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Parent(s):
ab37bbe
feat: add TQA prompt and db_schema with INSERT INTO
Browse files- prediction.py +19 -9
- test_get_db_schema_with_entries.py +15 -0
- utils_get_db_tables_info.py +35 -15
prediction.py
CHANGED
@@ -57,6 +57,16 @@ class ModelPrediction:
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"Database Schema\n"
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"{db_schema}\n"
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)
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def _reset_pipeline(self, model_name):
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if self._model_name != model_name:
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@@ -74,7 +84,7 @@ class ModelPrediction:
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return matches[-1].strip() if matches else pred
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-
def make_prediction(self, question, db_schema, model_name, prompt=None):
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if model_name not in self.model_name2pred_func:
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raise ValueError(
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"Model not supported",
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@@ -82,10 +92,13 @@ class ModelPrediction:
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self.model_name2pred_func.keys(),
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)
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-
prompt = prompt
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-
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-
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start_time = time.time()
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prediction = self.model_name2pred_func[model_name](prompt)
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end_time = time.time()
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@@ -93,7 +106,6 @@ class ModelPrediction:
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prediction["response"]
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)
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prediction['time'] = end_time - start_time
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-
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return prediction
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@@ -133,14 +145,12 @@ class ModelPrediction:
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model_name = "openai/gpt-3.5-turbo-0125"
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elif "gpt-4o-mini" in model_name:
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model_name = "openai/gpt-4o-mini-2024-07-18"
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-
elif "o1-mini" in model_name:
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model_name = "openai/o1-mini-2024-09-12"
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-
elif "QwQ" in model_name:
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model_name = "together_ai/Qwen/QwQ-32B"
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elif "DeepSeek-R1-Distill-Llama-70B" in model_name:
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model_name = "together_ai/deepseek-ai/DeepSeek-R1-Distill-Llama-70B"
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elif "llama-8" in model_name:
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model_name = "together_ai/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo"
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else:
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raise ValueError("Model forbidden")
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"Database Schema\n"
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"{db_schema}\n"
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)
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self.base_prompt_QA= (
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"Return the answer of the following question based on the provided database."
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" Return your answer as the result of a query executed over the database."
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" Namely, as a list of list where the first list represent the tuples and the second list the values in that tuple.\n"
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"Return the answer in answer tag as <answer> </answer>"
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" Question\n"
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"{question}\n"
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"Database Schema\n"
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"{db_schema}\n"
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)
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def _reset_pipeline(self, model_name):
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if self._model_name != model_name:
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return matches[-1].strip() if matches else pred
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def make_prediction(self, question, db_schema, model_name, prompt=None, task='SP'):
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if model_name not in self.model_name2pred_func:
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raise ValueError(
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"Model not supported",
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self.model_name2pred_func.keys(),
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)
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if task == 'SP':
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prompt = prompt or self.base_prompt
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else:
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prompt = prompt or self.base_prompt_QA
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prompt = prompt.format(question=question, db_schema=db_schema)
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start_time = time.time()
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prediction = self.model_name2pred_func[model_name](prompt)
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end_time = time.time()
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prediction["response"]
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)
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prediction['time'] = end_time - start_time
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return prediction
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model_name = "openai/gpt-3.5-turbo-0125"
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elif "gpt-4o-mini" in model_name:
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model_name = "openai/gpt-4o-mini-2024-07-18"
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elif "DeepSeek-R1-Distill-Llama-70B" in model_name:
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model_name = "together_ai/deepseek-ai/DeepSeek-R1-Distill-Llama-70B"
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elif "llama-8" in model_name:
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model_name = "together_ai/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo"
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elif "llama-70" in model_name:
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model_name = "together_ai/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo"
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else:
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raise ValueError("Model forbidden")
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test_get_db_schema_with_entries.py
ADDED
@@ -0,0 +1,15 @@
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from utils_get_db_tables_info import utils_extract_db_schema_as_string
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def main():
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db = utils_extract_db_schema_as_string(
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db_id='',
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base_path='mytable_7.sqlite',
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normalize=True,
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sql=None,
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get_insert_into=True
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)
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print(db)
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if __name__ == '__main__':
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main()
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utils_get_db_tables_info.py
CHANGED
@@ -4,7 +4,7 @@ import re
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def utils_extract_db_schema_as_string(
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db_id, base_path, normalize=False, sql: str | None = None
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):
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"""
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Extracts the full schema of an SQLite database into a single string.
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@@ -19,7 +19,7 @@ def utils_extract_db_schema_as_string(
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cursor = connection.cursor()
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# Get the schema entries based on the provided SQL query
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schema_entries = _get_schema_entries(cursor, sql)
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# Combine all schema definitions into a single string
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schema_string = _combine_schema_entries(schema_entries, normalize)
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@@ -27,27 +27,47 @@ def utils_extract_db_schema_as_string(
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return schema_string
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"""
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Retrieves schema entries from the SQLite database.
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:param cursor: SQLite cursor object.
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:param sql: Optional SQL query to filter specific tables.
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:
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"""
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if sql:
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cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
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tables = [tbl[0] for tbl in cursor.fetchall() if tbl[0].lower() in sql.lower()]
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if tables:
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tbl_names = ", ".join(f"'{tbl}'" for tbl in tables)
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query = f"SELECT sql FROM sqlite_master WHERE type='table' AND name IN ({tbl_names}) AND sql IS NOT NULL;"
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else:
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query = "SELECT sql FROM sqlite_master WHERE sql IS NOT NULL;"
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else:
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return cursor.fetchall()
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def _combine_schema_entries(schema_entries, normalize):
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:return: Combined schema string.
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"""
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if not normalize:
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return "\n".join(entry
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return "\n".join(
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re.sub(
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@@ -77,7 +97,7 @@ def _combine_schema_entries(schema_entries, normalize):
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re.sub(
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r"\s+",
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" ",
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entry
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).strip(),
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),
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),
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def utils_extract_db_schema_as_string(
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db_id, base_path, normalize=False, sql: str | None = None, get_insert_into: bool = False
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):
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"""
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Extracts the full schema of an SQLite database into a single string.
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cursor = connection.cursor()
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# Get the schema entries based on the provided SQL query
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schema_entries = _get_schema_entries(cursor, sql, get_insert_into)
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# Combine all schema definitions into a single string
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schema_string = _combine_schema_entries(schema_entries, normalize)
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return schema_string
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def _get_schema_entries(cursor, sql=None, get_insert_into=False):
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"""
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Retrieves schema entries and optionally data entries from the SQLite database.
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:param cursor: SQLite cursor object.
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:param sql: Optional SQL query to filter specific tables.
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:param get_insert_into: Boolean flag to include INSERT INTO statements.
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:return: List of schema and optionally data entries.
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"""
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entries = []
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if sql:
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# Extract table names from the provided SQL query
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cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
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tables = [tbl[0] for tbl in cursor.fetchall() if tbl[0].lower() in sql.lower()]
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else:
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# Retrieve all table names
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cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
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tables = [tbl[0] for tbl in cursor.fetchall()]
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for table in tables:
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# Retrieve the CREATE TABLE statement for each table
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cursor.execute(f"SELECT sql FROM sqlite_master WHERE type='table' AND name='{table}' AND sql IS NOT NULL;")
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create_table_stmt = cursor.fetchone()
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if create_table_stmt:
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entries.append(create_table_stmt[0])
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if get_insert_into:
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# Retrieve all data from the table
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cursor.execute(f"SELECT * FROM {table};")
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rows = cursor.fetchall()
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column_names = [description[0] for description in cursor.description]
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# Generate INSERT INTO statements for each row
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for row in rows:
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values = ', '.join(f"'{str(value)}'" if isinstance(value, str) else str(value) for value in row)
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insert_stmt = f"INSERT INTO {table} ({', '.join(column_names)}) VALUES ({values});"
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entries.append(insert_stmt)
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return entries
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def _combine_schema_entries(schema_entries, normalize):
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:return: Combined schema string.
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"""
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if not normalize:
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return "\n".join(entry for entry in schema_entries)
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return "\n".join(
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re.sub(
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re.sub(
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r"\s+",
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" ",
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entry.replace("CREATE TABLE", "").replace("\t", " "),
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).strip(),
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),
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),
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