + return ( + "Live Azure comparison upper-bounds provider calls at " + f"{total}: {dict(call_estimates)}" + ) + return ( + f"Live Azure comparison will run {total} provider calls: {dict(call_estimates)}" + ) + + def _aggregate_with_validity( evaluations: Sequence[EvaluationResult], per_query: Sequence[Mapping[str, object]], @@ -645,10 +923,10 @@ def _clean_env_value(value: str) -> str: def _dataset_name(args: argparse.Namespace) -> str: if args.dataset == "beir-scifact": - return args.dataset_name or "BEIR/SciFact" + return cast(str, args.dataset_name or "BEIR/SciFact") if args.dataset_name is not None and args.dataset_name.strip() != "": - return args.dataset_name.strip() - return args.cache_dir.name + return cast(str, args.dataset_name.strip()) + return cast(str, args.cache_dir.name) def _fixture_prefix(dataset_name: str) -> str: @@ -682,6 +960,15 @@ def _env_value( return default +def _env_value_required(name: str | None) -> str | None: + if name is None or name == "": + return None + value = os.environ.get(name) + if value is None or value == "": + raise SystemExit(f"Missing required environment variable: {name}") + return value + + def _env_float(name: str) -> float | None: value = os.environ.get(name) if value is None or value == "": @@ -701,6 +988,8 @@ def _estimate_provider_calls( ) -> int: if algorithm == "original_bm25": return 0 + if algorithm == "cbdr": + return document_count + 1 if algorithm in {"prp_sliding_k", "prp_sliding_p1", "prp_sliding_p3"}: return ( 2 diff --git a/scripts/generate_confidence_dataset.py b/scripts/generate_confidence_dataset.py new file mode 100644 index 0000000..f2b345a --- /dev/null +++ b/scripts/generate_confidence_dataset.py @@ -0,0 +1,104 @@ +from __future__ import annotations + +import argparse +import json +import sys +from collections.abc import Sequence +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "src")) + +from ranksmith.confidence_generation import ( # noqa: E402 + ConfidenceGenerationResult, + QueryAnswerabilityGenerationConfig, + QueryContextAnswerabilityGenerationConfig, + generate_query_answerability_confidence_dataset, + generate_query_context_answerability_confidence_dataset, +) +from ranksmith.integrations import LMStudioModelProvider # noqa: E402 + +TASK_QUERY = "query_answerability_confidence" +TASK_QUERY_CONTEXT = "query_context_answerability_confidence" +SUPPORTED_TASKS = (TASK_QUERY, TASK_QUERY_CONTEXT) + + +def main(argv: Sequence[str] | None = None) -> int: + args = _parse_args(argv) + provider = LMStudioModelProvider( + base_url=args.lmstudio_base_url, + model=args.lmstudio_model, + api_key=args.lmstudio_api_key, + max_tokens=args.lmstudio_max_tokens, + timeout=args.timeout, + ) + + if args.task == TASK_QUERY: + result = generate_query_answerability_confidence_dataset( + QueryAnswerabilityGenerationConfig( + input_path=args.input, + output_path=args.output, + provider=provider, + overwrite=args.overwrite, + resume=args.resume, + max_items=args.max_items, + source=args.source, + ) + ) + else: + result = generate_query_context_answerability_confidence_dataset( + QueryContextAnswerabilityGenerationConfig( + input_path=args.input, + output_path=args.output, + provider=provider, + overwrite=args.overwrite, + resume=args.resume, + max_items=args.max_items, + max_context_chars=args.max_context_chars, + source=args.source, + ) + ) + + print(json.dumps(_generation_summary(result), ensure_ascii=False, sort_keys=True)) + return 0 + + +def _parse_args(argv: Sequence[str] | None) -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Generate answerability confidence canonical JSONL datasets." + ) + parser.add_argument("--task", required=True, choices=SUPPORTED_TASKS) + parser.add_argument("--provider", required=True, choices=("lmstudio",)) + parser.add_argument("--input", required=True, type=Path) + parser.add_argument("--output", required=True, type=Path) + parser.add_argument("--overwrite", action="store_true") + parser.add_argument("--resume", action="store_true") + parser.add_argument("--max-items", type=int, default=None) + parser.add_argument("--source", default=None) + parser.add_argument("--max-context-chars", type=int, default=None) + parser.add_argument("--lmstudio-base-url", default=None) + parser.add_argument("--lmstudio-model", default=None) + parser.add_argument("--lmstudio-api-key", default=None) + parser.add_argument("--lmstudio-max-tokens", type=int, default=128) + parser.add_argument("--timeout", type=float, default=None) + args = parser.parse_args(argv) + if args.task == TASK_QUERY and args.max_context_chars is not None: + parser.error("--max-context-chars is only valid for query-context tasks") + if args.task == TASK_QUERY_CONTEXT and args.max_context_chars is None: + args.max_context_chars = 8000 + return args + + +def _generation_summary(result: ConfidenceGenerationResult) -> dict[str, int | str]: + return { + "output_path": str(result.output_path), + "input_count": result.input_count, + "generated_count": result.generated_count, + "skipped_count": result.skipped_count, + "positive_count": result.positive_count, + "negative_count": result.negative_count, + } + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/report_confidence_dataset.py b/scripts/report_confidence_dataset.py new file mode 100644 index 0000000..0bcfa84 --- /dev/null +++ b/scripts/report_confidence_dataset.py @@ -0,0 +1,38 @@ +from __future__ import annotations + +import argparse +import json +import sys +from collections.abc import Sequence +from pathlib import Path +from typing import cast + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "src")) + +from ranksmith.confidence import TaskType # noqa: E402 +from ranksmith.confidence_training.dataset_report import ( # noqa: E402 + build_dataset_report, +) + +TASK_QUERY = "query_answerability_confidence" +TASK_QUERY_CONTEXT = "query_context_answerability_confidence" +SUPPORTED_TASKS = (TASK_QUERY, TASK_QUERY_CONTEXT) + + +def main(argv: Sequence[str] | None = None) -> int: + args = _parse_args(argv) + report = build_dataset_report(args.dataset, cast(TaskType, args.task)) + print(json.dumps(report, ensure_ascii=False, indent=2, sort_keys=True)) + return 0 + + +def _parse_args(argv: Sequence[str] | None) -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Report confidence dataset balance.") + parser.add_argument("--task", required=True, choices=SUPPORTED_TASKS) + parser.add_argument("--dataset", required=True, type=Path) + return parser.parse_args(argv) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/run_lmstudio_confidence_pipeline.py b/scripts/run_lmstudio_confidence_pipeline.py new file mode 100644 index 0000000..3674486 --- /dev/null +++ b/scripts/run_lmstudio_confidence_pipeline.py @@ -0,0 +1,752 @@ +#!/usr/bin/env python +from __future__ import annotations + +import argparse +import json +import subprocess +import sys +from collections.abc import Mapping, Sequence +from dataclasses import dataclass +from pathlib import Path +from typing import Any, cast + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "src")) + +from ranksmith.confidence import TaskType # noqa: E402 +from ranksmith.confidence.encoder import FrozenAutoEncoder # noqa: E402 +from ranksmith.confidence_generation import ( # noqa: E402 + ConfidenceGenerationResult, + QueryAnswerabilityGenerationConfig, + QueryContextAnswerabilityGenerationConfig, + generate_query_answerability_confidence_dataset, + generate_query_context_answerability_confidence_dataset, +) +from ranksmith.confidence_training import ( # noqa: E402 + ConfidenceTrainingConfig, + ConfidenceTrainingResult, +) +from ranksmith.confidence_training.artifact import ( # noqa: E402 + export_scorer_artifact, + write_metadata_json, +) +from ranksmith.confidence_training.dataset import load_canonical_dataset # noqa: E402 +from ranksmith.confidence_training.dataset_report import ( # noqa: E402 + build_dataset_report, +) +from ranksmith.confidence_training.features import extract_feature_rows # noqa: E402 +from ranksmith.confidence_training.manifest import ( # noqa: E402 + build_dataset_manifest, + build_split_manifest, + json_hash, + training_config_hash, +) +from ranksmith.confidence_training.pipeline import ( # noqa: E402 + _feature_row_json, + _write_model, + _write_report_markdown, +) +from ranksmith.confidence_training.report import generate_training_report # noqa: E402 +from ranksmith.confidence_training.split import split_dataset # noqa: E402 +from ranksmith.confidence_training.train import ( # noqa: E402 + calibrate_classifier, + train_lightgbm_classifier, +) +from ranksmith.confidence_training.types import ConfidenceFeatureRow # noqa: E402 +from ranksmith.integrations import ( # noqa: E402 + LMStudioModelProvider, + ProviderAnswerGenerator, +) +from ranksmith.model import ModelProvider, ModelRequest, ModelResponse # noqa: E402 +from ranksmith.strategies import CBDRStrategy # noqa: E402 + +TASK_QUERY = "query_answerability_confidence" +TASK_QUERY_CONTEXT = "query_context_answerability_confidence" + + +@dataclass(frozen=True) +class _JsonObjectRetryProvider: + provider: ModelProvider + max_attempts: int + + def complete(self, request: ModelRequest) -> ModelResponse: + last_response: ModelResponse | None = None + for _ in range(self.max_attempts): + response = self.provider.complete(request) + last_response = response + if _is_valid_json_object(response.content): + return response + if last_response is not None: + return last_response + return self.provider.complete(request) + + +def _is_valid_json_object(content: str) -> bool: + try: + parsed = json.loads(content) + except json.JSONDecodeError: + return False + if not isinstance(parsed, dict): + return False + answer = parsed.get("answer") + return isinstance(answer, str) and answer.strip() != "" + + +def main(argv: Sequence[str] | None = None) -> int: + raw_argv = list(sys.argv[1:] if argv is None else argv) + if raw_argv[:1] == ["__extract_features"]: + return _extract_features_main(raw_argv[1:]) + + args = _parse_args(raw_argv) + run_dir = Path(args.run_dir) + if ( + run_dir.exists() + and any(run_dir.iterdir()) + and not (args.overwrite or args.resume_generation) + ): + raise SystemExit( + f"run dir already exists and is not empty: {run_dir}. " + "Use --resume-generation or --overwrite." + ) + + raw_dir = run_dir / "raw" + canonical_dir = run_dir / "canonical" + report_dir = run_dir / "reports" + training_dir = run_dir / "training" + artifact_dir = run_dir / "artifacts" + for directory in (raw_dir, canonical_dir, report_dir, training_dir, artifact_dir): + directory.mkdir(parents=True, exist_ok=True) + + query_raw = ( + Path(args.query_raw) + if args.query_raw is not None + else raw_dir / "query_answerability_raw.jsonl" + ) + query_context_raw = ( + Path(args.query_context_raw) + if args.query_context_raw is not None + else raw_dir / "query_context_answerability_raw.jsonl" + ) + if args.build_qa_raw: + _build_qa_raw_files( + raw_dir=raw_dir, + query_raw=query_raw, + query_context_raw=query_context_raw, + args=args, + ) + _require_input_file(query_raw, "--query-raw") + _require_input_file(query_context_raw, "--query-context-raw") + + provider: ModelProvider = _JsonObjectRetryProvider( + provider=LMStudioModelProvider( + model=args.lmstudio_model, + base_url=args.lmstudio_base_url, + api_key=args.lmstudio_api_key, + timeout=args.timeout, + max_tokens=args.lmstudio_max_tokens, + ), + max_attempts=args.generation_max_attempts, + ) + + query_canonical = canonical_dir / "query_answerability_confidence.jsonl" + context_canonical = canonical_dir / "query_context_answerability_confidence.jsonl" + + query_generation = generate_query_answerability_confidence_dataset( + QueryAnswerabilityGenerationConfig( + input_path=query_raw, + output_path=query_canonical, + provider=provider, + overwrite=args.overwrite, + resume=args.resume_generation, + max_items=args.max_items, + source=args.source, + ) + ) + context_generation = generate_query_context_answerability_confidence_dataset( + QueryContextAnswerabilityGenerationConfig( + input_path=query_context_raw, + output_path=context_canonical, + provider=provider, + overwrite=args.overwrite, + resume=args.resume_generation, + max_items=args.max_items, + max_context_chars=args.max_context_chars, + source=args.source, + ) + ) + + query_report = build_dataset_report(query_canonical, cast(TaskType, TASK_QUERY)) + context_report = build_dataset_report( + context_canonical, + cast(TaskType, TASK_QUERY_CONTEXT), + ) + _write_json(report_dir / "query_answerability_dataset_report.json", query_report) + _write_json( + report_dir / "query_context_answerability_dataset_report.json", + context_report, + ) + + base_artifact = artifact_dir / "query_answerability.joblib" + context_artifact = artifact_dir / "query_context_answerability.joblib" + query_training = _train_confidence_scorer_isolated( + _training_config( + task_type=TASK_QUERY, + dataset_path=query_canonical, + output_dir=training_dir / "query_answerability", + export_path=base_artifact, + args=args, + ) + ) + context_training = _train_confidence_scorer_isolated( + _training_config( + task_type=TASK_QUERY_CONTEXT, + dataset_path=context_canonical, + output_dir=training_dir / "query_context_answerability", + export_path=context_artifact, + args=args, + ) + ) + + strategy = CBDRStrategy.from_artifacts( + base_artifact_path=query_training.export_path, + context_artifact_path=context_training.export_path, + answer_generator=ProviderAnswerGenerator(provider=provider), + skip_threshold=args.skip_threshold, + cache_dir=args.cache_dir, + device=args.device, + local_files_only=args.local_files_only, + max_length=args.max_length, + allow_truncation=args.allow_truncation, + ) + benchmark_output = None + benchmark_checkpoint_output = None + if args.run_benchmark: + benchmark_output = run_dir / "benchmark" / "cbdr_report.json" + benchmark_checkpoint_output = run_dir / "benchmark" / "cbdr_checkpoint.jsonl" + _run_benchmark( + args=args, + output=benchmark_output, + checkpoint_output=benchmark_checkpoint_output, + base_artifact=query_training.export_path, + context_artifact=context_training.export_path, + ) + + summary = { + "run_dir": str(run_dir), + "generation": { + "query_answerability": _generation_summary(query_generation), + "query_context_answerability": _generation_summary(context_generation), + }, + "dataset_reports": { + "query_answerability": str( + report_dir / "query_answerability_dataset_report.json" + ), + "query_context_answerability": str( + report_dir / "query_context_answerability_dataset_report.json" + ), + }, + "training": { + "query_answerability": _training_summary(query_training), + "query_context_answerability": _training_summary(context_training), + }, + "artifacts": { + "query_answerability": str(query_training.export_path), + "query_context_answerability": str(context_training.export_path), + }, + "cbdr": { + "strategy_loaded": strategy.algorithm == "cbdr", + "skip_threshold": args.skip_threshold, + }, + "benchmark": { + "executed": args.run_benchmark, + "output": str(benchmark_output) if benchmark_output is not None else None, + "checkpoint_output": ( + str(benchmark_checkpoint_output) + if benchmark_checkpoint_output is not None + else None + ), + "command": _benchmark_command(run_dir, args=args), + }, + } + _write_json(report_dir / "summary.json", summary) + print(json.dumps(summary, ensure_ascii=False, indent=2, sort_keys=True)) + return 0 + + +def _parse_args(argv: Sequence[str] | None) -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=( + "Run the full LM Studio confidence pipeline: generation, reports, " + "training, artifact export, and CBDR loading." + ) + ) + parser.add_argument("--run-dir", required=True, type=Path) + parser.add_argument("--query-raw", type=Path, default=None) + parser.add_argument("--query-context-raw", type=Path, default=None) + parser.add_argument("--build-qa-raw", action="store_true") + parser.add_argument("--qa-source", choices=("triviaqa",), default="triviaqa") + parser.add_argument("--qa-dataset-name", default="mandarjoshi/trivia_qa") + parser.add_argument("--qa-dataset-config", default="rc") + parser.add_argument("--qa-split", default="train[:20000]") + parser.add_argument("--qa-input-jsonl", type=Path, default=None) + parser.add_argument("--qa-max-source-items", type=int, default=20000) + parser.add_argument("--qa-max-query-items", type=int, default=5000) + parser.add_argument("--qa-max-query-context-items", type=int, default=5000) + parser.add_argument("--lmstudio-model", default=None) + parser.add_argument("--lmstudio-base-url", default=None) + parser.add_argument("--lmstudio-api-key", default=None) + parser.add_argument("--lmstudio-max-tokens", type=int, default=128) + parser.add_argument("--generation-max-attempts", type=int, default=3) + parser.add_argument("--timeout", type=float, default=60.0) + parser.add_argument("--max-items", type=int, default=None) + parser.add_argument("--max-context-chars", type=int, default=8000) + parser.add_argument("--source", default=None) + parser.add_argument("--resume-generation", action="store_true") + parser.add_argument("--overwrite", action="store_true") + parser.add_argument("--encoder-name", default="bert-base-uncased") + parser.add_argument("--encoder-revision", default=None) + parser.add_argument("--tokenizer-name", default=None) + parser.add_argument("--tokenizer-revision", default=None) + parser.add_argument("--cache-dir", default=None) + parser.add_argument("--local-files-only", action="store_true") + parser.add_argument("--device", default="cpu") + parser.add_argument("--max-length", type=int, default=256) + parser.add_argument("--allow-truncation", action="store_true") + parser.add_argument("--skip-threshold", type=float, default=0.8) + parser.add_argument("--run-benchmark", action="store_true") + parser.add_argument("--allow-live", action="store_true") + parser.add_argument( + "--benchmark-dataset", + choices=("fixture", "benchmark-cache", "beir-scifact"), + default="benchmark-cache", + ) + parser.add_argument("--benchmark-cache-dir", type=Path) + parser.add_argument("--benchmark-candidates", type=Path) + parser.add_argument("--benchmark-max-cases", type=int) + parser.add_argument("--benchmark-top-k", type=int, default=5) + args = parser.parse_args(argv) + if args.build_qa_raw and ( + args.query_raw is not None or args.query_context_raw is not None + ): + parser.error("--build-qa-raw cannot be combined with explicit raw paths") + if not args.build_qa_raw and ( + args.query_raw is None or args.query_context_raw is None + ): + parser.error( + "--query-raw and --query-context-raw are required unless " + "--build-qa-raw is set" + ) + if args.qa_max_query_items < 30: + parser.error("--qa-max-query-items must be >= 30") + if args.qa_max_query_context_items < 30: + parser.error("--qa-max-query-context-items must be >= 30") + if args.qa_max_source_items < 2: + parser.error("--qa-max-source-items must be >= 2") + if args.qa_max_query_context_items % 2 != 0: + parser.error("--qa-max-query-context-items must be even") + if args.overwrite and args.resume_generation: + parser.error("--overwrite and --resume-generation cannot both be set") + if args.lmstudio_max_tokens < 1: + parser.error("--lmstudio-max-tokens must be >= 1") + if args.generation_max_attempts < 1: + parser.error("--generation-max-attempts must be >= 1") + if args.max_items is not None and args.max_items < 1: + parser.error("--max-items must be >= 1") + if args.max_context_chars < 1: + parser.error("--max-context-chars must be >= 1") + if args.skip_threshold < 0.0 or args.skip_threshold > 1.0: + parser.error("--skip-threshold must be in [0, 1]") + if args.run_benchmark and not args.allow_live: + parser.error("--run-benchmark requires --allow-live") + if args.run_benchmark and args.benchmark_dataset == "benchmark-cache": + if args.benchmark_cache_dir is None: + parser.error("--benchmark-cache-dir is required for benchmark-cache") + if args.benchmark_candidates is None: + parser.error("--benchmark-candidates is required for benchmark-cache") + if args.benchmark_max_cases is not None and args.benchmark_max_cases < 1: + parser.error("--benchmark-max-cases must be >= 1") + if args.benchmark_top_k < 1: + parser.error("--benchmark-top-k must be >= 1") + return args + + +def _training_config( + *, + task_type: str, + dataset_path: Path, + output_dir: Path, + export_path: Path, + args: argparse.Namespace, +) -> ConfidenceTrainingConfig: + return ConfidenceTrainingConfig( + task_type=cast(TaskType, task_type), + dataset_path=dataset_path, + output_dir=output_dir, + export_path=export_path, + encoder_name=args.encoder_name, + encoder_revision=args.encoder_revision, + tokenizer_name=args.tokenizer_name, + tokenizer_revision=args.tokenizer_revision, + cache_dir=args.cache_dir, + local_files_only=args.local_files_only, + max_length=args.max_length, + allow_truncation=args.allow_truncation, + ) + + +def _extract_features_main(argv: Sequence[str]) -> int: + args = _parse_extract_args(argv) + config = _training_config_from_extract_args(args) + output_dir = Path(config.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + samples = load_canonical_dataset(config.dataset_path, task_type=config.task_type) + split = split_dataset( + samples, + seed=config.seed, + train_ratio=config.train_ratio, + valid_ratio=config.valid_ratio, + test_ratio=config.test_ratio, + ) + encoder = FrozenAutoEncoder.from_pretrained( + encoder_name=config.encoder_name, + encoder_revision=config.encoder_revision, + tokenizer_name=config.tokenizer_name, + tokenizer_revision=config.tokenizer_revision, + hf_token=None, + local_files_only=config.local_files_only, + cache_dir=config.cache_dir, + device="cpu", + max_length=config.max_length, + allow_truncation=config.allow_truncation, + ) + for name, part in ( + ("train", split.train), + ("valid", split.valid), + ("test", split.test), + ): + rows = extract_feature_rows(part, encoder=encoder) + _write_feature_rows(output_dir / f"features_{name}.jsonl", rows) + return 0 + + +def _train_confidence_scorer_isolated( + config: ConfidenceTrainingConfig, +) -> ConfidenceTrainingResult: + output_dir = Path(config.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + export_path = Path(config.export_path) + + _run_feature_extraction_subprocess(config) + train_rows = _read_feature_rows(output_dir / "features_train.jsonl") + valid_rows = _read_feature_rows(output_dir / "features_valid.jsonl") + test_rows = _read_feature_rows(output_dir / "features_test.jsonl") + + model = train_lightgbm_classifier(train_rows, seed=config.seed) + scorer = calibrate_classifier(model, valid_rows) + report = generate_training_report( + model, + scorer, + valid_rows=valid_rows, + test_rows=test_rows, + ) + _write_json(output_dir / "report.json", report.to_dict()) + _write_report_markdown(output_dir / "report.md", report) + + samples = load_canonical_dataset(config.dataset_path, task_type=config.task_type) + split = split_dataset( + samples, + seed=config.seed, + train_ratio=config.train_ratio, + valid_ratio=config.valid_ratio, + test_ratio=config.test_ratio, + ) + dataset_manifest = build_dataset_manifest( + Path(config.dataset_path), + task_type=config.task_type, + sample_count=len(samples), + ) + split_manifest = build_split_manifest( + split, + seed=config.seed, + task_type=config.task_type, + ) + _write_json(output_dir / "dataset_manifest.json", dataset_manifest) + _write_json(output_dir / "split_manifest.json", split_manifest) + _write_model(output_dir / "model.joblib", scorer) + + metadata = export_scorer_artifact( + scorer, + config=config, + train_count=len(split.train), + valid_count=len(split.valid), + test_count=len(split.test), + dataset_manifest_hash=json_hash(dataset_manifest), + training_config_hash=training_config_hash(config), + ) + metadata_path = output_dir / "metadata.json" + write_metadata_json(metadata_path, metadata) + return ConfidenceTrainingResult( + output_dir=output_dir, + export_path=export_path, + report_path=output_dir / "report.json", + metadata_path=metadata_path, + ) + + +def _run_feature_extraction_subprocess(config: ConfidenceTrainingConfig) -> None: + command = [ + sys.executable, + str(Path(__file__).resolve()), + "__extract_features", + "--task", + config.task_type, + "--dataset", + str(config.dataset_path), + "--output-dir", + str(config.output_dir), + "--encoder-name", + config.encoder_name, + "--max-length", + str(config.max_length), + "--seed", + str(config.seed), + ] + if config.encoder_revision is not None: + command.extend(["--encoder-revision", config.encoder_revision]) + if config.tokenizer_name is not None: + command.extend(["--tokenizer-name", config.tokenizer_name]) + if config.tokenizer_revision is not None: + command.extend(["--tokenizer-revision", config.tokenizer_revision]) + if config.cache_dir is not None: + command.extend(["--cache-dir", config.cache_dir]) + if config.local_files_only: + command.append("--local-files-only") + if config.allow_truncation: + command.append("--allow-truncation") + subprocess.run(command, cwd=ROOT, check=True) + + +def _parse_extract_args(argv: Sequence[str]) -> argparse.Namespace: + parser = argparse.ArgumentParser(description=argparse.SUPPRESS) + parser.add_argument( + "--task", required=True, choices=(TASK_QUERY, TASK_QUERY_CONTEXT) + ) + parser.add_argument("--dataset", required=True, type=Path) + parser.add_argument("--output-dir", required=True, type=Path) + parser.add_argument("--encoder-name", required=True) + parser.add_argument("--encoder-revision", default=None) + parser.add_argument("--tokenizer-name", default=None) + parser.add_argument("--tokenizer-revision", default=None) + parser.add_argument("--cache-dir", default=None) + parser.add_argument("--local-files-only", action="store_true") + parser.add_argument("--max-length", type=int, required=True) + parser.add_argument("--allow-truncation", action="store_true") + parser.add_argument("--seed", type=int, required=True) + return parser.parse_args(argv) + + +def _training_config_from_extract_args( + args: argparse.Namespace, +) -> ConfidenceTrainingConfig: + return ConfidenceTrainingConfig( + task_type=cast(TaskType, args.task), + dataset_path=args.dataset, + output_dir=args.output_dir, + export_path=args.output_dir / "unused.joblib", + encoder_name=args.encoder_name, + encoder_revision=args.encoder_revision, + tokenizer_name=args.tokenizer_name, + tokenizer_revision=args.tokenizer_revision, + cache_dir=args.cache_dir, + local_files_only=args.local_files_only, + max_length=args.max_length, + allow_truncation=args.allow_truncation, + seed=args.seed, + ) + + +def _write_feature_rows(path: Path, rows: Sequence[ConfidenceFeatureRow]) -> None: + path.write_text( + "".join(json.dumps(_feature_row_json(row)) + "\n" for row in rows), + encoding="utf-8", + ) + + +def _read_feature_rows(path: Path) -> list[ConfidenceFeatureRow]: + rows: list[ConfidenceFeatureRow] = [] + for line in path.read_text(encoding="utf-8").splitlines(): + if not line.strip(): + continue + rows.append(ConfidenceFeatureRow(**json.loads(line))) + return rows + + +def _generation_summary(result: ConfidenceGenerationResult) -> Mapping[str, Any]: + return { + "output_path": str(result.output_path), + "input_count": result.input_count, + "generated_count": result.generated_count, + "skipped_count": result.skipped_count, + "positive_count": result.positive_count, + "negative_count": result.negative_count, + } + + +def _training_summary(result: ConfidenceTrainingResult) -> Mapping[str, str]: + return { + "output_dir": str(result.output_dir), + "export_path": str(result.export_path), + "report_path": str(result.report_path), + "metadata_path": str(result.metadata_path), + } + + +def _benchmark_command(run_dir: Path, *, args: argparse.Namespace) -> str: + parts = [ + "uv", + "run", + "python", + "scripts/compare_reranking.py", + "--dataset", + args.benchmark_dataset, + "--algorithm", + "cbdr", + "--cbdr-answer-provider", + "lmstudio", + "--cbdr-base-artifact", + str(run_dir / "artifacts/query_answerability.joblib"), + "--cbdr-context-artifact", + str(run_dir / "artifacts/query_context_answerability.joblib"), + "--lmstudio-model", + str(args.lmstudio_model or "$LMSTUDIO_MODEL"), + "--output", + str(run_dir / "benchmark/cbdr_report.json"), + "--checkpoint-output", + str(run_dir / "benchmark/cbdr_checkpoint.jsonl"), + "--allow-live", + ] + if args.benchmark_cache_dir is not None: + parts.extend(["--cache-dir", str(args.benchmark_cache_dir)]) + if args.benchmark_candidates is not None: + parts.extend(["--candidates", str(args.benchmark_candidates)]) + if args.benchmark_max_cases is not None: + parts.extend(["--max-cases", str(args.benchmark_max_cases)]) + parts.extend(["--top-k", str(args.benchmark_top_k)]) + return " ".join(parts) + + +def _run_benchmark( + *, + args: argparse.Namespace, + output: Path, + checkpoint_output: Path, + base_artifact: Path, + context_artifact: Path, +) -> None: + output.parent.mkdir(parents=True, exist_ok=True) + command = [ + sys.executable, + str(ROOT / "scripts/compare_reranking.py"), + "--dataset", + args.benchmark_dataset, + "--algorithm", + "cbdr", + "--cbdr-answer-provider", + "lmstudio", + "--cbdr-base-artifact", + str(base_artifact), + "--cbdr-context-artifact", + str(context_artifact), + "--cbdr-skip-threshold", + str(args.skip_threshold), + "--cbdr-device", + args.device, + "--cbdr-max-length", + str(args.max_length), + "--lmstudio-max-tokens", + str(args.lmstudio_max_tokens), + "--top-k", + str(args.benchmark_top_k), + "--output", + str(output), + "--checkpoint-output", + str(checkpoint_output), + "--allow-live", + ] + if args.lmstudio_model is not None: + command.extend(["--lmstudio-model", args.lmstudio_model]) + if args.lmstudio_base_url is not None: + command.extend(["--lmstudio-base-url", args.lmstudio_base_url]) + if args.lmstudio_api_key is not None: + command.extend(["--lmstudio-api-key", args.lmstudio_api_key]) + if args.benchmark_cache_dir is not None: + command.extend(["--cache-dir", str(args.benchmark_cache_dir)]) + if args.benchmark_candidates is not None: + command.extend(["--candidates", str(args.benchmark_candidates)]) + if args.benchmark_max_cases is not None: + command.extend(["--max-cases", str(args.benchmark_max_cases)]) + if args.cache_dir is not None: + command.extend(["--cbdr-cache-dir", args.cache_dir]) + if args.local_files_only: + command.append("--cbdr-local-files-only") + if args.allow_truncation: + command.append("--cbdr-allow-truncation") + subprocess.run(command, cwd=ROOT, check=True) + + +def _require_input_file(path: Path, option_name: str) -> None: + if not path.exists(): + raise SystemExit(f"{option_name} file does not exist: {path}") + if not path.is_file(): + raise SystemExit(f"{option_name} must be a file: {path}") + + +def _build_qa_raw_files( + *, + raw_dir: Path, + query_raw: Path, + query_context_raw: Path, + args: argparse.Namespace, +) -> None: + if args.resume_generation and query_raw.exists() and query_context_raw.exists(): + return + command = [ + sys.executable, + str(ROOT / "scripts/build_qa_confidence_raw_dataset.py"), + "--source", + args.qa_source, + "--dataset-name", + args.qa_dataset_name, + "--dataset-config", + args.qa_dataset_config, + "--split", + args.qa_split, + "--output-dir", + str(raw_dir), + "--max-source-items", + str(args.qa_max_source_items), + "--max-query-items", + str(args.qa_max_query_items), + "--max-query-context-items", + str(args.qa_max_query_context_items), + "--max-context-chars", + str(args.max_context_chars), + ] + if args.qa_input_jsonl is not None: + command.extend(["--input-jsonl", str(args.qa_input_jsonl)]) + if args.overwrite: + command.append("--overwrite") + subprocess.run(command, cwd=ROOT, check=True) + + +def _write_json(path: Path, value: Mapping[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text( + json.dumps(value, ensure_ascii=False, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/smoke_lmstudio_confidence_pipeline.py b/scripts/smoke_lmstudio_confidence_pipeline.py new file mode 100644 index 0000000..0e8b178 --- /dev/null +++ b/scripts/smoke_lmstudio_confidence_pipeline.py @@ -0,0 +1,791 @@ +#!/usr/bin/env python +from __future__ import annotations + +import argparse +import json +import subprocess +import sys +import tempfile +from collections.abc import Mapping, Sequence +from datetime import datetime +from pathlib import Path +from typing import Any, cast + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "src")) + +from ranksmith.confidence import StructuralConfidenceResult, TaskType # noqa: E402 +from ranksmith.confidence.encoder import FrozenAutoEncoder # noqa: E402 +from ranksmith.confidence_generation import ( # noqa: E402 + ConfidenceGenerationResult, + QueryAnswerabilityGenerationConfig, + QueryContextAnswerabilityGenerationConfig, + generate_query_answerability_confidence_dataset, + generate_query_context_answerability_confidence_dataset, +) +from ranksmith.confidence_training import ( # noqa: E402 + ConfidenceTrainingConfig, + ConfidenceTrainingResult, +) +from ranksmith.confidence_training.artifact import ( # noqa: E402 + export_scorer_artifact, + write_metadata_json, +) +from ranksmith.confidence_training.dataset import ( # noqa: E402 + load_canonical_dataset, +) +from ranksmith.confidence_training.dataset_report import ( # noqa: E402 + build_dataset_report, +) +from ranksmith.confidence_training.features import ( # noqa: E402 + extract_feature_rows, +) +from ranksmith.confidence_training.manifest import ( # noqa: E402 + build_dataset_manifest, + build_split_manifest, + json_hash, + training_config_hash, +) +from ranksmith.confidence_training.pipeline import ( # noqa: E402 + _feature_row_json, + _write_json, + _write_model, + _write_report_markdown, +) +from ranksmith.confidence_training.report import ( # noqa: E402 + generate_training_report, +) +from ranksmith.confidence_training.split import split_dataset # noqa: E402 +from ranksmith.confidence_training.train import ( # noqa: E402 + calibrate_classifier, + train_lightgbm_classifier, +) +from ranksmith.confidence_training.types import ( # noqa: E402 + ConfidenceFeatureRow, +) +from ranksmith.integrations import ( # noqa: E402 + LMStudioModelProvider, + ProviderAnswerGenerator, +) +from ranksmith.strategies import CBDRStrategy # noqa: E402 +from ranksmith.types import Document # noqa: E402 + +TASK_QUERY = "query_answerability_confidence" +TASK_QUERY_CONTEXT = "query_context_answerability_confidence" +DEFAULT_ENCODER = "hf-internal-testing/tiny-random-bert" + + +def main(argv: Sequence[str] | None = None) -> int: + raw_argv = list(sys.argv[1:] if argv is None else argv) + if raw_argv[:1] == ["__extract_features"]: + return _extract_features_main(raw_argv[1:]) + if raw_argv[:1] == ["__rerank_smoke"]: + return _rerank_smoke_main(raw_argv[1:]) + + args = _parse_args(raw_argv) + run_dir = _resolve_run_dir(args.output_dir) + _prepare_run_dir(run_dir, overwrite=args.overwrite) + + provider = LMStudioModelProvider( + model=args.lmstudio_model, + base_url=args.lmstudio_base_url, + api_key=args.lmstudio_api_key, + timeout=args.timeout, + max_tokens=args.lmstudio_max_tokens, + ) + + raw_dir = run_dir / "raw" + canonical_dir = run_dir / "canonical" + training_dir = run_dir / "training" + artifacts_dir = run_dir / "artifacts" + reports_dir = run_dir / "reports" + for directory in (raw_dir, canonical_dir, training_dir, artifacts_dir, reports_dir): + directory.mkdir(parents=True, exist_ok=True) + + live_query_raw = raw_dir / "live_query_answerability.jsonl" + live_context_raw = raw_dir / "live_query_context_answerability.jsonl" + live_query_canonical = canonical_dir / "live_query_answerability.jsonl" + live_context_canonical = canonical_dir / "live_query_context_answerability.jsonl" + training_query_canonical = canonical_dir / "training_query_answerability.jsonl" + training_context_canonical = ( + canonical_dir / "training_query_context_answerability.jsonl" + ) + + _write_jsonl(live_query_raw, _live_query_rows()) + _write_jsonl(live_context_raw, _live_context_rows()) + + live_query_result = generate_query_answerability_confidence_dataset( + QueryAnswerabilityGenerationConfig( + input_path=live_query_raw, + output_path=live_query_canonical, + provider=provider, + overwrite=True, + max_items=args.max_items, + source="lmstudio-smoke-live", + ) + ) + live_context_result = generate_query_context_answerability_confidence_dataset( + QueryContextAnswerabilityGenerationConfig( + input_path=live_context_raw, + output_path=live_context_canonical, + provider=provider, + overwrite=True, + max_items=args.max_items, + max_context_chars=args.max_context_chars, + source="lmstudio-smoke-live", + ) + ) + + _write_json( + reports_dir / "live_query_report.json", + _dataset_report(live_query_canonical, TASK_QUERY), + ) + _write_json( + reports_dir / "live_query_context_report.json", + _dataset_report(live_context_canonical, TASK_QUERY_CONTEXT), + ) + + _write_jsonl(training_query_canonical, _training_query_rows()) + _write_jsonl(training_context_canonical, _training_context_rows()) + _write_json( + reports_dir / "training_query_report.json", + _dataset_report(training_query_canonical, TASK_QUERY), + ) + _write_json( + reports_dir / "training_query_context_report.json", + _dataset_report(training_context_canonical, TASK_QUERY_CONTEXT), + ) + + base_artifact = artifacts_dir / "query_answerability.joblib" + context_artifact = artifacts_dir / "query_context_answerability.joblib" + base_training = _train_smoke_scorer( + _training_config( + task_type=TASK_QUERY, + dataset_path=training_query_canonical, + output_dir=training_dir / "query_answerability", + export_path=base_artifact, + args=args, + ) + ) + context_training = _train_smoke_scorer( + _training_config( + task_type=TASK_QUERY_CONTEXT, + dataset_path=training_context_canonical, + output_dir=training_dir / "query_context_answerability", + export_path=context_artifact, + args=args, + ) + ) + + answer_generator = ProviderAnswerGenerator(provider=provider) + strategy = CBDRStrategy.from_artifacts( + base_artifact_path=base_training.export_path, + context_artifact_path=context_training.export_path, + answer_generator=answer_generator, + skip_threshold=args.skip_threshold, + cache_dir=args.cache_dir, + device=args.device, + local_files_only=args.local_files_only, + max_length=args.max_length, + allow_truncation=args.allow_truncation, + ) + rerank_results = _run_optional_rerank_smoke( + include_rerank=args.include_rerank, + run_dir=run_dir, + base_artifact=base_training.export_path, + context_artifact=context_training.export_path, + args=args, + ) + + summary = { + "run_dir": str(run_dir), + "live_generation": { + "query_answerability": _generation_summary(live_query_result), + "query_context_answerability": _generation_summary(live_context_result), + }, + "training_artifacts": { + "query_answerability": str(base_training.export_path), + "query_context_answerability": str(context_training.export_path), + }, + "reports_dir": str(reports_dir), + "cbdr_smoke": { + "strategy_loaded": strategy.algorithm == "cbdr", + "rerank_executed": args.include_rerank, + "results": rerank_results, + }, + } + _write_json(reports_dir / "summary.json", summary) + print(json.dumps(summary, ensure_ascii=False, indent=2, sort_keys=True)) + return 0 + + +def _extract_features_main(argv: Sequence[str]) -> int: + args = _parse_extract_args(argv) + config = _training_config_from_extract_args(args) + output_dir = Path(config.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + samples = load_canonical_dataset(config.dataset_path, task_type=config.task_type) + split = split_dataset( + samples, + seed=config.seed, + train_ratio=config.train_ratio, + valid_ratio=config.valid_ratio, + test_ratio=config.test_ratio, + ) + encoder = FrozenAutoEncoder.from_pretrained( + encoder_name=config.encoder_name, + encoder_revision=config.encoder_revision, + tokenizer_name=config.tokenizer_name, + tokenizer_revision=config.tokenizer_revision, + hf_token=None, + local_files_only=config.local_files_only, + cache_dir=config.cache_dir, + device="cpu", + max_length=config.max_length, + allow_truncation=config.allow_truncation, + ) + for name, part in ( + ("train", split.train), + ("valid", split.valid), + ("test", split.test), + ): + rows = extract_feature_rows(part, encoder=encoder) + _write_feature_rows(output_dir / f"features_{name}.jsonl", rows) + return 0 + + +def _rerank_smoke_main(argv: Sequence[str]) -> int: + args = _parse_rerank_args(argv) + provider = LMStudioModelProvider( + model=args.lmstudio_model, + base_url=args.lmstudio_base_url, + api_key=args.lmstudio_api_key, + timeout=args.timeout, + max_tokens=args.lmstudio_max_tokens, + ) + answer_generator = _LoggingAnswerGenerator( + ProviderAnswerGenerator(provider=provider), + enabled=True, + ) + loaded = CBDRStrategy.from_artifacts( + base_artifact_path=args.base_artifact, + context_artifact_path=args.context_artifact, + answer_generator=answer_generator, + skip_threshold=args.skip_threshold, + cache_dir=args.cache_dir, + device=args.device, + local_files_only=args.local_files_only, + max_length=args.max_length, + allow_truncation=args.allow_truncation, + ) + strategy = CBDRStrategy( + base_estimator=_LoggingEstimator(loaded.base_estimator, label="base"), + context_estimator=_LoggingEstimator(loaded.context_estimator, label="context"), + answer_generator=answer_generator, + skip_threshold=args.skip_threshold, + max_document_chars=loaded.max_document_chars, + ) + results = _execute_rerank_smoke(strategy) + _write_json(args.output_json, {"results": results}) + return 0 + + +def _train_smoke_scorer(config: ConfidenceTrainingConfig) -> ConfidenceTrainingResult: + output_dir = Path(config.output_dir) + export_path = Path(config.export_path) + output_dir.mkdir(parents=True, exist_ok=True) + + _run_feature_extraction_subprocess(config) + train_rows = _read_feature_rows(output_dir / "features_train.jsonl") + valid_rows = _read_feature_rows(output_dir / "features_valid.jsonl") + test_rows = _read_feature_rows(output_dir / "features_test.jsonl") + + model = train_lightgbm_classifier(train_rows, seed=config.seed) + scorer = calibrate_classifier(model, valid_rows) + report = generate_training_report( + model, + scorer, + valid_rows=valid_rows, + test_rows=test_rows, + ) + _write_json(output_dir / "report.json", report.to_dict()) + _write_report_markdown(output_dir / "report.md", report) + + samples = load_canonical_dataset(config.dataset_path, task_type=config.task_type) + split = split_dataset( + samples, + seed=config.seed, + train_ratio=config.train_ratio, + valid_ratio=config.valid_ratio, + test_ratio=config.test_ratio, + ) + dataset_manifest = build_dataset_manifest( + Path(config.dataset_path), + task_type=config.task_type, + sample_count=len(samples), + ) + split_manifest = build_split_manifest( + split, + seed=config.seed, + task_type=config.task_type, + ) + _write_json(output_dir / "dataset_manifest.json", dataset_manifest) + _write_json(output_dir / "split_manifest.json", split_manifest) + _write_model(output_dir / "model.joblib", scorer) + + metadata = export_scorer_artifact( + scorer, + config=config, + train_count=len(split.train), + valid_count=len(split.valid), + test_count=len(split.test), + dataset_manifest_hash=json_hash(dataset_manifest), + training_config_hash=training_config_hash(config), + ) + metadata_path = output_dir / "metadata.json" + write_metadata_json(metadata_path, metadata) + + return ConfidenceTrainingResult( + output_dir=output_dir, + export_path=export_path, + report_path=output_dir / "report.json", + metadata_path=metadata_path, + ) + + +def _run_feature_extraction_subprocess(config: ConfidenceTrainingConfig) -> None: + command = [ + sys.executable, + str(Path(__file__).resolve()), + "__extract_features", + "--task", + config.task_type, + "--dataset", + str(config.dataset_path), + "--output-dir", + str(config.output_dir), + "--encoder-name", + config.encoder_name, + "--max-length", + str(config.max_length), + "--seed", + str(config.seed), + ] + if config.encoder_revision is not None: + command.extend(["--encoder-revision", config.encoder_revision]) + if config.tokenizer_name is not None: + command.extend(["--tokenizer-name", config.tokenizer_name]) + if config.tokenizer_revision is not None: + command.extend(["--tokenizer-revision", config.tokenizer_revision]) + if config.cache_dir is not None: + command.extend(["--cache-dir", config.cache_dir]) + if config.local_files_only: + command.append("--local-files-only") + if config.allow_truncation: + command.append("--allow-truncation") + subprocess.run(command, cwd=ROOT, check=True) + + +def _parse_args(argv: Sequence[str] | None) -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=( + "Run a live LM Studio smoke for confidence generation, " + "training, and CBDR loading." + ) + ) + parser.add_argument("--lmstudio-model", default=None) + parser.add_argument("--lmstudio-base-url", default=None) + parser.add_argument("--lmstudio-api-key", default=None) + parser.add_argument("--lmstudio-max-tokens", type=int, default=128) + parser.add_argument("--timeout", type=float, default=60.0) + parser.add_argument("--max-items", type=int, default=2) + parser.add_argument("--max-context-chars", type=int, default=8000) + parser.add_argument("--output-dir", type=Path, default=None) + parser.add_argument("--overwrite", action="store_true") + parser.add_argument("--encoder-name", default=DEFAULT_ENCODER) + parser.add_argument("--encoder-revision", default=None) + parser.add_argument("--tokenizer-name", default=None) + parser.add_argument("--tokenizer-revision", default=None) + parser.add_argument("--cache-dir", default=None) + parser.add_argument("--local-files-only", action="store_true") + parser.add_argument("--device", default="cpu") + parser.add_argument("--max-length", type=int, default=128) + parser.add_argument("--allow-truncation", action="store_true") + parser.add_argument("--include-rerank", action="store_true") + parser.add_argument("--skip-threshold", type=float, default=0.8) + args = parser.parse_args(argv) + if args.max_items < 1: + parser.error("--max-items must be >= 1") + if args.max_context_chars < 1: + parser.error("--max-context-chars must be >= 1") + if args.lmstudio_max_tokens < 1: + parser.error("--lmstudio-max-tokens must be >= 1") + if args.skip_threshold < 0.0 or args.skip_threshold > 1.0: + parser.error("--skip-threshold must be in [0, 1]") + return args + + +def _parse_extract_args(argv: Sequence[str]) -> argparse.Namespace: + parser = argparse.ArgumentParser(description=argparse.SUPPRESS) + parser.add_argument( + "--task", required=True, choices=(TASK_QUERY, TASK_QUERY_CONTEXT) + ) + parser.add_argument("--dataset", required=True, type=Path) + parser.add_argument("--output-dir", required=True, type=Path) + parser.add_argument("--encoder-name", required=True) + parser.add_argument("--encoder-revision", default=None) + parser.add_argument("--tokenizer-name", default=None) + parser.add_argument("--tokenizer-revision", default=None) + parser.add_argument("--cache-dir", default=None) + parser.add_argument("--local-files-only", action="store_true") + parser.add_argument("--max-length", type=int, required=True) + parser.add_argument("--allow-truncation", action="store_true") + parser.add_argument("--seed", type=int, required=True) + return parser.parse_args(argv) + + +def _parse_rerank_args(argv: Sequence[str]) -> argparse.Namespace: + parser = argparse.ArgumentParser(description=argparse.SUPPRESS) + parser.add_argument("--base-artifact", required=True, type=Path) + parser.add_argument("--context-artifact", required=True, type=Path) + parser.add_argument("--output-json", required=True, type=Path) + parser.add_argument("--lmstudio-model", default=None) + parser.add_argument("--lmstudio-base-url", default=None) + parser.add_argument("--lmstudio-api-key", default=None) + parser.add_argument("--lmstudio-max-tokens", type=int, required=True) + parser.add_argument("--timeout", type=float, required=True) + parser.add_argument("--skip-threshold", type=float, required=True) + parser.add_argument("--cache-dir", default=None) + parser.add_argument("--local-files-only", action="store_true") + parser.add_argument("--device", required=True) + parser.add_argument("--max-length", type=int, required=True) + parser.add_argument("--allow-truncation", action="store_true") + return parser.parse_args(argv) + + +def _training_config_from_extract_args( + args: argparse.Namespace, +) -> ConfidenceTrainingConfig: + return ConfidenceTrainingConfig( + task_type=cast(TaskType, args.task), + dataset_path=args.dataset, + output_dir=args.output_dir, + export_path=args.output_dir / "unused.joblib", + encoder_name=args.encoder_name, + encoder_revision=args.encoder_revision, + tokenizer_name=args.tokenizer_name, + tokenizer_revision=args.tokenizer_revision, + cache_dir=args.cache_dir, + local_files_only=args.local_files_only, + max_length=args.max_length, + allow_truncation=args.allow_truncation, + seed=args.seed, + ) + + +def _resolve_run_dir(output_dir: Path | None) -> Path: + if output_dir is not None: + return output_dir + timestamp = datetime.now().strftime("%Y%m%d-%H%M%S") + return Path(tempfile.gettempdir()) / f"ranksmith-lmstudio-smoke-{timestamp}" + + +def _prepare_run_dir(run_dir: Path, *, overwrite: bool) -> None: + if run_dir.exists() and any(run_dir.iterdir()) and not overwrite: + raise SystemExit(f"output dir already exists: {run_dir}. Use --overwrite.") + run_dir.mkdir(parents=True, exist_ok=True) + + +def _training_config( + *, + task_type: str, + dataset_path: Path, + output_dir: Path, + export_path: Path, + args: argparse.Namespace, +) -> ConfidenceTrainingConfig: + return ConfidenceTrainingConfig( + task_type=cast(TaskType, task_type), + dataset_path=dataset_path, + output_dir=output_dir, + export_path=export_path, + encoder_name=args.encoder_name, + encoder_revision=args.encoder_revision, + tokenizer_name=args.tokenizer_name, + tokenizer_revision=args.tokenizer_revision, + cache_dir=args.cache_dir, + local_files_only=args.local_files_only, + max_length=args.max_length, + allow_truncation=args.allow_truncation, + train_ratio=0.8, + valid_ratio=0.1, + test_ratio=0.1, + ) + + +def _dataset_report(path: Path, task_type: str) -> Mapping[str, Any]: + return build_dataset_report(path, cast(TaskType, task_type)) + + +def _generation_summary(result: ConfidenceGenerationResult) -> Mapping[str, Any]: + return { + "output_path": str(result.output_path), + "input_count": result.input_count, + "generated_count": result.generated_count, + "skipped_count": result.skipped_count, + "positive_count": result.positive_count, + "negative_count": result.negative_count, + } + + +def _live_query_rows() -> list[Mapping[str, Any]]: + return [ + { + "id": "live-q-1", + "query": "What is the capital of France?", + "gold_answer": "Paris", + "source": "lmstudio-smoke", + "group_id": "live-q", + }, + { + "id": "live-q-2", + "query": ( + "What exact color was the mayor's umbrella " + "in the unpublished town memo?" + ), + "gold_answer": "__NO_ANSWER__", + "source": "lmstudio-smoke", + "group_id": "live-q", + }, + ] + + +def _live_context_rows() -> list[Mapping[str, Any]]: + return [ + { + "id": "live-qc-1", + "query": "Who played Karen in Married to the Mob?", + "context": "Nancy Travis played Karen in the film Married to the Mob.", + "gold_answer": "Nancy Travis", + "source": "lmstudio-smoke", + "group_id": "live-qc", + }, + { + "id": "live-qc-2", + "query": "Who played Karen in Married to the Mob?", + "context": "Michelle Pfeiffer starred in Married to the Mob as Angela.", + "gold_answer": "__NO_ANSWER__", + "source": "lmstudio-smoke", + "group_id": "live-qc", + }, + ] + + +def _training_query_rows() -> list[Mapping[str, Any]]: + rows: list[Mapping[str, Any]] = [] + for index in range(15): + rows.append( + { + "id": f"train-q-pos-{index}", + "task_type": TASK_QUERY, + "query": f"What is synthetic known fact {index}?", + "answer": f"Known answer {index}", + "gold_answer": f"Known answer {index}", + "label": 1, + "source": "synthetic-smoke", + } + ) + rows.append( + { + "id": f"train-q-neg-{index}", + "task_type": TASK_QUERY, + "query": f"What is synthetic unknown fact {index}?", + "answer": "__NO_ANSWER__", + "gold_answer": "__NO_ANSWER__", + "label": 0, + "source": "synthetic-smoke", + } + ) + return rows + + +def _training_context_rows() -> list[Mapping[str, Any]]: + rows: list[Mapping[str, Any]] = [] + for index in range(15): + rows.append( + { + "id": f"train-qc-pos-{index}", + "task_type": TASK_QUERY_CONTEXT, + "query": f"Who is the synthetic actor {index}?", + "context": f"Context says Synthetic Actor {index} played the role.", + "answer": f"Synthetic Actor {index}", + "gold_answer": f"Synthetic Actor {index}", + "label": 1, + "source": "synthetic-smoke", + } + ) + rows.append( + { + "id": f"train-qc-neg-{index}", + "task_type": TASK_QUERY_CONTEXT, + "query": f"Who is the synthetic missing actor {index}?", + "context": "This context does not contain the requested actor.", + "answer": "__NO_ANSWER__", + "gold_answer": "__NO_ANSWER__", + "label": 0, + "source": "synthetic-smoke", + } + ) + return rows + + +def _write_jsonl(path: Path, rows: Sequence[Mapping[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text( + "".join( + json.dumps(row, ensure_ascii=False, sort_keys=True) + "\n" for row in rows + ), + encoding="utf-8", + ) + + +def _write_feature_rows(path: Path, rows: Sequence[ConfidenceFeatureRow]) -> None: + path.write_text( + "".join(json.dumps(_feature_row_json(row)) + "\n" for row in rows), + encoding="utf-8", + ) + + +def _read_feature_rows(path: Path) -> list[ConfidenceFeatureRow]: + rows: list[ConfidenceFeatureRow] = [] + for line in path.read_text(encoding="utf-8").splitlines(): + if not line.strip(): + continue + rows.append(ConfidenceFeatureRow(**json.loads(line))) + return rows + + +class _LoggingAnswerGenerator: + def __init__(self, inner: ProviderAnswerGenerator, *, enabled: bool) -> None: + self._inner = inner + self._enabled = enabled + + def answer_query(self, query: str) -> str: + if self._enabled: + print("[cbdr] answer_query start", flush=True) + answer = self._inner.answer_query(query) + if self._enabled: + print("[cbdr] answer_query done", flush=True) + return answer + + def answer_with_context(self, query: str, context: str) -> str: + if self._enabled: + print( + f"[cbdr] answer_with_context start chars={len(context)}", + flush=True, + ) + answer = self._inner.answer_with_context(query, context) + if self._enabled: + print("[cbdr] answer_with_context done", flush=True) + return answer + + +class _LoggingEstimator: + def __init__(self, inner: Any, *, label: str) -> None: + self._inner = inner + self._label = label + + @property + def task_type(self) -> str: + return str(self._inner.task_type) + + def score(self, item: object) -> StructuralConfidenceResult: + print(f"[cbdr] {self._label}_estimator score start", flush=True) + result = cast(StructuralConfidenceResult, self._inner.score(item)) + print( + f"[cbdr] {self._label}_estimator score done score={result.score}", + flush=True, + ) + return result + + +def _run_optional_rerank_smoke( + *, + include_rerank: bool, + run_dir: Path, + base_artifact: Path, + context_artifact: Path, + args: argparse.Namespace, +) -> list[Mapping[str, Any]]: + if not include_rerank: + return [] + output_json = run_dir / "reports" / "cbdr_rerank_smoke.json" + command = [ + sys.executable, + str(Path(__file__).resolve()), + "__rerank_smoke", + "--base-artifact", + str(base_artifact), + "--context-artifact", + str(context_artifact), + "--output-json", + str(output_json), + "--lmstudio-max-tokens", + str(args.lmstudio_max_tokens), + "--timeout", + str(args.timeout), + "--skip-threshold", + str(args.skip_threshold), + "--device", + args.device, + "--max-length", + str(args.max_length), + ] + if args.lmstudio_model is not None: + command.extend(["--lmstudio-model", args.lmstudio_model]) + if args.lmstudio_base_url is not None: + command.extend(["--lmstudio-base-url", args.lmstudio_base_url]) + if args.lmstudio_api_key is not None: + command.extend(["--lmstudio-api-key", args.lmstudio_api_key]) + if args.cache_dir is not None: + command.extend(["--cache-dir", args.cache_dir]) + if args.local_files_only: + command.append("--local-files-only") + if args.allow_truncation: + command.append("--allow-truncation") + subprocess.run(command, cwd=ROOT, check=True) + data = json.loads(output_json.read_text(encoding="utf-8")) + return list(data["results"]) + + +def _execute_rerank_smoke(strategy: CBDRStrategy) -> list[Mapping[str, Any]]: + print("[cbdr] rerank smoke start", flush=True) + results = strategy.rerank( + query="Who played Karen in Married to the Mob?", + documents=[ + Document( + id="nancy-travis", + text="Nancy Travis played Karen in the film Married to the Mob.", + ), + Document( + id="michelle-pfeiffer", + text="Michelle Pfeiffer starred in Married to the Mob as Angela.", + ), + ], + top_k=2, + ) + print("[cbdr] rerank smoke done", flush=True) + return [ + { + "rank": result.rank, + "document_id": result.document.id, + "original_index": result.original_index, + "metadata": result.metadata, + } + for result in results + ] + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/train_confidence_scorer.py b/scripts/train_confidence_scorer.py new file mode 100644 index 0000000..de52466 --- /dev/null +++ b/scripts/train_confidence_scorer.py @@ -0,0 +1,85 @@ +from __future__ import annotations + +import argparse +import json +import sys +from collections.abc import Sequence +from pathlib import Path +from typing import cast + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "src")) + +from ranksmith.confidence import TaskType # noqa: E402 +from ranksmith.confidence_training import ( # noqa: E402 + ConfidenceTrainingConfig, + ConfidenceTrainingResult, + train_confidence_scorer, +) + +TASK_QUERY = "query_answerability_confidence" +TASK_QUERY_CONTEXT = "query_context_answerability_confidence" +SUPPORTED_TASKS = (TASK_QUERY, TASK_QUERY_CONTEXT) + + +def main(argv: Sequence[str] | None = None) -> int: + args = _parse_args(argv) + result = train_confidence_scorer( + ConfidenceTrainingConfig( + task_type=cast(TaskType, args.task), + dataset_path=args.dataset, + output_dir=args.output_dir, + export_path=args.export_path, + encoder_name=args.encoder_name, + encoder_revision=args.encoder_revision, + tokenizer_name=args.tokenizer_name, + tokenizer_revision=args.tokenizer_revision, + cache_dir=args.cache_dir, + local_files_only=args.local_files_only, + max_length=args.max_length, + allow_truncation=args.allow_truncation, + seed=args.seed, + train_ratio=args.train_ratio, + valid_ratio=args.valid_ratio, + test_ratio=args.test_ratio, + calibration_method=args.calibration_method, + ) + ) + + print(json.dumps(_training_summary(result), ensure_ascii=False, sort_keys=True)) + return 0 + + +def _parse_args(argv: Sequence[str] | None) -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Train a confidence scorer artifact.") + parser.add_argument("--task", required=True, choices=SUPPORTED_TASKS) + parser.add_argument("--dataset", required=True, type=Path) + parser.add_argument("--output-dir", required=True, type=Path) + parser.add_argument("--export-path", required=True, type=Path) + parser.add_argument("--encoder-name", default="bert-base-uncased") + parser.add_argument("--encoder-revision", default=None) + parser.add_argument("--tokenizer-name", default=None) + parser.add_argument("--tokenizer-revision", default=None) + parser.add_argument("--cache-dir", default=None) + parser.add_argument("--local-files-only", action="store_true") + parser.add_argument("--max-length", type=int, default=256) + parser.add_argument("--allow-truncation", action="store_true") + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--train-ratio", type=float, default=0.8) + parser.add_argument("--valid-ratio", type=float, default=0.1) + parser.add_argument("--test-ratio", type=float, default=0.1) + parser.add_argument("--calibration-method", choices=("sigmoid",), default="sigmoid") + return parser.parse_args(argv) + + +def _training_summary(result: ConfidenceTrainingResult) -> dict[str, str]: + return { + "output_dir": str(result.output_dir), + "export_path": str(result.export_path), + "report_path": str(result.report_path), + "metadata_path": str(result.metadata_path), + } + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/ranksmith/azure.py b/src/ranksmith/azure.py index 7d7aa12..41d412e 100644 --- a/src/ranksmith/azure.py +++ b/src/ranksmith/azure.py @@ -28,6 +28,8 @@ AsyncPairwiseStrategy, AsyncSetwiseStrategy, AsyncTourRankStrategy, + CBDRStrategy, + ConfidenceGainStrategy, ListwiseStrategy, PairwiseStrategy, SetwiseStrategy, @@ -162,6 +164,8 @@ async def rerank( def _is_builtin_sync_strategy(strategy: object) -> bool: return type(strategy) in { AcuRankStrategy, + CBDRStrategy, + ConfidenceGainStrategy, ListwiseStrategy, PairwiseStrategy, SetwiseStrategy, diff --git a/src/ranksmith/confidence/__init__.py b/src/ranksmith/confidence/__init__.py index 488b61f..88343e8 100644 --- a/src/ranksmith/confidence/__init__.py +++ b/src/ranksmith/confidence/__init__.py @@ -9,6 +9,8 @@ from ranksmith.confidence.types import ( AnswerConfidenceInput, JudgmentConfidenceInput, + QueryAnswerabilityConfidenceInput, + QueryContextAnswerabilityConfidenceInput, ScoreOutput, ScorerMetadata, StructuralConfidenceInput, @@ -24,6 +26,8 @@ "ConfidenceError", "ConfidenceInputError", "JudgmentConfidenceInput", + "QueryAnswerabilityConfidenceInput", + "QueryContextAnswerabilityConfidenceInput", "ScorerMetadata", "ScoreOutput", "StructuralConfidenceEstimator", diff --git a/src/ranksmith/confidence/scorer.py b/src/ranksmith/confidence/scorer.py index 555409a..8346e08 100644 --- a/src/ranksmith/confidence/scorer.py +++ b/src/ranksmith/confidence/scorer.py @@ -293,7 +293,12 @@ def _validate_metadata_values(metadata: ScorerMetadata) -> None: raise ConfidenceArtifactError("unsupported local_stride") if metadata.input_template_version != INPUT_TEMPLATE_VERSION: raise ConfidenceArtifactError("unsupported input_template_version") - if metadata.task_type not in {"answer_confidence", "judgment_confidence"}: + if metadata.task_type not in { + "answer_confidence", + "judgment_confidence", + "query_answerability_confidence", + "query_context_answerability_confidence", + }: raise ConfidenceArtifactError("invalid task_type") if not metadata.scorer_type.strip(): raise ConfidenceArtifactError("scorer_type must be non-empty") @@ -333,6 +338,10 @@ def _task_type(value: object) -> TaskType: return "answer_confidence" if value == "judgment_confidence": return "judgment_confidence" + if value == "query_answerability_confidence": + return "query_answerability_confidence" + if value == "query_context_answerability_confidence": + return "query_context_answerability_confidence" raise ConfidenceArtifactError("invalid task_type") diff --git a/src/ranksmith/confidence/templates.py b/src/ranksmith/confidence/templates.py index 019de36..681d5d3 100644 --- a/src/ranksmith/confidence/templates.py +++ b/src/ranksmith/confidence/templates.py @@ -4,6 +4,8 @@ from ranksmith.confidence.types import ( AnswerConfidenceInput, JudgmentConfidenceInput, + QueryAnswerabilityConfidenceInput, + QueryContextAnswerabilityConfidenceInput, StructuralConfidenceInput, TaskType, ) @@ -40,4 +42,25 @@ def format_confidence_input( judgment = _require_non_empty(item.judgment, field_name="judgment") return f"Query:\n{query}\n\nDocument:\n{document}\n\nJudgment:\n{judgment}" + if task_type == "query_answerability_confidence": + if not isinstance(item, QueryAnswerabilityConfidenceInput): + raise ConfidenceInputError( + "query_answerability_confidence requires " + "QueryAnswerabilityConfidenceInput" + ) + query = _require_non_empty(item.query, field_name="query") + answer = _require_non_empty(item.answer, field_name="answer") + return f"Query:\n{query}\n\nAnswer:\n{answer}" + + if task_type == "query_context_answerability_confidence": + if not isinstance(item, QueryContextAnswerabilityConfidenceInput): + raise ConfidenceInputError( + "query_context_answerability_confidence requires " + "QueryContextAnswerabilityConfidenceInput" + ) + query = _require_non_empty(item.query, field_name="query") + context = _require_non_empty(item.context, field_name="context") + answer = _require_non_empty(item.answer, field_name="answer") + return f"Query:\n{query}\n\nContext:\n{context}\n\nAnswer:\n{answer}" + raise ConfidenceInputError(f"unsupported task_type: {task_type!r}") diff --git a/src/ranksmith/confidence/types.py b/src/ranksmith/confidence/types.py index c9db4a1..a95a5f2 100644 --- a/src/ranksmith/confidence/types.py +++ b/src/ranksmith/confidence/types.py @@ -5,7 +5,12 @@ from types import MappingProxyType from typing import Any, Literal, Protocol, TypeAlias -TaskType: TypeAlias = Literal["answer_confidence", "judgment_confidence"] +TaskType: TypeAlias = Literal[ + "answer_confidence", + "judgment_confidence", + "query_answerability_confidence", + "query_context_answerability_confidence", +] ScoreOutput: TypeAlias = Literal["probability"] @@ -22,7 +27,25 @@ class JudgmentConfidenceInput: judgment: str -StructuralConfidenceInput: TypeAlias = AnswerConfidenceInput | JudgmentConfidenceInput +@dataclass(frozen=True) +class QueryAnswerabilityConfidenceInput: + query: str + answer: str + + +@dataclass(frozen=True) +class QueryContextAnswerabilityConfidenceInput: + query: str + context: str + answer: str + + +StructuralConfidenceInput: TypeAlias = ( + AnswerConfidenceInput + | JudgmentConfidenceInput + | QueryAnswerabilityConfidenceInput + | QueryContextAnswerabilityConfidenceInput +) @dataclass(frozen=True) diff --git a/src/ranksmith/confidence_generation/__init__.py b/src/ranksmith/confidence_generation/__init__.py index d046e08..3c95e4c 100644 --- a/src/ranksmith/confidence_generation/__init__.py +++ b/src/ranksmith/confidence_generation/__init__.py @@ -6,10 +6,14 @@ from ranksmith.confidence_generation.pipeline import ( generate_answer_confidence_dataset, generate_judgment_confidence_dataset, + generate_query_answerability_confidence_dataset, + generate_query_context_answerability_confidence_dataset, ) from ranksmith.confidence_generation.types import ( AnswerGenerationConfig, ConfidenceGenerationResult, + QueryAnswerabilityGenerationConfig, + QueryContextAnswerabilityGenerationConfig, RelevanceGenerationConfig, ) @@ -19,7 +23,11 @@ "ConfidenceGenerationInputError", "ConfidenceGenerationParseError", "ConfidenceGenerationResult", + "QueryAnswerabilityGenerationConfig", + "QueryContextAnswerabilityGenerationConfig", "RelevanceGenerationConfig", "generate_answer_confidence_dataset", "generate_judgment_confidence_dataset", + "generate_query_answerability_confidence_dataset", + "generate_query_context_answerability_confidence_dataset", ] diff --git a/src/ranksmith/confidence_generation/io.py b/src/ranksmith/confidence_generation/io.py index fc7307d..2794c53 100644 --- a/src/ranksmith/confidence_generation/io.py +++ b/src/ranksmith/confidence_generation/io.py @@ -10,13 +10,34 @@ from ranksmith.confidence_generation.errors import ConfidenceGenerationInputError from ranksmith.confidence_generation.types import ( AnswerGenerationSample, + QueryAnswerabilityGenerationSample, + QueryContextAnswerabilityGenerationSample, RelevanceGenerationSample, ) -TaskType = Literal["answer_confidence", "judgment_confidence"] +TaskType = Literal[ + "answer_confidence", + "judgment_confidence", + "query_answerability_confidence", + "query_context_answerability_confidence", +] _ANSWER_REQUIRED = ("id", "query", "context", "gold_answer") _ANSWER_ALLOWED = {*_ANSWER_REQUIRED, "source", "group_id", "metadata"} +_QUERY_ANSWERABILITY_REQUIRED = ("id", "query", "gold_answer") +_QUERY_ANSWERABILITY_ALLOWED = { + *_QUERY_ANSWERABILITY_REQUIRED, + "source", + "group_id", + "metadata", +} +_QUERY_CONTEXT_ANSWERABILITY_REQUIRED = ("id", "query", "context", "gold_answer") +_QUERY_CONTEXT_ANSWERABILITY_ALLOWED = { + *_QUERY_CONTEXT_ANSWERABILITY_REQUIRED, + "source", + "group_id", + "metadata", +} _RELEVANCE_REQUIRED = ("id", "query", "document", "relevance_label") _RELEVANCE_ALLOWED = {*_RELEVANCE_REQUIRED, "source", "group_id", "metadata"} _ANSWER_OUTPUT_REQUIRED = ("id", "context", "answer", "label") @@ -27,6 +48,29 @@ "group_id", "metadata", } +_QUERY_ANSWERABILITY_OUTPUT_REQUIRED = ("id", "task_type", "query", "answer", "label") +_QUERY_ANSWERABILITY_OUTPUT_ALLOWED = { + *_QUERY_ANSWERABILITY_OUTPUT_REQUIRED, + "gold_answer", + "source", + "group_id", + "metadata", +} +_QUERY_CONTEXT_ANSWERABILITY_OUTPUT_REQUIRED = ( + "id", + "task_type", + "query", + "context", + "answer", + "label", +) +_QUERY_CONTEXT_ANSWERABILITY_OUTPUT_ALLOWED = { + *_QUERY_CONTEXT_ANSWERABILITY_OUTPUT_REQUIRED, + "gold_answer", + "source", + "group_id", + "metadata", +} _JUDGMENT_OUTPUT_REQUIRED = ( "id", "query", @@ -70,6 +114,63 @@ def load_answer_generation_samples( return samples +def load_query_answerability_generation_samples( + path: str | Path, +) -> list[QueryAnswerabilityGenerationSample]: + samples: list[QueryAnswerabilityGenerationSample] = [] + seen: set[str] = set() + for line_number, row in _read_jsonl_objects(Path(path)): + try: + _validate_keys( + row, + required=_QUERY_ANSWERABILITY_REQUIRED, + allowed=_QUERY_ANSWERABILITY_ALLOWED, + ) + sample = QueryAnswerabilityGenerationSample( + id=_required_text(row, "id"), + query=_required_text(row, "query"), + gold_answer=_gold_answer(row["gold_answer"]), + source=_optional_text(row.get("source"), "source"), + group_id=_optional_text(row.get("group_id"), "group_id"), + metadata=_metadata(row.get("metadata")), + ) + except ConfidenceGenerationInputError as exc: + raise ConfidenceGenerationInputError(f"line {line_number}: {exc}") from exc + _check_duplicate(sample.id, seen) + samples.append(sample) + return samples + + +def load_query_context_answerability_generation_samples( + path: str | Path, + *, + max_context_chars: int, +) -> list[QueryContextAnswerabilityGenerationSample]: + samples: list[QueryContextAnswerabilityGenerationSample] = [] + seen: set[str] = set() + for line_number, row in _read_jsonl_objects(Path(path)): + try: + _validate_keys( + row, + required=_QUERY_CONTEXT_ANSWERABILITY_REQUIRED, + allowed=_QUERY_CONTEXT_ANSWERABILITY_ALLOWED, + ) + sample = QueryContextAnswerabilityGenerationSample( + id=_required_text(row, "id"), + query=_required_text(row, "query"), + context=_bounded_text(row, "context", max_context_chars), + gold_answer=_gold_answer(row["gold_answer"]), + source=_optional_text(row.get("source"), "source"), + group_id=_optional_text(row.get("group_id"), "group_id"), + metadata=_metadata(row.get("metadata")), + ) + except ConfidenceGenerationInputError as exc: + raise ConfidenceGenerationInputError(f"line {line_number}: {exc}") from exc + _check_duplicate(sample.id, seen) + samples.append(sample) + return samples + + def load_relevance_generation_samples( path: str | Path, *, @@ -123,7 +224,7 @@ def open_output_path( def write_jsonl_row(handle: IO[str], row: Mapping[str, Any]) -> None: - handle.write(json.dumps(dict(row), ensure_ascii=False, allow_nan=False) + "\n") + handle.write(json.dumps(dict(row), ensure_ascii=True, allow_nan=False) + "\n") handle.flush() @@ -294,7 +395,7 @@ def _validate_output_task(row: Mapping[str, Any], task_type: TaskType) -> str: _label(row["label"]) if "gold_answer" in row: _gold_answer(row["gold_answer"]) - else: + elif task_type == "judgment_confidence": _validate_keys( row, required=_JUDGMENT_OUTPUT_REQUIRED, @@ -306,6 +407,33 @@ def _validate_output_task(row: Mapping[str, Any], task_type: TaskType) -> str: _judgment(row["judgment"]) _label(row["label"]) _relevance_label(row["relevance_label"]) + elif task_type == "query_answerability_confidence": + _validate_keys( + row, + required=_QUERY_ANSWERABILITY_OUTPUT_REQUIRED, + allowed=_QUERY_ANSWERABILITY_OUTPUT_ALLOWED, + ) + _validate_task_type_field(row, task_type) + row_id = _required_text(row, "id") + _required_text(row, "query") + _required_text(row, "answer") + _label(row["label"]) + if "gold_answer" in row: + _gold_answer(row["gold_answer"]) + else: + _validate_keys( + row, + required=_QUERY_CONTEXT_ANSWERABILITY_OUTPUT_REQUIRED, + allowed=_QUERY_CONTEXT_ANSWERABILITY_OUTPUT_ALLOWED, + ) + _validate_task_type_field(row, task_type) + row_id = _required_text(row, "id") + _required_text(row, "query") + _required_text(row, "context") + _required_text(row, "answer") + _label(row["label"]) + if "gold_answer" in row: + _gold_answer(row["gold_answer"]) if "source" in row: _required_text(row, "source") @@ -316,6 +444,14 @@ def _validate_output_task(row: Mapping[str, Any], task_type: TaskType) -> str: return row_id +def _validate_task_type_field(row: Mapping[str, Any], task_type: TaskType) -> None: + value = row["task_type"] + if value != task_type: + raise ConfidenceGenerationInputError( + f"task_type must be {task_type!r} for requested task" + ) + + def _label(value: object) -> int: if type(value) is not int or value not in {0, 1}: raise ConfidenceGenerationInputError("label must be integer 0 or 1") diff --git a/src/ranksmith/confidence_generation/pipeline.py b/src/ranksmith/confidence_generation/pipeline.py index b7687d6..3948300 100644 --- a/src/ranksmith/confidence_generation/pipeline.py +++ b/src/ranksmith/confidence_generation/pipeline.py @@ -7,6 +7,8 @@ from ranksmith.confidence_generation.io import ( load_answer_generation_samples, load_completed_ids, + load_query_answerability_generation_samples, + load_query_context_answerability_generation_samples, load_relevance_generation_samples, open_output_path, write_jsonl_row, @@ -22,14 +24,22 @@ ) from ranksmith.confidence_generation.prompts import ( ANSWER_SYSTEM_PROMPT, + QUERY_ANSWERABILITY_SYSTEM_PROMPT, + QUERY_CONTEXT_ANSWERABILITY_SYSTEM_PROMPT, RELEVANCE_SYSTEM_PROMPT, build_answer_prompt, + build_query_answerability_prompt, + build_query_context_answerability_prompt, build_relevance_prompt, ) from ranksmith.confidence_generation.types import ( AnswerGenerationConfig, AnswerGenerationSample, ConfidenceGenerationResult, + QueryAnswerabilityGenerationConfig, + QueryAnswerabilityGenerationSample, + QueryContextAnswerabilityGenerationConfig, + QueryContextAnswerabilityGenerationSample, RelevanceGenerationConfig, RelevanceGenerationSample, UsageCallback, @@ -93,6 +103,64 @@ def generate_judgment_confidence_dataset( ) +def generate_query_answerability_confidence_dataset( + config: QueryAnswerabilityGenerationConfig, +) -> ConfidenceGenerationResult: + samples = load_query_answerability_generation_samples(config.input_path) + output_path = Path(config.output_path) + completed_ids = ( + load_completed_ids(output_path, task_type="query_answerability_confidence") + if config.resume + else set() + ) + + return _write_generation_dataset( + samples=samples, + output_path=output_path, + completed_ids=completed_ids, + overwrite=config.overwrite, + resume=config.resume, + max_items=config.max_items, + get_id=lambda sample: sample.id, + build_row=lambda sample: _build_query_answerability_generated_row( + sample, + config, + ), + ) + + +def generate_query_context_answerability_confidence_dataset( + config: QueryContextAnswerabilityGenerationConfig, +) -> ConfidenceGenerationResult: + samples = load_query_context_answerability_generation_samples( + config.input_path, + max_context_chars=config.max_context_chars, + ) + output_path = Path(config.output_path) + completed_ids = ( + load_completed_ids( + output_path, + task_type="query_context_answerability_confidence", + ) + if config.resume + else set() + ) + + return _write_generation_dataset( + samples=samples, + output_path=output_path, + completed_ids=completed_ids, + overwrite=config.overwrite, + resume=config.resume, + max_items=config.max_items, + get_id=lambda sample: sample.id, + build_row=lambda sample: _build_query_context_answerability_generated_row( + sample, + config, + ), + ) + + def _call_provider( provider: ModelProvider, *, @@ -307,3 +375,136 @@ def _judgment_canonical_row( if sample.group_id is not None: row["group_id"] = sample.group_id return row + + +def _build_query_answerability_generated_row( + sample: QueryAnswerabilityGenerationSample, + config: QueryAnswerabilityGenerationConfig, +) -> Mapping[str, Any]: + raw_output = _call_provider( + config.provider, + system=QUERY_ANSWERABILITY_SYSTEM_PROMPT, + user=build_query_answerability_prompt( + sample, + no_answer_value=config.no_answer_value, + ), + on_usage=config.on_usage, + ) + answer = parse_answer_output(raw_output) + label = int( + normalized_exact_match( + answer, + sample.gold_answer, + no_answer_value=config.no_answer_value, + ) + ) + return _query_answerability_canonical_row( + sample, + answer=answer, + label=label, + raw_output=raw_output, + config=config, + ) + + +def _query_answerability_canonical_row( + sample: QueryAnswerabilityGenerationSample, + *, + answer: str, + label: int, + raw_output: str, + config: QueryAnswerabilityGenerationConfig, +) -> dict[str, Any]: + generation: dict[str, Any] = { + "generation_task": "query_answerability", + "match_policy": "normalized_exact", + "no_answer_value": config.no_answer_value, + } + if config.include_raw_model_output: + generation["raw_model_output"] = raw_output + + row: dict[str, Any] = { + "id": sample.id, + "task_type": "query_answerability_confidence", + "query": sample.query, + "answer": answer, + "gold_answer": sample.gold_answer, + "label": label, + "metadata": { + "input_metadata": dict(sample.metadata), + "generation": generation, + }, + } + source = sample.source if sample.source is not None else config.source + if source is not None: + row["source"] = source + if sample.group_id is not None: + row["group_id"] = sample.group_id + return row + + +def _build_query_context_answerability_generated_row( + sample: QueryContextAnswerabilityGenerationSample, + config: QueryContextAnswerabilityGenerationConfig, +) -> Mapping[str, Any]: + raw_output = _call_provider( + config.provider, + system=QUERY_CONTEXT_ANSWERABILITY_SYSTEM_PROMPT, + user=build_query_context_answerability_prompt( + sample, + no_answer_value=config.no_answer_value, + ), + on_usage=config.on_usage, + ) + answer = parse_answer_output(raw_output) + label = int( + normalized_exact_match( + answer, + sample.gold_answer, + no_answer_value=config.no_answer_value, + ) + ) + return _query_context_answerability_canonical_row( + sample, + answer=answer, + label=label, + raw_output=raw_output, + config=config, + ) + + +def _query_context_answerability_canonical_row( + sample: QueryContextAnswerabilityGenerationSample, + *, + answer: str, + label: int, + raw_output: str, + config: QueryContextAnswerabilityGenerationConfig, +) -> dict[str, Any]: + generation: dict[str, Any] = { + "generation_task": "query_context_answerability", + "match_policy": "normalized_exact", + "no_answer_value": config.no_answer_value, + } + if config.include_raw_model_output: + generation["raw_model_output"] = raw_output + + row: dict[str, Any] = { + "id": sample.id, + "task_type": "query_context_answerability_confidence", + "query": sample.query, + "context": sample.context, + "answer": answer, + "gold_answer": sample.gold_answer, + "label": label, + "metadata": { + "input_metadata": dict(sample.metadata), + "generation": generation, + }, + } + source = sample.source if sample.source is not None else config.source + if source is not None: + row["source"] = source + if sample.group_id is not None: + row["group_id"] = sample.group_id + return row diff --git a/src/ranksmith/confidence_generation/prompts.py b/src/ranksmith/confidence_generation/prompts.py index ac5f496..80b67e7 100644 --- a/src/ranksmith/confidence_generation/prompts.py +++ b/src/ranksmith/confidence_generation/prompts.py @@ -4,6 +4,8 @@ from ranksmith.confidence_generation.types import ( AnswerGenerationSample, + QueryAnswerabilityGenerationSample, + QueryContextAnswerabilityGenerationSample, RelevanceGenerationSample, ) @@ -17,6 +19,18 @@ 'Return only JSON with a "judgment" value of "relevant" or "not_relevant".' ) +QUERY_ANSWERABILITY_SYSTEM_PROMPT = ( + "You are a strict JSON answer API. " + "Answer questions using your parametric knowledge. " + "Never reason or explain. Return exactly one JSON object and stop." +) + +QUERY_CONTEXT_ANSWERABILITY_SYSTEM_PROMPT = ( + "You are a strict JSON answer API. " + "Answer questions using only the provided context. " + "Never reason or explain. Return exactly one JSON object and stop." +) + def build_answer_prompt( sample: AnswerGenerationSample, @@ -49,3 +63,48 @@ def build_relevance_prompt(sample: RelevanceGenerationSample) -> str: "answering the query.\n" 'Use "not_relevant" otherwise.' ) + + +def build_query_answerability_prompt( + sample: QueryAnswerabilityGenerationSample, + *, + no_answer_value: str, +) -> str: + no_answer_contract = json.dumps( + {"answer": no_answer_value}, + ensure_ascii=False, + separators=(",", ":"), + ) + return ( + f"Question:\n{sample.query}\n\n" + "Return JSON only. Valid examples:\n" + '{"answer":"short answer"}\n' + f"{no_answer_contract}\n\n" + "The answer string must be a short answer only, not an explanation. " + "Do not output any other text. " + "Answer from your parametric knowledge. If you are not immediately " + f"certain, return {no_answer_contract}." + ) + + +def build_query_context_answerability_prompt( + sample: QueryContextAnswerabilityGenerationSample, + *, + no_answer_value: str, +) -> str: + no_answer_contract = json.dumps( + {"answer": no_answer_value}, + ensure_ascii=False, + separators=(",", ":"), + ) + return ( + f"Question:\n{sample.query}\n\n" + f"Context:\n{sample.context}\n\n" + "Return JSON only. Valid examples:\n" + '{"answer":"short answer"}\n' + f"{no_answer_contract}\n\n" + "The answer string must be a short answer only, not an explanation. " + "Do not output any other text. " + "Use only the context. If the context does not directly contain the answer, " + f"return {no_answer_contract}." + ) diff --git a/src/ranksmith/confidence_generation/types.py b/src/ranksmith/confidence_generation/types.py index dcc8e06..7af4f89 100644 --- a/src/ranksmith/confidence_generation/types.py +++ b/src/ranksmith/confidence_generation/types.py @@ -47,6 +47,56 @@ def __post_init__(self) -> None: ) +@dataclass(frozen=True) +class QueryAnswerabilityGenerationConfig: + input_path: str | Path + output_path: str | Path + provider: ModelProvider + overwrite: bool = False + resume: bool = False + max_items: int | None = None + include_raw_model_output: bool = True + on_usage: UsageCallback | None = None + source: str | None = None + no_answer_value: str = "__NO_ANSWER__" + + def __post_init__(self) -> None: + _validate_common_config( + provider=self.provider, + overwrite=self.overwrite, + resume=self.resume, + max_items=self.max_items, + source=self.source, + ) + _validate_no_answer_value(self.no_answer_value) + + +@dataclass(frozen=True) +class QueryContextAnswerabilityGenerationConfig: + input_path: str | Path + output_path: str | Path + provider: ModelProvider + overwrite: bool = False + resume: bool = False + max_items: int | None = None + max_context_chars: int = 4000 + include_raw_model_output: bool = True + on_usage: UsageCallback | None = None + source: str | None = None + no_answer_value: str = "__NO_ANSWER__" + + def __post_init__(self) -> None: + _validate_common_config( + provider=self.provider, + overwrite=self.overwrite, + resume=self.resume, + max_items=self.max_items, + source=self.source, + ) + _validate_positive_int("max_context_chars", self.max_context_chars) + _validate_no_answer_value(self.no_answer_value) + + @dataclass(frozen=True) class RelevanceGenerationConfig: input_path: str | Path @@ -112,6 +162,33 @@ def __post_init__(self) -> None: object.__setattr__(self, "metadata", MappingProxyType(dict(self.metadata))) +@dataclass(frozen=True) +class QueryAnswerabilityGenerationSample: + id: str + query: str + gold_answer: str | list[str] + source: str | None = None + group_id: str | None = None + metadata: Mapping[str, Any] = field(default_factory=dict) + + def __post_init__(self) -> None: + object.__setattr__(self, "metadata", MappingProxyType(dict(self.metadata))) + + +@dataclass(frozen=True) +class QueryContextAnswerabilityGenerationSample: + id: str + query: str + context: str + gold_answer: str | list[str] + source: str | None = None + group_id: str | None = None + metadata: Mapping[str, Any] = field(default_factory=dict) + + def __post_init__(self) -> None: + object.__setattr__(self, "metadata", MappingProxyType(dict(self.metadata))) + + @dataclass(frozen=True) class RelevanceGenerationSample: id: str @@ -149,3 +226,10 @@ def _validate_positive_int(name: str, value: object) -> None: raise ConfidenceGenerationInputError(f"{name} must be an int") if value < 1: raise ConfidenceGenerationInputError(f"{name} must be >= 1") + + +def _validate_no_answer_value(value: object) -> None: + if not isinstance(value, str) or not value.strip(): + raise ConfidenceGenerationInputError( + "no_answer_value must be a non-empty string" + ) diff --git a/src/ranksmith/confidence_training/dataset.py b/src/ranksmith/confidence_training/dataset.py index 1844c80..fcffed4 100644 --- a/src/ranksmith/confidence_training/dataset.py +++ b/src/ranksmith/confidence_training/dataset.py @@ -15,6 +15,8 @@ _ANSWER_REQUIRED = ("id", "context", "answer", "label") _JUDGMENT_REQUIRED = ("id", "query", "document", "judgment", "label") +_QUERY_ANSWERABILITY_REQUIRED = ("id", "query", "answer", "label") +_QUERY_CONTEXT_ANSWERABILITY_REQUIRED = ("id", "query", "context", "answer", "label") _ANSWER_ALLOWED = {*_ANSWER_REQUIRED, "gold_answer", "source", "group_id", "metadata"} _JUDGMENT_ALLOWED = { *_JUDGMENT_REQUIRED, @@ -23,6 +25,21 @@ "group_id", "metadata", } +_ANSWERABILITY_OPTIONAL_ALLOWED = { + "task_type", + "gold_answer", + "source", + "group_id", + "metadata", +} +_QUERY_ANSWERABILITY_ALLOWED = { + *_QUERY_ANSWERABILITY_REQUIRED, + *_ANSWERABILITY_OPTIONAL_ALLOWED, +} +_QUERY_CONTEXT_ANSWERABILITY_ALLOWED = { + *_QUERY_CONTEXT_ANSWERABILITY_REQUIRED, + *_ANSWERABILITY_OPTIONAL_ALLOWED, +} @dataclass(frozen=True) @@ -64,6 +81,37 @@ class _TaskDatasetSchema: metadata=_metadata(row.get("metadata")), ), ), + "query_answerability_confidence": _TaskDatasetSchema( + required=_QUERY_ANSWERABILITY_REQUIRED, + allowed=_QUERY_ANSWERABILITY_ALLOWED, + parser=lambda row: CanonicalConfidenceSample( + id=_required_text(row, "id"), + task_type="query_answerability_confidence", + label=_label(row["label"]), + query=_required_text(row, "query"), + answer=_required_text(row, "answer"), + gold_answer=_optional_gold_answer(row.get("gold_answer")), + source=_optional_text(row.get("source"), "source"), + group_id=_optional_text(row.get("group_id"), "group_id"), + metadata=_metadata(row.get("metadata")), + ), + ), + "query_context_answerability_confidence": _TaskDatasetSchema( + required=_QUERY_CONTEXT_ANSWERABILITY_REQUIRED, + allowed=_QUERY_CONTEXT_ANSWERABILITY_ALLOWED, + parser=lambda row: CanonicalConfidenceSample( + id=_required_text(row, "id"), + task_type="query_context_answerability_confidence", + label=_label(row["label"]), + query=_required_text(row, "query"), + context=_required_text(row, "context"), + answer=_required_text(row, "answer"), + gold_answer=_optional_gold_answer(row.get("gold_answer")), + source=_optional_text(row.get("source"), "source"), + group_id=_optional_text(row.get("group_id"), "group_id"), + metadata=_metadata(row.get("metadata")), + ), + ), } @@ -120,6 +168,7 @@ def _parse_row( raise ConfidenceDatasetError(f"unsupported task_type: {task_type}") try: _validate_keys(row, required=schema.required, allowed=schema.allowed) + _validate_row_task_type(row, task_type=task_type) return schema.parser(row) except (ConfidenceDatasetError, ConfidenceLabelError) as exc: raise type(exc)(f"line {line_number}: {exc}") from exc @@ -144,6 +193,14 @@ def _validate_keys( raise ConfidenceDatasetError(f"unexpected field for task: {unexpected[0]}") +def _validate_row_task_type(row: Mapping[str, Any], *, task_type: TaskType) -> None: + value = row.get("task_type") + if value is None: + return + if value != task_type: + raise ConfidenceDatasetError("task_type field must match requested task_type") + + def _required_text(row: Mapping[str, Any], name: str) -> str: value = row[name] if not isinstance(value, str): diff --git a/src/ranksmith/confidence_training/dataset_report.py b/src/ranksmith/confidence_training/dataset_report.py new file mode 100644 index 0000000..8285f6d --- /dev/null +++ b/src/ranksmith/confidence_training/dataset_report.py @@ -0,0 +1,72 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Any + +from ranksmith.confidence import TaskType +from ranksmith.confidence_training.dataset import load_canonical_dataset +from ranksmith.confidence_training.types import CanonicalConfidenceSample + +MISSING_SOURCE_KEY = "__MISSING__" + + +def build_dataset_report(path: str | Path, task_type: TaskType) -> dict[str, Any]: + samples = load_canonical_dataset(path, task_type=task_type) + positive_count = sum(sample.label for sample in samples) + negative_count = len(samples) - positive_count + present_sources = {sample.source for sample in samples if sample.source is not None} + present_groups = { + sample.group_id for sample in samples if sample.group_id is not None + } + + sample_count = len(samples) + missing_source_count = sum(1 for sample in samples if sample.source is None) + missing_group_id_count = sum(1 for sample in samples if sample.group_id is None) + + return { + "task_type": task_type, + "sample_count": sample_count, + "positive_count": positive_count, + "negative_count": negative_count, + "positive_rate": _rate(positive_count, sample_count), + "source_count": len(present_sources), + "group_count": len(present_groups), + "missing_source_count": missing_source_count, + "missing_source_rate": _rate(missing_source_count, sample_count), + "missing_group_id_count": missing_group_id_count, + "missing_group_id_rate": _rate(missing_group_id_count, sample_count), + "sources": _source_reports(samples), + } + + +def _source_reports( + samples: list[CanonicalConfidenceSample], +) -> dict[str, dict[str, int | float]]: + buckets: dict[str, list[CanonicalConfidenceSample]] = {} + for sample in samples: + source = sample.source if sample.source is not None else MISSING_SOURCE_KEY + buckets.setdefault(source, []).append(sample) + + return { + source: _source_report(source_samples) + for source, source_samples in sorted(buckets.items()) + } + + +def _source_report( + samples: list[CanonicalConfidenceSample], +) -> dict[str, int | float]: + positive_count = sum(sample.label for sample in samples) + sample_count = len(samples) + return { + "sample_count": sample_count, + "positive_count": positive_count, + "negative_count": sample_count - positive_count, + "positive_rate": _rate(positive_count, sample_count), + } + + +def _rate(numerator: int, denominator: int) -> float: + if denominator == 0: + return 0.0 + return numerator / denominator diff --git a/src/ranksmith/confidence_training/features.py b/src/ranksmith/confidence_training/features.py index ebb6b4f..e1de6e0 100644 --- a/src/ranksmith/confidence_training/features.py +++ b/src/ranksmith/confidence_training/features.py @@ -2,7 +2,12 @@ from typing import Protocol -from ranksmith.confidence import AnswerConfidenceInput, JudgmentConfidenceInput +from ranksmith.confidence import ( + AnswerConfidenceInput, + JudgmentConfidenceInput, + QueryAnswerabilityConfidenceInput, + QueryContextAnswerabilityConfidenceInput, +) from ranksmith.confidence.errors import ConfidenceError from ranksmith.confidence.features import ( FEATURE_DIM, @@ -82,4 +87,23 @@ def _sample_input(sample: CanonicalConfidenceSample) -> StructuralConfidenceInpu document=sample.document, judgment=sample.judgment, ) + if sample.task_type == "query_answerability_confidence": + if sample.query is None or sample.answer is None: + raise ConfidenceTrainingError( + "query answerability sample is missing text fields" + ) + return QueryAnswerabilityConfidenceInput( + query=sample.query, + answer=sample.answer, + ) + if sample.task_type == "query_context_answerability_confidence": + if sample.query is None or sample.context is None or sample.answer is None: + raise ConfidenceTrainingError( + "query context answerability sample is missing text fields" + ) + return QueryContextAnswerabilityConfidenceInput( + query=sample.query, + context=sample.context, + answer=sample.answer, + ) raise ConfidenceTrainingError(f"unsupported task_type: {sample.task_type}") diff --git a/src/ranksmith/confidence_training/types.py b/src/ranksmith/confidence_training/types.py index 18496fd..e8f7f14 100644 --- a/src/ranksmith/confidence_training/types.py +++ b/src/ranksmith/confidence_training/types.py @@ -34,7 +34,12 @@ class ConfidenceTrainingConfig: calibration_method: CalibrationMethod = "sigmoid" def __post_init__(self) -> None: - if self.task_type not in {"answer_confidence", "judgment_confidence"}: + if self.task_type not in { + "answer_confidence", + "judgment_confidence", + "query_answerability_confidence", + "query_context_answerability_confidence", + }: raise ConfidenceTrainingConfigError("unsupported task_type") if self.calibration_method != "sigmoid": raise ConfidenceTrainingConfigError("unsupported calibration_method") diff --git a/src/ranksmith/integrations/__init__.py b/src/ranksmith/integrations/__init__.py new file mode 100644 index 0000000..ec3e2bb --- /dev/null +++ b/src/ranksmith/integrations/__init__.py @@ -0,0 +1,9 @@ +from ranksmith.integrations.answer_generator import ProviderAnswerGenerator +from ranksmith.integrations.azure_answer_generator import AzureAnswerGenerator +from ranksmith.integrations.lmstudio_provider import LMStudioModelProvider + +__all__ = [ + "AzureAnswerGenerator", + "LMStudioModelProvider", + "ProviderAnswerGenerator", +] diff --git a/src/ranksmith/integrations/answer_generator.py b/src/ranksmith/integrations/answer_generator.py new file mode 100644 index 0000000..7252394 --- /dev/null +++ b/src/ranksmith/integrations/answer_generator.py @@ -0,0 +1,101 @@ +from __future__ import annotations + +import json +from dataclasses import dataclass + +from ranksmith.errors import RerankParseError, RerankProviderError +from ranksmith.integrations.validation import validate_no_answer_value +from ranksmith.model import ModelMessage, ModelProvider, ModelRequest + + +@dataclass(frozen=True) +class ProviderAnswerGenerator: + provider: ModelProvider + no_answer_value: str = "__NO_ANSWER__" + + def __post_init__(self) -> None: + validate_no_answer_value(self.no_answer_value) + + def answer_query(self, query: str) -> str: + return self._complete( + system=( + "You are a strict JSON answer API for confidence estimation. " + "Never reason or explain. Return exactly one JSON object and stop." + ), + user=( + f"Question:\n{query}\n\n" + "Return JSON only. Valid examples:\n" + '{"answer":"short answer"}\n' + f"{_answer_contract(self.no_answer_value)}\n\n" + "The answer string must be a short answer only, not an " + "explanation. Do not output any other text. " + "Answer from your parametric knowledge. If you are not " + "immediately certain, return " + f"{_answer_contract(self.no_answer_value)}." + ), + ) + + def answer_with_context(self, query: str, context: str) -> str: + return self._complete( + system=( + "You are a strict JSON answer API for confidence estimation. " + "Use only the provided context. Never reason or explain. " + "Return exactly one JSON object and stop." + ), + user=( + f"Question:\n{query}\n\n" + f"Context:\n{context}\n\n" + "Return JSON only. Valid examples:\n" + '{"answer":"short answer"}\n' + f"{_answer_contract(self.no_answer_value)}\n\n" + "The answer string must be a short answer only, not an " + "explanation. Do not output any other text. " + "Use only the context. If the context does not directly contain " + "the answer, " + f"return {_answer_contract(self.no_answer_value)}." + ), + ) + + def _complete(self, *, system: str, user: str) -> str: + try: + response = self.provider.complete( + ModelRequest( + messages=[ + ModelMessage(role="system", content=system), + ModelMessage(role="user", content=user), + ], + response_format="json_object", + temperature=0, + ) + ) + except RerankProviderError: + raise + except Exception as exc: + message = str(exc) + if message: + raise RerankProviderError( + f"Answer generation provider failed: {message}" + ) from exc + raise RerankProviderError("Answer generation provider failed.") from exc + return _parse_answer(response.content) + + +def _parse_answer(content: str) -> str: + try: + parsed = json.loads(content) + except json.JSONDecodeError as exc: + raise RerankParseError("answer response must be valid JSON") from exc + if not isinstance(parsed, dict): + raise RerankParseError("answer response must be a JSON object") + answer = parsed.get("answer") + if not isinstance(answer, str) or answer.strip() == "": + raise RerankParseError('answer response must contain a non-empty "answer"') + return answer + + +def _answer_contract(no_answer_value: str) -> str: + return json.dumps( + {"answer": no_answer_value}, + ensure_ascii=False, + separators=(",", ":"), + ) diff --git a/src/ranksmith/integrations/azure_answer_generator.py b/src/ranksmith/integrations/azure_answer_generator.py new file mode 100644 index 0000000..54aef56 --- /dev/null +++ b/src/ranksmith/integrations/azure_answer_generator.py @@ -0,0 +1,113 @@ +from __future__ import annotations + +import os +from dataclasses import dataclass, field + +from ranksmith.errors import RerankInputError +from ranksmith.integrations.answer_generator import ProviderAnswerGenerator +from ranksmith.integrations.validation import validate_no_answer_value +from ranksmith.model import ModelProvider +from ranksmith.providers import AzureAOAIProvider + + +@dataclass(frozen=True) +class AzureAnswerGenerator: + provider: ModelProvider = field(init=False) + no_answer_value: str = field(init=False, default="__NO_ANSWER__") + _generator: ProviderAnswerGenerator = field( + init=False, + repr=False, + compare=False, + ) + + def __init__( + self, + *, + api_key: str | None = None, + azure_endpoint: str | None = None, + azure_deployment: str | None = None, + api_version: str = "2024-08-01-preview", + timeout: float | None = None, + provider: ModelProvider | None = None, + no_answer_value: str = "__NO_ANSWER__", + ) -> None: + validate_no_answer_value(no_answer_value) + if provider is not None: + resolved_provider = provider + else: + if api_key is None: + raise RerankInputError("AZURE_OPENAI_API_KEY is required") + if azure_endpoint is None: + raise RerankInputError("AZURE_OPENAI_ENDPOINT is required") + if azure_deployment is None: + raise RerankInputError( + "AZURE_OPENAI_LLM_DEPLOYMENT or AZURE_OPENAI_DEPLOYMENT is required" + ) + resolved_provider = AzureAOAIProvider( + api_key=api_key, + azure_endpoint=azure_endpoint, + azure_deployment=azure_deployment, + api_version=api_version, + timeout=timeout, + ) + resolved_generator = ProviderAnswerGenerator( + provider=resolved_provider, + no_answer_value=no_answer_value, + ) + object.__setattr__(self, "provider", resolved_provider) + object.__setattr__(self, "no_answer_value", no_answer_value) + object.__setattr__(self, "_generator", resolved_generator) + + @classmethod + def from_env( + cls, + *, + timeout: float | None = None, + no_answer_value: str = "__NO_ANSWER__", + ) -> AzureAnswerGenerator: + return cls( + api_key=_required_env("AZURE_OPENAI_API_KEY"), + azure_endpoint=_required_env("AZURE_OPENAI_ENDPOINT"), + azure_deployment=_required_env( + "AZURE_OPENAI_LLM_DEPLOYMENT", + fallback="AZURE_OPENAI_DEPLOYMENT", + ), + api_version=_env_value( + "AZURE_OPENAI_LLM_API_VERSION", + fallback="AZURE_OPENAI_API_VERSION", + default="2024-08-01-preview", + ) + or "2024-08-01-preview", + timeout=timeout, + no_answer_value=no_answer_value, + ) + + def answer_query(self, query: str) -> str: + return self._generator.answer_query(query) + + def answer_with_context(self, query: str, context: str) -> str: + return self._generator.answer_with_context(query, context) + + +def _required_env(name: str, *, fallback: str | None = None) -> str: + value = _env_value(name, fallback=fallback) + if value is None or value == "": + names = name if fallback is None else f"{name} or {fallback}" + raise RerankInputError(f"{names} is required") + return value + + +def _env_value( + name: str, + *, + fallback: str | None = None, + default: str | None = None, +) -> str | None: + value = os.environ.get(name) + if value is not None and value != "": + return value + if fallback is not None: + fallback_value = os.environ.get(fallback) + if fallback_value is not None and fallback_value != "": + return fallback_value + return default diff --git a/src/ranksmith/integrations/lmstudio_provider.py b/src/ranksmith/integrations/lmstudio_provider.py new file mode 100644 index 0000000..84b82a2 --- /dev/null +++ b/src/ranksmith/integrations/lmstudio_provider.py @@ -0,0 +1,183 @@ +from __future__ import annotations + +import os +from typing import Any, Protocol, cast + +from openai import OpenAI + +from ranksmith.errors import RerankInputError, RerankProviderError +from ranksmith.model import ModelRequest, ModelResponse +from ranksmith.types import RerankUsage + + +class _CompletionsClient(Protocol): + def create( + self, + *, + model: str, + messages: list[dict[str, str]], + response_format: dict[str, object], + temperature: float, + max_tokens: int, + reasoning_effort: str, + ) -> object: ... + + +class _ChatClient(Protocol): + completions: _CompletionsClient + + +class _OpenAICompatibleClient(Protocol): + chat: _ChatClient + + +class LMStudioModelProvider: + def __init__( + self, + *, + model: str | None = None, + base_url: str | None = None, + api_key: str | None = None, + timeout: float | None = None, + max_tokens: int = 128, + client: _OpenAICompatibleClient | None = None, + ) -> None: + resolved_model = ( + model if model is not None else os.environ.get("LMSTUDIO_MODEL") + ) + if resolved_model is None or resolved_model.strip() == "": + raise RerankInputError("LMSTUDIO_MODEL is required") + if max_tokens <= 0: + raise RerankInputError("max_tokens must be greater than 0") + + resolved_base_url = _resolve_optional_setting( + explicit=base_url, + env_name="LMSTUDIO_BASE_URL", + default="http://localhost:1234/v1", + setting_name="base_url", + ) + resolved_api_key = _resolve_optional_setting( + explicit=api_key, + env_name="LMSTUDIO_API_KEY", + default="lm-studio", + setting_name="api_key", + ) + + self._model = resolved_model + self._base_url = resolved_base_url + self._api_key_configured = resolved_api_key != "" + self._max_tokens = max_tokens + self._client: _OpenAICompatibleClient = ( + client + if client is not None + else cast( + _OpenAICompatibleClient, + OpenAI( + api_key=resolved_api_key, + base_url=resolved_base_url, + timeout=timeout, + ), + ) + ) + + @property + def model(self) -> str: + return self._model + + @property + def base_url(self) -> str: + return self._base_url + + @property + def api_key_configured(self) -> bool: + return self._api_key_configured + + @property + def max_tokens(self) -> int: + return self._max_tokens + + def complete(self, request: ModelRequest) -> ModelResponse: + response_format = _to_lmstudio_response_format(request) + try: + response = self._client.chat.completions.create( + model=self._model, + messages=_to_openai_messages(request), + response_format=response_format, + temperature=request.temperature, + max_tokens=self._max_tokens, + reasoning_effort="none", + ) + except Exception as exc: + message = str(exc) + if message: + raise RerankProviderError( + f"LM Studio request failed: {message}" + ) from exc + raise RerankProviderError("LM Studio request failed.") from exc + + content = _extract_content(response) + if content is None or content == "": + raise RerankProviderError("LM Studio returned an empty response.") + return ModelResponse(content=content, usage=_extract_usage(response)) + + +def _to_lmstudio_response_format(request: ModelRequest) -> dict[str, Any]: + if request.response_format != "json_object": + raise RerankProviderError("LM Studio received unsupported response_format.") + return { + "type": "json_schema", + "json_schema": { + "name": "ranksmith_json_response", + "schema": {"type": "object"}, + }, + } + + +def _resolve_optional_setting( + *, + explicit: str | None, + env_name: str, + default: str, + setting_name: str, +) -> str: + if explicit is not None: + if explicit.strip() == "": + raise RerankInputError(f"{setting_name} must not be blank") + return explicit + value = os.environ.get(env_name) + if value is not None: + if value.strip() == "": + raise RerankInputError(f"{env_name} must not be blank") + return value + return default + + +def _extract_usage(response: object) -> RerankUsage | None: + usage = getattr(response, "usage", None) + if usage is None: + return None + return RerankUsage( + prompt_tokens=int(getattr(usage, "prompt_tokens", 0) or 0), + completion_tokens=int(getattr(usage, "completion_tokens", 0) or 0), + total_tokens=int(getattr(usage, "total_tokens", 0) or 0), + ) + + +def _extract_content(response: object) -> str | None: + choices = getattr(response, "choices", None) + if not choices: + raise RerankProviderError("LM Studio returned an invalid response.") + try: + content = choices[0].message.content + except AttributeError as exc: + raise RerankProviderError("LM Studio returned an invalid response.") from exc + if content is not None and not isinstance(content, str): + raise RerankProviderError("LM Studio returned an invalid response.") + return content + + +def _to_openai_messages(request: ModelRequest) -> list[dict[str, str]]: + return [ + {"role": message.role, "content": message.content} + for message in request.messages + ] diff --git a/src/ranksmith/integrations/validation.py b/src/ranksmith/integrations/validation.py new file mode 100644 index 0000000..25dfd85 --- /dev/null +++ b/src/ranksmith/integrations/validation.py @@ -0,0 +1,6 @@ +from __future__ import annotations + + +def validate_no_answer_value(value: object) -> None: + if not isinstance(value, str) or value.strip() == "": + raise ValueError("no_answer_value must be a non-empty string") diff --git a/src/ranksmith/model.py b/src/ranksmith/model.py index 969b9c6..c06f98f 100644 --- a/src/ranksmith/model.py +++ b/src/ranksmith/model.py @@ -32,6 +32,8 @@ class ModelMessage: @dataclass(frozen=True) class ModelRequest: messages: Sequence[ModelMessage] + response_format: Literal["json_object"] = "json_object" + temperature: float = 0 @dataclass(frozen=True) diff --git a/src/ranksmith/strategies/__init__.py b/src/ranksmith/strategies/__init__.py index 222d0d4..a708325 100644 --- a/src/ranksmith/strategies/__init__.py +++ b/src/ranksmith/strategies/__init__.py @@ -1,4 +1,11 @@ from ranksmith.strategies.acurank import AcuRankStrategy, AsyncAcuRankStrategy +from ranksmith.strategies.cbdr import CBDRStrategy +from ranksmith.strategies.confidence_gain import ( + AnswerGenerator, + ConfidenceEstimator, + ConfidenceGainResult, + ConfidenceGainStrategy, +) from ranksmith.strategies.listwise import AsyncListwiseStrategy, ListwiseStrategy from ranksmith.strategies.pairwise import AsyncPairwiseStrategy, PairwiseStrategy from ranksmith.strategies.setwise import AsyncSetwiseStrategy, SetwiseStrategy @@ -10,11 +17,16 @@ __all__ = [ "AcuRankStrategy", + "AnswerGenerator", "AsyncAcuRankStrategy", "AsyncListwiseStrategy", "AsyncPairwiseStrategy", "AsyncSetwiseStrategy", "AsyncTourRankStrategy", + "CBDRStrategy", + "ConfidenceEstimator", + "ConfidenceGainResult", + "ConfidenceGainStrategy", "ListwiseStrategy", "PairwiseStrategy", "SetwiseStrategy", diff --git a/src/ranksmith/strategies/cbdr.py b/src/ranksmith/strategies/cbdr.py new file mode 100644 index 0000000..b06cd93 --- /dev/null +++ b/src/ranksmith/strategies/cbdr.py @@ -0,0 +1,222 @@ +from __future__ import annotations + +import math +from collections.abc import Sequence +from dataclasses import dataclass +from numbers import Real +from pathlib import Path +from typing import Literal + +from ranksmith.confidence import StructuralConfidenceEstimator +from ranksmith.errors import RerankInputError +from ranksmith.types import Document, RerankResult + +from .common import validate_documents_max_chars, validate_top_k +from .confidence_gain import ( + AnswerGenerator, + ConfidenceEstimator, + _call_answer_query, + _call_answer_with_context, + _confidence_gain, + _score_base_answerability, + _score_context_answerability, + _validate_confidence_score, + _validate_estimator_tasks, +) + +CBDRAlgorithm = Literal["cbdr"] + + +@dataclass(frozen=True) +class CBDRStrategy: + base_estimator: ConfidenceEstimator + context_estimator: ConfidenceEstimator + answer_generator: AnswerGenerator + skip_threshold: float = 0.8 + max_document_chars: int = 4000 + algorithm: CBDRAlgorithm = "cbdr" + + @classmethod + def from_artifacts( + cls, + *, + base_artifact_path: str | Path, + context_artifact_path: str | Path, + base_metadata_path: str | Path | None = None, + context_metadata_path: str | Path | None = None, + answer_generator: AnswerGenerator, + skip_threshold: float = 0.8, + max_document_chars: int = 4000, + hf_token: str | None = None, + cache_dir: str | None = None, + device: str = "cpu", + local_files_only: bool = False, + max_length: int | None = None, + allow_truncation: bool = False, + ) -> CBDRStrategy: + return cls( + base_estimator=StructuralConfidenceEstimator.from_artifact( + base_artifact_path, + metadata_path=base_metadata_path, + task_type="query_answerability_confidence", + hf_token=hf_token, + cache_dir=cache_dir, + device=device, + local_files_only=local_files_only, + max_length=max_length, + allow_truncation=allow_truncation, + ), + context_estimator=StructuralConfidenceEstimator.from_artifact( + context_artifact_path, + metadata_path=context_metadata_path, + task_type="query_context_answerability_confidence", + hf_token=hf_token, + cache_dir=cache_dir, + device=device, + local_files_only=local_files_only, + max_length=max_length, + allow_truncation=allow_truncation, + ), + answer_generator=answer_generator, + skip_threshold=skip_threshold, + max_document_chars=max_document_chars, + ) + + def __post_init__(self) -> None: + if self.algorithm != "cbdr": + raise ValueError('algorithm must be "cbdr"') + if self.max_document_chars < 1: + raise ValueError("max_document_chars must be greater than 0") + _validate_probability_config(self.skip_threshold, "skip_threshold") + _validate_estimator_tasks( + base_estimator=self.base_estimator, + context_estimator=self.context_estimator, + ) + + def rerank( + self, + *, + query: str, + documents: Sequence[Document], + model_client: object | None = None, + top_k: int | None = None, + ) -> list[RerankResult]: + del model_client + validate_top_k(top_k) + if query.strip() == "": + raise RerankInputError("query must not be empty") + if not documents or top_k == 0: + return [] + + base_answer = _call_answer_query(self.answer_generator, query) + base_result = _score_base_answerability( + estimator=self.base_estimator, + query=query, + answer=base_answer, + ) + base_score = _validate_confidence_score(base_result.score, "base") + + if base_score >= self.skip_threshold: + return _original_order_results( + documents=documents, + top_k=top_k, + algorithm=self.algorithm, + base_score=base_score, + skip_threshold=self.skip_threshold, + ) + + validate_documents_max_chars( + documents, + max_document_chars=self.max_document_chars, + ) + scored = [] + for original_index, document in enumerate(documents): + context_answer = _call_answer_with_context( + self.answer_generator, + query, + document.text, + ) + context_result = _score_context_answerability( + estimator=self.context_estimator, + query=query, + context=document.text, + answer=context_answer, + ) + context_score = _validate_confidence_score( + context_result.score, + "context", + ) + scored.append( + ( + original_index, + context_score, + _confidence_gain( + base_score=base_score, + context_score=context_score, + ), + ) + ) + + scored.sort(key=lambda item: (-item[2], item[0])) + if top_k is not None: + scored = scored[:top_k] + + return [ + RerankResult( + document=documents[original_index], + rank=rank, + original_index=original_index, + metadata={ + "strategy": "cbdr", + "algorithm": self.algorithm, + "cbdr_skipped": False, + "base_confidence": base_score, + "skip_threshold": self.skip_threshold, + "context_confidence": context_score, + "confidence_gain": gain, + }, + ) + for rank, (original_index, context_score, gain) in enumerate( + scored, + start=1, + ) + ] + + +def _validate_probability_config(value: object, name: str) -> float: + if isinstance(value, bool) or not isinstance(value, Real): + raise ValueError(f"{name} must be a finite probability in [0, 1]") + probability = float(value) + if not math.isfinite(probability) or probability < 0.0 or probability > 1.0: + raise ValueError(f"{name} must be a finite probability in [0, 1]") + return probability + + +def _original_order_results( + *, + documents: Sequence[Document], + top_k: int | None, + algorithm: CBDRAlgorithm, + base_score: float, + skip_threshold: float, +) -> list[RerankResult]: + indexed = list(enumerate(documents)) + if top_k is not None: + indexed = indexed[:top_k] + return [ + RerankResult( + document=document, + rank=rank, + original_index=original_index, + metadata={ + "strategy": "cbdr", + "algorithm": algorithm, + "cbdr_skipped": True, + "base_confidence": base_score, + "skip_threshold": skip_threshold, + "context_confidence": None, + "confidence_gain": None, + }, + ) + for rank, (original_index, document) in enumerate(indexed, start=1) + ] diff --git a/src/ranksmith/strategies/confidence_gain.py b/src/ranksmith/strategies/confidence_gain.py new file mode 100644 index 0000000..f989bd1 --- /dev/null +++ b/src/ranksmith/strategies/confidence_gain.py @@ -0,0 +1,245 @@ +from __future__ import annotations + +import math +from collections.abc import Sequence +from dataclasses import dataclass +from numbers import Real +from typing import Literal, Protocol + +from ranksmith.confidence import ( + QueryAnswerabilityConfidenceInput, + QueryContextAnswerabilityConfidenceInput, + StructuralConfidenceResult, +) +from ranksmith.errors import ( + RerankInputError, + RerankProviderError, + RerankStrategyError, +) +from ranksmith.types import Document, RerankResult + +from .common import validate_documents_max_chars, validate_top_k + +ConfidenceGainAlgorithm = Literal["confidence_gain"] +QUERY_ANSWERABILITY_TASK = "query_answerability_confidence" +QUERY_CONTEXT_ANSWERABILITY_TASK = "query_context_answerability_confidence" +AnswerabilityConfidenceInput = ( + QueryAnswerabilityConfidenceInput | QueryContextAnswerabilityConfidenceInput +) + + +class AnswerGenerator(Protocol): + def answer_query(self, query: str) -> str: ... + + def answer_with_context(self, query: str, context: str) -> str: ... + + +class ConfidenceEstimator(Protocol): + @property + def task_type(self) -> str: ... + + def score( + self, + item: AnswerabilityConfidenceInput, + ) -> StructuralConfidenceResult: ... + + +@dataclass(frozen=True) +class ConfidenceGainResult: + base_score: float + context_score: float + gain: float + base_result: StructuralConfidenceResult + context_result: StructuralConfidenceResult + + +@dataclass(frozen=True) +class ConfidenceGainStrategy: + base_estimator: ConfidenceEstimator + context_estimator: ConfidenceEstimator + answer_generator: AnswerGenerator + max_document_chars: int = 4000 + algorithm: ConfidenceGainAlgorithm = "confidence_gain" + + def __post_init__(self) -> None: + if self.algorithm != "confidence_gain": + raise ValueError('algorithm must be "confidence_gain"') + if self.max_document_chars < 1: + raise ValueError("max_document_chars must be greater than 0") + _validate_estimator_tasks( + base_estimator=self.base_estimator, + context_estimator=self.context_estimator, + ) + + def rerank( + self, + *, + query: str, + documents: Sequence[Document], + model_client: object, + top_k: int | None = None, + ) -> list[RerankResult]: + del model_client + validate_top_k(top_k) + if query.strip() == "": + raise RerankInputError("query must not be empty") + validate_documents_max_chars( + documents, + max_document_chars=self.max_document_chars, + ) + if not documents: + return [] + + base_answer = _call_answer_query(self.answer_generator, query) + base_result = _score_base_answerability( + estimator=self.base_estimator, + query=query, + answer=base_answer, + ) + base_score = _validate_confidence_score(base_result.score, "base") + + scored: list[tuple[int, ConfidenceGainResult]] = [] + for original_index, document in enumerate(documents): + context_answer = _call_answer_with_context( + self.answer_generator, + query, + document.text, + ) + context_result = _score_context_answerability( + estimator=self.context_estimator, + query=query, + context=document.text, + answer=context_answer, + ) + context_score = _validate_confidence_score( + context_result.score, + "context", + ) + gain = _confidence_gain( + base_score=base_score, + context_score=context_score, + ) + scored.append( + ( + original_index, + ConfidenceGainResult( + base_score=base_score, + context_score=context_score, + gain=gain, + base_result=base_result, + context_result=context_result, + ), + ) + ) + + scored.sort(key=lambda item: (-item[1].gain, item[0])) + if top_k is not None: + scored = scored[:top_k] + + return [ + RerankResult( + document=documents[original_index], + rank=rank, + original_index=original_index, + metadata={ + "strategy": "confidence_gain", + "algorithm": self.algorithm, + "base_confidence": result.base_score, + "context_confidence": result.context_score, + "confidence_gain": result.gain, + }, + ) + for rank, (original_index, result) in enumerate(scored, start=1) + ] + + +def _validate_estimator_tasks( + *, + base_estimator: ConfidenceEstimator, + context_estimator: ConfidenceEstimator, +) -> None: + if base_estimator.task_type != QUERY_ANSWERABILITY_TASK: + raise RerankInputError( + f"base_estimator task_type must be {QUERY_ANSWERABILITY_TASK!r}" + ) + if context_estimator.task_type != QUERY_CONTEXT_ANSWERABILITY_TASK: + raise RerankInputError( + f"context_estimator task_type must be {QUERY_CONTEXT_ANSWERABILITY_TASK!r}" + ) + + +def _call_answer_query(generator: AnswerGenerator, query: str) -> str: + try: + answer = generator.answer_query(query) + except RerankProviderError: + raise + except Exception as exc: + raise RerankProviderError(str(exc)) from exc + return _validate_answer(answer, "answer_query") + + +def _call_answer_with_context( + generator: AnswerGenerator, + query: str, + context: str, +) -> str: + try: + answer = generator.answer_with_context(query, context) + except RerankProviderError: + raise + except Exception as exc: + raise RerankProviderError(str(exc)) from exc + return _validate_answer(answer, "answer_with_context") + + +def _validate_answer(answer: object, method_name: str) -> str: + if not isinstance(answer, str) or answer.strip() == "": + raise RerankProviderError(f"{method_name} must return a non-empty string") + return answer + + +def _score_base_answerability( + *, + estimator: ConfidenceEstimator, + query: str, + answer: str, +) -> StructuralConfidenceResult: + return estimator.score( + QueryAnswerabilityConfidenceInput(query=query, answer=answer) + ) + + +def _score_context_answerability( + *, + estimator: ConfidenceEstimator, + query: str, + context: str, + answer: str, +) -> StructuralConfidenceResult: + return estimator.score( + QueryContextAnswerabilityConfidenceInput( + query=query, + context=context, + answer=answer, + ) + ) + + +def _validate_confidence_score(score: object, label: str) -> float: + if isinstance(score, bool) or not isinstance(score, Real): + raise RerankStrategyError(f"{label} confidence score must be numeric") + value = float(score) + if not math.isfinite(value) or value < 0.0 or value > 1.0: + raise RerankStrategyError(f"{label} confidence score must be finite in [0, 1]") + return value + + +def _confidence_gain( + *, + base_score: float, + context_score: float, +) -> float: + gain = context_score - base_score + if not math.isfinite(gain) or gain < -1.0 or gain > 1.0: + raise RerankStrategyError("confidence gain must be finite in [-1, 1]") + return gain diff --git a/tests/fixtures/confidence_query_answerability_raw.jsonl b/tests/fixtures/confidence_query_answerability_raw.jsonl new file mode 100644 index 0000000..bce08d1 --- /dev/null +++ b/tests/fixtures/confidence_query_answerability_raw.jsonl @@ -0,0 +1,2 @@ +{"id":"q1","query":"What is the capital of France?","gold_answer":"Paris","source":"fixture","group_id":"geo"} +{"id":"q2","query":"Who wrote an unknown private diary?","gold_answer":"__NO_ANSWER__","source":"fixture","group_id":"unknown"} diff --git a/tests/fixtures/confidence_query_context_answerability_raw.jsonl b/tests/fixtures/confidence_query_context_answerability_raw.jsonl new file mode 100644 index 0000000..061497d --- /dev/null +++ b/tests/fixtures/confidence_query_context_answerability_raw.jsonl @@ -0,0 +1,2 @@ +{"id":"qc1","query":"Who played Karen?","context":"Nancy Travis played Karen.","gold_answer":"Nancy Travis","source":"fixture","group_id":"tv"} +{"id":"qc2","query":"What color is the hidden key?","context":"The passage does not mention a key.","gold_answer":"__NO_ANSWER__","source":"fixture","group_id":"missing"} diff --git a/tests/test_azure_answer_generator.py b/tests/test_azure_answer_generator.py new file mode 100644 index 0000000..af94d7a --- /dev/null +++ b/tests/test_azure_answer_generator.py @@ -0,0 +1,129 @@ +from __future__ import annotations + +import importlib +from dataclasses import dataclass + +import pytest + +from ranksmith.errors import RerankInputError, RerankParseError, RerankProviderError +from ranksmith.model import ModelRequest, ModelResponse + + +@dataclass +class FakeProvider: + responses: list[str] + requests: list[ModelRequest] + + def complete(self, request: ModelRequest) -> ModelResponse: + self.requests.append(request) + return ModelResponse(content=self.responses.pop(0)) + + +def test_integrations_exports_are_submodule_only() -> None: + integrations = importlib.import_module("ranksmith.integrations") + root = importlib.import_module("ranksmith") + + assert integrations.AzureAnswerGenerator is not None + assert not hasattr(root, "AzureAnswerGenerator") + + +def test_azure_answer_generator_parses_query_answer_json() -> None: + from ranksmith.integrations import AzureAnswerGenerator + + provider = FakeProvider(responses=['{"answer": "Nancy Travis"}'], requests=[]) + generator = AzureAnswerGenerator(provider=provider) + + assert generator.answer_query("who played karen?") == "Nancy Travis" + assert provider.requests[0].response_format == "json_object" + assert provider.requests[0].temperature == 0 + assert provider.requests[0].messages[0].role == "system" + assert provider.requests[0].messages[1].role == "user" + assert "who played karen?" in provider.requests[0].messages[1].content + assert "__NO_ANSWER__" in provider.requests[0].messages[1].content + assert "best concise answer" not in provider.requests[0].messages[0].content + + +def test_azure_answer_generator_parses_context_answer_json() -> None: + from ranksmith.integrations import AzureAnswerGenerator + + provider = FakeProvider(responses=['{"answer": "Nancy Travis"}'], requests=[]) + generator = AzureAnswerGenerator(provider=provider) + + assert ( + generator.answer_with_context( + "who played karen?", + "Nancy Travis played Karen.", + ) + == "Nancy Travis" + ) + assert "Nancy Travis played Karen." in provider.requests[0].messages[1].content + assert "__NO_ANSWER__" in provider.requests[0].messages[1].content + + +def test_azure_answer_generator_uses_configured_no_answer_value() -> None: + from ranksmith.integrations import AzureAnswerGenerator + + provider = FakeProvider(responses=['{"answer": "UNKNOWN"}'], requests=[]) + generator = AzureAnswerGenerator(provider=provider, no_answer_value="UNKNOWN") + + assert generator.answer_query("query") == "UNKNOWN" + assert '{"answer":"UNKNOWN"}' in provider.requests[0].messages[1].content + + +def test_azure_answer_generator_rejects_empty_no_answer_value() -> None: + from ranksmith.integrations import AzureAnswerGenerator + + with pytest.raises(ValueError, match="no_answer_value"): + AzureAnswerGenerator( + provider=FakeProvider(responses=[], requests=[]), + no_answer_value=" ", + ) + + +@pytest.mark.parametrize( + "content", + [ + "not json", + "{}", + '{"answer": ""}', + '{"answer": " "}', + '{"answer": 123}', + ], +) +def test_azure_answer_generator_rejects_invalid_answer_json(content: str) -> None: + from ranksmith.integrations import AzureAnswerGenerator + + generator = AzureAnswerGenerator( + provider=FakeProvider(responses=[content], requests=[]), + ) + + with pytest.raises(RerankParseError): + generator.answer_query("query") + + +def test_azure_answer_generator_preserves_provider_error() -> None: + from ranksmith.integrations import AzureAnswerGenerator + + class FailingProvider: + def complete(self, request: ModelRequest) -> ModelResponse: + del request + raise RerankProviderError("provider failed") + + generator = AzureAnswerGenerator(provider=FailingProvider()) + + with pytest.raises(RerankProviderError, match="provider failed"): + generator.answer_query("query") + + +def test_azure_answer_generator_requires_azure_config_without_provider( + monkeypatch: pytest.MonkeyPatch, +) -> None: + from ranksmith.integrations import AzureAnswerGenerator + + monkeypatch.delenv("AZURE_OPENAI_API_KEY", raising=False) + monkeypatch.delenv("AZURE_OPENAI_ENDPOINT", raising=False) + monkeypatch.delenv("AZURE_OPENAI_LLM_DEPLOYMENT", raising=False) + monkeypatch.delenv("AZURE_OPENAI_DEPLOYMENT", raising=False) + + with pytest.raises(RerankInputError, match="AZURE_OPENAI_API_KEY"): + AzureAnswerGenerator.from_env() diff --git a/tests/test_cbdr_strategy.py b/tests/test_cbdr_strategy.py new file mode 100644 index 0000000..914a714 --- /dev/null +++ b/tests/test_cbdr_strategy.py @@ -0,0 +1,684 @@ +from __future__ import annotations + +import importlib +import math +import sys +from dataclasses import dataclass +from pathlib import Path +from types import ModuleType +from typing import Any, cast + +import pytest + +from ranksmith import AzureOpenAIReranker +from ranksmith.confidence import ( + StructuralConfidenceEstimator, + StructuralConfidenceResult, + TaskType, +) +from ranksmith.confidence.scorer import ARTIFACT_SCHEMA_VERSION +from ranksmith.errors import ( + DocumentTooLongError, + RerankInputError, + RerankProviderError, + RerankStrategyError, +) +from ranksmith.strategies import CBDRStrategy +from ranksmith.types import Document + + +@dataclass +class FakeEstimator: + task_type: TaskType + scores: list[Any] + calls: list[object] | None = None + + def score(self, item: object) -> StructuralConfidenceResult: + if self.calls is not None: + self.calls.append(item) + value = self.scores.pop(0) + if isinstance(value, BaseException): + raise value + return StructuralConfidenceResult( + score=value, + task_type=self.task_type, + feature_schema_version="structural-v1", + ) + + +class FakeGenerator: + def __init__( + self, + *, + base_answer: object = "base answer", + context_answers: list[object] | None = None, + ) -> None: + self.base_answer = base_answer + self.context_answers = context_answers or [] + self.query_calls: list[str] = [] + self.context_calls: list[tuple[str, str]] = [] + + def answer_query(self, query: str) -> Any: + self.query_calls.append(query) + if isinstance(self.base_answer, BaseException): + raise self.base_answer + return self.base_answer + + def answer_with_context(self, query: str, context: str) -> Any: + self.context_calls.append((query, context)) + answer = self.context_answers.pop(0) + if isinstance(answer, BaseException): + raise answer + return answer + + +class ArtifactScorer: + def __init__(self, scores: list[float]) -> None: + self.scores = scores + + def predict_confidence(self, features: object) -> float: + del features + return self.scores.pop(0) + + +class ArtifactEncoder: + encoder_name = "bert-base-uncased" + encoder_revision = None + tokenizer_name = "bert-base-uncased" + tokenizer_revision = None + + def __init__(self, *, max_length: int) -> None: + self.max_length = max_length + + def encode(self, text: str) -> tuple[list[list[float]], list[int]]: + seed = float(len(text) % 7 + 1) + hidden = [[seed + row * 0.01, row * 0.02, seed * 0.03] for row in range(40)] + return hidden, [1] * len(hidden) + + +def _artifact_metadata(task_type: TaskType) -> dict[str, object]: + return { + "artifact_schema_version": ARTIFACT_SCHEMA_VERSION, + "scorer_type": "joblib-wrapper", + "task_type": task_type, + "encoder_name": "bert-base-uncased", + "encoder_revision": None, + "tokenizer_name": "bert-base-uncased", + "tokenizer_revision": None, + "input_template_version": "structural-template-v1", + "feature_schema_version": "structural-v1", + "feature_dim": 70, + "feature_dtype": "float64", + "max_length": 64, + "granularity": "two_scale", + "local_window_size": 5, + "local_stride": 2, + "score_output": "probability", + "positive_class_index": 1, + } + + +def _install_artifact_joblib( + monkeypatch: pytest.MonkeyPatch, + artifacts: dict[Path, object], +) -> None: + module = ModuleType("joblib") + + def load(path: str | Path) -> object: + return artifacts[Path(path)] + + module.load = load # type: ignore[attr-defined] + monkeypatch.setitem(sys.modules, "joblib", module) + + +def _artifact_strategy( + *, + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + base_scores: list[float], + context_scores: list[float], + generator: FakeGenerator, + skip_threshold: float, +) -> CBDRStrategy: + base_artifact_path = tmp_path / "query_answerability.joblib" + context_artifact_path = tmp_path / "query_context_answerability.joblib" + _install_artifact_joblib( + monkeypatch, + { + base_artifact_path: { + "metadata": _artifact_metadata("query_answerability_confidence"), + "scorer": ArtifactScorer(base_scores), + }, + context_artifact_path: { + "metadata": _artifact_metadata( + "query_context_answerability_confidence" + ), + "scorer": ArtifactScorer(context_scores), + }, + }, + ) + + def fake_from_pretrained(**kwargs: object) -> ArtifactEncoder: + return ArtifactEncoder(max_length=cast(int, kwargs["max_length"])) + + monkeypatch.setattr( + "ranksmith.confidence.structural.FrozenAutoEncoder.from_pretrained", + fake_from_pretrained, + ) + + return CBDRStrategy( + base_estimator=StructuralConfidenceEstimator.from_artifact(base_artifact_path), + context_estimator=StructuralConfidenceEstimator.from_artifact( + context_artifact_path + ), + answer_generator=generator, + skip_threshold=skip_threshold, + ) + + +def _strategy( + *, + base_scores: list[Any] | None = None, + context_scores: list[Any] | None = None, + generator: FakeGenerator | None = None, + skip_threshold: float = 0.8, + max_document_chars: int = 4000, +) -> CBDRStrategy: + return CBDRStrategy( + base_estimator=FakeEstimator( + task_type="query_answerability_confidence", + scores=base_scores or [0.2], + ), + context_estimator=FakeEstimator( + task_type="query_context_answerability_confidence", + scores=context_scores or [0.7], + ), + answer_generator=generator or FakeGenerator(context_answers=["context answer"]), + skip_threshold=skip_threshold, + max_document_chars=max_document_chars, + ) + + +def _unused_model_client() -> Any: + return object() + + +def test_cbdr_exports_are_submodule_only() -> None: + strategies = importlib.import_module("ranksmith.strategies") + root = importlib.import_module("ranksmith") + + assert strategies.CBDRStrategy is not None + assert not hasattr(root, "CBDRStrategy") + + +def test_cbdr_from_artifacts_builds_estimators_with_hf_options( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + calls: list[dict[str, object]] = [] + base_estimator = FakeEstimator( + task_type="query_answerability_confidence", + scores=[0.9], + ) + context_estimator = FakeEstimator( + task_type="query_context_answerability_confidence", + scores=[0.5], + ) + + def fake_from_artifact(path: str | Path, **kwargs: object) -> FakeEstimator: + calls.append({"path": Path(path), **kwargs}) + return base_estimator if len(calls) == 1 else context_estimator + + monkeypatch.setattr( + "ranksmith.confidence.StructuralConfidenceEstimator.from_artifact", + fake_from_artifact, + ) + generator = FakeGenerator(context_answers=[]) + + strategy = CBDRStrategy.from_artifacts( + base_artifact_path=tmp_path / "base.joblib", + context_artifact_path=tmp_path / "context.joblib", + base_metadata_path=tmp_path / "base.metadata.json", + context_metadata_path=tmp_path / "context.metadata.json", + answer_generator=generator, + skip_threshold=0.7, + max_document_chars=123, + hf_token="token", + cache_dir="/tmp/hf", + device="cpu", + local_files_only=True, + max_length=128, + allow_truncation=True, + ) + + assert strategy.base_estimator is base_estimator + assert strategy.context_estimator is context_estimator + assert strategy.answer_generator is generator + assert strategy.skip_threshold == 0.7 + assert strategy.max_document_chars == 123 + assert calls == [ + { + "path": tmp_path / "base.joblib", + "metadata_path": tmp_path / "base.metadata.json", + "task_type": "query_answerability_confidence", + "hf_token": "token", + "cache_dir": "/tmp/hf", + "device": "cpu", + "local_files_only": True, + "max_length": 128, + "allow_truncation": True, + }, + { + "path": tmp_path / "context.joblib", + "metadata_path": tmp_path / "context.metadata.json", + "task_type": "query_context_answerability_confidence", + "hf_token": "token", + "cache_dir": "/tmp/hf", + "device": "cpu", + "local_files_only": True, + "max_length": 128, + "allow_truncation": True, + }, + ] + + +def test_cbdr_empty_documents_returns_empty_without_calls() -> None: + generator = FakeGenerator(context_answers=[]) + strategy = _strategy(generator=generator) + + assert ( + strategy.rerank( + query="Who?", + documents=[], + model_client=object(), + ) + == [] + ) + assert generator.query_calls == [] + assert generator.context_calls == [] + + +def test_cbdr_top_k_zero_returns_empty_without_calls() -> None: + generator = FakeGenerator(context_answers=["a"]) + strategy = _strategy(generator=generator) + + assert ( + strategy.rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + top_k=0, + ) + == [] + ) + assert generator.query_calls == [] + assert generator.context_calls == [] + + +def test_cbdr_skip_path_preserves_original_order_and_metadata() -> None: + generator = FakeGenerator(context_answers=[]) + strategy = _strategy(base_scores=[0.91], generator=generator, skip_threshold=0.8) + documents = [ + Document(id="a", text="alpha"), + Document(id="b", text="beta"), + ] + + results = strategy.rerank(query="Who?", documents=documents, model_client=object()) + + assert [result.document.id for result in results] == ["a", "b"] + assert [result.rank for result in results] == [1, 2] + assert [result.original_index for result in results] == [0, 1] + assert [dict(result.metadata) for result in results] == [ + { + "strategy": "cbdr", + "algorithm": "cbdr", + "cbdr_skipped": True, + "base_confidence": 0.91, + "skip_threshold": 0.8, + "context_confidence": None, + "confidence_gain": None, + }, + { + "strategy": "cbdr", + "algorithm": "cbdr", + "cbdr_skipped": True, + "base_confidence": 0.91, + "skip_threshold": 0.8, + "context_confidence": None, + "confidence_gain": None, + }, + ] + assert generator.query_calls == ["Who?"] + assert generator.context_calls == [] + + +def test_cbdr_skip_path_applies_top_k_after_original_order() -> None: + strategy = _strategy(base_scores=[0.9], skip_threshold=0.8) + + results = strategy.rerank( + query="Who?", + documents=[ + Document(id="a", text="alpha"), + Document(id="b", text="beta"), + ], + model_client=object(), + top_k=1, + ) + + assert [result.document.id for result in results] == ["a"] + assert [result.rank for result in results] == [1] + assert [result.original_index for result in results] == [0] + + +def test_cbdr_skip_path_does_not_validate_long_documents() -> None: + strategy = _strategy( + base_scores=[0.9], + context_scores=[], + generator=FakeGenerator(context_answers=[]), + skip_threshold=0.8, + max_document_chars=3, + ) + + results = strategy.rerank( + query="Who?", + documents=[Document(text="abcdef")], + model_client=object(), + ) + + assert len(results) == 1 + assert results[0].metadata["cbdr_skipped"] is True + + +def test_cbdr_rerank_path_sorts_by_gain_and_preserves_ties() -> None: + generator = FakeGenerator(context_answers=["answer a", "answer b", "answer c"]) + strategy = _strategy( + base_scores=[0.4], + context_scores=[0.6, 0.8, 0.8], + generator=generator, + skip_threshold=0.9, + ) + + results = strategy.rerank( + query="Who?", + documents=[ + Document(id="a", text="alpha"), + Document(id="b", text="beta"), + Document(id="c", text="gamma"), + ], + model_client=object(), + top_k=2, + ) + + assert [result.document.id for result in results] == ["b", "c"] + assert [result.rank for result in results] == [1, 2] + assert [result.original_index for result in results] == [1, 2] + assert [result.metadata["cbdr_skipped"] for result in results] == [False, False] + assert [result.metadata["base_confidence"] for result in results] == [0.4, 0.4] + assert [result.metadata["context_confidence"] for result in results] == [0.8, 0.8] + assert [result.metadata["confidence_gain"] for result in results] == pytest.approx( + [0.4, 0.4] + ) + assert generator.context_calls == [ + ("Who?", "alpha"), + ("Who?", "beta"), + ("Who?", "gamma"), + ] + + +@pytest.mark.parametrize("skip_threshold", [math.nan, math.inf, -0.1, 1.1, True]) +def test_cbdr_invalid_skip_threshold_fails(skip_threshold: object) -> None: + with pytest.raises(ValueError, match="skip_threshold"): + CBDRStrategy( + base_estimator=FakeEstimator( + task_type="query_answerability_confidence", + scores=[0.1], + ), + context_estimator=FakeEstimator( + task_type="query_context_answerability_confidence", + scores=[0.2], + ), + answer_generator=FakeGenerator(), + skip_threshold=cast(float, skip_threshold), + ) + + +def test_cbdr_threshold_zero_always_skips_non_empty_documents() -> None: + strategy = _strategy(base_scores=[0.0], skip_threshold=0.0) + + results = strategy.rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + assert results[0].metadata["cbdr_skipped"] is True + + +def test_cbdr_threshold_one_skips_only_at_exact_one() -> None: + skipped = _strategy(base_scores=[1.0], skip_threshold=1.0).rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + reranked = _strategy( + base_scores=[0.999], + context_scores=[1.0], + generator=FakeGenerator(context_answers=["a"]), + skip_threshold=1.0, + ).rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + assert skipped[0].metadata["cbdr_skipped"] is True + assert reranked[0].metadata["cbdr_skipped"] is False + + +def test_cbdr_rerank_path_validates_long_documents() -> None: + strategy = _strategy(base_scores=[0.2], skip_threshold=0.8, max_document_chars=3) + + with pytest.raises(DocumentTooLongError): + strategy.rerank( + query="Who?", + documents=[Document(text="abcdef")], + model_client=object(), + ) + + +def test_cbdr_empty_query_fails() -> None: + with pytest.raises(RerankInputError, match="query"): + _strategy().rerank( + query=" ", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +def test_cbdr_negative_top_k_fails() -> None: + with pytest.raises(RerankInputError, match="top_k"): + _strategy().rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + top_k=-1, + ) + + +def test_cbdr_invalid_task_types_fail() -> None: + with pytest.raises(RerankInputError, match="base_estimator"): + CBDRStrategy( + base_estimator=FakeEstimator( + task_type="query_context_answerability_confidence", + scores=[0.1], + ), + context_estimator=FakeEstimator( + task_type="query_context_answerability_confidence", + scores=[0.2], + ), + answer_generator=FakeGenerator(), + ) + + with pytest.raises(RerankInputError, match="context_estimator"): + CBDRStrategy( + base_estimator=FakeEstimator( + task_type="query_answerability_confidence", + scores=[0.1], + ), + context_estimator=FakeEstimator( + task_type="query_answerability_confidence", + scores=[0.2], + ), + answer_generator=FakeGenerator(), + ) + + +@pytest.mark.parametrize("score", [math.nan, math.inf, -0.1, 1.1, "0.5", True]) +def test_cbdr_invalid_base_score_fails(score: object) -> None: + with pytest.raises(RerankStrategyError): + _strategy(base_scores=[score]).rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +@pytest.mark.parametrize("score", [math.nan, math.inf, -0.1, 1.1, "0.5", True]) +def test_cbdr_invalid_context_score_fails(score: object) -> None: + with pytest.raises(RerankStrategyError): + _strategy( + base_scores=[0.1], + context_scores=[score], + skip_threshold=0.8, + ).rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +def test_cbdr_answer_generator_empty_output_fails() -> None: + with pytest.raises(RerankProviderError): + _strategy(generator=FakeGenerator(base_answer="")).rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +def test_cbdr_generator_unexpected_exception_wraps_provider_error() -> None: + with pytest.raises(RerankProviderError) as exc_info: + _strategy(generator=FakeGenerator(base_answer=RuntimeError("boom"))).rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + assert isinstance(exc_info.value.__cause__, RuntimeError) + + +def test_cbdr_direct_estimator_unexpected_exception_propagates() -> None: + error = RuntimeError("confidence failed") + + with pytest.raises(RuntimeError, match="confidence failed"): + _strategy(base_scores=[error]).rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +def test_cbdr_facade_wraps_unexpected_estimator_error() -> None: + reranker = AzureOpenAIReranker( + model_client=_unused_model_client(), + strategy=_strategy(base_scores=[RuntimeError("confidence failed")]), + ) + + with pytest.raises(RerankProviderError) as exc_info: + reranker.rerank("Who?", [Document(text="alpha")]) + + assert isinstance(exc_info.value.__cause__, RuntimeError) + + +def test_cbdr_artifact_e2e_skip_path_through_azure_facade( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + strategy = _artifact_strategy( + monkeypatch=monkeypatch, + tmp_path=tmp_path, + base_scores=[0.91], + context_scores=[], + generator=FakeGenerator(context_answers=[]), + skip_threshold=0.8, + ) + reranker = AzureOpenAIReranker( + model_client=_unused_model_client(), + strategy=strategy, + ) + + results = reranker.rerank( + "who played karen in married to the mob?", + [ + Document( + id="similar-but-weak", + text="Michelle Pfeiffer appears in the film.", + ), + Document(id="direct-evidence", text="Nancy Travis played Karen."), + ], + ) + + assert [result.document.id for result in results] == [ + "similar-but-weak", + "direct-evidence", + ] + assert [result.metadata["cbdr_skipped"] for result in results] == [True, True] + assert [result.metadata["base_confidence"] for result in results] == [0.91, 0.91] + assert [result.metadata["context_confidence"] for result in results] == [ + None, + None, + ] + assert [result.metadata["confidence_gain"] for result in results] == [None, None] + + +def test_cbdr_artifact_e2e_rerank_path_through_azure_facade( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + strategy = _artifact_strategy( + monkeypatch=monkeypatch, + tmp_path=tmp_path, + base_scores=[0.3], + context_scores=[0.45, 0.9], + generator=FakeGenerator( + base_answer="base answer", + context_answers=["low answer", "high answer"], + ), + skip_threshold=0.8, + ) + reranker = AzureOpenAIReranker( + model_client=_unused_model_client(), + strategy=strategy, + ) + + results = reranker.rerank( + "who played karen in married to the mob?", + [ + Document( + id="similar-but-weak", + text="Michelle Pfeiffer appears in the film.", + ), + Document(id="direct-evidence", text="Nancy Travis played Karen."), + ], + ) + + assert [result.document.id for result in results] == [ + "direct-evidence", + "similar-but-weak", + ] + assert [result.metadata["cbdr_skipped"] for result in results] == [False, False] + assert [result.metadata["base_confidence"] for result in results] == [0.3, 0.3] + assert [result.metadata["context_confidence"] for result in results] == [0.9, 0.45] + assert [result.metadata["confidence_gain"] for result in results] == pytest.approx( + [0.6, 0.15] + ) diff --git a/tests/test_compare_reranking.py b/tests/test_compare_reranking.py index 35528ba..b5feacf 100644 --- a/tests/test_compare_reranking.py +++ b/tests/test_compare_reranking.py @@ -4,6 +4,7 @@ import importlib.util import sys from pathlib import Path +from typing import Any, cast import pytest @@ -96,6 +97,37 @@ def test_compare_optional_prp_sliding_p1_is_preserved() -> None: assert compare_reranking._selected_algorithms(args, cases) == ("prp_sliding_p1",) +def test_compare_explicit_cbdr_is_preserved() -> None: + args = argparse.Namespace(algorithm="cbdr") + cases: list[BenchmarkCase] = [] + + assert compare_reranking._selected_algorithms(args, cases) == ("cbdr",) + + +def test_compare_estimates_cbdr_provider_calls_as_upper_bound() -> None: + assert ( + compare_reranking._estimate_provider_calls( + 20, + "cbdr", + window_size=20, + stride=10, + top_k=5, + ) + == 21 + ) + + +def test_compare_describes_cbdr_estimates_as_upper_bounds() -> None: + assert ( + compare_reranking._call_estimate_message( + needs_live=True, + algorithms=("cbdr",), + call_estimates={"cbdr": 21}, + ) + == "Live Azure comparison upper-bounds provider calls at 21: {'cbdr': 21}" + ) + + def test_compare_explicit_tourrank_is_preserved_for_non_100_candidate_cases() -> None: args = argparse.Namespace(algorithm="tourrank_r") cases: list[BenchmarkCase] = [] @@ -242,6 +274,16 @@ def test_compare_report_records_method_settings() -> None: passes=10, tourrank_rounds=2, set_size=3, + cbdr_base_artifact=None, + cbdr_context_artifact=None, + cbdr_skip_threshold=0.8, + cbdr_device="cpu", + cbdr_cache_dir=None, + cbdr_local_files_only=False, + cbdr_hf_token_env=None, + cbdr_max_length=None, + cbdr_max_document_chars=4000, + cbdr_allow_truncation=False, query_id=[], timeout=None, checkpoint_output=None, @@ -266,6 +308,167 @@ def test_compare_report_records_method_settings() -> None: } +def test_compare_cbdr_method_settings_record_runtime_options() -> None: + args = argparse.Namespace( + candidate_count=20, + cbdr_base_artifact=Path("base.joblib"), + cbdr_context_artifact=Path("context.joblib"), + cbdr_answer_provider="lmstudio", + cbdr_skip_threshold=0.7, + cbdr_device="cpu", + cbdr_cache_dir=Path(".hf-cache"), + cbdr_local_files_only=True, + cbdr_hf_token_env="HF_TOKEN", + cbdr_max_length=128, + cbdr_max_document_chars=1234, + cbdr_allow_truncation=True, + lmstudio_base_url="http://localhost:1234/v1", + lmstudio_model="google/gemma-4-12b", + lmstudio_api_key="local-key", + lmstudio_max_tokens=64, + ) + + assert compare_reranking._method_setting(args, "cbdr") == { + "candidate_count": 20, + "base_artifact": "base.joblib", + "context_artifact": "context.joblib", + "answer_provider": "lmstudio", + "skip_threshold": 0.7, + "device": "cpu", + "cache_dir": ".hf-cache", + "local_files_only": True, + "hf_token_env": "HF_TOKEN", + "max_length": 128, + "max_document_chars": 1234, + "allow_truncation": True, + "lmstudio_base_url": "http://localhost:1234/v1", + "lmstudio_model": "google/gemma-4-12b", + "lmstudio_api_key": "configured", + "lmstudio_max_tokens": 64, + "provider_call_estimate": "upper_bound", + "top_k_early_stop": False, + } + + +def test_compare_cbdr_method_settings_record_lmstudio_env_defaults( + monkeypatch: pytest.MonkeyPatch, +) -> None: + args = argparse.Namespace( + candidate_count=20, + cbdr_base_artifact=Path("base.joblib"), + cbdr_context_artifact=Path("context.joblib"), + cbdr_answer_provider="lmstudio", + cbdr_skip_threshold=0.7, + cbdr_device="cpu", + cbdr_cache_dir=None, + cbdr_local_files_only=False, + cbdr_hf_token_env=None, + cbdr_max_length=128, + cbdr_max_document_chars=1234, + cbdr_allow_truncation=False, + lmstudio_base_url=None, + lmstudio_model=None, + lmstudio_api_key=None, + lmstudio_max_tokens=64, + ) + monkeypatch.setenv("LMSTUDIO_MODEL", "env-model") + monkeypatch.setenv("LMSTUDIO_API_KEY", "env-key") + monkeypatch.delenv("LMSTUDIO_BASE_URL", raising=False) + + settings = compare_reranking._method_setting(args, "cbdr") + + assert settings["lmstudio_base_url"] == "http://localhost:1234/v1" + assert settings["lmstudio_model"] == "env-model" + assert settings["lmstudio_api_key"] == "configured" + + +def test_compare_cbdr_requires_artifacts() -> None: + args = argparse.Namespace( + algorithm="cbdr", + cbdr_base_artifact=None, + cbdr_context_artifact=Path("context.joblib"), + cbdr_skip_threshold=0.8, + cbdr_max_length=None, + cbdr_max_document_chars=4000, + window_size=20, + stride=10, + passes=10, + tourrank_rounds=2, + set_size=3, + top_k=5, + candidate_count=20, + max_cases=None, + timeout=None, + checkpoint_output=None, + dataset="fixture", + cache_dir=None, + dataset_name=None, + candidates=None, + candidate_strategy="candidate_file", + ) + + with pytest.raises(SystemExit, match="--cbdr-base-artifact"): + compare_reranking._validate_args(args) + + +def test_compare_cbdr_requires_context_artifact() -> None: + args = argparse.Namespace( + algorithm="cbdr", + cbdr_base_artifact=Path("base.joblib"), + cbdr_context_artifact=None, + cbdr_skip_threshold=0.8, + cbdr_max_length=None, + cbdr_max_document_chars=4000, + window_size=20, + stride=10, + passes=10, + tourrank_rounds=2, + set_size=3, + top_k=5, + candidate_count=20, + max_cases=None, + timeout=None, + checkpoint_output=None, + dataset="fixture", + cache_dir=None, + dataset_name=None, + candidates=None, + candidate_strategy="candidate_file", + ) + + with pytest.raises(SystemExit, match="--cbdr-context-artifact"): + compare_reranking._validate_args(args) + + +def test_compare_rejects_non_positive_lmstudio_max_tokens() -> None: + args = argparse.Namespace( + algorithm="original_bm25", + cbdr_skip_threshold=0.8, + cbdr_max_length=None, + cbdr_max_document_chars=4000, + lmstudio_max_tokens=0, + window_size=20, + stride=10, + passes=10, + tourrank_rounds=2, + set_size=3, + top_k=5, + candidate_count=20, + max_cases=None, + timeout=None, + checkpoint_output=None, + output=None, + dataset="fixture", + cache_dir=None, + dataset_name=None, + candidates=None, + candidate_strategy="candidate_file", + ) + + with pytest.raises(SystemExit, match="--lmstudio-max-tokens"): + compare_reranking._validate_args(args) + + def test_compare_setwise_heapsort_uses_set_size( monkeypatch: pytest.MonkeyPatch, ) -> None: @@ -312,7 +515,7 @@ def rerank( set_size=5, ) - assert captured["strategy"].set_size == 5 + assert cast(Any, captured["strategy"]).set_size == 5 def test_compare_setwise_hs_s10_uses_fixed_set_size( @@ -361,7 +564,7 @@ def rerank( set_size=5, ) - assert captured["strategy"].set_size == 10 + assert cast(Any, captured["strategy"]).set_size == 10 def test_compare_setwise_heapsort_forwards_top_k( @@ -462,7 +665,7 @@ def rerank( tourrank_rounds=2, ) - assert captured["strategy"].max_adaptive_reranker_calls == 4 + assert cast(Any, captured["strategy"]).max_adaptive_reranker_calls == 4 def test_compare_acurank_b1_uses_budget_one(monkeypatch: pytest.MonkeyPatch) -> None: @@ -508,7 +711,7 @@ def rerank( tourrank_rounds=2, ) - assert captured["strategy"].max_adaptive_reranker_calls == 1 + assert cast(Any, captured["strategy"]).max_adaptive_reranker_calls == 1 def test_compare_acurank_k5_b1_uses_target_rank_five( @@ -556,8 +759,8 @@ def rerank( tourrank_rounds=2, ) - assert captured["strategy"].target_rank == 5 - assert captured["strategy"].max_adaptive_reranker_calls == 1 + assert cast(Any, captured["strategy"]).target_rank == 5 + assert cast(Any, captured["strategy"]).max_adaptive_reranker_calls == 1 def test_compare_rankgpt_sw_w5_uses_small_sliding_window( @@ -605,8 +808,8 @@ def rerank( tourrank_rounds=2, ) - assert captured["strategy"].window_size == 5 - assert captured["strategy"].stride == 2 + assert cast(Any, captured["strategy"]).window_size == 5 + assert cast(Any, captured["strategy"]).stride == 2 def test_compare_original_bm25_returns_candidate_order() -> None: @@ -658,6 +861,52 @@ def test_compare_original_bm25_only_does_not_require_live_flag( assert output_path.exists() +def test_compare_cbdr_requires_live_flag( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setattr( + sys, + "argv", + [ + "compare_reranking.py", + "--algorithm", + "cbdr", + "--cbdr-base-artifact", + str(tmp_path / "base.joblib"), + "--cbdr-context-artifact", + str(tmp_path / "context.joblib"), + ], + ) + + with pytest.raises(SystemExit, match="--allow-live"): + compare_reranking.main() + + +def test_compare_cbdr_lmstudio_requires_live_flag( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setattr( + sys, + "argv", + [ + "compare_reranking.py", + "--algorithm", + "cbdr", + "--cbdr-base-artifact", + str(tmp_path / "base.joblib"), + "--cbdr-context-artifact", + str(tmp_path / "context.joblib"), + "--cbdr-answer-provider", + "lmstudio", + ], + ) + + with pytest.raises(SystemExit, match="--allow-live"): + compare_reranking.main() + + def test_compare_cli_defaults_match_top20_evaluate_at_5( monkeypatch: pytest.MonkeyPatch, ) -> None: @@ -670,6 +919,366 @@ def test_compare_cli_defaults_match_top20_evaluate_at_5( assert args.stride == 10 assert args.set_size == 3 assert args.top_k == 5 + assert args.cbdr_skip_threshold == 0.8 + assert args.cbdr_device == "cpu" + assert args.cbdr_max_document_chars == 4000 + assert args.cbdr_answer_provider == "azure" + assert args.lmstudio_base_url is None + assert args.lmstudio_model is None + assert args.lmstudio_api_key is None + assert args.lmstudio_max_tokens == 128 + + +def test_compare_cbdr_rank_case_builds_strategy_from_artifacts( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + captured: dict[str, object] = {} + base_estimator = object() + context_estimator = object() + + def fake_cached_cbdr_estimators(**kwargs: object) -> tuple[object, object]: + captured["estimator_kwargs"] = kwargs + return base_estimator, context_estimator + + class FakeCBDRStrategy: + def __init__(self, **kwargs: object) -> None: + captured["strategy_kwargs"] = kwargs + + def rerank( + self, + *, + query: str, + documents: list[object], + model_client: object | None = None, + top_k: int | None = None, + ) -> list[object]: + del query, model_client, top_k + return [ + type("Result", (), {"document": document})() for document in documents + ] + + class FakeAnswerGenerator: + @classmethod + def from_env(cls, **kwargs: object) -> object: + captured["generator_kwargs"] = kwargs + generator = cls() + captured["answer_generator"] = generator + return generator + + class FakeReranker: + def __init__(self, **kwargs: object) -> None: + del kwargs + raise AssertionError("CBDR benchmark must not create AzureOpenAIReranker") + + def rerank( + self, + query: str, + documents: list[object], + *, + top_k: int | None = None, + ) -> list[object]: + del query, top_k + return [ + type("Result", (), {"document": document})() for document in documents + ] + + monkeypatch.setenv("AZURE_OPENAI_API_KEY", "key") + monkeypatch.setenv("AZURE_OPENAI_ENDPOINT", "https://example.openai.azure.com") + monkeypatch.setenv("AZURE_OPENAI_DEPLOYMENT", "deployment") + monkeypatch.setenv("HF_TOKEN", "hf-token") + monkeypatch.setattr("ranksmith.AzureOpenAIReranker", FakeReranker) + monkeypatch.setattr("ranksmith.strategies.CBDRStrategy", FakeCBDRStrategy) + monkeypatch.setattr( + compare_reranking, + "_cached_cbdr_estimators", + fake_cached_cbdr_estimators, + ) + monkeypatch.setattr( + "ranksmith.integrations.AzureAnswerGenerator", + FakeAnswerGenerator, + ) + + ranked = compare_reranking._rank_case( + case=BenchmarkCase( + fixture_id="fixture", + dataset="dataset", + source="source", + license="license", + query_id="q1", + query="query", + documents=tuple( + BenchmarkDocument(id=str(index), title="", text="") + for index in range(2) + ), + qrels={}, + ), + algorithm="cbdr", + window_size=20, + stride=10, + passes=10, + tourrank_rounds=2, + top_k=5, + cbdr_base_artifact=tmp_path / "base.joblib", + cbdr_context_artifact=tmp_path / "context.joblib", + cbdr_skip_threshold=0.7, + cbdr_device="cpu", + cbdr_cache_dir=tmp_path / "hf", + cbdr_local_files_only=True, + cbdr_hf_token_env="HF_TOKEN", + cbdr_max_length=128, + cbdr_max_document_chars=1234, + cbdr_allow_truncation=True, + ) + + assert ranked == ("0", "1") + assert captured["generator_kwargs"] == {"timeout": None} + assert captured["estimator_kwargs"] == { + "base_artifact_path": tmp_path / "base.joblib", + "context_artifact_path": tmp_path / "context.joblib", + "hf_token": "hf-token", + "cache_dir": str(tmp_path / "hf"), + "device": "cpu", + "local_files_only": True, + "max_length": 128, + "allow_truncation": True, + } + assert captured["strategy_kwargs"] == { + "base_estimator": base_estimator, + "context_estimator": context_estimator, + "answer_generator": captured["answer_generator"], + "skip_threshold": 0.7, + "max_document_chars": 1234, + } + + +def test_compare_cbdr_rank_case_rejects_unsupported_answer_provider( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + class FakeAnswerGenerator: + @classmethod + def from_env(cls, **kwargs: object) -> object: + del kwargs + raise AssertionError("unsupported provider must not create Azure generator") + + class FakeLMStudioModelProvider: + def __init__(self, **kwargs: object) -> None: + del kwargs + raise AssertionError("unsupported provider must not create LM Studio") + + monkeypatch.setattr( + "ranksmith.integrations.AzureAnswerGenerator", + FakeAnswerGenerator, + ) + monkeypatch.setattr( + "ranksmith.integrations.LMStudioModelProvider", + FakeLMStudioModelProvider, + ) + + with pytest.raises(SystemExit, match="--cbdr-answer-provider"): + compare_reranking._rank_case( + case=BenchmarkCase( + fixture_id="fixture", + dataset="dataset", + source="source", + license="license", + query_id="q1", + query="query", + documents=(BenchmarkDocument(id="d1", title="", text=""),), + qrels={"d1": 1}, + ), + algorithm="cbdr", + window_size=20, + stride=10, + passes=10, + tourrank_rounds=2, + set_size=3, + cbdr_base_artifact=tmp_path / "base.joblib", + cbdr_context_artifact=tmp_path / "context.joblib", + cbdr_answer_provider="bad", + ) + + +def test_compare_cbdr_rank_case_uses_lmstudio_answer_provider( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + captured: dict[str, object] = {} + + class FakeLMStudioModelProvider: + def __init__(self, **kwargs: object) -> None: + captured["provider_kwargs"] = kwargs + + class FakeProviderAnswerGenerator: + def __init__(self, *, provider: object) -> None: + captured["provider"] = provider + self.no_answer_value = "__NO_ANSWER__" + + base_estimator = object() + context_estimator = object() + + def fake_cached_cbdr_estimators(**kwargs: object) -> tuple[object, object]: + captured["estimator_kwargs"] = kwargs + return base_estimator, context_estimator + + class FakeCBDRStrategy: + def __init__(self, **kwargs: object) -> None: + captured["strategy_kwargs"] = kwargs + + def rerank( + self, + *, + query: str, + documents: list[object], + model_client: object | None = None, + top_k: int | None = None, + ) -> list[object]: + del query, model_client, top_k + return [ + type("Result", (), {"document": document})() for document in documents + ] + + class FakeAzureAnswerGenerator: + @classmethod + def from_env(cls, **kwargs: object) -> object: + del kwargs + raise AssertionError("LM Studio CBDR must not create Azure generator") + + monkeypatch.setattr("ranksmith.strategies.CBDRStrategy", FakeCBDRStrategy) + monkeypatch.setattr( + compare_reranking, + "_cached_cbdr_estimators", + fake_cached_cbdr_estimators, + ) + monkeypatch.setattr( + "ranksmith.integrations.AzureAnswerGenerator", + FakeAzureAnswerGenerator, + ) + monkeypatch.setattr( + "ranksmith.integrations.LMStudioModelProvider", + FakeLMStudioModelProvider, + ) + monkeypatch.setattr( + "ranksmith.integrations.ProviderAnswerGenerator", + FakeProviderAnswerGenerator, + ) + + ranked = compare_reranking._rank_case( + case=BenchmarkCase( + fixture_id="fixture", + dataset="dataset", + source="source", + license="license", + query_id="q1", + query="query", + documents=(BenchmarkDocument(id="d1", title="", text=""),), + qrels={"d1": 1}, + ), + algorithm="cbdr", + window_size=20, + stride=10, + passes=10, + tourrank_rounds=2, + set_size=3, + cbdr_base_artifact=tmp_path / "base.joblib", + cbdr_context_artifact=tmp_path / "context.joblib", + cbdr_answer_provider="lmstudio", + lmstudio_base_url="http://localhost:1234/v1", + lmstudio_model="google/gemma-4-12b", + lmstudio_api_key="local-key", + lmstudio_max_tokens=64, + timeout=2.5, + ) + + assert ranked == ("d1",) + assert captured["provider_kwargs"] == { + "base_url": "http://localhost:1234/v1", + "model": "google/gemma-4-12b", + "api_key": "local-key", + "timeout": 2.5, + "max_tokens": 64, + } + assert captured["estimator_kwargs"] == { + "base_artifact_path": tmp_path / "base.joblib", + "context_artifact_path": tmp_path / "context.joblib", + "hf_token": None, + "cache_dir": None, + "device": "cpu", + "local_files_only": False, + "max_length": None, + "allow_truncation": False, + } + strategy_kwargs = cast(dict[str, object], captured["strategy_kwargs"]) + answer_generator = strategy_kwargs["answer_generator"] + assert strategy_kwargs == { + "base_estimator": base_estimator, + "context_estimator": context_estimator, + "answer_generator": answer_generator, + "skip_threshold": 0.8, + "max_document_chars": 4000, + } + assert cast(Any, answer_generator).no_answer_value == "__NO_ANSWER__" + assert isinstance(captured["provider"], FakeLMStudioModelProvider) + + +def test_compare_evaluate_cases_forwards_cbdr_answer_provider_options( + monkeypatch: pytest.MonkeyPatch, +) -> None: + captured: dict[str, object] = {} + args = argparse.Namespace( + window_size=20, + stride=10, + passes=10, + tourrank_rounds=2, + set_size=3, + top_k=5, + timeout=1.5, + cbdr_base_artifact=Path("base.joblib"), + cbdr_context_artifact=Path("context.joblib"), + cbdr_skip_threshold=0.8, + cbdr_device="cpu", + cbdr_cache_dir=None, + cbdr_local_files_only=False, + cbdr_hf_token_env=None, + cbdr_max_length=None, + cbdr_max_document_chars=4000, + cbdr_allow_truncation=False, + cbdr_answer_provider="lmstudio", + lmstudio_base_url="http://localhost:1234/v1", + lmstudio_model="google/gemma-4-12b", + lmstudio_api_key="local-key", + lmstudio_max_tokens=64, + checkpoint_output=None, + ) + case = BenchmarkCase( + fixture_id="fixture", + dataset="dataset", + source="source", + license="license", + query_id="q1", + query="query", + documents=(BenchmarkDocument(id="d1", title="", text=""),), + qrels={"d1": 1}, + ) + + def fake_rank_case(**kwargs: object) -> tuple[str, ...]: + captured.update(kwargs) + return ("d1",) + + monkeypatch.setattr(compare_reranking, "_rank_case", fake_rank_case) + + compare_reranking._evaluate_cases( + args=args, + algorithms=("cbdr",), + cases=(case,), + ) + + assert captured["cbdr_answer_provider"] == "lmstudio" + assert captured["lmstudio_base_url"] == "http://localhost:1234/v1" + assert captured["lmstudio_model"] == "google/gemma-4-12b" + assert captured["lmstudio_api_key"] == "local-key" + assert captured["lmstudio_max_tokens"] == 64 def test_compare_live_invalid_output_is_recorded( diff --git a/tests/test_confidence_answerability_tasks.py b/tests/test_confidence_answerability_tasks.py new file mode 100644 index 0000000..3d21209 --- /dev/null +++ b/tests/test_confidence_answerability_tasks.py @@ -0,0 +1,126 @@ +from __future__ import annotations + +import importlib +from collections.abc import Sequence +from dataclasses import dataclass + +import pytest + +from ranksmith.confidence import ( + QueryAnswerabilityConfidenceInput, + QueryContextAnswerabilityConfidenceInput, + ScorerMetadata, + StructuralConfidenceEstimator, + TaskType, +) + + +@dataclass +class FakeEncoder: + encoder_name: str = "bert-base-uncased" + encoder_revision: str | None = None + tokenizer_name: str = "bert-base-uncased" + tokenizer_revision: str | None = None + max_length: int = 64 + last_text: str | None = None + + def encode(self, text: str) -> tuple[list[list[float]], list[int]]: + self.last_text = text + hidden = [[float(row + col) for col in range(4)] for row in range(8)] + mask = [1] * 8 + return hidden, mask + + +class FakeScorer: + def __init__( + self, + *, + task_type: TaskType, + score: float = 0.75, + ) -> None: + self.metadata = ScorerMetadata( + artifact_schema_version="structural-artifact-v1", + scorer_type="fake", + task_type=task_type, + encoder_name="bert-base-uncased", + encoder_revision=None, + tokenizer_name="bert-base-uncased", + tokenizer_revision=None, + input_template_version="structural-template-v1", + feature_schema_version="structural-v1", + feature_dim=70, + feature_dtype="float64", + max_length=64, + granularity="two_scale", + local_window_size=5, + local_stride=2, + score_output="probability", + positive_class_index=1, + ) + self.score = score + + def predict_confidence(self, features: Sequence[float]) -> float: + assert len(features) == 70 + return self.score + + +@pytest.fixture(autouse=True) +def fake_structural_features(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setattr( + "ranksmith.confidence.structural.extract_structural_features", + lambda hidden_states, mask, *, max_length: [0.0] * 70, + ) + + +def test_confidence_submodule_exports_answerability_inputs() -> None: + confidence = importlib.import_module("ranksmith.confidence") + + assert confidence.QueryAnswerabilityConfidenceInput is ( + QueryAnswerabilityConfidenceInput + ) + assert confidence.QueryContextAnswerabilityConfidenceInput is ( + QueryContextAnswerabilityConfidenceInput + ) + + +def test_estimator_scores_query_answerability_input_with_exact_template() -> None: + encoder = FakeEncoder() + estimator = StructuralConfidenceEstimator( + encoder=encoder, + scorer=FakeScorer(task_type="query_answerability_confidence"), + task_type="query_answerability_confidence", + ) + + result = estimator.score( + QueryAnswerabilityConfidenceInput(query="Who?", answer="Nancy Travis") + ) + + assert result.score == 0.75 + assert result.task_type == "query_answerability_confidence" + assert encoder.last_text == "Query:\nWho?\n\nAnswer:\nNancy Travis" + + +def test_estimator_scores_query_context_answerability_input_with_exact_template() -> ( + None +): + encoder = FakeEncoder() + estimator = StructuralConfidenceEstimator( + encoder=encoder, + scorer=FakeScorer(task_type="query_context_answerability_confidence"), + task_type="query_context_answerability_confidence", + ) + + result = estimator.score( + QueryContextAnswerabilityConfidenceInput( + query="Who?", + context="Karen was played by Nancy Travis.", + answer="Nancy Travis", + ) + ) + + assert result.score == 0.75 + assert result.task_type == "query_context_answerability_confidence" + assert encoder.last_text == ( + "Query:\nWho?\n\nContext:\nKaren was played by Nancy Travis." + "\n\nAnswer:\nNancy Travis" + ) diff --git a/tests/test_confidence_gain_strategy.py b/tests/test_confidence_gain_strategy.py new file mode 100644 index 0000000..68d5d98 --- /dev/null +++ b/tests/test_confidence_gain_strategy.py @@ -0,0 +1,658 @@ +from __future__ import annotations + +import importlib +import math +import sys +from dataclasses import dataclass +from pathlib import Path +from types import ModuleType +from typing import Any, cast + +import pytest + +from ranksmith import AzureOpenAIReranker +from ranksmith.confidence import ( + QueryAnswerabilityConfidenceInput, + QueryContextAnswerabilityConfidenceInput, + StructuralConfidenceEstimator, + StructuralConfidenceResult, + TaskType, +) +from ranksmith.confidence.scorer import ARTIFACT_SCHEMA_VERSION +from ranksmith.errors import ( + DocumentTooLongError, + RerankInputError, + RerankProviderError, + RerankStrategyError, +) +from ranksmith.strategies import ConfidenceGainStrategy +from ranksmith.types import Document + + +@dataclass +class FakeEstimator: + task_type: TaskType + scores: list[Any] + calls: list[object] | None = None + + def score(self, item: object) -> StructuralConfidenceResult: + if self.calls is not None: + self.calls.append(item) + value = self.scores.pop(0) + if isinstance(value, BaseException): + raise value + return StructuralConfidenceResult( + score=value, + task_type=self.task_type, + feature_schema_version="structural-v1", + ) + + +class FakeGenerator: + def __init__( + self, + *, + base_answer: object = "base answer", + context_answers: list[object] | None = None, + ) -> None: + self.base_answer = base_answer + self.context_answers = context_answers or [] + self.query_calls: list[str] = [] + self.context_calls: list[tuple[str, str]] = [] + + def answer_query(self, query: str) -> Any: + self.query_calls.append(query) + if isinstance(self.base_answer, BaseException): + raise self.base_answer + return self.base_answer + + def answer_with_context(self, query: str, context: str) -> Any: + self.context_calls.append((query, context)) + answer = self.context_answers.pop(0) + if isinstance(answer, BaseException): + raise answer + return answer + + +class ArtifactScorer: + def __init__(self, scores: list[float]) -> None: + self.scores = scores + + def predict_confidence(self, features: object) -> float: + del features + return self.scores.pop(0) + + +class ArtifactEncoder: + encoder_name = "bert-base-uncased" + encoder_revision = None + tokenizer_name = "bert-base-uncased" + tokenizer_revision = None + + def __init__(self, *, max_length: int) -> None: + self.max_length = max_length + + def encode(self, text: str) -> tuple[list[list[float]], list[int]]: + seed = float(len(text) % 7 + 1) + hidden = [[seed + row * 0.01, row * 0.02, seed * 0.03] for row in range(40)] + return hidden, [1] * len(hidden) + + +def _artifact_metadata(task_type: TaskType) -> dict[str, object]: + return { + "artifact_schema_version": ARTIFACT_SCHEMA_VERSION, + "scorer_type": "joblib-wrapper", + "task_type": task_type, + "encoder_name": "bert-base-uncased", + "encoder_revision": None, + "tokenizer_name": "bert-base-uncased", + "tokenizer_revision": None, + "input_template_version": "structural-template-v1", + "feature_schema_version": "structural-v1", + "feature_dim": 70, + "feature_dtype": "float64", + "max_length": 64, + "granularity": "two_scale", + "local_window_size": 5, + "local_stride": 2, + "score_output": "probability", + "positive_class_index": 1, + } + + +def _install_artifact_joblib( + monkeypatch: pytest.MonkeyPatch, + artifacts: dict[Path, object], +) -> None: + module = ModuleType("joblib") + + def load(path: str | Path) -> object: + return artifacts[Path(path)] + + module.load = load # type: ignore[attr-defined] + monkeypatch.setitem(sys.modules, "joblib", module) + + +def _strategy( + *, + base_scores: list[Any] | None = None, + context_scores: list[Any] | None = None, + generator: FakeGenerator | None = None, +) -> ConfidenceGainStrategy: + return ConfidenceGainStrategy( + base_estimator=FakeEstimator( + task_type="query_answerability_confidence", + scores=base_scores or [0.2], + ), + context_estimator=FakeEstimator( + task_type="query_context_answerability_confidence", + scores=context_scores or [0.7], + ), + answer_generator=generator or FakeGenerator(context_answers=["context answer"]), + ) + + +def _unused_model_client() -> Any: + return object() + + +def test_confidence_gain_exports_are_submodule_only() -> None: + strategies = importlib.import_module("ranksmith.strategies") + root = importlib.import_module("ranksmith") + + assert strategies.AnswerGenerator is not None + assert strategies.ConfidenceEstimator is not None + assert strategies.ConfidenceGainResult is not None + assert strategies.ConfidenceGainStrategy is not None + assert not hasattr(root, "AnswerGenerator") + assert not hasattr(root, "ConfidenceEstimator") + assert not hasattr(root, "ConfidenceGainResult") + assert not hasattr(root, "ConfidenceGainStrategy") + + +def test_confidence_gain_sorts_by_gain_desc_and_preserves_ties() -> None: + generator = FakeGenerator(context_answers=["answer a", "answer b", "answer c"]) + strategy = _strategy( + base_scores=[0.4], + context_scores=[0.6, 0.8, 0.8], + generator=generator, + ) + documents = [ + Document(id="a", text="alpha"), + Document(id="b", text="beta"), + Document(id="c", text="gamma"), + ] + + results = strategy.rerank( + query="Who?", + documents=documents, + model_client=object(), + ) + + assert [result.document.id for result in results] == ["b", "c", "a"] + assert [result.rank for result in results] == [1, 2, 3] + assert [result.original_index for result in results] == [1, 2, 0] + assert [dict(result.metadata) for result in results] == [ + { + "strategy": "confidence_gain", + "algorithm": "confidence_gain", + "base_confidence": 0.4, + "context_confidence": 0.8, + "confidence_gain": 0.4, + }, + { + "strategy": "confidence_gain", + "algorithm": "confidence_gain", + "base_confidence": 0.4, + "context_confidence": 0.8, + "confidence_gain": 0.4, + }, + { + "strategy": "confidence_gain", + "algorithm": "confidence_gain", + "base_confidence": 0.4, + "context_confidence": 0.6, + "confidence_gain": 0.19999999999999996, + }, + ] + + +def test_confidence_gain_applies_top_k_after_sorting() -> None: + strategy = _strategy( + base_scores=[0.1], + context_scores=[0.2, 0.9, 0.5], + generator=FakeGenerator(context_answers=["a", "b", "c"]), + ) + + results = strategy.rerank( + query="Who?", + documents=[Document(text="a"), Document(text="b"), Document(text="c")], + model_client=object(), + top_k=2, + ) + + assert [result.original_index for result in results] == [1, 2] + assert [result.rank for result in results] == [1, 2] + + +def test_confidence_gain_calls_answer_generator_expected_number_of_times() -> None: + generator = FakeGenerator(context_answers=["a", "b"]) + strategy = _strategy( + base_scores=[0.3], + context_scores=[0.4, 0.5], + generator=generator, + ) + + strategy.rerank( + query="Who?", + documents=[Document(text="alpha"), Document(text="beta")], + model_client=object(), + ) + + assert generator.query_calls == ["Who?"] + assert generator.context_calls == [("Who?", "alpha"), ("Who?", "beta")] + + +def test_confidence_gain_task_mismatch_fails_for_base_and_context() -> None: + from ranksmith.strategies import ConfidenceGainStrategy # noqa: PLC0415 + + with pytest.raises(RerankInputError, match="base_estimator"): + ConfidenceGainStrategy( + base_estimator=FakeEstimator( + task_type="query_context_answerability_confidence", + scores=[0.1], + ), + context_estimator=FakeEstimator( + task_type="query_context_answerability_confidence", + scores=[0.2], + ), + answer_generator=FakeGenerator(), + ) + + with pytest.raises(RerankInputError, match="context_estimator"): + ConfidenceGainStrategy( + base_estimator=FakeEstimator( + task_type="query_answerability_confidence", + scores=[0.1], + ), + context_estimator=FakeEstimator( + task_type="query_answerability_confidence", + scores=[0.2], + ), + answer_generator=FakeGenerator(), + ) + + +def test_confidence_gain_invalid_algorithm_fails() -> None: + with pytest.raises(ValueError, match="algorithm"): + ConfidenceGainStrategy( + base_estimator=FakeEstimator( + task_type="query_answerability_confidence", + scores=[0.1], + ), + context_estimator=FakeEstimator( + task_type="query_context_answerability_confidence", + scores=[0.2], + ), + answer_generator=FakeGenerator(), + algorithm=cast(Any, "other"), + ) + + +def test_confidence_gain_invalid_max_document_chars_fails() -> None: + with pytest.raises(ValueError, match="max_document_chars"): + ConfidenceGainStrategy( + base_estimator=FakeEstimator( + task_type="query_answerability_confidence", + scores=[0.1], + ), + context_estimator=FakeEstimator( + task_type="query_context_answerability_confidence", + scores=[0.2], + ), + answer_generator=FakeGenerator(), + max_document_chars=0, + ) + + +def test_confidence_gain_negative_top_k_fails() -> None: + with pytest.raises(RerankInputError, match="top_k"): + _strategy().rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + top_k=-1, + ) + + +def test_confidence_gain_empty_query_fails() -> None: + with pytest.raises(RerankInputError, match="query"): + _strategy().rerank( + query=" ", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +def test_confidence_gain_empty_documents_returns_empty_without_calls() -> None: + generator = FakeGenerator(context_answers=[]) + strategy = _strategy(generator=generator) + + assert strategy.rerank(query="Who?", documents=[], model_client=object()) == [] + assert generator.query_calls == [] + assert generator.context_calls == [] + + +def test_confidence_gain_long_document_fails() -> None: + from ranksmith.strategies import ConfidenceGainStrategy # noqa: PLC0415 + + strategy = ConfidenceGainStrategy( + base_estimator=FakeEstimator( + task_type="query_answerability_confidence", + scores=[0.2], + ), + context_estimator=FakeEstimator( + task_type="query_context_answerability_confidence", + scores=[0.5], + ), + answer_generator=FakeGenerator(context_answers=["a"]), + max_document_chars=3, + ) + + with pytest.raises(DocumentTooLongError): + strategy.rerank( + query="Who?", + documents=[Document(text="abcdef")], + model_client=object(), + top_k=None, + ) + + +@pytest.mark.parametrize("base_answer", ["", " ", 123]) +def test_confidence_gain_empty_or_non_string_answer_query_fails( + base_answer: object, +) -> None: + strategy = _strategy( + generator=FakeGenerator(base_answer=base_answer, context_answers=["a"]) + ) + + with pytest.raises(RerankProviderError): + strategy.rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +@pytest.mark.parametrize("context_answer", ["", " ", 123]) +def test_confidence_gain_empty_or_non_string_answer_with_context_fails( + context_answer: object, +) -> None: + strategy = _strategy(generator=FakeGenerator(context_answers=[context_answer])) + + with pytest.raises(RerankProviderError): + strategy.rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +def test_confidence_gain_generator_unexpected_exception_wraps_provider_error() -> None: + strategy = _strategy( + generator=FakeGenerator( + base_answer=TimeoutError("timeout"), + context_answers=["a"], + ) + ) + + with pytest.raises(RerankProviderError) as exc_info: + strategy.rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + assert isinstance(exc_info.value.__cause__, TimeoutError) + + +def test_confidence_gain_preserves_answer_query_provider_error() -> None: + error = RerankProviderError("provider failed") + strategy = _strategy( + generator=FakeGenerator( + base_answer=error, + context_answers=["a"], + ) + ) + + with pytest.raises(RerankProviderError) as exc_info: + strategy.rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + assert exc_info.value is error + assert exc_info.value.__cause__ is None + + +def test_confidence_gain_preserves_answer_with_context_provider_error() -> None: + error = RerankProviderError("provider failed") + strategy = _strategy( + generator=FakeGenerator( + context_answers=[error], + ) + ) + + with pytest.raises(RerankProviderError) as exc_info: + strategy.rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + assert exc_info.value is error + assert exc_info.value.__cause__ is None + + +def test_confidence_gain_facade_preserves_generator_provider_error() -> None: + error = RerankProviderError("provider failed") + reranker = AzureOpenAIReranker( + model_client=_unused_model_client(), + strategy=_strategy( + generator=FakeGenerator( + base_answer=error, + context_answers=["a"], + ) + ), + ) + + with pytest.raises(RerankProviderError) as exc_info: + reranker.rerank("Who?", [Document(text="alpha")]) + + assert exc_info.value is error + + +def test_confidence_gain_facade_wraps_unexpected_generator_error() -> None: + reranker = AzureOpenAIReranker( + model_client=_unused_model_client(), + strategy=_strategy( + generator=FakeGenerator( + base_answer=RuntimeError("generation failed"), + context_answers=["a"], + ) + ), + ) + + with pytest.raises(RerankProviderError) as exc_info: + reranker.rerank("Who?", [Document(text="alpha")]) + + assert isinstance(exc_info.value.__cause__, RuntimeError) + + +def test_confidence_gain_facade_preserves_estimator_provider_error() -> None: + error = RerankProviderError("confidence provider failed") + reranker = AzureOpenAIReranker( + model_client=_unused_model_client(), + strategy=_strategy(base_scores=[error]), + ) + + with pytest.raises(RerankProviderError) as exc_info: + reranker.rerank("Who?", [Document(text="alpha")]) + + assert exc_info.value is error + + +def test_confidence_gain_facade_wraps_unexpected_estimator_error() -> None: + reranker = AzureOpenAIReranker( + model_client=_unused_model_client(), + strategy=_strategy(base_scores=[RuntimeError("confidence failed")]), + ) + + with pytest.raises(RerankProviderError) as exc_info: + reranker.rerank("Who?", [Document(text="alpha")]) + + assert isinstance(exc_info.value.__cause__, RuntimeError) + + +@pytest.mark.parametrize("score", [math.nan, math.inf, -0.1, 1.1, "0.5", True]) +def test_confidence_gain_invalid_base_score_fails(score: object) -> None: + strategy = _strategy(base_scores=[score], context_scores=[0.5]) + + with pytest.raises(RerankStrategyError): + strategy.rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +@pytest.mark.parametrize("score", [math.nan, math.inf, -0.1, 1.1, "0.5", True]) +def test_confidence_gain_invalid_context_score_fails(score: object) -> None: + strategy = _strategy(context_scores=[score]) + + with pytest.raises(RerankStrategyError): + strategy.rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +def test_confidence_gain_estimator_confidence_error_propagates() -> None: + error = RuntimeError("confidence scorer failed") + strategy = _strategy(base_scores=[error]) + + with pytest.raises(RuntimeError, match="confidence scorer failed"): + strategy.rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + +def test_confidence_gain_passes_expected_confidence_inputs() -> None: + base_calls: list[object] = [] + context_calls: list[object] = [] + from ranksmith.strategies import ConfidenceGainStrategy # noqa: PLC0415 + + strategy = ConfidenceGainStrategy( + base_estimator=FakeEstimator( + task_type="query_answerability_confidence", + scores=[0.2], + calls=base_calls, + ), + context_estimator=FakeEstimator( + task_type="query_context_answerability_confidence", + scores=[0.5], + calls=context_calls, + ), + answer_generator=FakeGenerator( + base_answer="base", + context_answers=["context"], + ), + ) + + strategy.rerank( + query="Who?", + documents=[Document(text="alpha")], + model_client=object(), + ) + + assert base_calls == [ + QueryAnswerabilityConfidenceInput(query="Who?", answer="base") + ] + assert context_calls == [ + QueryContextAnswerabilityConfidenceInput( + query="Who?", + context="alpha", + answer="context", + ) + ] + + +def test_confidence_gain_e2e_smoke_from_artifacts_through_azure_facade( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + base_artifact_path = tmp_path / "query_answerability.joblib" + context_artifact_path = tmp_path / "query_context_answerability.joblib" + _install_artifact_joblib( + monkeypatch, + { + base_artifact_path: { + "metadata": _artifact_metadata("query_answerability_confidence"), + "scorer": ArtifactScorer([0.3]), + }, + context_artifact_path: { + "metadata": _artifact_metadata( + "query_context_answerability_confidence" + ), + "scorer": ArtifactScorer([0.45, 0.9]), + }, + }, + ) + + def fake_from_pretrained(**kwargs: object) -> ArtifactEncoder: + return ArtifactEncoder(max_length=cast(int, kwargs["max_length"])) + + monkeypatch.setattr( + "ranksmith.confidence.structural.FrozenAutoEncoder.from_pretrained", + fake_from_pretrained, + ) + + strategy = ConfidenceGainStrategy( + base_estimator=StructuralConfidenceEstimator.from_artifact(base_artifact_path), + context_estimator=StructuralConfidenceEstimator.from_artifact( + context_artifact_path + ), + answer_generator=FakeGenerator( + base_answer="base answer", + context_answers=["low answer", "high answer"], + ), + ) + reranker = AzureOpenAIReranker( + model_client=_unused_model_client(), + strategy=strategy, + ) + + results = reranker.rerank( + "who played karen in married to the mob?", + [ + Document( + id="similar-but-weak", + text="Michelle Pfeiffer appears in the film.", + ), + Document(id="direct-evidence", text="Nancy Travis played Karen."), + ], + ) + + assert [result.document.id for result in results] == [ + "direct-evidence", + "similar-but-weak", + ] + assert [result.metadata["base_confidence"] for result in results] == [0.3, 0.3] + assert [result.metadata["context_confidence"] for result in results] == [0.9, 0.45] + assert [result.metadata["confidence_gain"] for result in results] == pytest.approx( + [0.6, 0.15] + ) diff --git a/tests/test_confidence_generation_answerability.py b/tests/test_confidence_generation_answerability.py new file mode 100644 index 0000000..ec46c4e --- /dev/null +++ b/tests/test_confidence_generation_answerability.py @@ -0,0 +1,508 @@ +from __future__ import annotations + +import importlib +import json +from pathlib import Path +from typing import Any + +import pytest + +from ranksmith.confidence_generation.errors import ( + ConfidenceGenerationInputError, + ConfidenceGenerationParseError, +) +from ranksmith.confidence_generation.io import ( + load_completed_ids, + load_query_answerability_generation_samples, + load_query_context_answerability_generation_samples, +) +from ranksmith.confidence_generation.prompts import ( + QUERY_ANSWERABILITY_SYSTEM_PROMPT, + QUERY_CONTEXT_ANSWERABILITY_SYSTEM_PROMPT, + build_query_answerability_prompt, + build_query_context_answerability_prompt, +) +from ranksmith.confidence_generation.types import ( + QueryAnswerabilityGenerationSample, + QueryContextAnswerabilityGenerationSample, +) +from ranksmith.model import ModelMessage, ModelRequest, ModelResponse +from ranksmith.types import RerankUsage + +ANSWERABILITY_PUBLIC_NAMES = ( + "QueryAnswerabilityGenerationConfig", + "QueryContextAnswerabilityGenerationConfig", + "generate_query_answerability_confidence_dataset", + "generate_query_context_answerability_confidence_dataset", +) + + +class RecordingProvider: + def __init__(self, outputs: list[ModelResponse]) -> None: + self.outputs = list(outputs) + self.requests: list[ModelRequest] = [] + + def complete(self, request: ModelRequest) -> ModelResponse: + self.requests.append(request) + return self.outputs.pop(0) + + +def _write_jsonl(path: Path, rows: list[dict[str, object]]) -> None: + path.write_text( + "".join(json.dumps(row, ensure_ascii=False) + "\n" for row in rows), + encoding="utf-8", + ) + + +def _read_jsonl(path: Path) -> list[dict[str, Any]]: + return [ + json.loads(line) + for line in path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + + +def test_query_answerability_prompt_uses_parametric_knowledge_contract() -> None: + prompt = build_query_answerability_prompt( + QueryAnswerabilityGenerationSample( + id="q1", + query="Who played Karen?", + gold_answer="Nancy Travis", + ), + no_answer_value="UNKNOWN", + ) + + assert "Question:\nWho played Karen?" in prompt + assert "Context:" not in prompt + assert '{"answer":"short answer"}' in prompt + assert '{"answer":"UNKNOWN"}' in prompt + assert "parametric knowledge" in prompt + + +def test_query_context_answerability_prompt_uses_context_contract() -> None: + prompt = build_query_context_answerability_prompt( + QueryContextAnswerabilityGenerationSample( + id="c1", + query="Who played Karen?", + context="Nancy Travis played Karen.", + gold_answer="Nancy Travis", + ), + no_answer_value="NO_CONTEXT_ANSWER", + ) + + assert "Question:\nWho played Karen?" in prompt + assert "Context:\nNancy Travis played Karen." in prompt + assert '{"answer":"short answer"}' in prompt + assert '{"answer":"NO_CONTEXT_ANSWER"}' in prompt + assert "Use only the context" in prompt + + +def test_answerability_public_exports_are_submodule_only() -> None: + generation = importlib.import_module("ranksmith.confidence_generation") + ranksmith = importlib.import_module("ranksmith") + + for name in ANSWERABILITY_PUBLIC_NAMES: + assert hasattr(generation, name) + assert not hasattr(ranksmith, name) + + +def test_query_answerability_config_rejects_invalid_options(tmp_path: Path) -> None: + generation = importlib.import_module("ranksmith.confidence_generation") + provider = RecordingProvider([]) + + with pytest.raises(generation.ConfidenceGenerationInputError): + generation.QueryAnswerabilityGenerationConfig( + input_path=tmp_path / "in.jsonl", + output_path=tmp_path / "out.jsonl", + provider=object(), + ) + + with pytest.raises(generation.ConfidenceGenerationInputError): + generation.QueryAnswerabilityGenerationConfig( + input_path=tmp_path / "in.jsonl", + output_path=tmp_path / "out.jsonl", + provider=provider, + overwrite=True, + resume=True, + ) + + with pytest.raises(generation.ConfidenceGenerationInputError): + generation.QueryAnswerabilityGenerationConfig( + input_path=tmp_path / "in.jsonl", + output_path=tmp_path / "out.jsonl", + provider=provider, + max_items=0, + ) + + with pytest.raises(generation.ConfidenceGenerationInputError): + generation.QueryAnswerabilityGenerationConfig( + input_path=tmp_path / "in.jsonl", + output_path=tmp_path / "out.jsonl", + provider=provider, + no_answer_value=" ", + ) + + +def test_query_context_answerability_config_rejects_invalid_options( + tmp_path: Path, +) -> None: + generation = importlib.import_module("ranksmith.confidence_generation") + + with pytest.raises(generation.ConfidenceGenerationInputError): + generation.QueryContextAnswerabilityGenerationConfig( + input_path=tmp_path / "in.jsonl", + output_path=tmp_path / "out.jsonl", + provider=RecordingProvider([]), + max_context_chars=0, + ) + + +def test_answerability_raw_loaders_validate_and_preserve_text( + tmp_path: Path, +) -> None: + query_path = tmp_path / "query.jsonl" + context_path = tmp_path / "context.jsonl" + _write_jsonl( + query_path, + [ + { + "id": " q1 ", + "query": " Who? ", + "gold_answer": [" Gold "], + "source": " source ", + "group_id": " group ", + "metadata": {"dataset": "unit"}, + } + ], + ) + _write_jsonl( + context_path, + [ + { + "id": "c1", + "query": "q", + "context": " 12345 ", + "gold_answer": "g", + } + ], + ) + + query_samples = load_query_answerability_generation_samples(query_path) + assert query_samples[0].id == " q1 " + assert query_samples[0].query == " Who? " + assert query_samples[0].gold_answer == [" Gold "] + assert query_samples[0].source == " source " + assert query_samples[0].group_id == " group " + assert query_samples[0].metadata["dataset"] == "unit" + + with pytest.raises(ConfidenceGenerationInputError, match="context"): + load_query_context_answerability_generation_samples( + context_path, + max_context_chars=5, + ) + + +@pytest.mark.parametrize( + "row", + [ + {"id": "q1", "query": "q"}, + {"id": "q1", "query": " ", "gold_answer": "g"}, + {"id": "q1", "query": "q", "gold_answer": "", "extra": "nope"}, + {"id": "q1", "query": "q", "gold_answer": "g", "metadata": []}, + ], +) +def test_query_answerability_loader_rejects_invalid_rows( + tmp_path: Path, + row: dict[str, object], +) -> None: + path = tmp_path / "query.jsonl" + _write_jsonl(path, [row]) + + with pytest.raises(ConfidenceGenerationInputError): + load_query_answerability_generation_samples(path) + + +def test_generate_answerability_datasets_write_canonical_rows_and_usage( + tmp_path: Path, +) -> None: + generation = importlib.import_module("ranksmith.confidence_generation") + query_input = tmp_path / "query_in.jsonl" + query_output = tmp_path / "query_out.jsonl" + context_input = tmp_path / "context_in.jsonl" + context_output = tmp_path / "context_out.jsonl" + _write_jsonl( + query_input, + [ + { + "id": "q1", + "query": "Who played Karen?", + "gold_answer": ["nancy travis"], + "metadata": {"dataset": "unit"}, + } + ], + ) + _write_jsonl( + context_input, + [ + { + "id": "c1", + "query": "Who played Karen?", + "context": "Nancy Travis played Karen.", + "gold_answer": "Nancy Travis", + "source": "row-source", + } + ], + ) + usage = RerankUsage(prompt_tokens=1, completion_tokens=2, total_tokens=3) + query_provider = RecordingProvider( + [ModelResponse(content='{"answer":" Nancy Travis "}', usage=usage)] + ) + context_provider = RecordingProvider( + [ModelResponse(content='{"answer":"__NO_ANSWER__"}')] + ) + seen_usage: list[RerankUsage] = [] + + query_result = generation.generate_query_answerability_confidence_dataset( + generation.QueryAnswerabilityGenerationConfig( + input_path=query_input, + output_path=query_output, + provider=query_provider, + source="config-source", + on_usage=seen_usage.append, + ) + ) + context_result = generation.generate_query_context_answerability_confidence_dataset( + generation.QueryContextAnswerabilityGenerationConfig( + input_path=context_input, + output_path=context_output, + provider=context_provider, + include_raw_model_output=False, + ) + ) + + query_rows = _read_jsonl(query_output) + context_rows = _read_jsonl(context_output) + assert query_result.generated_count == 1 + assert query_result.positive_count == 1 + assert query_rows[0]["task_type"] == "query_answerability_confidence" + assert query_rows[0]["id"] == "q1" + assert query_rows[0]["query"] == "Who played Karen?" + assert query_rows[0]["answer"] == " Nancy Travis " + assert query_rows[0]["gold_answer"] == ["nancy travis"] + assert query_rows[0]["label"] == 1 + assert query_rows[0]["source"] == "config-source" + assert query_rows[0]["metadata"]["input_metadata"] == {"dataset": "unit"} + assert query_rows[0]["metadata"]["generation"]["generation_task"] == ( + "query_answerability" + ) + assert query_rows[0]["metadata"]["generation"]["match_policy"] == ( + "normalized_exact" + ) + assert query_rows[0]["metadata"]["generation"]["raw_model_output"] == ( + '{"answer":" Nancy Travis "}' + ) + assert context_result.negative_count == 1 + assert context_rows[0]["task_type"] == "query_context_answerability_confidence" + assert context_rows[0]["context"] == "Nancy Travis played Karen." + assert context_rows[0]["source"] == "row-source" + assert "raw_model_output" not in context_rows[0]["metadata"]["generation"] + assert seen_usage == [usage] + + +def test_answerability_pipelines_use_expected_model_request_messages( + tmp_path: Path, +) -> None: + generation = importlib.import_module("ranksmith.confidence_generation") + query_input = tmp_path / "query_in.jsonl" + query_output = tmp_path / "query_out.jsonl" + context_input = tmp_path / "context_in.jsonl" + context_output = tmp_path / "context_out.jsonl" + _write_jsonl( + query_input, + [{"id": "q1", "query": "Who played Karen?", "gold_answer": "Nancy Travis"}], + ) + _write_jsonl( + context_input, + [ + { + "id": "c1", + "query": "Who played Karen?", + "context": "Nancy Travis played Karen.", + "gold_answer": "Nancy Travis", + } + ], + ) + query_provider = RecordingProvider([ModelResponse(content='{"answer":"Nancy"}')]) + context_provider = RecordingProvider([ModelResponse(content='{"answer":"Nancy"}')]) + + generation.generate_query_answerability_confidence_dataset( + generation.QueryAnswerabilityGenerationConfig( + input_path=query_input, + output_path=query_output, + provider=query_provider, + no_answer_value="UNKNOWN", + ) + ) + generation.generate_query_context_answerability_confidence_dataset( + generation.QueryContextAnswerabilityGenerationConfig( + input_path=context_input, + output_path=context_output, + provider=context_provider, + no_answer_value="NO_CONTEXT_ANSWER", + ) + ) + + assert query_provider.requests[0].messages == [ + ModelMessage(role="system", content=QUERY_ANSWERABILITY_SYSTEM_PROMPT), + ModelMessage( + role="user", + content=build_query_answerability_prompt( + QueryAnswerabilityGenerationSample( + id="q1", + query="Who played Karen?", + gold_answer="Nancy Travis", + ), + no_answer_value="UNKNOWN", + ), + ), + ] + assert context_provider.requests[0].messages == [ + ModelMessage( + role="system", + content=QUERY_CONTEXT_ANSWERABILITY_SYSTEM_PROMPT, + ), + ModelMessage( + role="user", + content=build_query_context_answerability_prompt( + QueryContextAnswerabilityGenerationSample( + id="c1", + query="Who played Karen?", + context="Nancy Travis played Karen.", + gold_answer="Nancy Travis", + ), + no_answer_value="NO_CONTEXT_ANSWER", + ), + ), + ] + + +def test_answerability_resume_accepts_completed_rows_and_skips( + tmp_path: Path, +) -> None: + generation = importlib.import_module("ranksmith.confidence_generation") + input_path = tmp_path / "query_in.jsonl" + output_path = tmp_path / "query_out.jsonl" + _write_jsonl( + input_path, + [ + {"id": "q1", "query": "q", "gold_answer": "g"}, + {"id": "q2", "query": "q", "gold_answer": "g"}, + ], + ) + _write_jsonl( + output_path, + [ + { + "id": "q1", + "task_type": "query_answerability_confidence", + "query": "q", + "answer": "g", + "label": 1, + } + ], + ) + provider = RecordingProvider([ModelResponse(content='{"answer":"g"}')]) + + result = generation.generate_query_answerability_confidence_dataset( + generation.QueryAnswerabilityGenerationConfig( + input_path=input_path, + output_path=output_path, + provider=provider, + resume=True, + ) + ) + + rows = _read_jsonl(output_path) + assert result.skipped_count == 1 + assert result.generated_count == 1 + assert [row["id"] for row in rows] == ["q1", "q2"] + + +def test_query_context_answerability_resume_after_partial_parse_failure( + tmp_path: Path, +) -> None: + generation = importlib.import_module("ranksmith.confidence_generation") + input_path = tmp_path / "context_in.jsonl" + output_path = tmp_path / "context_out.jsonl" + _write_jsonl( + input_path, + [ + { + "id": "c1", + "query": "q", + "context": "gold answer appears here", + "gold_answer": "gold", + }, + { + "id": "c2", + "query": "q", + "context": "next answer appears here", + "gold_answer": "next", + }, + ], + ) + first_provider = RecordingProvider( + [ + ModelResponse(content='{"answer":"gold"}'), + ModelResponse(content='{"answer":}'), + ] + ) + + with pytest.raises(ConfidenceGenerationParseError, match="valid JSON"): + generation.generate_query_context_answerability_confidence_dataset( + generation.QueryContextAnswerabilityGenerationConfig( + input_path=input_path, + output_path=output_path, + provider=first_provider, + ) + ) + + assert len(first_provider.requests) == 2 + assert [row["id"] for row in _read_jsonl(output_path)] == ["c1"] + + second_provider = RecordingProvider([ModelResponse(content='{"answer":"next"}')]) + result = generation.generate_query_context_answerability_confidence_dataset( + generation.QueryContextAnswerabilityGenerationConfig( + input_path=input_path, + output_path=output_path, + provider=second_provider, + resume=True, + ) + ) + + rows = _read_jsonl(output_path) + assert len(second_provider.requests) == 1 + assert result.skipped_count == 1 + assert result.generated_count == 1 + assert [row["id"] for row in rows] == ["c1", "c2"] + + +def test_answerability_resume_rejects_mismatched_task_type( + tmp_path: Path, +) -> None: + path = tmp_path / "out.jsonl" + _write_jsonl( + path, + [ + { + "id": "q1", + "task_type": "query_context_answerability_confidence", + "query": "q", + "answer": "a", + "label": 1, + } + ], + ) + + with pytest.raises(ConfidenceGenerationInputError, match="task_type"): + load_completed_ids(path, task_type="query_answerability_confidence") diff --git a/tests/test_confidence_scorer.py b/tests/test_confidence_scorer.py index 613c7c6..49fc260 100644 --- a/tests/test_confidence_scorer.py +++ b/tests/test_confidence_scorer.py @@ -8,7 +8,7 @@ import pytest -from ranksmith.confidence import ConfidenceArtifactError, ScorerMetadata +from ranksmith.confidence import ConfidenceArtifactError, ScorerMetadata, TaskType from ranksmith.confidence.scorer import ( ARTIFACT_SCHEMA_VERSION, JoblibScorerWrapper, @@ -138,6 +138,33 @@ def test_validate_scorer_metadata_accepts_matching_metadata() -> None: ) +@pytest.mark.parametrize( + "task_type", + [ + "query_answerability_confidence", + "query_context_answerability_confidence", + ], +) +def test_metadata_parser_and_validator_accept_answerability_task_types( + task_type: TaskType, +) -> None: + parsed = metadata_from_dict(metadata_dict(task_type=task_type)) + + assert parsed.task_type == task_type + validate_scorer_metadata( + parsed, + encoder_name="bert-base-uncased", + encoder_revision=None, + tokenizer_name="bert-base-uncased", + tokenizer_revision=None, + task_type=task_type, + max_length=256, + input_template_version="structural-template-v1", + feature_schema_version="structural-v1", + feature_dim=70, + ) + + def test_validate_scorer_metadata_rejects_mismatch() -> None: with pytest.raises(ConfidenceArtifactError): validate_scorer_metadata( diff --git a/tests/test_confidence_templates.py b/tests/test_confidence_templates.py index 513c951..c2451af 100644 --- a/tests/test_confidence_templates.py +++ b/tests/test_confidence_templates.py @@ -6,6 +6,9 @@ AnswerConfidenceInput, ConfidenceInputError, JudgmentConfidenceInput, + QueryAnswerabilityConfidenceInput, + QueryContextAnswerabilityConfidenceInput, + TaskType, ) from ranksmith.confidence.templates import ( format_confidence_input, @@ -34,6 +37,31 @@ def test_formats_judgment_confidence_template() -> None: assert text == "Query:\nquery\n\nDocument:\ndocument\n\nJudgment:\ndirect evidence" +def test_formats_query_answerability_confidence_template() -> None: + text = format_confidence_input( + "query_answerability_confidence", + QueryAnswerabilityConfidenceInput(query="Who?", answer="Nancy Travis"), + ) + + assert text == "Query:\nWho?\n\nAnswer:\nNancy Travis" + + +def test_formats_query_context_answerability_confidence_template() -> None: + text = format_confidence_input( + "query_context_answerability_confidence", + QueryContextAnswerabilityConfidenceInput( + query="Who?", + context="Karen was played by Nancy Travis.", + answer="Nancy Travis", + ), + ) + + assert text == ( + "Query:\nWho?\n\nContext:\nKaren was played by Nancy Travis." + "\n\nAnswer:\nNancy Travis" + ) + + def test_rejects_mismatched_input_type() -> None: with pytest.raises(ConfidenceInputError): format_confidence_input( @@ -46,9 +74,79 @@ def test_rejects_mismatched_input_type() -> None: ) +@pytest.mark.parametrize( + ("task_type", "item"), + [ + ( + "query_answerability_confidence", + QueryContextAnswerabilityConfidenceInput( + query="Who?", + context="Karen was played by Nancy Travis.", + answer="Nancy Travis", + ), + ), + ( + "query_context_answerability_confidence", + QueryAnswerabilityConfidenceInput(query="Who?", answer="Nancy Travis"), + ), + ], +) +def test_rejects_mismatched_answerability_input_type( + task_type: TaskType, + item: QueryAnswerabilityConfidenceInput | QueryContextAnswerabilityConfidenceInput, +) -> None: + with pytest.raises(ConfidenceInputError): + format_confidence_input(task_type, item) + + def test_rejects_whitespace_required_field() -> None: with pytest.raises(ConfidenceInputError): format_confidence_input( "answer_confidence", AnswerConfidenceInput(context=" ", answer="answer"), ) + + +@pytest.mark.parametrize( + ("task_type", "item"), + [ + ( + "query_answerability_confidence", + QueryAnswerabilityConfidenceInput(query=" ", answer="Nancy Travis"), + ), + ( + "query_answerability_confidence", + QueryAnswerabilityConfidenceInput(query="Who?", answer="\t"), + ), + ( + "query_context_answerability_confidence", + QueryContextAnswerabilityConfidenceInput( + query=" ", + context="Karen was played by Nancy Travis.", + answer="Nancy Travis", + ), + ), + ( + "query_context_answerability_confidence", + QueryContextAnswerabilityConfidenceInput( + query="Who?", + context="\n", + answer="Nancy Travis", + ), + ), + ( + "query_context_answerability_confidence", + QueryContextAnswerabilityConfidenceInput( + query="Who?", + context="Karen was played by Nancy Travis.", + answer="\t", + ), + ), + ], +) +def test_answerability_templates_reject_whitespace_required_fields( + task_type: TaskType, + item: QueryAnswerabilityConfidenceInput | QueryContextAnswerabilityConfidenceInput, +) -> None: + with pytest.raises(ConfidenceInputError): + format_confidence_input(task_type, item) diff --git a/tests/test_confidence_training_answerability.py b/tests/test_confidence_training_answerability.py new file mode 100644 index 0000000..8627a1b --- /dev/null +++ b/tests/test_confidence_training_answerability.py @@ -0,0 +1,184 @@ +from __future__ import annotations + +import json +from pathlib import Path + +import pytest + +from ranksmith.confidence_training import ( + ConfidenceDatasetError, + ConfidenceTrainingConfig, +) +from ranksmith.confidence_training.dataset import load_canonical_dataset +from ranksmith.confidence_training.features import extract_feature_rows +from ranksmith.confidence_training.types import CanonicalConfidenceSample + + +class RecordingEncoder: + max_length = 34 + + def __init__(self) -> None: + self.texts: list[str] = [] + + def encode(self, text: str) -> tuple[list[list[float]], list[int]]: + self.texts.append(text) + return [[0.0, 1.0], [1.0, 2.0]], [1, 1] + + +def _write_jsonl(path: Path, rows: list[dict[str, object]]) -> None: + path.write_text( + "".join(json.dumps(row) + "\n" for row in rows), + encoding="utf-8", + ) + + +def test_config_accepts_query_answerability_confidence(tmp_path: Path) -> None: + config = ConfidenceTrainingConfig( + task_type="query_answerability_confidence", + dataset_path=tmp_path / "dataset.jsonl", + output_dir=tmp_path / "run", + export_path=tmp_path / "artifact.joblib", + ) + + assert config.task_type == "query_answerability_confidence" + + +def test_config_accepts_query_context_answerability_confidence( + tmp_path: Path, +) -> None: + config = ConfidenceTrainingConfig( + task_type="query_context_answerability_confidence", + dataset_path=tmp_path / "dataset.jsonl", + output_dir=tmp_path / "run", + export_path=tmp_path / "artifact.joblib", + ) + + assert config.task_type == "query_context_answerability_confidence" + + +def test_load_query_answerability_confidence_canonical_jsonl( + tmp_path: Path, +) -> None: + path = tmp_path / "query-answerability.jsonl" + _write_jsonl( + path, + [ + { + "id": "qa1", + "query": "Who played Karen?", + "answer": "Nancy Travis", + "label": 1, + "gold_answer": ["Nancy Travis"], + "metadata": {"split": "fixture"}, + } + ], + ) + + samples = load_canonical_dataset( + path, + task_type="query_answerability_confidence", + ) + + assert len(samples) == 1 + assert samples[0].task_type == "query_answerability_confidence" + assert samples[0].query == "Who played Karen?" + assert samples[0].answer == "Nancy Travis" + assert samples[0].gold_answer == ["Nancy Travis"] + assert samples[0].metadata["split"] == "fixture" + + +def test_load_query_context_answerability_confidence_canonical_jsonl( + tmp_path: Path, +) -> None: + path = tmp_path / "query-context-answerability.jsonl" + _write_jsonl( + path, + [ + { + "id": "qca1", + "query": "Who played Karen?", + "context": "Karen was played by Nancy Travis.", + "answer": "Nancy Travis", + "label": 1, + "gold_answer": "Nancy Travis", + "group_id": "question-1", + } + ], + ) + + samples = load_canonical_dataset( + path, + task_type="query_context_answerability_confidence", + ) + + assert len(samples) == 1 + assert samples[0].task_type == "query_context_answerability_confidence" + assert samples[0].query == "Who played Karen?" + assert samples[0].context == "Karen was played by Nancy Travis." + assert samples[0].answer == "Nancy Travis" + assert samples[0].gold_answer == "Nancy Travis" + assert samples[0].group_id == "question-1" + + +def test_query_context_answerability_missing_context_fails( + tmp_path: Path, +) -> None: + path = tmp_path / "missing-context.jsonl" + _write_jsonl( + path, + [ + { + "id": "qca1", + "query": "Who played Karen?", + "answer": "Nancy Travis", + "label": 1, + } + ], + ) + + with pytest.raises(ConfidenceDatasetError, match="missing required field: context"): + load_canonical_dataset( + path, + task_type="query_context_answerability_confidence", + ) + + +def test_extract_feature_rows_formats_answerability_samples( + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setattr( + "ranksmith.confidence_training.features.extract_structural_features", + lambda hidden_states, attention_mask, *, max_length: [0.0] * 70, + ) + encoder = RecordingEncoder() + samples = [ + CanonicalConfidenceSample( + id="qa1", + task_type="query_answerability_confidence", + label=1, + query="Who played Karen?", + answer="Nancy Travis", + ), + CanonicalConfidenceSample( + id="qca1", + task_type="query_context_answerability_confidence", + label=0, + query="Who played Karen?", + context="Karen was played by Nancy Travis.", + answer="Nancy Travis", + ), + ] + + rows = extract_feature_rows(samples, encoder=encoder) + + assert [row.task_type for row in rows] == [ + "query_answerability_confidence", + "query_context_answerability_confidence", + ] + assert encoder.texts == [ + "Query:\nWho played Karen?\n\nAnswer:\nNancy Travis", + ( + "Query:\nWho played Karen?\n\nContext:\n" + "Karen was played by Nancy Travis.\n\nAnswer:\nNancy Travis" + ), + ] diff --git a/tests/test_confidence_training_dataset_report.py b/tests/test_confidence_training_dataset_report.py new file mode 100644 index 0000000..870c4ce --- /dev/null +++ b/tests/test_confidence_training_dataset_report.py @@ -0,0 +1,160 @@ +from __future__ import annotations + +import importlib.util +import json +import subprocess +from pathlib import Path +from typing import Any + +import pytest + +from ranksmith.confidence_training import ConfidenceDatasetError +from ranksmith.confidence_training.dataset_report import build_dataset_report + +REPORT_SCRIPT_PATH = Path("scripts/report_confidence_dataset.py") + + +def _write_jsonl(path: Path, rows: list[dict[str, object]]) -> None: + path.write_text( + "".join(json.dumps(row, ensure_ascii=False) + "\n" for row in rows), + encoding="utf-8", + ) + + +def _load_report_script() -> Any: + spec = importlib.util.spec_from_file_location( + "report_confidence_dataset", + REPORT_SCRIPT_PATH, + ) + assert spec is not None + assert spec.loader is not None + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def test_report_confidence_dataset_help_exits_successfully() -> None: + result = subprocess.run( + ["uv", "run", "python", str(REPORT_SCRIPT_PATH), "--help"], + check=False, + capture_output=True, + text=True, + ) + + assert result.returncode == 0 + assert "--task" in result.stdout + + +def test_build_dataset_report_counts_sources_groups_and_missing_values( + tmp_path: Path, +) -> None: + dataset = tmp_path / "dataset.jsonl" + _write_jsonl( + dataset, + [ + { + "id": "q1", + "task_type": "query_answerability_confidence", + "query": "q1", + "answer": "a", + "label": 1, + "source": "alpha", + "group_id": "g1", + }, + { + "id": "q2", + "task_type": "query_answerability_confidence", + "query": "q2", + "answer": "a", + "label": 0, + "source": "alpha", + }, + { + "id": "q3", + "task_type": "query_answerability_confidence", + "query": "q3", + "answer": "a", + "label": 1, + "source": "beta", + "group_id": "g2", + }, + { + "id": "q4", + "task_type": "query_answerability_confidence", + "query": "q4", + "answer": "a", + "label": 0, + }, + ], + ) + + report = build_dataset_report(dataset, "query_answerability_confidence") + + assert report["task_type"] == "query_answerability_confidence" + assert report["sample_count"] == 4 + assert report["positive_count"] == 2 + assert report["negative_count"] == 2 + assert report["positive_rate"] == 0.5 + assert report["source_count"] == 2 + assert report["group_count"] == 2 + assert report["missing_source_count"] == 1 + assert report["missing_source_rate"] == 0.25 + assert report["missing_group_id_count"] == 2 + assert report["missing_group_id_rate"] == 0.5 + assert report["sources"] == { + "__MISSING__": { + "sample_count": 1, + "positive_count": 0, + "negative_count": 1, + "positive_rate": 0.0, + }, + "alpha": { + "sample_count": 2, + "positive_count": 1, + "negative_count": 1, + "positive_rate": 0.5, + }, + "beta": { + "sample_count": 1, + "positive_count": 1, + "negative_count": 0, + "positive_rate": 1.0, + }, + } + + +def test_build_dataset_report_inherits_duplicate_id_error(tmp_path: Path) -> None: + dataset = tmp_path / "duplicate.jsonl" + _write_jsonl( + dataset, + [ + {"id": "q1", "query": "q", "answer": "a", "label": 1}, + {"id": "q1", "query": "q", "answer": "a", "label": 0}, + ], + ) + + with pytest.raises(ConfidenceDatasetError, match="duplicate id"): + build_dataset_report(dataset, "query_answerability_confidence") + + +def test_report_cli_prints_pretty_json(tmp_path: Path, capsys: Any) -> None: + script = _load_report_script() + dataset = tmp_path / "dataset.jsonl" + _write_jsonl( + dataset, + [{"id": "q1", "query": "q", "answer": "a", "label": 1, "source": "fixture"}], + ) + + status = script.main( + [ + "--task", + "query_answerability_confidence", + "--dataset", + str(dataset), + ] + ) + + assert status == 0 + output = capsys.readouterr().out + assert '\n "sample_count": 1' in output + assert json.loads(output)["sources"]["fixture"]["positive_rate"] == 1.0 diff --git a/tests/test_generate_confidence_dataset_script.py b/tests/test_generate_confidence_dataset_script.py new file mode 100644 index 0000000..21b1bb0 --- /dev/null +++ b/tests/test_generate_confidence_dataset_script.py @@ -0,0 +1,237 @@ +from __future__ import annotations + +import importlib.util +import json +import subprocess +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import pytest + +SCRIPT_PATH = Path("scripts/generate_confidence_dataset.py") + + +@dataclass(frozen=True) +class FakeGenerationResult: + output_path: Path + input_count: int = 2 + generated_count: int = 2 + skipped_count: int = 0 + positive_count: int = 1 + negative_count: int = 1 + + +class FakeLMStudioProvider: + instances: list[FakeLMStudioProvider] = [] + + def __init__( + self, + *, + base_url: str | None, + model: str | None, + api_key: str | None, + max_tokens: int, + timeout: float | None, + ) -> None: + self.base_url = base_url + self.model = model + self.api_key = api_key + self.max_tokens = max_tokens + self.timeout = timeout + self.instances.append(self) + + def complete(self, request: object) -> object: + raise AssertionError("live provider must not be called in CLI tests") + + +def _load_script() -> Any: + spec = importlib.util.spec_from_file_location( + "generate_confidence_dataset", + SCRIPT_PATH, + ) + assert spec is not None + assert spec.loader is not None + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def test_generate_confidence_dataset_help_exits_successfully() -> None: + result = subprocess.run( + ["uv", "run", "python", str(SCRIPT_PATH), "--help"], + check=False, + capture_output=True, + text=True, + ) + + assert result.returncode == 0 + assert "--task" in result.stdout + + +def test_query_answerability_cli_builds_lmstudio_provider_and_config( + monkeypatch: Any, + tmp_path: Path, + capsys: Any, +) -> None: + script = _load_script() + calls: list[Any] = [] + + def fake_generate(config: Any) -> FakeGenerationResult: + calls.append(config) + return FakeGenerationResult(output_path=Path(config.output_path)) + + monkeypatch.setattr(script, "LMStudioModelProvider", FakeLMStudioProvider) + monkeypatch.setattr( + script, + "generate_query_answerability_confidence_dataset", + fake_generate, + ) + + status = script.main( + [ + "--task", + "query_answerability_confidence", + "--provider", + "lmstudio", + "--lmstudio-base-url", + "http://localhost:1234/v1", + "--lmstudio-model", + "local-model", + "--lmstudio-api-key", + "key", + "--timeout", + "15", + "--input", + str(tmp_path / "raw.jsonl"), + "--output", + str(tmp_path / "canonical.jsonl"), + "--overwrite", + "--max-items", + "3", + "--source", + "unit", + ] + ) + + assert status == 0 + provider = FakeLMStudioProvider.instances[-1] + assert provider.base_url == "http://localhost:1234/v1" + assert provider.model == "local-model" + assert provider.api_key == "key" + assert provider.max_tokens == 128 + assert provider.timeout == 15 + config = calls[0] + assert config.overwrite is True + assert config.resume is False + assert config.max_items == 3 + assert config.source == "unit" + summary = json.loads(capsys.readouterr().out) + assert summary["output_path"] == str(tmp_path / "canonical.jsonl") + assert summary["generated_count"] == 2 + + +def test_query_context_cli_passes_max_context_chars_and_resume( + monkeypatch: Any, + tmp_path: Path, + capsys: Any, +) -> None: + script = _load_script() + calls: list[Any] = [] + + def fake_generate(config: Any) -> FakeGenerationResult: + calls.append(config) + return FakeGenerationResult(output_path=Path(config.output_path)) + + monkeypatch.setattr(script, "LMStudioModelProvider", FakeLMStudioProvider) + monkeypatch.setattr( + script, + "generate_query_context_answerability_confidence_dataset", + fake_generate, + ) + + status = script.main( + [ + "--task", + "query_context_answerability_confidence", + "--provider", + "lmstudio", + "--lmstudio-model", + "local-model", + "--lmstudio-max-tokens", + "64", + "--input", + str(tmp_path / "raw.jsonl"), + "--output", + str(tmp_path / "canonical.jsonl"), + "--resume", + "--max-context-chars", + "1234", + ] + ) + + assert status == 0 + assert FakeLMStudioProvider.instances[-1].max_tokens == 64 + config = calls[0] + assert config.resume is True + assert config.max_context_chars == 1234 + assert json.loads(capsys.readouterr().out)["output_path"] == str( + tmp_path / "canonical.jsonl" + ) + + +def test_query_context_cli_defaults_max_context_chars( + monkeypatch: Any, + tmp_path: Path, +) -> None: + script = _load_script() + calls: list[Any] = [] + + def fake_generate(config: Any) -> FakeGenerationResult: + calls.append(config) + return FakeGenerationResult(output_path=Path(config.output_path)) + + monkeypatch.setattr(script, "LMStudioModelProvider", FakeLMStudioProvider) + monkeypatch.setattr( + script, + "generate_query_context_answerability_confidence_dataset", + fake_generate, + ) + + status = script.main( + [ + "--task", + "query_context_answerability_confidence", + "--provider", + "lmstudio", + "--input", + str(tmp_path / "raw.jsonl"), + "--output", + str(tmp_path / "canonical.jsonl"), + ] + ) + + assert status == 0 + assert calls[0].max_context_chars == 8000 + + +def test_query_answerability_cli_rejects_max_context_chars(tmp_path: Path) -> None: + script = _load_script() + + with pytest.raises(SystemExit) as exc_info: + script._parse_args( + [ + "--task", + "query_answerability_confidence", + "--provider", + "lmstudio", + "--input", + str(tmp_path / "raw.jsonl"), + "--output", + str(tmp_path / "canonical.jsonl"), + "--max-context-chars", + "1234", + ] + ) + + assert exc_info.value.code != 0 diff --git a/tests/test_lmstudio_provider.py b/tests/test_lmstudio_provider.py new file mode 100644 index 0000000..3f35cb3 --- /dev/null +++ b/tests/test_lmstudio_provider.py @@ -0,0 +1,327 @@ +from __future__ import annotations + +import importlib +from dataclasses import dataclass +from typing import Any, cast + +import pytest + +from ranksmith.errors import RerankInputError, RerankProviderError +from ranksmith.model import ModelMessage, ModelRequest +from ranksmith.types import RerankUsage + + +@dataclass +class FakeUsage: + prompt_tokens: int + completion_tokens: int + total_tokens: int + + +@dataclass +class FakeMessage: + content: str | None + + +@dataclass +class FakeChoice: + message: FakeMessage + + +@dataclass +class FakeResponse: + choices: list[FakeChoice] + usage: FakeUsage | None = None + + +class FakeCompletions: + def __init__(self, response: object | None = None, error: Exception | None = None): + self.response = response + self.error = error + self.calls: list[dict[str, Any]] = [] + + def create(self, **kwargs: Any) -> object: + self.calls.append(kwargs) + if self.error is not None: + raise self.error + return self.response + + +class FakeClient: + def __init__(self, completions: FakeCompletions): + self.chat = type( + "FakeChat", + (), + {"completions": completions}, + )() + + +class RecordingOpenAI(FakeClient): + instances: list[RecordingOpenAI] = [] + + def __init__(self, *, api_key: str, base_url: str, timeout: float | None): + self.api_key = api_key + self.base_url = base_url + self.timeout = timeout + self.completions = FakeCompletions( + FakeResponse(choices=[FakeChoice(FakeMessage('{"ranking": [1]}'))]) + ) + super().__init__(self.completions) + self.instances.append(self) + + +def _request(response_format: str = "json_object") -> ModelRequest: + return ModelRequest( + messages=[ + ModelMessage(role="system", content="system"), + ModelMessage(role="user", content="user"), + ], + response_format=cast(Any, response_format), + temperature=0.2, + ) + + +def test_lmstudio_provider_export_is_submodule_only() -> None: + integrations = importlib.import_module("ranksmith.integrations") + root = importlib.import_module("ranksmith") + + assert integrations.LMStudioModelProvider is not None + assert not hasattr(root, "LMStudioModelProvider") + + +def test_lmstudio_provider_converts_json_object_to_json_schema() -> None: + from ranksmith.integrations import LMStudioModelProvider + + completions = FakeCompletions( + FakeResponse( + choices=[FakeChoice(FakeMessage('{"ranking": [1]}'))], + usage=FakeUsage(prompt_tokens=3, completion_tokens=4, total_tokens=7), + ) + ) + provider = LMStudioModelProvider( + model="local-model", + client=FakeClient(completions), + ) + + response = provider.complete(_request()) + + assert response.content == '{"ranking": [1]}' + assert response.usage == RerankUsage( + prompt_tokens=3, + completion_tokens=4, + total_tokens=7, + ) + assert completions.calls == [ + { + "model": "local-model", + "messages": [ + {"role": "system", "content": "system"}, + {"role": "user", "content": "user"}, + ], + "response_format": { + "type": "json_schema", + "json_schema": { + "name": "ranksmith_json_response", + "schema": {"type": "object"}, + }, + }, + "temperature": 0.2, + "max_tokens": 128, + "reasoning_effort": "none", + } + ] + + +def test_lmstudio_provider_passes_custom_max_tokens() -> None: + from ranksmith.integrations import LMStudioModelProvider + + completions = FakeCompletions( + FakeResponse(choices=[FakeChoice(FakeMessage('{"ranking": [1]}'))]) + ) + provider = LMStudioModelProvider( + model="local-model", + max_tokens=64, + client=FakeClient(completions), + ) + + provider.complete(_request()) + + assert completions.calls[0]["max_tokens"] == 64 + + +def test_lmstudio_provider_uses_lmstudio_model_env_fallback( + monkeypatch: pytest.MonkeyPatch, +) -> None: + from ranksmith.integrations import LMStudioModelProvider + + monkeypatch.setenv("LMSTUDIO_MODEL", "env-model") + completions = FakeCompletions( + FakeResponse(choices=[FakeChoice(FakeMessage('{"ranking": [1]}'))]) + ) + provider = LMStudioModelProvider(client=FakeClient(completions)) + + provider.complete(_request()) + + assert completions.calls[0]["model"] == "env-model" + + +def test_lmstudio_provider_constructs_client_from_env_fallbacks( + monkeypatch: pytest.MonkeyPatch, +) -> None: + import ranksmith.integrations.lmstudio_provider as lmstudio_provider + from ranksmith.integrations import LMStudioModelProvider + + RecordingOpenAI.instances = [] + monkeypatch.setattr(lmstudio_provider, "OpenAI", RecordingOpenAI) + monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://localhost:4321/v1") + monkeypatch.setenv("LMSTUDIO_API_KEY", "env-key") + monkeypatch.setenv("LMSTUDIO_MODEL", "env-model") + + provider = LMStudioModelProvider(timeout=2.5) + provider.complete(_request()) + + assert len(RecordingOpenAI.instances) == 1 + constructed = RecordingOpenAI.instances[0] + assert constructed.base_url == "http://localhost:4321/v1" + assert constructed.api_key == "env-key" + assert constructed.timeout == 2.5 + assert constructed.completions.calls[0]["model"] == "env-model" + + +def test_lmstudio_provider_constructs_client_from_default_fallbacks( + monkeypatch: pytest.MonkeyPatch, +) -> None: + import ranksmith.integrations.lmstudio_provider as lmstudio_provider + from ranksmith.integrations import LMStudioModelProvider + + RecordingOpenAI.instances = [] + monkeypatch.setattr(lmstudio_provider, "OpenAI", RecordingOpenAI) + monkeypatch.setenv("LMSTUDIO_MODEL", "env-model") + monkeypatch.delenv("LMSTUDIO_BASE_URL", raising=False) + monkeypatch.delenv("LMSTUDIO_API_KEY", raising=False) + + provider = LMStudioModelProvider() + + assert len(RecordingOpenAI.instances) == 1 + constructed = RecordingOpenAI.instances[0] + assert constructed.base_url == "http://localhost:1234/v1" + assert constructed.api_key == "lm-studio" + assert provider.model == "env-model" + assert provider.base_url == "http://localhost:1234/v1" + assert provider.api_key_configured is True + assert provider.max_tokens == 128 + + +def test_lmstudio_provider_missing_model_raises( + monkeypatch: pytest.MonkeyPatch, +) -> None: + from ranksmith.integrations import LMStudioModelProvider + + monkeypatch.delenv("LMSTUDIO_MODEL", raising=False) + + with pytest.raises(RerankInputError, match="LMSTUDIO_MODEL is required"): + LMStudioModelProvider(model=" ") + + +def test_lmstudio_provider_rejects_blank_explicit_base_url() -> None: + from ranksmith.integrations import LMStudioModelProvider + + with pytest.raises(RerankInputError, match="base_url"): + LMStudioModelProvider(model="local-model", base_url=" ") + + +def test_lmstudio_provider_rejects_blank_explicit_api_key() -> None: + from ranksmith.integrations import LMStudioModelProvider + + with pytest.raises(RerankInputError, match="api_key"): + LMStudioModelProvider(model="local-model", api_key=" ") + + +@pytest.mark.parametrize( + ("env_name", "match"), + [ + ("LMSTUDIO_BASE_URL", "LMSTUDIO_BASE_URL"), + ("LMSTUDIO_API_KEY", "LMSTUDIO_API_KEY"), + ], +) +def test_lmstudio_provider_rejects_blank_env_runtime_options( + monkeypatch: pytest.MonkeyPatch, + env_name: str, + match: str, +) -> None: + from ranksmith.integrations import LMStudioModelProvider + + monkeypatch.setenv(env_name, " ") + + with pytest.raises(RerankInputError, match=match): + LMStudioModelProvider(model="local-model") + + +@pytest.mark.parametrize("max_tokens", [0, -1]) +def test_lmstudio_provider_rejects_non_positive_max_tokens(max_tokens: int) -> None: + from ranksmith.integrations import LMStudioModelProvider + + with pytest.raises(RerankInputError, match="max_tokens"): + LMStudioModelProvider( + model="local-model", + max_tokens=max_tokens, + client=FakeClient(FakeCompletions()), + ) + + +def test_lmstudio_provider_wraps_client_errors() -> None: + from ranksmith.integrations import LMStudioModelProvider + + provider = LMStudioModelProvider( + model="local-model", + client=FakeClient(FakeCompletions(error=RuntimeError("boom"))), + ) + + with pytest.raises(RerankProviderError, match="boom"): + provider.complete(_request()) + + +def test_lmstudio_provider_wraps_empty_client_error_with_context() -> None: + from ranksmith.integrations import LMStudioModelProvider + + provider = LMStudioModelProvider( + model="local-model", + client=FakeClient(FakeCompletions(error=RuntimeError())), + ) + + with pytest.raises(RerankProviderError, match="LM Studio request failed"): + provider.complete(_request()) + + +def test_lmstudio_provider_rejects_unexpected_response_format() -> None: + from ranksmith.integrations import LMStudioModelProvider + + provider = LMStudioModelProvider( + model="local-model", + client=FakeClient(FakeCompletions()), + ) + + with pytest.raises(RerankProviderError, match="response_format"): + provider.complete(_request("text")) + + +@pytest.mark.parametrize( + "response", + [ + FakeResponse(choices=[]), + FakeResponse(choices=[FakeChoice(FakeMessage(None))]), + FakeResponse(choices=[FakeChoice(FakeMessage(""))]), + ], +) +def test_lmstudio_provider_rejects_invalid_or_empty_content( + response: FakeResponse, +) -> None: + from ranksmith.integrations import LMStudioModelProvider + + provider = LMStudioModelProvider( + model="local-model", + client=FakeClient(FakeCompletions(response)), + ) + + with pytest.raises(RerankProviderError): + provider.complete(_request()) diff --git a/tests/test_provider_answer_generator.py b/tests/test_provider_answer_generator.py new file mode 100644 index 0000000..41783d0 --- /dev/null +++ b/tests/test_provider_answer_generator.py @@ -0,0 +1,134 @@ +from __future__ import annotations + +import importlib +from dataclasses import dataclass + +import pytest + +from ranksmith.errors import RerankParseError, RerankProviderError +from ranksmith.model import ModelRequest, ModelResponse + + +@dataclass +class FakeProvider: + responses: list[str] + requests: list[ModelRequest] + + def complete(self, request: ModelRequest) -> ModelResponse: + self.requests.append(request) + return ModelResponse(content=self.responses.pop(0)) + + +def test_provider_answer_generator_export_is_submodule_only() -> None: + integrations = importlib.import_module("ranksmith.integrations") + root = importlib.import_module("ranksmith") + + assert integrations.ProviderAnswerGenerator is not None + assert not hasattr(root, "ProviderAnswerGenerator") + + +def test_provider_answer_generator_parses_query_answer_json() -> None: + from ranksmith.integrations import ProviderAnswerGenerator + + provider = FakeProvider(responses=['{"answer": "Paris"}'], requests=[]) + generator = ProviderAnswerGenerator(provider=provider) + + assert generator.answer_query("capital of france?") == "Paris" + assert provider.requests[0].response_format == "json_object" + assert provider.requests[0].temperature == 0 + assert provider.requests[0].messages[0].role == "system" + assert provider.requests[0].messages[1].role == "user" + assert "capital of france?" in provider.requests[0].messages[1].content + assert "Answer from your parametric knowledge" in ( + provider.requests[0].messages[1].content + ) + assert "__NO_ANSWER__" in provider.requests[0].messages[1].content + assert "best concise answer" not in provider.requests[0].messages[0].content + + +def test_provider_answer_generator_parses_context_answer_json() -> None: + from ranksmith.integrations import ProviderAnswerGenerator + + provider = FakeProvider(responses=['{"answer": "Nancy Travis"}'], requests=[]) + generator = ProviderAnswerGenerator(provider=provider) + + assert ( + generator.answer_with_context( + "who played karen?", + "Nancy Travis played Karen.", + ) + == "Nancy Travis" + ) + assert "Nancy Travis played Karen." in provider.requests[0].messages[1].content + assert "Use only the context" in provider.requests[0].messages[1].content + assert "__NO_ANSWER__" in provider.requests[0].messages[1].content + + +def test_provider_answer_generator_uses_configured_no_answer_value() -> None: + from ranksmith.integrations import ProviderAnswerGenerator + + provider = FakeProvider(responses=['{"answer": "UNKNOWN"}'], requests=[]) + generator = ProviderAnswerGenerator(provider=provider, no_answer_value="UNKNOWN") + + assert generator.answer_query("query") == "UNKNOWN" + assert '{"answer":"UNKNOWN"}' in provider.requests[0].messages[1].content + + +def test_provider_answer_generator_rejects_empty_no_answer_value() -> None: + from ranksmith.integrations import ProviderAnswerGenerator + + with pytest.raises(ValueError, match="no_answer_value"): + ProviderAnswerGenerator( + provider=FakeProvider(responses=[], requests=[]), + no_answer_value=" ", + ) + + +@pytest.mark.parametrize( + "content", + [ + "not json", + "[]", + "{}", + '{"answer": ""}', + '{"answer": " "}', + '{"answer": 123}', + ], +) +def test_provider_answer_generator_rejects_invalid_answer_json(content: str) -> None: + from ranksmith.integrations import ProviderAnswerGenerator + + generator = ProviderAnswerGenerator( + provider=FakeProvider(responses=[content], requests=[]), + ) + + with pytest.raises(RerankParseError): + generator.answer_query("query") + + +def test_provider_answer_generator_preserves_provider_error() -> None: + from ranksmith.integrations import ProviderAnswerGenerator + + class FailingProvider: + def complete(self, request: ModelRequest) -> ModelResponse: + del request + raise RerankProviderError("provider failed") + + generator = ProviderAnswerGenerator(provider=FailingProvider()) + + with pytest.raises(RerankProviderError, match="provider failed"): + generator.answer_query("query") + + +def test_provider_answer_generator_wraps_unexpected_provider_error() -> None: + from ranksmith.integrations import ProviderAnswerGenerator + + class FailingProvider: + def complete(self, request: ModelRequest) -> ModelResponse: + del request + raise RuntimeError("boom") + + generator = ProviderAnswerGenerator(provider=FailingProvider()) + + with pytest.raises(RerankProviderError, match="boom"): + generator.answer_query("query") diff --git a/tests/test_train_confidence_scorer_script.py b/tests/test_train_confidence_scorer_script.py new file mode 100644 index 0000000..1b836a3 --- /dev/null +++ b/tests/test_train_confidence_scorer_script.py @@ -0,0 +1,126 @@ +from __future__ import annotations + +import importlib.util +import json +import subprocess +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +SCRIPT_PATH = Path("scripts/train_confidence_scorer.py") + + +@dataclass(frozen=True) +class FakeTrainingResult: + output_dir: Path + export_path: Path + report_path: Path + metadata_path: Path + + +def _load_script() -> Any: + spec = importlib.util.spec_from_file_location( + "train_confidence_scorer", + SCRIPT_PATH, + ) + assert spec is not None + assert spec.loader is not None + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def test_train_confidence_scorer_help_exits_successfully() -> None: + result = subprocess.run( + ["uv", "run", "python", str(SCRIPT_PATH), "--help"], + check=False, + capture_output=True, + text=True, + ) + + assert result.returncode == 0 + assert "--task" in result.stdout + + +def test_training_cli_builds_config_and_prints_result_paths( + monkeypatch: Any, + tmp_path: Path, + capsys: Any, +) -> None: + script = _load_script() + calls: list[Any] = [] + + def fake_train(config: Any) -> FakeTrainingResult: + calls.append(config) + return FakeTrainingResult( + output_dir=Path(config.output_dir), + export_path=Path(config.export_path), + report_path=Path(config.output_dir) / "report.json", + metadata_path=Path(config.output_dir) / "metadata.json", + ) + + monkeypatch.setattr(script, "train_confidence_scorer", fake_train) + + status = script.main( + [ + "--task", + "query_context_answerability_confidence", + "--dataset", + str(tmp_path / "dataset.jsonl"), + "--output-dir", + str(tmp_path / "training"), + "--export-path", + str(tmp_path / "artifact.joblib"), + "--encoder-name", + "encoder", + "--encoder-revision", + "rev1", + "--tokenizer-name", + "tokenizer", + "--tokenizer-revision", + "rev2", + "--cache-dir", + str(tmp_path / "cache"), + "--local-files-only", + "--max-length", + "384", + "--allow-truncation", + "--seed", + "7", + "--train-ratio", + "0.6", + "--valid-ratio", + "0.2", + "--test-ratio", + "0.2", + "--calibration-method", + "sigmoid", + ] + ) + + assert status == 0 + config = calls[0] + assert config.task_type == "query_context_answerability_confidence" + assert config.dataset_path == tmp_path / "dataset.jsonl" + assert config.output_dir == tmp_path / "training" + assert config.export_path == tmp_path / "artifact.joblib" + assert config.encoder_name == "encoder" + assert config.encoder_revision == "rev1" + assert config.tokenizer_name == "tokenizer" + assert config.tokenizer_revision == "rev2" + assert config.cache_dir == str(tmp_path / "cache") + assert config.local_files_only is True + assert config.max_length == 384 + assert config.allow_truncation is True + assert config.seed == 7 + assert config.train_ratio == 0.6 + assert config.valid_ratio == 0.2 + assert config.test_ratio == 0.2 + assert config.calibration_method == "sigmoid" + summary = json.loads(capsys.readouterr().out) + assert summary == { + "output_dir": str(tmp_path / "training"), + "export_path": str(tmp_path / "artifact.joblib"), + "report_path": str(tmp_path / "training" / "report.json"), + "metadata_path": str(tmp_path / "training" / "metadata.json"), + }