diff --git a/README.md b/README.md index b08af30..31a23b6 100644 --- a/README.md +++ b/README.md @@ -122,7 +122,7 @@ print(metric(**datum)) Code Generation Deterministic - CodeStringMatch, PythonASTSimilarity + CodeStringMatch, PythonASTSimilarity, SQLSyntaxMatch, SQLASTSimilarity LLM-based diff --git a/continuous_eval/metrics/code/__init__.py b/continuous_eval/metrics/code/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/continuous_eval/metrics/code/python/__init__.py b/continuous_eval/metrics/code/python/__init__.py deleted file mode 100644 index d3b6965..0000000 --- a/continuous_eval/metrics/code/python/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from continuous_eval.metrics.code.python.code_deterministic_metrics import CodeStringMatch, PythonASTSimilarity \ No newline at end of file diff --git a/continuous_eval/metrics/code/python/code_deterministic_metrics.py b/continuous_eval/metrics/code/python/code_deterministic_metrics.py index 735e634..c240cc8 100644 --- a/continuous_eval/metrics/code/python/code_deterministic_metrics.py +++ b/continuous_eval/metrics/code/python/code_deterministic_metrics.py @@ -2,6 +2,8 @@ from typing import List, Union from munkres import Munkres +from sqlglot import diff, parse_one +from sqlglot.diff import Keep from thefuzz import fuzz from continuous_eval.metrics.base import Metric diff --git a/continuous_eval/metrics/code/sql/deterministic.py b/continuous_eval/metrics/code/sql/deterministic.py new file mode 100644 index 0000000..817d79f --- /dev/null +++ b/continuous_eval/metrics/code/sql/deterministic.py @@ -0,0 +1,135 @@ +import logging +from dataclasses import dataclass +from typing import Dict, List, Optional, Union + +from sqlglot import diff, parse_one, transpile +from sqlglot.diff import Insert, Keep, Move, Remove, Update +from sqlglot.optimizer import optimize + +from continuous_eval.metrics.base import Metric + +logger = logging.getLogger("metrics") + + +@dataclass(frozen=True) +class ASTDiffWeightConfig: + """ + Configuration for assigning weights to different types of changes in the AST diff. + Higher weights indicate more significant changes, which are expected to have a greater impact on query semantics. + """ + + keep: float = 0.0 + # Updates are significant as they imply a modification in function or value. + update: float = 1.5 + # Inserts affect the structure and content but are simpler than updates. + insert: float = 1.0 + # Removes affect the structure and content but are simpler than updates. + remove: float = 1.0 + # Moves are generally less impactful as they simply change the order. + move: float = 0.5 + # Default weight for other types of changes + default: float = 1.0 + + +class _SQLMetric: + def __init__(self, optimize: bool = False, schema: Optional[Dict] = None): + self._optimize = optimize + self._schema = schema + + def _prepare_query(self, sql: str): + """ + Parse, transpile, and optionally optimize a SQL query. + """ + formatted_sql = transpile(sql, pretty=True, comments=False)[0] + if self._optimize: + try: + optimized_sql = optimize(parse_one(formatted_sql), schema=self._schema).sql(pretty=True) + return optimized_sql + except Exception as e: + logger.warning(f"Failed to optimize SQL query given schema: {e}. Using unoptimized query.") + return formatted_sql + return formatted_sql + + +class SQLSyntaxMatch(Metric, _SQLMetric): + """ + This metric evaluates the syntactic similarity between the generated SQL query and a set of ground truth queries. + It uses the sqlglot library to format and compare the SQL queries. + """ + + def __init__(self, optimize: bool = False, schema: Optional[Dict] = None): + super(SQLSyntaxMatch, self).__init__() + _SQLMetric.__init__(self, optimize=optimize, schema=schema) + + def __call__(self, answer: str, ground_truth_answers: Union[List[str], str]): + + transformed_answer = self._prepare_query(answer) + transformed_ground_truths = [self._prepare_query(gt) for gt in ground_truth_answers] + + max_match_score = 0.0 + + for transformed_gt in transformed_ground_truths: + match_score = float(transformed_answer == transformed_gt) + if match_score > max_match_score: + max_match_score = match_score + + return {"SQL_Syntax_Match": max_match_score} + + +class SQLASTSimilarity(Metric, _SQLMetric): + """ + Compare SQL queries using AST similarity, considering different types of changes differently and improving normalization. + """ + + def __init__( + self, + optimize: bool = False, + schema: Optional[Dict] = None, + diff_weights: ASTDiffWeightConfig = ASTDiffWeightConfig(), + ): + super(SQLASTSimilarity, self).__init__() + _SQLMetric.__init__(self, optimize=optimize, schema=schema) + self._diff_weights = diff_weights + + def __call__(self, answer: str, ground_truth_answers: Union[List[str], str], **kwargs): + + transformed_answer = self._prepare_query(answer) + transformed_ground_truths = [self._prepare_query(gt) for gt in ground_truth_answers] + + try: + answer_tree = parse_one(transformed_answer) + ground_truth_trees = [parse_one(gt) for gt in transformed_ground_truths] + except Exception: + return {"SQL_AST_Similarity": -1.0} + + similarity_scores = [ + self._calculate_similarity(answer_tree, ground_truth_tree) for ground_truth_tree in ground_truth_trees + ] + + return { + "SQL_AST_Similarity": max(similarity_scores) if similarity_scores else -1.0, + } + + def _calculate_similarity(self, tree1, tree2): + diff_result = diff(tree1, tree2) + total_changes = sum(self._apply_weights(change) for change in diff_result) + max_nodes = max(len(list(tree1.walk())), len(list(tree2.walk()))) + similarity_score = 1 - (total_changes / max_nodes) if max_nodes > 0 else 1 + return similarity_score + + def _apply_weights(self, change): + """ + Assign weights to different types of changes based on their expected impact on query semantics. + """ + if isinstance(change, Keep): + return self._diff_weights.keep + elif isinstance(change, Update): + return self._diff_weights.update + elif isinstance(change, Insert): + return self._diff_weights.insert + elif isinstance(change, Remove): + return self._diff_weights.remove + elif isinstance(change, Move): + return self._diff_weights.move + else: + return self._diff_weights.default diff --git a/docs/src/content/docs/metrics/Code/Deterministic/string_match.md b/docs/src/content/docs/metrics/Code/Deterministic/code_string_match.md similarity index 89% rename from docs/src/content/docs/metrics/Code/Deterministic/string_match.md rename to docs/src/content/docs/metrics/Code/Deterministic/code_string_match.md index 2f3ed6d..44f2453 100644 --- a/docs/src/content/docs/metrics/Code/Deterministic/string_match.md +++ b/docs/src/content/docs/metrics/Code/Deterministic/code_string_match.md @@ -1,5 +1,5 @@ --- -title: StringMatch +title: Code String Match sidebar: order: 1 --- @@ -18,7 +18,7 @@ It outputs both the binary exact match score and the fuzzy match score in the ra Required data items: `answer`, `ground_truth_answers` ```python -from continuous_eval.metrics.code.python import CodeStringMatch +from continuous_eval.metrics.code import CodeStringMatch datum = { "answer": "def function(x, y):\n return x + y", diff --git a/docs/src/content/docs/metrics/Code/Deterministic/python_ast_similarity.md b/docs/src/content/docs/metrics/Code/Deterministic/python_ast_similarity.md index 76ccc25..bcbc78c 100644 --- a/docs/src/content/docs/metrics/Code/Deterministic/python_ast_similarity.md +++ b/docs/src/content/docs/metrics/Code/Deterministic/python_ast_similarity.md @@ -21,7 +21,7 @@ The metric depends on syntactically correct Python scripts to produce the Abstra Required data items: `answer`, `ground_truth_answers` ```python -from continuous_eval.metrics.code.python import PythonASTSimilarity +from continuous_eval.metrics.code import PythonASTSimilarity datum = { "answer": "def function(x, y):\n return x + y", diff --git a/docs/src/content/docs/metrics/Code/Deterministic/sql_ast_similarity.md b/docs/src/content/docs/metrics/Code/Deterministic/sql_ast_similarity.md new file mode 100644 index 0000000..a027efa --- /dev/null +++ b/docs/src/content/docs/metrics/Code/Deterministic/sql_ast_similarity.md @@ -0,0 +1,70 @@ +--- +title: SQL AST Similarity +sidebar: + order: 1 +--- + +### Definitions + +**SQL AST Similarity** compares the structure of two SQL queries by analyzing their Abstract Syntax Trees (ASTs). This metric assesses similarity by matching the nodes within these trees, taking into account the statement types and their arrangement. Different types of tree differences (such as insert, remove, update, move, etc.) are weighted differently to calculate the final similarity score. + +
+ +$$ +\text{SQL AST Similarity} = 1 - \frac{\text{Total Weight Changes}}{\text{Maximum Possible Nodes}} +$$ + +
+ +:::note +The metric depends on syntactically correct SQL queries to produce the Abstract Syntax Trees (ASTs). If the scripts contain syntax errors and cannot be parsed, the metric will yield a score of -1.0. +::: + +
+ +### Example Usage + +Required data items: `answer`, `ground_truth_answers` + +```python +from continuous_eval.metrics.code import SQLASTSimilarity + +datum = { + "answer": "SELECT name, age FROM customers", + "ground_truth_answers": ["SELECT age, name FROM customers"], +}, + +metric = SQLASTSimilarity() +print(metric(**datum)) +``` + +You can optionally initialize the metric to use optimized SQL queries using the [sqlglot optimizer](https://github.com/tobymao/sqlglot?tab=readme-ov-file#sql-optimizer) and optionally pass in the schema. For example: +```python +schema={"x": {"A": "INT", "B": "INT", "C": "INT", "D": "INT", "Z": "STRING"}} +sql_syntax_match_optimized = SQLASTSimilarity(optimized=True, schema=schema) +``` + +You can also customize weights to different types of nodes in the AST diff. +Higher weights indicate more significant changes, which are expected to have a greater impact on query semantics. + +```python +from continuous_eval.metrics.code.sql.deterministic import ASTDiffWeightConfig + +weights = ASTDiffWeightConfig( + keep_weight=0.0, + update_weight=2, + insert_weight=1.0, + remove_weight=1.5, + move_weight=0, + default_weight=0, +) +ASTSimilarity = SQLASTSimilarity(diff_weights=weights) +``` + +### Example Output + +```JSON +{ + "SQL_AST_Similarity": 0.9375 +} +``` diff --git a/docs/src/content/docs/metrics/Code/Deterministic/sql_syntax_match.md b/docs/src/content/docs/metrics/Code/Deterministic/sql_syntax_match.md new file mode 100644 index 0000000..f35c678 --- /dev/null +++ b/docs/src/content/docs/metrics/Code/Deterministic/sql_syntax_match.md @@ -0,0 +1,43 @@ +--- +title: SQL Syntax Match +sidebar: + order: 2 +--- + +## Definitions + +**SQL Syntax Match** evaluates the syntactic equivalence between generated SQL queries and a set of ground truth queries. The strict comparison can tolerate formatting changes. + +## Example Usage + +Required data items: `answer`, `ground_truth_answers` + +```python +from continuous_eval.metrics.code import SQLSyntaxMatch + +sql_syntax_match = SQLSyntaxMatch() + +datum = { + "answer": "SELECT * FROM users;"", + "ground_truth_answers": [ + "SELECT * from users;" + ], +}, + +metric = SQLSyntaxMatch() +print(metric(**datum)) +``` + +You can optionally initialize the metric to use optimized SQL queries using the [sqlglot optimizer](https://github.com/tobymao/sqlglot?tab=readme-ov-file#sql-optimizer) and optionally pass in the schema. For example: +```python +schema={"x": {"A": "INT", "B": "INT", "C": "INT", "D": "INT", "Z": "STRING"}} +sql_syntax_match_optimized = SQLSyntaxSimilarity(optimized=True, schema=schema) +``` + +## Example Output + +```JSON +{ + "SQL_Syntax_Match": 1.0 +} +``` \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 0851278..3cd90f3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -112,13 +112,13 @@ frozenlist = ">=1.1.0" [[package]] name = "annotated-types" -version = "0.6.0" +version = "0.7.0" description = "Reusable constraint types to use with typing.Annotated" optional = false python-versions = ">=3.8" files = [ - {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"}, - {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = 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"tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"] +dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] +flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"] flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] ftfy = ["ftfy"] integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"] @@ -4709,7 +4720,7 @@ natten = ["natten (>=0.14.6,<0.15.0)"] onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"] onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"] optuna = ["optuna"] -quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"] +quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"] ray = ["ray[tune] (>=2.7.0)"] retrieval = ["datasets (!=2.5.0)", "faiss-cpu"] sagemaker = ["sagemaker (>=2.31.0)"] @@ -4718,16 +4729,16 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"] sigopt = ["sigopt"] sklearn = ["scikit-learn"] speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] -testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] -tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] -tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] +testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] +tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] +tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] timm = ["timm"] tokenizers = ["tokenizers (>=0.19,<0.20)"] torch = ["accelerate (>=0.21.0)", "torch"] torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"] -torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"] +torchhub = ["filelock", "huggingface-hub (>=0.23.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"] video = ["av (==9.2.0)", "decord (==0.6.0)"] vision = ["Pillow (>=10.0.1,<=15.0)"] @@ -5531,8 +5542,9 @@ bedrock = ["boto3"] cohere = ["cohere"] gemini = ["google-generativeai"] langchain = ["langchain-community"] +sqlglot = ["sqlglot"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.12" -content-hash = "893b2c7e785564deb85fb26ca6b40ce39dd6ee04ab6914ee6d4d57d3fa3f0abc" +content-hash = "5ba2dbf3ac49f82f5f1cc37b733d5a9d5e63b2e2d76d27eb88a5b69d327bafbe" diff --git a/pyproject.toml b/pyproject.toml index e784d44..74899fe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "continuous-eval" -version = "0.3.8" +version = "0.3.9" description = "Open-Source Evaluation for GenAI Application Pipelines." authors = ["Yi Zhang ", "Pasquale Antonante "] readme = "README.md" @@ -26,6 +26,7 @@ thefuzz = "^0.22.1" sentencepiece = "^0.2.0" tenacity = "^8.2.3" +sqlglot = {version = "^23.17.0", optional = true} langchain-community = {version = "^0.0.29", optional = true} cohere = {version = "^4.54", optional = true} boto3 = {version = "^1.34.70", optional = true} @@ -59,6 +60,7 @@ anthropic = ["anthropic"] bedrock = ["boto3"] gemini = ["google-generativeai"] cohere = ["cohere"] +sqlglot = ["sqlglot"] [tool.isort] diff --git a/tests/code_metrics_test.py b/tests/code_metrics_test.py index a1b8d4c..facd2e9 100644 --- a/tests/code_metrics_test.py +++ b/tests/code_metrics_test.py @@ -1,6 +1,7 @@ import pytest from continuous_eval.metrics.code.python.code_deterministic_metrics import CodeStringMatch, PythonASTSimilarity +from continuous_eval.metrics.code.sql.deterministic import ASTDiffWeightConfig, SQLASTSimilarity, SQLSyntaxMatch from tests.helpers import example_datum from tests.helpers.utils import all_close @@ -17,7 +18,7 @@ def test_code_string_match(): metric = CodeStringMatch() assert all( all_close( - metric(answer=datum["answer"], ground_truth_answers=datum["ground_truths"]), # type: ignore + metric(answer=datum["answer"], ground_truth_answers=datum["ground_truths"]), expected, ) for datum, expected in zip(example_datum.PYTHON_CODE_EXAMPLES, expected_results) @@ -36,8 +37,52 @@ def test_python_ast_similarity(): metric = PythonASTSimilarity() assert all( all_close( - metric(answer=datum["answer"], ground_truth_answers=datum["ground_truths"]), # type: ignore - expected, # type: ignore + metric(answer=datum["answer"], ground_truth_answers=datum["ground_truths"]), + expected, ) for datum, expected in zip(example_datum.PYTHON_CODE_EXAMPLES, expected_results) ) + + +def test_sql_syntax_match(): + expected_results = [{'SQL_Syntax_Match': 1.0}, {'SQL_Syntax_Match': 0}, {'SQL_Syntax_Match': 0}] + metric = SQLSyntaxMatch() + assert all( + all_close( + metric(answer=datum["answer"], ground_truth_answers=datum["ground_truths"]), + expected, + ) + for datum, expected in zip(example_datum.SQL_CODE_EXAMPLES, expected_results) + ) + + +def test_sql_ast_similarity(): + expected_results = [{'SQL_AST_Similarity': 1.0}, {'SQL_AST_Similarity': 0.9375}, {'SQL_AST_Similarity': 0.8}] + metric = SQLASTSimilarity() + assert all( + all_close( + metric(answer=datum["answer"], ground_truth_answers=datum["ground_truths"]), + expected, + ) + for datum, expected in zip(example_datum.SQL_CODE_EXAMPLES, expected_results) + ) + + +def test_sql_optimized_ast_similarity(): + expected_results = [{'SQL_AST_Similarity': 1.0}, {'SQL_AST_Similarity': 1.0}, {'SQL_AST_Similarity': 0.75}] + weights = ASTDiffWeightConfig( + keep=0.0, + update=2, + insert=1.0, + remove=1.5, + move=0, + default=0, + ) + metric = SQLASTSimilarity(optimize=True, diff_weights=weights) + assert all( + all_close( + metric(answer=datum["answer"], ground_truth_answers=datum["ground_truths"]), + expected, + ) + for datum, expected in zip(example_datum.SQL_CODE_EXAMPLES, expected_results) + ) diff --git a/tests/helpers/example_datum.py b/tests/helpers/example_datum.py index a7f8393..1d1ddd3 100644 --- a/tests/helpers/example_datum.py +++ b/tests/helpers/example_datum.py @@ -135,3 +135,24 @@ ], }, ] + +SQL_CODE_EXAMPLES = [ + { + "answer": """--comments + SELECT * FROM users WHERE id = 1""", + "ground_truths": [ + "SELECT * FROM users where id = 2", + "Select * from users WHERE id = 1", + ], + }, + { + "answer": "SELECT name, age FROM customers", + "ground_truths": ["SELECT age, name FROM customers"], + }, + { + "answer": "INSERT INTO orders (order_id, product_id) VALUES (1, 100)", + "ground_truths": [ + "INSERT INTO orders (order_id, product_id) VALUES (1, 101)", + ], + }, +]