From 17f1b1f8c70a93fb2e6b9e1b6b854dceb5684138 Mon Sep 17 00:00:00 2001 From: yuyr Date: Tue, 17 Jun 2025 09:53:40 +0000 Subject: [PATCH] clean code --- random_sample/generate_tasks copy 2.py | 364 - random_sample/generate_tasks copy 3.py | 370 - random_sample/generate_tasks copy 4.py | 408 - random_sample/generate_tasks copy 5.py | 437 -- random_sample/generate_tasks copy.py | 246 - random_sample/generate_tasks.py | 4 +- random_sample/generated_tasks.json.bak | 3539 --------- random_sample/generated_tasks.json.bak2 | 8182 -------------------- random_sample/generated_tasks.json.bak3 | 9329 ----------------------- random_sample/requirements.txt | 3 + random_sample/tes1.json | 1088 +++ random_sample/test1.json | 282 + scripts/portforward.sh | 2 +- 13 files changed, 1377 insertions(+), 22877 deletions(-) delete mode 100644 random_sample/generate_tasks copy 2.py delete mode 100644 random_sample/generate_tasks copy 3.py delete mode 100644 random_sample/generate_tasks copy 4.py delete mode 100644 random_sample/generate_tasks copy 5.py delete mode 100644 random_sample/generate_tasks copy.py delete mode 100644 random_sample/generated_tasks.json.bak delete mode 100644 random_sample/generated_tasks.json.bak2 delete mode 100644 random_sample/generated_tasks.json.bak3 create mode 100644 random_sample/requirements.txt create mode 100644 random_sample/tes1.json create mode 100644 random_sample/test1.json diff --git a/random_sample/generate_tasks copy 2.py b/random_sample/generate_tasks copy 2.py deleted file mode 100644 index 33601b6..0000000 --- a/random_sample/generate_tasks copy 2.py +++ /dev/null @@ -1,364 +0,0 @@ -import os -import random -import json -import mysql.connector -from openai import OpenAI -from dotenv import load_dotenv - -# --- Configuration --- -load_dotenv() - -MYSQL_CONFIG = { - "host": "localhost", - "port": "23306", - "user": "mcpuser", - "password": "StrongPass123!", - "database": "magentodb" -} - -OPENAI_CONFIG = { - "api_key": os.getenv("OPENAI_API_KEY"), - "base_url": os.getenv("OPENAI_BASE_URL"), - "model": "gpt-4o" -} - -# --- Prompt Template --- -# This is a carefully engineered prompt to guide the LLM's output. -PROMPT_TEMPLATE = """ -You are an expert database analyst and a creative test case designer for e-commerce web applications. -Your goal is to generate realistic administrative tasks that can be solved by a Web Agent navigating an admin panel. - -I will provide you with the following context: -1. **Full Database Schema**: A list of `CREATE TABLE` statements for the core tables of a Magento e-commerce platform. -2. **Sampled Data**: A JSON object containing 5 random rows of data from 5 randomly selected core tables. This data is REAL and should be used to inspire specific, answerable questions. - -## Your Task - -Based on the provided schema and sample data, create a JSON object containing a single key, "questions", which holds an array of up to 10 unique task objects. - -### Requirements for Each Question: -- **Web Agent Solvable**: The task must represent a realistic action an administrator would perform in a web UI (e.g., "Find all orders for customer X", "Update the stock for product Y", "Approve a pending review"). -- **Grounded in Data**: The questions should be specific, using names, IDs, or values from the provided **Sampled Data** to make them concrete. -- **Utilize Schema**: You can formulate questions that require joining tables, even if not all tables were sampled. The full schema is your guide. - -### Output Format -The final output MUST be a single, valid JSON object. Do not include any other text, explanations, or markdown formatting like ```json. -The JSON object must have one key: "questions", containing a JSON array of task objects. - -Each object in the array must contain exactly three keys: `question`, `answer`, and `sql`. - -- **`question`**: (string) A natural language description of the task for a web agent. -- **`answer`**: (string, integer, float, or list) The precise and concise answer to the question, derived by running the SQL query against the database. -- **`sql`**: (string) The exact, runnable MySQL query that was used to find the answer. - -### Output Format Example -```json -{{ - "questions": [ - {{ - "question": "What is the email address for customer with ID 5?", - "answer": "customer5@example.com", - "sql": "SELECT email FROM customer_entity WHERE entity_id = 5;" - }}, - {{ - "question": "Find the total quantity of item with SKU 'ABC-123' in the cart.", - "answer": 3, - "sql": "SELECT SUM(qty) FROM quote_item WHERE sku = 'ABC-123';" - }} - ] -}} -``` - ---- -### Full Database Schema -{schema_context} - ---- -### Sampled Data -Here is the sample data from randomly selected tables. Use this to make your questions specific. - -{sampled_data_str} - ---- -Now, generate the JSON object based on these instructions. -""" - -# This is a carefully engineered prompt to verify the LLM's own output. -SEMANTIC_VERIFICATION_PROMPT_TEMPLATE = """ -You are a meticulous data verifier. Your task is to determine if a given "answer" is semantically correct and accurately supported by the "SQL query result". - -I will provide you with a JSON object containing: -1. `question`: The original question asked. -2. `sql`: The SQL query used to find the answer. -3. `answer`: The answer generated by a previous AI. -4. `sql_result`: The actual data returned by executing the SQL query. - -## Your Task -Carefully analyze the `sql_result` and compare it to the `answer`. The match should be semantic, not just a simple substring match. For example, if the question is "How many products are in stock?", an answer of "5" should be verifiable from the SQL result which might be `[(5,)]`. - -### Requirements: -- Respond with a single JSON object. -- Do not include any other text, explanations, or markdown formatting. -- The JSON object must have exactly two keys: - - `is_match`: (boolean) `true` if the `answer` is fully and accurately supported by the `sql_result`, otherwise `false`. - - `reason`: (string) A brief explanation for your decision. If it's a mismatch, explain why (e.g., "The answer is 'John Doe' but the result contains 'Jane Doe'", "The answer is a count but the result is a list of names"). - ---- -### Verification Data -{task_data_json} ---- - -Now, provide your verification as a JSON object. -""" - -def get_db_connection(): - """Establishes a connection to the MySQL database.""" - try: - conn = mysql.connector.connect(**MYSQL_CONFIG) - return conn - except mysql.connector.Error as err: - print(f"Error connecting to MySQL: {err}") - return None - -def get_full_schema(cursor, tables): - """Fetches the CREATE TABLE statements for all core tables.""" - schema_parts = [] - for table_name in tables: - try: - cursor.execute(f"SHOW CREATE TABLE `{table_name}`") - result = cursor.fetchone() - if result: - schema_parts.append(result[1]) # result[1] is the CREATE TABLE statement - except mysql.connector.Error as err: - print(f"Warning: Could not get schema for table {table_name}: {err}") - return "\n\n".join(schema_parts) - -def get_random_tables_and_samples(cursor, tables, num_tables=5, num_samples=5): - """Selects random tables and samples random rows from them.""" - selected_tables = random.sample(tables, num_tables) - sampled_data = {} - - for table_name in selected_tables: - try: - # Use ORDER BY RAND() for random sampling. Can be slow on very large tables. - query = f"SELECT * FROM `{table_name}` ORDER BY RAND() LIMIT {num_samples}" - cursor.execute(query) - - rows = cursor.fetchall() - if not rows: - sampled_data[table_name] = [] - continue - - columns = [desc[0] for desc in cursor.description] - - # Convert rows (tuples) to a list of dictionaries - sampled_rows = [] - for row in rows: - row_dict = {} - for i, col_value in enumerate(row): - # Handle bytes by decoding, fall back to string representation - if isinstance(col_value, bytes): - try: - row_dict[columns[i]] = col_value.decode('utf-8') - except UnicodeDecodeError: - row_dict[columns[i]] = str(col_value) - else: - row_dict[columns[i]] = col_value - sampled_rows.append(row_dict) - - sampled_data[table_name] = sampled_rows - - except mysql.connector.Error as err: - print(f"Warning: Could not sample data from table {table_name}: {err}") - sampled_data[table_name] = f"Error: {err}" - - return sampled_data - -def generate_questions(client, schema_context, sampled_data): - """Generates questions by calling the OpenAI API.""" - if not client: - raise ValueError("OpenAI client not provided.") - - sampled_data_str = json.dumps(sampled_data, indent=2, default=str) - - prompt = PROMPT_TEMPLATE.format( - schema_context=schema_context, - sampled_data_str=sampled_data_str - ) - - try: - response = client.chat.completions.create( - model=OPENAI_CONFIG["model"], - messages=[ - {"role": "system", "content": "You are a helpful assistant designed to output JSON."}, - {"role": "user", "content": prompt} - ], - temperature=0.7, - response_format={"type": "json_object"}, - ) - content = response.choices[0].message.content - data = json.loads(content) - - # The prompt asks for {"questions": [...]}, so we extract the list. - if isinstance(data, dict) and "questions" in data and isinstance(data["questions"], list): - return data["questions"] - elif isinstance(data, list): - # Fallback in case the model returns a list directly - print("Warning: Model returned a raw list instead of an object with a 'questions' key.") - return data - else: - print(f"Warning: Failed to find a 'questions' list in the model's output. Got: {content}") - return None - - except Exception as e: - print(f"Error calling OpenAI API or parsing JSON: {e}") - return None - -def semantic_validate_tasks(tasks, client): - """ - Uses an LLM to semantically validate if the task's answer matches the SQL result. - """ - if not tasks: - return [] - - final_validated_tasks = [] - print("\nPerforming semantic validation with GPT-4o...") - - for task in tasks: - # Prepare data for the prompt, including the SQL result - task_data_for_prompt = { - "question": task["question"], - "sql": task["sql"], - "answer": task["answer"], - "sql_result": task["sql_result"] - } - task_data_json = json.dumps(task_data_for_prompt, indent=2, default=str) - - prompt = SEMANTIC_VERIFICATION_PROMPT_TEMPLATE.format(task_data_json=task_data_json) - - try: - print(f" - Verifying question: \"{task['question'][:80]}...\"") - response = client.chat.completions.create( - model=OPENAI_CONFIG["model"], - messages=[ - {"role": "system", "content": "You are a helpful assistant designed to output JSON."}, - {"role": "user", "content": prompt} - ], - temperature=0.0, # We want deterministic validation - response_format={"type": "json_object"}, - ) - content = response.choices[0].message.content - verification_result = json.loads(content) - - if verification_result.get("is_match") is True: - # Task is valid. Rename sql_result for the final output. - print(f" - Validation PASSED.") - task['sql_execute_result'] = task.pop('sql_result') - final_validated_tasks.append(task) - else: - reason = verification_result.get('reason', 'No reason provided.') - print(f" - Validation FAILED. Filtering task.") - print(f" - Reason: {reason}") - print(f" - Question: {task['question']}") - print(f" - Expected Answer: {json.dumps(task['answer'], default=str)}") - print(f" - SQL: {task['sql']}") - sql_result_str = json.dumps(task['sql_result'], indent=2, default=str) - print(f" - SQL Result: {sql_result_str}") - - except Exception as e: - print(f" - An error occurred during semantic validation for task, filtering it out: {e}") - print(f" - Question: {task.get('question', 'N/A')}") - print(f" - SQL: {task.get('sql', 'N/A')}") - - return final_validated_tasks - -def main(): - """Main function to run the script.""" - # 1. Load the list of core tables - try: - with open('core_tables.json', 'r') as f: - core_tables = json.load(f) - except FileNotFoundError: - print("Error: core_tables.json not found. Please create it.") - return - - # 2. Connect to the database - conn = get_db_connection() - if not conn: - return - - cursor = conn.cursor() - - # 3. Setup OpenAI Client - if not OPENAI_CONFIG["api_key"]: - print("Error: OPENAI_API_KEY environment variable not set.") - return - client = OpenAI(api_key=OPENAI_CONFIG["api_key"], base_url=OPENAI_CONFIG["base_url"]) - - try: - # 4. Get full schema context - print("Fetching full database schema...") - schema_context = get_full_schema(cursor, core_tables) - - # 5. Get random samples and print them - print("Sampling data from 5 random tables...") - sampled_data = get_random_tables_and_samples(cursor, core_tables, num_tables=5, num_samples=5) - print(f"Sampled from tables: {list(sampled_data.keys())}") - print("\n--- Sampled Data ---") - print(json.dumps(sampled_data, indent=2, default=str)) - print("---------------------\n") - - # 6. Generate questions using the LLM - print("Generating questions with GPT-4o...") - generated_tasks = generate_questions(client, schema_context, sampled_data) - - # 7. Initial validation (SQL execution and substring check) - pre_validated_tasks = [] - if generated_tasks: - print("\nPerforming initial validation (SQL execution and substring match)...") - for task in generated_tasks: - if not isinstance(task, dict) or not all(k in task for k in ['sql', 'answer', 'question']): - print(f"Filtering task due to malformed structure or missing keys: {task}") - continue - - try: - cursor.execute(task['sql']) - sql_result = cursor.fetchall() - answer_str = str(task['answer']) - result_str = str(sql_result) - - if answer_str in result_str: - task['sql_result'] = sql_result # Attach result for the next validation step - pre_validated_tasks.append(task) - else: - print(f"Filtering task: Answer '{answer_str}' not found in SQL result.") - print(f" - Question: {task['question']}") - print(f" - SQL: {task['sql']}") - print(f" - Result: {result_str[:250]}...") - - except mysql.connector.Error as err: - print(f"Filtering task due to SQL error: {err}") - print(f" - Question: {task['question']}") - print(f" - SQL: {task['sql']}") - except Exception as e: - print(f"An unexpected error occurred during initial validation for task {task}: {e}") - - # 8. Semantic validation using LLM - validated_tasks = semantic_validate_tasks(pre_validated_tasks, client) - - # 9. Print the final JSON output - if validated_tasks: - print("\n--- Final Validated Tasks ---") - print(json.dumps(validated_tasks, indent=2, default=str)) - else: - print("Failed to generate any valid tasks after all validation steps.") - - finally: - # 10. Close the database connection - if conn.is_connected(): - cursor.close() - conn.close() - print("\nDatabase connection closed.") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/random_sample/generate_tasks copy 3.py b/random_sample/generate_tasks copy 3.py deleted file mode 100644 index 7dc7598..0000000 --- a/random_sample/generate_tasks copy 3.py +++ /dev/null @@ -1,370 +0,0 @@ -import os -import random -import json -import mysql.connector -from openai import OpenAI -from dotenv import load_dotenv - -# --- Configuration --- -load_dotenv() - -MYSQL_CONFIG = { - "host": "localhost", - "port": "23306", - "user": "mcpuser", - "password": "StrongPass123!", - "database": "magentodb" -} - -OPENAI_CONFIG = { - "api_key": os.getenv("OPENAI_API_KEY"), - "base_url": os.getenv("OPENAI_BASE_URL"), - "model": "gpt-4o" -} - -# --- Prompt Template --- -# This is a carefully engineered prompt to guide the LLM's output. -PROMPT_TEMPLATE = """ -You are an expert database analyst and a creative test case designer for e-commerce web applications. -Your goal is to generate realistic administrative tasks that can be solved by a Web Agent navigating an admin panel. - -I will provide you with the following context: -1. **Full Database Schema**: A list of `CREATE TABLE` statements for the core tables of a Magento e-commerce platform. -2. **Sampled Data**: A JSON object containing 5 random rows of data from 5 randomly selected core tables. This data is REAL and should be used to inspire specific, answerable questions. - -## Your Task - -Based on the provided schema and sample data, create a JSON object containing a single key, "questions", which holds an array of up to 10 unique task objects. - -### Requirements for Each Question: -- **Web Agent Solvable**: The task must represent a realistic action an administrator would perform in a web UI (e.g., "Find all orders for customer X", "Update the stock for product Y", "Approve a pending review"). -- **Grounded in Data**: The questions should be specific, using names, IDs, or values from the provided **Sampled Data** to make them concrete. -- **Utilize Schema**: You can formulate questions that require joining tables, even if not all tables were sampled. The full schema is your guide. - -### Output Format -The final output MUST be a single, valid JSON object. Do not include any other text, explanations, or markdown formatting like ```json. -The JSON object must have one key: "questions", containing a JSON array of task objects. - -Each object in the array must contain exactly three keys: `question`, `answer`, and `sql`. - -- **`question`**: (string) A natural language description of the task for a web agent. -- **`answer`**: (string, integer, float, or list) The precise and concise answer to the question, derived by running the SQL query against the database. -- **`sql`**: (string) The exact, runnable MySQL query that was used to find the answer. - -### Output Format Example -```json -{{ - "questions": [ - {{ - "question": "What is the email address for customer with ID 5?", - "answer": "customer5@example.com", - "sql": "SELECT email FROM customer_entity WHERE entity_id = 5;" - }}, - {{ - "question": "Find the total quantity of item with SKU 'ABC-123' in the cart.", - "answer": 3, - "sql": "SELECT SUM(qty) FROM quote_item WHERE sku = 'ABC-123';" - }} - ] -}} -``` - ---- -### Full Database Schema -{schema_context} - ---- -### Sampled Data -Here is the sample data from randomly selected tables. Use this to make your questions specific. - -{sampled_data_str} - ---- -Now, generate the JSON object based on these instructions. -""" - -# This is a new prompt to evaluate results and generate a corrected answer. -SEMANTIC_EVALUATION_PROMPT_TEMPLATE = """ -You are a precise data analyst. Your task is to evaluate if a SQL query's result adequately answers a given natural language question. If it does, you must formulate a concise, natural-language answer. - -I will provide you with a JSON object containing: -1. `question`: The original question asked. -2. `sql`: The SQL query that was executed. -3. `sql_result`: The actual data returned by executing the SQL query. - -## Your Task -1. **Analyze**: Determine if the `sql_result` contains the necessary information to definitively answer the `question`. -2. **Respond**: Based on your analysis, generate a JSON object with one of two structures. - -### Case 1: The question CAN be answered -If the `sql_result` provides a clear answer, respond with: -```json -{{ - "can_answer": true, - "new_answer": "..." -}} -``` -- `can_answer`: (boolean) Must be `true`. -- `new_answer`: (string, integer, float, or list) A concise, human-readable answer derived *only* from the `sql_result`. For example, if the result is `[(52.00,)]`, the answer can be "52.00" or 52.00. - -### Case 2: The question CANNOT be answered -If the `sql_result` is empty, irrelevant, or insufficient to answer the question, respond with: -```json -{{ - "can_answer": false, - "reason": "..." -}} -``` -- `can_answer`: (boolean) Must be `false`. -- `reason`: (string) A brief explanation for why the question cannot be answered from the given data (e.g., "The query returned no results.", "The result contains internal IDs, not the requested customer names."). - ---- -### Evaluation Data -{task_data_json} ---- - -Now, provide your evaluation as a JSON object. -""" - -def get_db_connection(): - """Establishes a connection to the MySQL database.""" - try: - conn = mysql.connector.connect(**MYSQL_CONFIG) - return conn - except mysql.connector.Error as err: - print(f"Error connecting to MySQL: {err}") - return None - -def get_full_schema(cursor, tables): - """Fetches the CREATE TABLE statements for all core tables.""" - schema_parts = [] - for table_name in tables: - try: - cursor.execute(f"SHOW CREATE TABLE `{table_name}`") - result = cursor.fetchone() - if result: - schema_parts.append(result[1]) # result[1] is the CREATE TABLE statement - except mysql.connector.Error as err: - print(f"Warning: Could not get schema for table {table_name}: {err}") - return "\n\n".join(schema_parts) - -def get_random_tables_and_samples(cursor, tables, num_tables=5, num_samples=5): - """Selects random tables and samples random rows from them.""" - selected_tables = random.sample(tables, num_tables) - sampled_data = {} - - for table_name in selected_tables: - try: - # Use ORDER BY RAND() for random sampling. Can be slow on very large tables. - query = f"SELECT * FROM `{table_name}` ORDER BY RAND() LIMIT {num_samples}" - cursor.execute(query) - - rows = cursor.fetchall() - if not rows: - sampled_data[table_name] = [] - continue - - columns = [desc[0] for desc in cursor.description] - - # Convert rows (tuples) to a list of dictionaries - sampled_rows = [] - for row in rows: - row_dict = {} - for i, col_value in enumerate(row): - # Handle bytes by decoding, fall back to string representation - if isinstance(col_value, bytes): - try: - row_dict[columns[i]] = col_value.decode('utf-8') - except UnicodeDecodeError: - row_dict[columns[i]] = str(col_value) - else: - row_dict[columns[i]] = col_value - sampled_rows.append(row_dict) - - sampled_data[table_name] = sampled_rows - - except mysql.connector.Error as err: - print(f"Warning: Could not sample data from table {table_name}: {err}") - sampled_data[table_name] = f"Error: {err}" - - return sampled_data - -def generate_questions(client, schema_context, sampled_data): - """Generates questions by calling the OpenAI API.""" - if not client: - raise ValueError("OpenAI client not provided.") - - sampled_data_str = json.dumps(sampled_data, indent=2, default=str) - - prompt = PROMPT_TEMPLATE.format( - schema_context=schema_context, - sampled_data_str=sampled_data_str - ) - - try: - response = client.chat.completions.create( - model=OPENAI_CONFIG["model"], - messages=[ - {"role": "system", "content": "You are a helpful assistant designed to output JSON."}, - {"role": "user", "content": prompt} - ], - temperature=0.7, - response_format={"type": "json_object"}, - ) - content = response.choices[0].message.content - data = json.loads(content) - - # The prompt asks for {"questions": [...]}, so we extract the list. - if isinstance(data, dict) and "questions" in data and isinstance(data["questions"], list): - return data["questions"] - elif isinstance(data, list): - # Fallback in case the model returns a list directly - print("Warning: Model returned a raw list instead of an object with a 'questions' key.") - return data - else: - print(f"Warning: Failed to find a 'questions' list in the model's output. Got: {content}") - return None - - except Exception as e: - print(f"Error calling OpenAI API or parsing JSON: {e}") - return None - -def evaluate_and_refine_tasks(tasks, client): - """ - Uses an LLM to evaluate if a SQL result answers the question and refines the answer. - """ - if not tasks: - return [] - - final_validated_tasks = [] - print("\nPerforming semantic evaluation and answer refinement with GPT-4o...") - - for task in tasks: - # Prepare data for the prompt, excluding the original 'answer' - task_data_for_prompt = { - "question": task["question"], - "sql": task["sql"], - "sql_result": task["sql_result"] - } - task_data_json = json.dumps(task_data_for_prompt, indent=2, default=str) - - prompt = SEMANTIC_EVALUATION_PROMPT_TEMPLATE.format(task_data_json=task_data_json) - - try: - print(f" - Evaluating question: \"{task['question'][:80]}...\"") - response = client.chat.completions.create( - model=OPENAI_CONFIG["model"], - messages=[ - {"role": "system", "content": "You are a helpful assistant designed to output JSON."}, - {"role": "user", "content": prompt} - ], - temperature=0.0, # We want deterministic evaluation - response_format={"type": "json_object"}, - ) - content = response.choices[0].message.content - evaluation_result = json.loads(content) - - if evaluation_result.get("can_answer") is True and "new_answer" in evaluation_result: - # Task is valid. Update the answer with the refined one from the LLM. - task['answer'] = evaluation_result['new_answer'] - task['sql_execute_result'] = task.pop('sql_result') - final_validated_tasks.append(task) - print(f" - Evaluation PASSED. New answer: {json.dumps(task['answer'])}") - else: - reason = evaluation_result.get('reason', 'No reason provided.') - print(f" - Evaluation FAILED. Filtering task.") - print(f" - Reason: {reason}") - print(f" - Question: {task['question']}") - print(f" - Original Answer: {json.dumps(task['answer'], default=str)}") - print(f" - SQL: {task['sql']}") - sql_result_str = json.dumps(task['sql_result'], indent=2, default=str) - print(f" - SQL Result: {sql_result_str}") - - except Exception as e: - print(f" - An error occurred during semantic evaluation for task, filtering it out: {e}") - print(f" - Question: {task.get('question', 'N/A')}") - print(f" - SQL: {task.get('sql', 'N/A')}") - - return final_validated_tasks - -def main(): - """Main function to run the script.""" - # 1. Load the list of core tables - try: - with open('core_tables.json', 'r') as f: - core_tables = json.load(f) - except FileNotFoundError: - print("Error: core_tables.json not found. Please create it.") - return - - # 2. Connect to the database - conn = get_db_connection() - if not conn: - return - - cursor = conn.cursor() - - # 3. Setup OpenAI Client - if not OPENAI_CONFIG["api_key"]: - print("Error: OPENAI_API_KEY environment variable not set.") - return - client = OpenAI(api_key=OPENAI_CONFIG["api_key"], base_url=OPENAI_CONFIG["base_url"]) - - try: - # 4. Get full schema context - print("Fetching full database schema...") - schema_context = get_full_schema(cursor, core_tables) - - # 5. Get random samples and print them - print("Sampling data from 5 random tables...") - sampled_data = get_random_tables_and_samples(cursor, core_tables, num_tables=5, num_samples=5) - print(f"Sampled from tables: {list(sampled_data.keys())}") - print("\n--- Sampled Data ---") - print(json.dumps(sampled_data, indent=2, default=str)) - print("---------------------\n") - - # 6. Generate questions using the LLM - print("Generating questions with GPT-4o...") - generated_tasks = generate_questions(client, schema_context, sampled_data) - - # 7. Execute SQL for all generated tasks - tasks_for_evaluation = [] - if generated_tasks: - print("\nExecuting SQL for generated tasks...") - for task in generated_tasks: - if not isinstance(task, dict) or not all(k in task for k in ['sql', 'answer', 'question']): - print(f"Filtering task due to malformed structure or missing keys: {task}") - continue - - try: - cursor.execute(task['sql']) - sql_result = cursor.fetchall() - task['sql_result'] = sql_result - tasks_for_evaluation.append(task) - - except mysql.connector.Error as err: - print(f"Filtering task due to SQL error: {err}") - print(f" - Question: {task['question']}") - print(f" - SQL: {task['sql']}") - except Exception as e: - print(f"An unexpected error occurred during SQL execution for task {task}: {e}") - - # 8. Semantic evaluation and answer refinement - validated_tasks = evaluate_and_refine_tasks(tasks_for_evaluation, client) - - # 9. Print the final JSON output - if validated_tasks: - print("\n--- Final Validated Tasks ---") - print(json.dumps(validated_tasks, indent=2, default=str)) - else: - print("Failed to generate any valid tasks after all validation steps.") - - finally: - # 10. Close the database connection - if conn.is_connected(): - cursor.close() - conn.close() - print("\nDatabase connection closed.") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/random_sample/generate_tasks copy 4.py b/random_sample/generate_tasks copy 4.py deleted file mode 100644 index 85ce167..0000000 --- a/random_sample/generate_tasks copy 4.py +++ /dev/null @@ -1,408 +0,0 @@ -import os -import random -import json -import mysql.connector -import argparse -from openai import OpenAI -from dotenv import load_dotenv - -# --- Configuration --- -load_dotenv() - -MYSQL_CONFIG = { - "host": "localhost", - "port": "23306", - "user": "mcpuser", - "password": "StrongPass123!", - "database": "magentodb" -} - -OPENAI_CONFIG = { - "api_key": os.getenv("OPENAI_API_KEY"), - "base_url": os.getenv("OPENAI_BASE_URL"), - "model": "gpt-4o" -} - -# --- Prompt Template --- -# This is a carefully engineered prompt to guide the LLM's output. -PROMPT_TEMPLATE = """ -You are an expert database analyst and a creative test case designer for e-commerce web applications. -Your goal is to generate realistic administrative tasks that can be solved by a Web Agent navigating an admin panel. - -I will provide you with the following context: -1. **Full Database Schema**: A list of `CREATE TABLE` statements for the core tables of a Magento e-commerce platform. -2. **Sampled Data**: A JSON object containing 5 random rows of data from 5 randomly selected core tables. This data is REAL and should be used to inspire specific, answerable questions. - -## Your Task - -Based on the provided schema and sample data, create a JSON object containing a single key, "questions", which holds an array of up to 10 unique task objects. - -### Requirements for Each Question: -- **Web Agent Solvable**: The task must represent a realistic action an administrator would perform in a web UI (e.g., "Find all orders for customer X", "Update the stock for product Y", "Approve a pending review"). -- **Grounded in Data**: The questions should be specific, using names, IDs, or values from the provided **Sampled Data** to make them concrete. -- **Utilize Schema**: You can formulate questions that require joining tables, even if not all tables were sampled. The full schema is your guide. - -### Output Format -The final output MUST be a single, valid JSON object. Do not include any other text, explanations, or markdown formatting like ```json. -The JSON object must have one key: "questions", containing a JSON array of task objects. - -Each object in the array must contain exactly three keys: `question`, `answer`, and `sql`. - -- **`question`**: (string) A natural language description of the task for a web agent. -- **`answer`**: (string, integer, float, or list) The precise and concise answer to the question, derived by running the SQL query against the database. -- **`sql`**: (string) The exact, runnable MySQL query that was used to find the answer. - -### Output Format Example -```json -{{ - "questions": [ - {{ - "question": "What is the email address for customer with ID 5?", - "answer": "customer5@example.com", - "sql": "SELECT email FROM customer_entity WHERE entity_id = 5;" - }}, - {{ - "question": "Find the total quantity of item with SKU 'ABC-123' in the cart.", - "answer": 3, - "sql": "SELECT SUM(qty) FROM quote_item WHERE sku = 'ABC-123';" - }} - ] -}} -``` - ---- -### Full Database Schema -{schema_context} - ---- -### Sampled Data -Here is the sample data from randomly selected tables. Use this to make your questions specific. - -{sampled_data_str} - ---- -Now, generate the JSON object based on these instructions. -""" - -# This is a new prompt to evaluate results and generate a corrected answer. -SEMANTIC_EVALUATION_PROMPT_TEMPLATE = """ -You are a precise data analyst. Your task is to evaluate if a SQL query's result adequately answers a given natural language question. If it does, you must formulate a concise, natural-language answer. - -I will provide you with a JSON object containing: -1. `question`: The original question asked. -2. `sql`: The SQL query that was executed. -3. `sql_result`: The actual data returned by executing the SQL query. - -## Your Task -1. **Analyze**: Determine if the `sql_result` contains the necessary information to definitively answer the `question`. -2. **Respond**: Based on your analysis, generate a JSON object with one of two structures. - -### Case 1: The question CAN be answered -If the `sql_result` provides a clear answer, respond with: -```json -{{ - "can_answer": true, - "new_answer": "..." -}} -``` -- `can_answer`: (boolean) Must be `true`. -- `new_answer`: (string, integer, float, or list) A concise, human-readable answer derived *only* from the `sql_result`. For example, if the result is `[(52.00,)]`, the answer can be "52.00" or 52.00. - -### Case 2: The question CANNOT be answered -If the `sql_result` is empty, irrelevant, or insufficient to answer the question, respond with: -```json -{{ - "can_answer": false, - "reason": "..." -}} -``` -- `can_answer`: (boolean) Must be `false`. -- `reason`: (string) A brief explanation for why the question cannot be answered from the given data (e.g., "The query returned no results.", "The result contains internal IDs, not the requested customer names."). - ---- -### Evaluation Data -{task_data_json} ---- - -Now, provide your evaluation as a JSON object. -""" - -def get_db_connection(): - """Establishes a connection to the MySQL database.""" - try: - conn = mysql.connector.connect(**MYSQL_CONFIG) - return conn - except mysql.connector.Error as err: - print(f"Error connecting to MySQL: {err}") - return None - -def get_full_schema(cursor, tables): - """Fetches the CREATE TABLE statements for all core tables.""" - schema_parts = [] - for table_name in tables: - try: - cursor.execute(f"SHOW CREATE TABLE `{table_name}`") - result = cursor.fetchone() - if result: - schema_parts.append(result[1]) # result[1] is the CREATE TABLE statement - except mysql.connector.Error as err: - print(f"Warning: Could not get schema for table {table_name}: {err}") - return "\n\n".join(schema_parts) - -def get_random_tables_and_samples(cursor, tables, num_tables=5, num_samples=5): - """Selects random tables and samples random rows from them.""" - selected_tables = random.sample(tables, num_tables) - sampled_data = {} - - for table_name in selected_tables: - try: - # Use ORDER BY RAND() for random sampling. Can be slow on very large tables. - query = f"SELECT * FROM `{table_name}` ORDER BY RAND() LIMIT {num_samples}" - cursor.execute(query) - - rows = cursor.fetchall() - if not rows: - sampled_data[table_name] = [] - continue - - columns = [desc[0] for desc in cursor.description] - - # Convert rows (tuples) to a list of dictionaries - sampled_rows = [] - for row in rows: - row_dict = {} - for i, col_value in enumerate(row): - # Handle bytes by decoding, fall back to string representation - if isinstance(col_value, bytes): - try: - row_dict[columns[i]] = col_value.decode('utf-8') - except UnicodeDecodeError: - row_dict[columns[i]] = str(col_value) - else: - row_dict[columns[i]] = col_value - sampled_rows.append(row_dict) - - sampled_data[table_name] = sampled_rows - - except mysql.connector.Error as err: - print(f"Warning: Could not sample data from table {table_name}: {err}") - sampled_data[table_name] = f"Error: {err}" - - return sampled_data - -def generate_questions(client, schema_context, sampled_data): - """Generates questions by calling the OpenAI API.""" - if not client: - raise ValueError("OpenAI client not provided.") - - sampled_data_str = json.dumps(sampled_data, indent=2, default=str) - - prompt = PROMPT_TEMPLATE.format( - schema_context=schema_context, - sampled_data_str=sampled_data_str - ) - - try: - response = client.chat.completions.create( - model=OPENAI_CONFIG["model"], - messages=[ - {"role": "system", "content": "You are a helpful assistant designed to output JSON."}, - {"role": "user", "content": prompt} - ], - temperature=0.7, - response_format={"type": "json_object"}, - ) - content = response.choices[0].message.content - data = json.loads(content) - - # The prompt asks for {"questions": [...]}, so we extract the list. - if isinstance(data, dict) and "questions" in data and isinstance(data["questions"], list): - return data["questions"] - elif isinstance(data, list): - # Fallback in case the model returns a list directly - print("Warning: Model returned a raw list instead of an object with a 'questions' key.") - return data - else: - print(f"Warning: Failed to find a 'questions' list in the model's output. Got: {content}") - return None - - except Exception as e: - print(f"Error calling OpenAI API or parsing JSON: {e}") - return None - -def load_existing_tasks(filepath): - """Loads tasks from a JSON file if it exists.""" - if not os.path.exists(filepath): - return [] - try: - with open(filepath, 'r') as f: - content = f.read() - if not content: # Handle empty file - return [] - return json.loads(content) - except (json.JSONDecodeError, FileNotFoundError): - print(f"Warning: Could not read or parse {filepath}. Starting with an empty list.") - return [] - -def evaluate_and_refine_tasks(tasks, client): - """ - Uses an LLM to evaluate if a SQL result answers the question and refines the answer. - """ - if not tasks: - return [] - - final_validated_tasks = [] - print("\nPerforming semantic evaluation and answer refinement with GPT-4o...") - - for task in tasks: - # Prepare data for the prompt, excluding the original 'answer' - task_data_for_prompt = { - "question": task["question"], - "sql": task["sql"], - "sql_result": task["sql_result"] - } - task_data_json = json.dumps(task_data_for_prompt, indent=2, default=str) - - prompt = SEMANTIC_EVALUATION_PROMPT_TEMPLATE.format(task_data_json=task_data_json) - - try: - print(f" - Evaluating question: \"{task['question'][:80]}...\"") - response = client.chat.completions.create( - model=OPENAI_CONFIG["model"], - messages=[ - {"role": "system", "content": "You are a helpful assistant designed to output JSON."}, - {"role": "user", "content": prompt} - ], - temperature=0.0, # We want deterministic evaluation - response_format={"type": "json_object"}, - ) - content = response.choices[0].message.content - evaluation_result = json.loads(content) - - if evaluation_result.get("can_answer") is True and "new_answer" in evaluation_result: - # Task is valid. Update the answer with the refined one from the LLM. - task['answer'] = evaluation_result['new_answer'] - task['sql_execute_result'] = task.pop('sql_result') - final_validated_tasks.append(task) - print(f" - Evaluation PASSED. New answer: {json.dumps(task['answer'])}") - else: - reason = evaluation_result.get('reason', 'No reason provided.') - print(f" - Evaluation FAILED. Filtering task.") - print(f" - Reason: {reason}") - print(f" - Question: {task['question']}") - print(f" - Original Answer: {json.dumps(task['answer'], default=str)}") - print(f" - SQL: {task['sql']}") - sql_result_str = json.dumps(task['sql_result'], indent=2, default=str) - print(f" - SQL Result: {sql_result_str}") - - except Exception as e: - print(f" - An error occurred during semantic evaluation for task, filtering it out: {e}") - print(f" - Question: {task.get('question', 'N/A')}") - print(f" - SQL: {task.get('sql', 'N/A')}") - - return final_validated_tasks - -def main(): - """Main function to run the script.""" - parser = argparse.ArgumentParser(description="Generate and validate e-commerce admin tasks.") - parser.add_argument( - "--target-count", - type=int, - required=True, - help="The total number of questions to generate." - ) - parser.add_argument( - "--output-file", - type=str, - default="generated_tasks.json", - help="The file to save the generated tasks to (in JSON format)." - ) - args = parser.parse_args() - - # Load existing tasks from the output file - all_tasks = load_existing_tasks(args.output_file) - print(f"Found {len(all_tasks)} existing valid tasks in '{args.output_file}'.") - - # Connect to DB and set up client - conn = get_db_connection() - if not conn: - return - cursor = conn.cursor() - - if not OPENAI_CONFIG["api_key"]: - print("Error: OPENAI_API_KEY environment variable not set.") - return - client = OpenAI(api_key=OPENAI_CONFIG["api_key"], base_url=OPENAI_CONFIG["base_url"]) - - try: - # Load core tables and schema once - try: - with open('core_tables.json', 'r') as f: - core_tables = json.load(f) - except FileNotFoundError: - print("Error: core_tables.json not found. Please create it.") - return - - print("Fetching full database schema...") - schema_context = get_full_schema(cursor, core_tables) - - # Start the generation loop - round_num = 1 - while len(all_tasks) < args.target_count: - print(f"\n--- Starting Generation Round {round_num} ---") - print(f"Goal: {args.target_count} | Current: {len(all_tasks)} | Needed: {args.target_count - len(all_tasks)}") - - # Get random samples for this round - print("Sampling data from 5 random tables...") - sampled_data = get_random_tables_and_samples(cursor, core_tables, num_tables=5, num_samples=5) - - # Generate questions - print("Generating questions with GPT-4o...") - generated_tasks = generate_questions(client, schema_context, sampled_data) - - # Execute SQL for generated tasks - tasks_for_evaluation = [] - if generated_tasks: - print("\nExecuting SQL for generated tasks...") - for task in generated_tasks: - if not isinstance(task, dict) or not all(k in task for k in ['sql', 'answer', 'question']): - print(f"Filtering task due to malformed structure: {task}") - continue - try: - cursor.execute(task['sql']) - sql_result = cursor.fetchall() - task['sql_result'] = sql_result - tasks_for_evaluation.append(task) - except mysql.connector.Error as err: - print(f"Filtering task due to SQL error: {err} on SQL: {task['sql']}") - - # Perform semantic evaluation and get validated tasks - validated_tasks = evaluate_and_refine_tasks(tasks_for_evaluation, client) - - # Append new tasks and save to file - if validated_tasks: - all_tasks.extend(validated_tasks) - with open(args.output_file, 'w') as f: - json.dump(all_tasks, f, indent=2, default=str) - - print("\n--- Round Summary ---") - print(f"Generated {len(validated_tasks)} new valid tasks in this round.") - print(f"Progress: {len(all_tasks)} / {args.target_count} tasks.") - else: - print("\n--- Round Summary ---") - print("No new valid tasks were generated in this round. Retrying...") - - round_num += 1 - - finally: - # Close the database connection - if conn.is_connected(): - cursor.close() - conn.close() - print("\nDatabase connection closed.") - - print(f"\nTarget of {args.target_count} tasks reached. Final output saved to {args.output_file}.") - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/random_sample/generate_tasks copy 5.py b/random_sample/generate_tasks copy 5.py deleted file mode 100644 index 2c0b0e2..0000000 --- a/random_sample/generate_tasks copy 5.py +++ /dev/null @@ -1,437 +0,0 @@ -import os -import random -import json -import mysql.connector -import argparse -from openai import OpenAI -from dotenv import load_dotenv - -# --- Configuration --- -load_dotenv() - -MYSQL_CONFIG = { - "host": "localhost", - "port": "23306", - "user": "mcpuser", - "password": "StrongPass123!", - "database": "magentodb" -} - -OPENAI_CONFIG = { - "api_key": os.getenv("OPENAI_API_KEY"), - "base_url": os.getenv("OPENAI_BASE_URL"), - "model": "gpt-4o" -} - -# --- Prompt Template --- -# This is a carefully engineered prompt to guide the LLM's output. -PROMPT_TEMPLATE = """ -You are an expert database analyst and a creative test case designer for e-commerce web applications. -Your goal is to generate realistic administrative tasks that can be solved by a Web Agent navigating an admin panel. - -I will provide you with the following context: -1. **Full Database Schema**: A list of `CREATE TABLE` statements for the core tables of a Magento e-commerce platform. -2. **Sampled Data**: A JSON object containing 5 random rows of data from 5 randomly selected core tables. This data is REAL and should be used to inspire specific, answerable questions. - -## Your Task - -Based on the provided schema and sample data, create a JSON object containing a single key, "questions", which holds an array of up to 10 unique task objects. - -### Requirements for Each Question: -- **Web Agent Solvable**: The task must represent a realistic action an administrator would perform in a web UI (e.g., "Find all orders for customer X", "Update the stock for product Y", "Approve a pending review"). -- **Grounded in Data**: The questions should be specific, using names, IDs, or values from the provided **Sampled Data** to make them concrete. -- **Utilize Schema**: You can formulate questions that require joining tables, even if not all tables were sampled. The full schema is your guide. - -### Output Format -The final output MUST be a single, valid JSON object. Do not include any other text, explanations, or markdown formatting like ```json. -The JSON object must have one key: "questions", containing a JSON array of task objects. - -Each object in the array must contain exactly three keys: `question`, `answer`, and `sql`. - -- **`question`**: (string) A natural language description of the task for a web agent. -- **`answer`**: (string, integer, float, or list) The precise and concise answer to the question, derived by running the SQL query against the database. -- **`sql`**: (string) The exact, runnable MySQL query that was used to find the answer. - -### Output Format Example -```json -{{ - "questions": [ - {{ - "question": "What is the email address for customer with ID 5?", - "answer": "customer5@example.com", - "sql": "SELECT email FROM customer_entity WHERE entity_id = 5;" - }}, - {{ - "question": "Find the total quantity of item with SKU 'ABC-123' in the cart.", - "answer": 3, - "sql": "SELECT SUM(qty) FROM quote_item WHERE sku = 'ABC-123';" - }} - ] -}} -``` - ---- -### Full Database Schema -{schema_context} - ---- -### Sampled Data -Here is the sample data from randomly selected tables. Use this to make your questions specific. - -{sampled_data_str} - ---- -Now, generate the JSON object based on these instructions. -""" - -# This is a new prompt to evaluate results and generate a corrected answer. -SEMANTIC_EVALUATION_PROMPT_TEMPLATE = """ -You are a precise data analyst. Your task is to evaluate if a SQL query's result adequately answers a given natural language question. You will then either refine the answer, or completely rephrase the question if the result set is large. - -I will provide you with a JSON object containing: -1. `question`: The original question asked. -2. `sql`: The SQL query that was executed. -3. `sql_result`: The actual data returned by executing the SQL query. -4. `row_count`: The number of rows in `sql_result`. - -## Your Task -Analyze the inputs and respond with a JSON object. You have three cases. The `new_answer` field MUST always be an array of strings. - -### Case 1: Large Result Set (Question Transformation) -If `row_count` is greater than 10 AND the original `question` does NOT already ask for a count (e.g., it is not phrased like "How many..."), you must transform the question. -Respond with: -```json -{{ - "can_answer": true, - "new_question": "How many items were found?", - "new_answer": ["42"] -}} -``` -- `can_answer`: (boolean) Must be `true`. -- `new_question`: (string) A rephrased question that asks for the quantity of items. For example, if the original question was "List all products", the new question should be "How many products were found?". -- `new_answer`: (array of strings) An array containing the `row_count` as a single string element. - -### Case 2: Standard Answer (No Transformation) -If Case 1 does not apply, but the `sql_result` still provides a clear answer to the original `question`, respond with: -```json -{{ - "can_answer": true, - "new_answer": ["value1", "value2", ...] -}} -``` -- `can_answer`: (boolean) Must be `true`. -- `new_answer`: (array of strings) An array containing all the essential parts of the answer extracted from `sql_result`. Every value from the result set that contributes to the answer should be included as a string in the array. This ensures answer completeness. - - **Example 1**: If `question` is "What is the status of order 123?" and `sql_result` is `[["processing"]]`, `new_answer` should be `["processing"]`. - - **Example 2**: If `question` is "List emails for pending customers" and `sql_result` is `[["test@a.com"], ["test@b.com"]]`, `new_answer` should be `["test@a.com", "test@b.com"]`. - - **Example 3**: If `question` is "Get product name and price for SKU 'XYZ'" and `sql_result` is `[["My Product", 19.99]]`, `new_answer` should be `["My Product", "19.99"]`. - -### Case 3: The question CANNOT be answered -If the `sql_result` is empty, irrelevant, or insufficient to answer the question, respond with: -```json -{{ - "can_answer": false, - "reason": "..." -}} -``` -- `can_answer`: (boolean) Must be `false`. -- `reason`: (string) A brief explanation for why the question cannot be answered. - ---- -### Evaluation Data -{task_data_json} ---- - -Now, provide your evaluation as a JSON object. -""" - -def get_db_connection(): - """Establishes a connection to the MySQL database.""" - try: - conn = mysql.connector.connect(**MYSQL_CONFIG) - return conn - except mysql.connector.Error as err: - print(f"Error connecting to MySQL: {err}") - return None - -def get_full_schema(cursor, tables): - """Fetches the CREATE TABLE statements for all core tables.""" - schema_parts = [] - for table_name in tables: - try: - cursor.execute(f"SHOW CREATE TABLE `{table_name}`") - result = cursor.fetchone() - if result: - schema_parts.append(result[1]) # result[1] is the CREATE TABLE statement - except mysql.connector.Error as err: - print(f"Warning: Could not get schema for table {table_name}: {err}") - return "\n\n".join(schema_parts) - -def get_random_tables_and_samples(cursor, tables, num_tables=5, num_samples=5): - """Selects random tables and samples random rows from them.""" - selected_tables = random.sample(tables, num_tables) - sampled_data = {} - - for table_name in selected_tables: - try: - # Use ORDER BY RAND() for random sampling. Can be slow on very large tables. - query = f"SELECT * FROM `{table_name}` ORDER BY RAND() LIMIT {num_samples}" - cursor.execute(query) - - rows = cursor.fetchall() - if not rows: - sampled_data[table_name] = [] - continue - - columns = [desc[0] for desc in cursor.description] - - # Convert rows (tuples) to a list of dictionaries - sampled_rows = [] - for row in rows: - row_dict = {} - for i, col_value in enumerate(row): - # Handle bytes by decoding, fall back to string representation - if isinstance(col_value, bytes): - try: - row_dict[columns[i]] = col_value.decode('utf-8') - except UnicodeDecodeError: - row_dict[columns[i]] = str(col_value) - else: - row_dict[columns[i]] = col_value - sampled_rows.append(row_dict) - - sampled_data[table_name] = sampled_rows - - except mysql.connector.Error as err: - print(f"Warning: Could not sample data from table {table_name}: {err}") - sampled_data[table_name] = f"Error: {err}" - - return sampled_data - -def generate_questions(client, schema_context, sampled_data): - """Generates questions by calling the OpenAI API.""" - if not client: - raise ValueError("OpenAI client not provided.") - - sampled_data_str = json.dumps(sampled_data, indent=2, default=str) - - prompt = PROMPT_TEMPLATE.format( - schema_context=schema_context, - sampled_data_str=sampled_data_str - ) - - try: - response = client.chat.completions.create( - model=OPENAI_CONFIG["model"], - messages=[ - {"role": "system", "content": "You are a helpful assistant designed to output JSON."}, - {"role": "user", "content": prompt} - ], - temperature=0.7, - response_format={"type": "json_object"}, - ) - content = response.choices[0].message.content - data = json.loads(content) - - # The prompt asks for {"questions": [...]}, so we extract the list. - if isinstance(data, dict) and "questions" in data and isinstance(data["questions"], list): - return data["questions"] - elif isinstance(data, list): - # Fallback in case the model returns a list directly - print("Warning: Model returned a raw list instead of an object with a 'questions' key.") - return data - else: - print(f"Warning: Failed to find a 'questions' list in the model's output. Got: {content}") - return None - - except Exception as e: - print(f"Error calling OpenAI API or parsing JSON: {e}") - return None - -def load_existing_tasks(filepath): - """Loads tasks from a JSON file if it exists.""" - if not os.path.exists(filepath): - return [] - try: - with open(filepath, 'r') as f: - content = f.read() - if not content: # Handle empty file - return [] - return json.loads(content) - except (json.JSONDecodeError, FileNotFoundError): - print(f"Warning: Could not read or parse {filepath}. Starting with an empty list.") - return [] - -def evaluate_and_refine_tasks(tasks, client): - """ - Uses an LLM to evaluate if a SQL result answers the question and refines the answer. - """ - if not tasks: - return [] - - final_validated_tasks = [] - print("\nPerforming semantic evaluation and answer refinement with GPT-4o...") - - for task in tasks: - # Prepare data for the prompt, excluding the original 'answer' - task_data_for_prompt = { - "question": task["question"], - "sql": task["sql"], - "sql_result": task["sql_result"], - "row_count": task["row_count"] - } - task_data_json = json.dumps(task_data_for_prompt, indent=2, default=str) - - prompt = SEMANTIC_EVALUATION_PROMPT_TEMPLATE.format(task_data_json=task_data_json) - - try: - print(f" - Evaluating question: \"{task['question'][:80]}...\"") - response = client.chat.completions.create( - model=OPENAI_CONFIG["model"], - messages=[ - {"role": "system", "content": "You are a helpful assistant designed to output JSON."}, - {"role": "user", "content": prompt} - ], - temperature=0.0, # We want deterministic evaluation - response_format={"type": "json_object"}, - ) - content = response.choices[0].message.content - evaluation_result = json.loads(content) - - if evaluation_result.get("can_answer") is True and "new_answer" in evaluation_result: - # Task is valid. Update the answer with the refined one from the LLM. - task['answer'] = evaluation_result['new_answer'] - - # If the LLM provides a new question, update it. - if 'new_question' in evaluation_result: - task['question'] = evaluation_result['new_question'] - print(f" - Question was rephrased: \"{task['question']}\"") - - task['sql_execute_result'] = task.pop('sql_result') - task.pop('row_count', None) # Clean up temp key - final_validated_tasks.append(task) - print(f" - Evaluation PASSED. New answer: {json.dumps(task['answer'])}") - else: - reason = evaluation_result.get('reason', 'No reason provided.') - print(f" - Evaluation FAILED. Filtering task.") - print(f" - Reason: {reason}") - print(f" - Question: {task['question']}") - print(f" - SQL: {task['sql']}") - sql_result_str = json.dumps(task['sql_result'], indent=2, default=str) - print(f" - SQL Result: {sql_result_str}") - - except Exception as e: - print(f" - An error occurred during semantic evaluation for task, filtering it out: {e}") - print(f" - Question: {task.get('question', 'N/A')}") - print(f" - SQL: {task.get('sql', 'N/A')}") - - return final_validated_tasks - -def main(): - """Main function to run the script.""" - parser = argparse.ArgumentParser(description="Generate and validate e-commerce admin tasks.") - parser.add_argument( - "--target-count", - type=int, - required=True, - help="The total number of questions to generate." - ) - parser.add_argument( - "--output-file", - type=str, - default="generated_tasks.json", - help="The file to save the generated tasks to (in JSON format)." - ) - args = parser.parse_args() - - # Load existing tasks from the output file - all_tasks = load_existing_tasks(args.output_file) - print(f"Found {len(all_tasks)} existing valid tasks in '{args.output_file}'.") - - # Connect to DB and set up client - conn = get_db_connection() - if not conn: - return - cursor = conn.cursor() - - if not OPENAI_CONFIG["api_key"]: - print("Error: OPENAI_API_KEY environment variable not set.") - return - client = OpenAI(api_key=OPENAI_CONFIG["api_key"], base_url=OPENAI_CONFIG["base_url"]) - - try: - # Load core tables and schema once - try: - with open('core_tables.json', 'r') as f: - core_tables = json.load(f) - except FileNotFoundError: - print("Error: core_tables.json not found. Please create it.") - return - - print("Fetching full database schema...") - schema_context = get_full_schema(cursor, core_tables) - - # Start the generation loop - round_num = 1 - while len(all_tasks) < args.target_count: - print(f"\n--- Starting Generation Round {round_num} ---") - print(f"Goal: {args.target_count} | Current: {len(all_tasks)} | Needed: {args.target_count - len(all_tasks)}") - - # Get random samples for this round - print("Sampling data from 5 random tables...") - sampled_data = get_random_tables_and_samples(cursor, core_tables, num_tables=5, num_samples=5) - - # Generate questions - print("Generating questions with GPT-4o...") - generated_tasks = generate_questions(client, schema_context, sampled_data) - - # Execute SQL for generated tasks - tasks_for_evaluation = [] - if generated_tasks: - print("\nExecuting SQL for generated tasks...") - for task in generated_tasks: - if not isinstance(task, dict) or not all(k in task for k in ['sql', 'answer', 'question']): - print(f"Filtering task due to malformed structure: {task}") - continue - try: - cursor.execute(task['sql']) - sql_result = cursor.fetchall() - # Create a new dict for evaluation, excluding the original 'answer'. - tasks_for_evaluation.append({ - 'question': task['question'], - 'sql': task['sql'], - 'sql_result': sql_result, - 'row_count': len(sql_result) - }) - except mysql.connector.Error as err: - print(f"Filtering task due to SQL error: {err} on SQL: {task['sql']}") - - # Perform semantic evaluation and get validated tasks - validated_tasks = evaluate_and_refine_tasks(tasks_for_evaluation, client) - - # Append new tasks and save to file - if validated_tasks: - all_tasks.extend(validated_tasks) - with open(args.output_file, 'w') as f: - json.dump(all_tasks, f, indent=2, default=str) - - print("\n--- Round Summary ---") - print(f"Generated {len(validated_tasks)} new valid tasks in this round.") - print(f"Progress: {len(all_tasks)} / {args.target_count} tasks.") - else: - print("\n--- Round Summary ---") - print("No new valid tasks were generated in this round. Retrying...") - - round_num += 1 - - finally: - # Close the database connection - if conn.is_connected(): - cursor.close() - conn.close() - print("\nDatabase connection closed.") - - print(f"\nTarget of {args.target_count} tasks reached. Final output saved to {args.output_file}.") - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/random_sample/generate_tasks copy.py b/random_sample/generate_tasks copy.py deleted file mode 100644 index 43324cc..0000000 --- a/random_sample/generate_tasks copy.py +++ /dev/null @@ -1,246 +0,0 @@ -import os -import random -import json -import mysql.connector -from openai import OpenAI -from dotenv import load_dotenv - -# --- Configuration --- -load_dotenv() - -MYSQL_CONFIG = { - "host": "localhost", - "port": "23306", - "user": "mcpuser", - "password": "StrongPass123!", - "database": "magentodb" -} - -OPENAI_CONFIG = { - "api_key": os.getenv("OPENAI_API_KEY"), - "base_url": os.getenv("OPENAI_BASE_URL"), - "model": "gpt-4o" -} - -# --- Prompt Template --- -# This is a carefully engineered prompt to guide the LLM's output. -PROMPT_TEMPLATE = """ -You are an expert database analyst and a creative test case designer for e-commerce web applications. -Your goal is to generate realistic administrative tasks that can be solved by a Web Agent navigating an admin panel. - -I will provide you with the following context: -1. **Full Database Schema**: A list of `CREATE TABLE` statements for the core tables of a Magento e-commerce platform. -2. **Sampled Data**: A JSON object containing 5 random rows of data from 5 randomly selected core tables. This data is REAL and should be used to inspire specific, answerable questions. - -## Your Task - -Based on the provided schema and sample data, create a JSON array of up to 10 unique questions. - -### Requirements for Each Question: -- **Web Agent Solvable**: The task must represent a realistic action an administrator would perform in a web UI (e.g., "Find all orders for customer X", "Update the stock for product Y", "Approve a pending review"). -- **Grounded in Data**: The questions should be specific, using names, IDs, or values from the provided **Sampled Data** to make them concrete. -- **Utilize Schema**: You can formulate questions that require joining tables, even if not all tables were sampled. The full schema is your guide. - -### Output Format -The final output MUST be a single, valid JSON array of objects. Do not include any other text, explanations, or markdown formatting like ```json. - -Each object in the array must contain exactly three keys: `question`, `answer`, and `sql`. - -- **`question`**: (string) A natural language description of the task for a web agent. -- **`answer`**: (string, integer, float, or list) The precise and concise answer to the question, derived by running the SQL query against the database. -- **`sql`**: (string) The exact, runnable MySQL query that was used to find the answer. - ---- -### Full Database Schema -{schema_context} - ---- -### Sampled Data -Here is the sample data from randomly selected tables. Use this to make your questions specific. - -{sampled_data_str} - ---- -Now, generate the JSON array based on these instructions. -""" - -def get_db_connection(): - """Establishes a connection to the MySQL database.""" - try: - conn = mysql.connector.connect(**MYSQL_CONFIG) - return conn - except mysql.connector.Error as err: - print(f"Error connecting to MySQL: {err}") - return None - -def get_full_schema(cursor, tables): - """Fetches the CREATE TABLE statements for all core tables.""" - schema_parts = [] - for table_name in tables: - try: - cursor.execute(f"SHOW CREATE TABLE `{table_name}`") - result = cursor.fetchone() - if result: - schema_parts.append(result[1]) # result[1] is the CREATE TABLE statement - except mysql.connector.Error as err: - print(f"Warning: Could not get schema for table {table_name}: {err}") - return "\n\n".join(schema_parts) - -def get_random_tables_and_samples(cursor, tables, num_tables=5, num_samples=5): - """Selects random tables and samples random rows from them.""" - selected_tables = random.sample(tables, num_tables) - sampled_data = {} - - for table_name in selected_tables: - try: - # Use ORDER BY RAND() for random sampling. Can be slow on very large tables. - query = f"SELECT * FROM `{table_name}` ORDER BY RAND() LIMIT {num_samples}" - cursor.execute(query) - - rows = cursor.fetchall() - if not rows: - sampled_data[table_name] = [] - continue - - columns = [desc[0] for desc in cursor.description] - - # Convert rows (tuples) to a list of dictionaries - sampled_rows = [] - for row in rows: - row_dict = {} - for i, col_value in enumerate(row): - # Handle bytes by decoding, fall back to string representation - if isinstance(col_value, bytes): - try: - row_dict[columns[i]] = col_value.decode('utf-8') - except UnicodeDecodeError: - row_dict[columns[i]] = str(col_value) - else: - row_dict[columns[i]] = col_value - sampled_rows.append(row_dict) - - sampled_data[table_name] = sampled_rows - - except mysql.connector.Error as err: - print(f"Warning: Could not sample data from table {table_name}: {err}") - sampled_data[table_name] = f"Error: {err}" - - return sampled_data - -def generate_questions(schema_context, sampled_data): - """Generates questions by calling the OpenAI API.""" - if not OPENAI_CONFIG["api_key"]: - raise ValueError("OPENAI_API_KEY environment variable not set.") - - client = OpenAI(api_key=OPENAI_CONFIG["api_key"], base_url=OPENAI_CONFIG["base_url"]) - - sampled_data_str = json.dumps(sampled_data, indent=2, default=str) - - prompt = PROMPT_TEMPLATE.format( - schema_context=schema_context, - sampled_data_str=sampled_data_str - ) - - try: - response = client.chat.completions.create( - model=OPENAI_CONFIG["model"], - messages=[ - {"role": "system", "content": "You are a helpful assistant designed to output JSON."}, - {"role": "user", "content": prompt} - ], - temperature=0.7, - ) - content = response.choices[0].message.content - return json.loads(content) - except Exception as e: - print(f"Error calling OpenAI API: {e}") - return None - -def main(): - """Main function to run the script.""" - # 1. Load the list of core tables - try: - with open('core_tables.json', 'r') as f: - core_tables = json.load(f) - except FileNotFoundError: - print("Error: core_tables.json not found. Please create it.") - return - - # 2. Connect to the database - conn = get_db_connection() - if not conn: - return - - cursor = conn.cursor() - - try: - # 3. Get full schema context - print("Fetching full database schema...") - schema_context = get_full_schema(cursor, core_tables) - - # 4. Get random samples and print them - print("Sampling data from 5 random tables...") - sampled_data = get_random_tables_and_samples(cursor, core_tables, num_tables=5, num_samples=5) - print(f"Sampled from tables: {list(sampled_data.keys())}") - print("\n--- Sampled Data ---") - print(json.dumps(sampled_data, indent=2, default=str)) - print("---------------------\n") - - # 5. Generate questions using the LLM - print("Generating questions with GPT-4o...") - generated_tasks = generate_questions(schema_context, sampled_data) - - # 6. Validate and filter the generated tasks - validated_tasks = [] - if generated_tasks: - print("\nValidating generated tasks...") - for task in generated_tasks: - # Basic validation for task structure - if not isinstance(task, dict) or not all(k in task for k in ['sql', 'answer', 'question']): - print(f"Filtering task due to malformed structure or missing keys: {task}") - continue - - try: - # Execute the SQL query from the task - cursor.execute(task['sql']) - sql_result = cursor.fetchall() - - # Convert both answer and result to string for flexible substring matching - answer_str = str(task['answer']) - result_str = str(sql_result) - - # If the answer exists in the result, the task is valid - if answer_str in result_str: - validated_tasks.append(task) - else: - # Log tasks that are filtered because the answer doesn't match - print(f"Filtering task: Answer '{answer_str}' not found in SQL result.") - print(f" - Question: {task['question']}") - print(f" - SQL: {task['sql']}") - # Showing a snippet of a large result is helpful for debugging - print(f" - Result: {result_str[:250]}...") - - except mysql.connector.Error as err: - # Log tasks that are filtered due to SQL errors - print(f"Filtering task due to SQL error: {err}") - print(f" - Question: {task['question']}") - print(f" - SQL: {task['sql']}") - except Exception as e: - print(f"An unexpected error occurred during validation for task {task}: {e}") - - # 7. Print the final JSON output - if validated_tasks: - print("\n--- Generated and Validated Tasks ---") - print(json.dumps(validated_tasks, indent=2)) - else: - print("Failed to generate any valid tasks.") - - finally: - # 8. Close the database connection - if conn.is_connected(): - cursor.close() - conn.close() - print("\nDatabase connection closed.") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/random_sample/generate_tasks.py b/random_sample/generate_tasks.py index 1aa2095..f5bf278 100644 --- a/random_sample/generate_tasks.py +++ b/random_sample/generate_tasks.py @@ -9,8 +9,10 @@ from dotenv import load_dotenv # --- Configuration --- load_dotenv() +server_address = "localhost" + MYSQL_CONFIG = { - "host": "localhost", + "host": server_address, "port": "23306", "user": "mcpuser", "password": "StrongPass123!", diff --git a/random_sample/generated_tasks.json.bak b/random_sample/generated_tasks.json.bak deleted file mode 100644 index d19033a..0000000 --- a/random_sample/generated_tasks.json.bak +++ /dev/null @@ -1,3539 +0,0 @@ -[ - { - "question": "What is the SKU of the product with entity_id 1304?", - "answer": "adrienne-trek-jacket-s-gray", - "sql": "SELECT value FROM catalog_product_entity_varchar WHERE entity_id = 1304 AND attribute_id = 121 AND store_id = 0;", - "sql_execute_result": [ - [ - "adrienne-trek-jacket-s-gray" - ] - ] - }, - { - "question": "What is the shipping method for the order with increment ID '000000092'?", - "answer": "flatrate_flatrate", - "sql": "SELECT shipping_method FROM sales_order WHERE increment_id = '000000092';", - "sql_execute_result": [ - [ - "flatrate_flatrate" - ] - ] - }, - { - "question": "What is the total quantity ordered for the order with entity ID 166?", - "answer": "5.0000", - "sql": "SELECT total_qty_ordered FROM sales_order WHERE entity_id = 166;", - "sql_execute_result": [ - [ - "5.0000" - ] - ] - }, - { - "question": "What is the base grand total for the sales order with entity ID 190?", - "answer": "145.0000", - "sql": "SELECT base_grand_total FROM sales_order WHERE entity_id = 190;", - "sql_execute_result": [ - [ - "145.0000" - ] - ] - }, - { - "question": "Has the email for the order with increment ID '000000092' been sent?", - "answer": "Yes, the email for the order with increment ID '000000092' has been sent.", - "sql": "SELECT email_sent FROM sales_order WHERE increment_id = '000000092';", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "List all complete orders with a flat rate shipping method.", - "answer": [ - 4, - 9, - 11, - 13, - 16, - 17, - 20, - 21, - 22, - 23, - 24, - 27, - 28, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 43, - 45, - 47, - 48, - 50, - 51, - 53, - 54, - 55, - 57, - 61, - 62, - 64, - 69, - 70, - 71, - 73, - 75, - 78, - 79, - 82, - 83, - 84, - 87, - 89, - 90, - 91, - 92, - 93, - 96, - 97, - 99, - 100, - 102, - 104, - 105, - 112, - 113, - 114, - 115, - 116, - 119, - 121, - 127, - 128, - 130, - 131, - 133, - 137, - 138, - 139, - 140, - 145, - 146, - 147, - 148, - 150, - 154, - 155, - 156, - 158, - 160, - 161, - 163, - 164, - 166, - 169, - 179, - 181, - 182, - 184, - 186, - 187, - 188, - 189, - 190, - 192, - 196, - 197, - 199, - 200, - 201, - 202, - 203, - 205, - 207, - 208, - 213, - 214, - 215, - 216, - 217, - 218, - 223, - 225, - 228, - 230, - 231, - 233, - 235, - 236, - 237, - 238, - 239, - 240, - 243, - 247, - 250, - 251, - 253, - 256, - 257, - 258, - 260, - 262, - 263, - 264, - 268, - 269, - 270, - 274, - 276, - 277, - 281, - 282, - 284, - 285, - 286, - 287, - 288, - 295, - 297, - 298 - ], - "sql": "SELECT entity_id FROM sales_order WHERE status = 'complete' AND shipping_method = 'flatrate_flatrate';", - "sql_execute_result": [ - [ - 4 - ], - [ - 9 - ], - [ - 11 - ], - [ - 13 - ], - [ - 16 - ], - [ - 17 - ], - [ - 20 - ], - [ - 21 - ], - [ - 22 - ], - [ - 23 - ], - [ - 24 - ], - [ - 27 - ], - [ - 28 - ], - [ - 31 - ], - [ - 32 - ], - [ - 33 - ], - [ - 34 - ], - [ - 35 - ], - [ - 36 - ], - [ - 37 - ], - [ - 43 - ], - [ - 45 - ], - [ - 47 - ], - [ - 48 - ], - [ - 50 - ], - [ - 51 - ], - [ - 53 - ], - [ - 54 - ], - [ - 55 - ], - [ - 57 - ], - [ - 61 - ], - [ - 62 - ], - [ - 64 - ], - [ - 69 - ], - [ - 70 - ], - [ - 71 - ], - [ - 73 - ], - [ - 75 - ], - [ - 78 - ], - [ - 79 - ], - [ - 82 - ], - [ - 83 - ], - [ - 84 - ], - [ - 87 - ], - [ - 89 - ], - [ - 90 - ], - [ - 91 - ], - [ - 92 - ], - [ - 93 - ], - [ - 96 - ], - [ - 97 - ], - [ - 99 - ], - [ - 100 - ], - [ - 102 - ], - [ - 104 - ], - [ - 105 - ], - [ - 112 - ], - [ - 113 - ], - [ - 114 - ], - [ - 115 - ], - [ - 116 - ], - [ - 119 - ], - [ - 121 - ], - [ - 127 - ], - [ - 128 - ], - [ - 130 - ], - [ - 131 - ], - [ - 133 - ], - [ - 137 - ], - [ - 138 - ], - [ - 139 - ], - [ - 140 - ], - [ - 145 - ], - [ - 146 - ], - [ - 147 - ], - [ - 148 - ], - [ - 150 - ], - [ - 154 - ], - [ - 155 - ], - [ - 156 - ], - [ - 158 - ], - [ - 160 - ], - [ - 161 - ], - [ - 163 - ], - [ - 164 - ], - [ - 166 - ], - [ - 169 - ], - [ - 179 - ], - [ - 181 - ], - [ - 182 - ], - [ - 184 - ], - [ - 186 - ], - [ - 187 - ], - [ - 188 - ], - [ - 189 - ], - [ - 190 - ], - [ - 192 - ], - [ - 196 - ], - [ - 197 - ], - [ - 199 - ], - [ - 200 - ], - [ - 201 - ], - [ - 202 - ], - [ - 203 - ], - [ - 205 - ], - [ - 207 - ], - [ - 208 - ], - [ - 213 - ], - [ - 214 - ], - [ - 215 - ], - [ - 216 - ], - [ - 217 - ], - [ - 218 - ], - [ - 223 - ], - [ - 225 - ], - [ - 228 - ], - [ - 230 - ], - [ - 231 - ], - [ - 233 - ], - [ - 235 - ], - [ - 236 - ], - [ - 237 - ], - [ - 238 - ], - [ - 239 - ], - [ - 240 - ], - [ - 243 - ], - [ - 247 - ], - [ - 250 - ], - [ - 251 - ], - [ - 253 - ], - [ - 256 - ], - [ - 257 - ], - [ - 258 - ], - [ - 260 - ], - [ - 262 - ], - [ - 263 - ], - [ - 264 - ], - [ - 268 - ], - [ - 269 - ], - [ - 270 - ], - [ - 274 - ], - [ - 276 - ], - [ - 277 - ], - [ - 281 - ], - [ - 282 - ], - [ - 284 - ], - [ - 285 - ], - [ - 286 - ], - [ - 287 - ], - [ - 288 - ], - [ - 295 - ], - [ - 297 - ], - [ - 298 - ] - ] - }, - { - "question": "How many items are ordered in total for the order with increment ID '000000190'?", - "answer": 2.0, - "sql": "SELECT total_qty_ordered FROM sales_order WHERE increment_id = '000000190';", - "sql_execute_result": [ - [ - "2.0000" - ] - ] - }, - { - "question": "What is the total weight of the order with entity ID 193?", - "answer": "4.0000", - "sql": "SELECT weight FROM sales_order WHERE entity_id = 193;", - "sql_execute_result": [ - [ - "4.0000" - ] - ] - }, - { - "question": "Is the order with increment ID '000000023' a virtual order?", - "answer": "No, the order with increment ID '000000023' is not a virtual order.", - "sql": "SELECT is_virtual FROM sales_order WHERE increment_id = '000000023';", - "sql_execute_result": [ - [ - 0 - ] - ] - }, - { - "question": "What is the total number of completed orders for customer Matt Baker?", - "answer": 7, - "sql": "SELECT COUNT(*) FROM sales_order_grid WHERE customer_id = 13 AND status = 'complete';", - "sql_execute_result": [ - [ - 7 - ] - ] - }, - { - "question": "Find the email of the customer who placed the order with increment ID '000000290'.", - "answer": "jla_7781@gmail.com", - "sql": "SELECT customer_email FROM sales_order_grid WHERE increment_id = '000000290';", - "sql_execute_result": [ - [ - "jla_7781@gmail.com" - ] - ] - }, - { - "question": "What is the name of the category with the entity ID 22?", - "answer": "bottoms-women", - "sql": "SELECT value FROM catalog_category_entity_varchar WHERE entity_id = 22 AND attribute_id = 119;", - "sql_execute_result": [ - [ - "bottoms-women" - ] - ] - }, - { - "question": "What is the status of the order with increment ID '000000126'?", - "answer": "canceled", - "sql": "SELECT status FROM sales_order_grid WHERE increment_id = '000000126';", - "sql_execute_result": [ - [ - "canceled" - ] - ] - }, - { - "question": "Determine the shipping method for Matt Baker's order created on '2022-03-31'.", - "answer": "Flat Rate - Fixed", - "sql": "SELECT shipping_information FROM sales_order_grid WHERE customer_email = 'matt.baker@yahoo.com' AND created_at = '2022-03-31 05:17:09';", - "sql_execute_result": [ - [ - "Flat Rate - Fixed" - ] - ] - }, - { - "question": "What is the default state for the status 'complete'?", - "answer": "complete", - "sql": "SELECT state FROM sales_order_status_state WHERE status = 'complete';", - "sql_execute_result": [ - [ - "complete" - ] - ] - }, - { - "question": "Find the customer group ID for the customer who placed the order with increment ID '000000092'.", - "answer": "1", - "sql": "SELECT customer_group FROM sales_order_grid WHERE increment_id = '000000092';", - "sql_execute_result": [ - [ - "1" - ] - ] - }, - { - "question": "What is the label for the status 'processing'?", - "answer": "Processing", - "sql": "SELECT label FROM sales_order_status WHERE status = 'processing';", - "sql_execute_result": [ - [ - "Processing" - ] - ] - }, - { - "question": "Find all orders for customer with email 'soccerfanatic22@gmail.com'.", - "answer": [ - { - "entity_id": 123, - "increment_id": "000000123", - "grand_total": "209.0000" - }, - { - "entity_id": 129, - "increment_id": "000000129", - "grand_total": "151.0000" - }, - { - "entity_id": 155, - "increment_id": "000000155", - "grand_total": "191.0000" - }, - { - "entity_id": 162, - "increment_id": "000000162", - "grand_total": "152.2000" - }, - { - "entity_id": 170, - "increment_id": "000000170", - "grand_total": "66.0000" - }, - { - "entity_id": 242, - "increment_id": "000000242", - "grand_total": "183.0000" - }, - { - "entity_id": 285, - "increment_id": "000000285", - "grand_total": "82.0000" - }, - { - "entity_id": 286, - "increment_id": "000000286", - "grand_total": "101.2500" - } - ], - "sql": "SELECT entity_id, increment_id, grand_total FROM sales_order WHERE customer_email = 'soccerfanatic22@gmail.com';", - "sql_execute_result": [ - [ - 123, - "000000123", - "209.0000" - ], - [ - 129, - "000000129", - "151.0000" - ], - [ - 155, - "000000155", - "191.0000" - ], - [ - 162, - "000000162", - "152.2000" - ], - [ - 170, - "000000170", - "66.0000" - ], - [ - 242, - "000000242", - "183.0000" - ], - [ - 285, - "000000285", - "82.0000" - ], - [ - 286, - "000000286", - "101.2500" - ] - ] - }, - { - "question": "What is the total quantity ordered for the order with increment ID '000000141'?", - "answer": "4.0000", - "sql": "SELECT total_qty_ordered FROM sales_order WHERE increment_id = '000000141';", - "sql_execute_result": [ - [ - "4.0000" - ] - ] - }, - { - "question": "Find the region code for 'Kauno Apskritis' in Lithuania.", - "answer": "LT-KU", - "sql": "SELECT code FROM directory_country_region WHERE default_name = 'Kauno Apskritis' AND country_id = 'LT';", - "sql_execute_result": [ - [ - "LT-KU" - ] - ] - }, - { - "question": "List the products with SKU containing 'Sprite Stasis Ball 75 cm'.", - "answer": [ - "24-WG083-blue", - "24-WG083-gray", - "24-WG083-pink" - ], - "sql": "SELECT product_id, sku, name FROM sales_order_item WHERE name LIKE '%Sprite Stasis Ball 75 cm%';", - "sql_execute_result": [ - [ - 32, - "24-WG083-blue", - "Sprite Stasis Ball 75 cm" - ], - [ - 30, - "24-WG083-gray", - "Sprite Stasis Ball 75 cm" - ], - [ - 32, - "24-WG083-blue", - "Sprite Stasis Ball 75 cm" - ], - [ - 31, - "24-WG083-pink", - "Sprite Stasis Ball 75 cm" - ], - [ - 30, - "24-WG083-gray", - "Sprite Stasis Ball 75 cm" - ], - [ - 31, - "24-WG083-pink", - "Sprite Stasis Ball 75 cm" - ], - [ - 32, - "24-WG083-blue", - "Sprite Stasis Ball 75 cm" - ], - [ - 32, - "24-WG083-blue", - "Sprite Stasis Ball 75 cm" - ], - [ - 31, - "24-WG083-pink", - "Sprite Stasis Ball 75 cm" - ], - [ - 31, - "24-WG083-pink", - "Sprite Stasis Ball 75 cm" - ], - [ - 30, - "24-WG083-gray", - "Sprite Stasis Ball 75 cm" - ], - [ - 30, - "24-WG083-gray", - "Sprite Stasis Ball 75 cm" - ] - ] - }, - { - "question": "What is the average rating value for product ID 1620?", - "answer": 3.0, - "sql": "SELECT AVG(value) AS average_rating FROM rating_option_vote WHERE entity_pk_value = 1620;", - "sql_execute_result": [ - [ - "3.0000" - ] - ] - }, - { - "question": "Find the order ID for the order that contains the product with SKU 'WJ04-M-Red'.", - "answer": [ - 124, - 244 - ], - "sql": "SELECT order_id FROM sales_order_item WHERE sku = 'WJ04-M-Red';", - "sql_execute_result": [ - [ - 124 - ], - [ - 124 - ], - [ - 244 - ], - [ - 244 - ] - ] - }, - { - "question": "What is the customer name for the billing address ID 570?", - "answer": "Olivia Lee", - "sql": "SELECT customer_firstname, customer_lastname FROM sales_order WHERE billing_address_id = 570;", - "sql_execute_result": [ - [ - "Olivia", - "Lee" - ] - ] - }, - { - "question": "Find the order status for the order with increment ID '000000206'.", - "answer": "canceled", - "sql": "SELECT status FROM sales_order WHERE increment_id = '000000206';", - "sql_execute_result": [ - [ - "canceled" - ] - ] - }, - { - "question": "List order IDs with the shipping method 'flatrate_flatrate'.", - "answer": [ - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69, - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, - 94, - 95, - 96, - 97, - 98, - 99, - 100, - 101, - 102, - 103, - 104, - 105, - 106, - 107, - 108, - 109, - 110, - 111, - 112, - 113, - 114, - 115, - 116, - 117, - 118, - 119, - 120, - 121, - 122, - 123, - 124, - 125, - 126, - 127, - 128, - 129, - 130, - 131, - 132, - 133, - 134, - 135, - 136, - 137, - 138, - 139, - 140, - 141, - 142, - 143, - 144, - 145, - 146, - 147, - 148, - 149, - 150, - 151, - 152, - 153, - 154, - 155, - 156, - 157, - 158, - 159, - 160, - 161, - 162, - 163, - 164, - 165, - 166, - 167, - 168, - 169, - 170, - 171, - 172, - 173, - 174, - 175, - 176, - 177, - 178, - 179, - 180, - 181, - 182, - 183, - 184, - 185, - 186, - 187, - 188, - 189, - 190, - 191, - 192, - 193, - 194, - 195, - 196, - 197, - 198, - 199, - 200, - 201, - 202, - 203, - 204, - 205, - 206, - 207, - 208, - 209, - 210, - 211, - 212, - 213, - 214, - 215, - 216, - 217, - 218, - 219, - 220, - 221, - 222, - 223, - 224, - 225, - 226, - 227, - 228, - 229, - 230, - 231, - 232, - 233, - 234, - 235, - 236, - 237, - 238, - 239, - 240, - 241, - 242, - 243, - 244, - 245, - 246, - 247, - 248, - 249, - 250, - 251, - 252, - 253, - 254, - 255, - 256, - 257, - 258, - 259, - 260, - 261, - 262, - 263, - 264, - 265, - 266, - 267, - 268, - 269, - 270, - 271, - 272, - 273, - 274, - 275, - 276, - 277, - 278, - 279, - 280, - 281, - 282, - 283, - 284, - 285, - 286, - 287, - 288, - 289, - 290, - 291, - 292, - 293, - 294, - 295, - 296, - 297, - 298, - 299, - 300, - 301, - 302, - 303, - 304, - 305, - 306, - 307, - 308 - ], - "sql": "SELECT entity_id FROM sales_order WHERE shipping_method = 'flatrate_flatrate';", - "sql_execute_result": [ - [ - 1 - ], - [ - 2 - ], - [ - 3 - ], - [ - 4 - ], - [ - 5 - ], - [ - 6 - ], - [ - 7 - ], - [ - 8 - ], - [ - 9 - ], - [ - 10 - ], - [ - 11 - ], - [ - 12 - ], - [ - 13 - ], - [ - 14 - ], - [ - 15 - ], - [ - 16 - ], - [ - 17 - ], - [ - 18 - ], - [ - 19 - ], - [ - 20 - ], - [ - 21 - ], - [ - 22 - ], - [ - 23 - ], - [ - 24 - ], - [ - 25 - ], - [ - 26 - ], - [ - 27 - ], - [ - 28 - ], - [ - 29 - ], - [ - 30 - ], - [ - 31 - ], - [ - 32 - 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], - [ - 305 - ], - [ - 306 - ], - [ - 307 - ], - [ - 308 - ] - ] - }, - { - "question": "What is the email address for the customer named 'Hannah Lim'?", - "answer": "hannah.lim@gmail.com", - "sql": "SELECT email FROM customer_grid_flat WHERE name = 'Hannah Lim';", - "sql_execute_result": [ - [ - "hannah.lim@gmail.com" - ] - ] - }, - { - "question": "How many search results are there for the query 'Joust Bag'?", - "answer": 10, - "sql": "SELECT num_results FROM search_query WHERE query_text = 'Joust Bag';", - "sql_execute_result": [ - [ - 10 - ] - ] - }, - { - "question": "List all customers located in Hoboken, New Jersey.", - "answer": [ - "Amanda Kim", - "Matthew Kim", - "Natalie Kim" - ], - "sql": "SELECT name FROM customer_grid_flat WHERE billing_city = 'Hoboken' AND billing_region = 'New Jersey';", - "sql_execute_result": [ - [ - "Amanda Kim" - ], - [ - "Matthew Kim" - ], - [ - "Natalie Kim" - ] - ] - }, - { - "question": "What is the ISO3 code for the country with ISO2 code 'AE'?", - "answer": "ARE", - "sql": "SELECT iso3_code FROM directory_country WHERE iso2_code = 'AE';", - "sql_execute_result": [ - [ - "ARE" - ] - ] - }, - { - "question": "What is the name of the store group with ID 1?", - "answer": "Main Website Store", - "sql": "SELECT name FROM store_group WHERE group_id = 1;", - "sql_execute_result": [ - [ - "Main Website Store" - ] - ] - }, - { - "question": "Find the rating value for the rating option with code '3'.", - "answer": 3, - "sql": "SELECT value FROM rating_option WHERE code = '3';", - "sql_execute_result": [ - [ - 3 - ], - [ - 3 - ], - [ - 3 - ], - [ - 3 - ] - ] - }, - { - "question": "Is the search query 'Antonia Racer Tank' active?", - "answer": "Yes, the search query 'Antonia Racer Tank' is active.", - "sql": "SELECT is_active FROM search_query WHERE query_text = 'Antonia Racer Tank';", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "What is the default store ID for the group named 'Default'?", - "answer": 0, - "sql": "SELECT default_store_id FROM store_group WHERE name = 'Default';", - "sql_execute_result": [ - [ - 0 - ] - ] - }, - { - "question": "What is the billing telephone number for the customer 'Matt Baker'?", - "answer": "4045551234", - "sql": "SELECT billing_telephone FROM customer_grid_flat WHERE name = 'Matt Baker';", - "sql_execute_result": [ - [ - "4045551234" - ] - ] - }, - { - "question": "What is the stock quantity for product with ID 1502?", - "answer": "100.0000", - "sql": "SELECT qty FROM cataloginventory_stock_item WHERE product_id = 1502;", - "sql_execute_result": [ - [ - "100.0000" - ] - ] - }, - { - "question": "List all order statuses and their labels.", - "answer": [ - { - "status": "canceled", - "label": "Canceled" - }, - { - "status": "closed", - "label": "Closed" - }, - { - "status": "complete", - "label": "Complete" - }, - { - "status": "fraud", - "label": "Suspected Fraud" - }, - { - "status": "holded", - "label": "On Hold" - }, - { - "status": "payment_review", - "label": "Payment Review" - }, - { - "status": "paypal_canceled_reversal", - "label": "PayPal Canceled Reversal" - }, - { - "status": "paypal_reversed", - "label": "PayPal Reversed" - }, - { - "status": "pending", - "label": "Pending" - }, - { - "status": "pending_payment", - "label": "Pending Payment" - }, - { - "status": "pending_paypal", - "label": "Pending PayPal" - }, - { - "status": "processing", - "label": "Processing" - } - ], - "sql": "SELECT status, label FROM sales_order_status;", - "sql_execute_result": [ - [ - "canceled", - "Canceled" - ], - [ - "closed", - "Closed" - ], - [ - "complete", - "Complete" - ], - [ - "fraud", - "Suspected Fraud" - ], - [ - "holded", - "On Hold" - ], - [ - "payment_review", - "Payment Review" - ], - [ - "paypal_canceled_reversal", - "PayPal Canceled Reversal" - ], - [ - "paypal_reversed", - "PayPal Reversed" - ], - [ - "pending", - "Pending" - ], - [ - "pending_payment", - "Pending Payment" - ], - [ - "pending_paypal", - "Pending PayPal" - ], - [ - "processing", - "Processing" - ] - ] - }, - { - "question": "What is the average rating value for product with ID 11 based on votes?", - "answer": "3.6667", - "sql": "SELECT AVG(value) AS average_rating FROM rating_option_vote WHERE entity_pk_value = 11;", - "sql_execute_result": [ - [ - "3.6667" - ] - ] - }, - { - "question": "Find the sequence table for invoices in store with ID 1.", - "answer": "sequence_invoice_1", - "sql": "SELECT sequence_table FROM sales_sequence_meta WHERE entity_type = 'invoice' AND store_id = 1;", - "sql_execute_result": [ - [ - "sequence_invoice_1" - ] - ] - }, - { - "question": "Is product with ID 889 in stock?", - "answer": "Yes, the product with ID 889 is in stock.", - "sql": "SELECT is_in_stock FROM cataloginventory_stock_item WHERE product_id = 889;", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "Retrieve all reviews and their percent rating for product with ID 11.", - "answer": [ - { - "review_id": 169, - "percent": 100 - }, - { - "review_id": 170, - "percent": 100 - }, - { - "review_id": 171, - "percent": 20 - } - ], - "sql": "SELECT review_id, percent FROM rating_option_vote WHERE entity_pk_value = 11;", - "sql_execute_result": [ - [ - 169, - 100 - ], - [ - 170, - 100 - ], - [ - 171, - 20 - ] - ] - }, - { - "question": "How many sequence values are present in the order sequence table?", - "answer": 308, - "sql": "SELECT COUNT(sequence_value) FROM sequence_order_1;", - "sql_execute_result": [ - [ - 308 - ] - ] - }, - { - "question": "What is the entity type for sequence table 'sequence_shipment_1'?", - "answer": "shipment", - "sql": "SELECT entity_type FROM sales_sequence_meta WHERE sequence_table = 'sequence_shipment_1';", - "sql_execute_result": [ - [ - "shipment" - ] - ] - }, - { - "question": "Find the minimum sale quantity configuration for product with ID 598.", - "answer": "1.0000", - "sql": "SELECT min_sale_qty FROM cataloginventory_stock_item WHERE product_id = 598;", - "sql_execute_result": [ - [ - "1.0000" - ] - ] - }, - { - "question": "Does the product with ID 1229 allow backorders?", - "answer": "No, the product with ID 1229 does not allow backorders.", - "sql": "SELECT backorders FROM cataloginventory_stock_item WHERE product_id = 1229;", - "sql_execute_result": [ - [ - 0 - ] - ] - }, - { - "question": "What is the status of the order with increment ID '000000152'?", - "answer": "canceled", - "sql": "SELECT status FROM sales_order_grid WHERE increment_id = '000000152';", - "sql_execute_result": [ - [ - "canceled" - ] - ] - }, - { - "question": "Find the total grand total for orders placed by customer with email 'jane.doe@hotmail.com'.", - "answer": 1634.72, - "sql": "SELECT SUM(grand_total) FROM sales_order_grid WHERE customer_email = 'jane.doe@hotmail.com';", - "sql_execute_result": [ - [ - "1634.7200" - ] - ] - }, - { - "question": "Which CMS page is currently inactive?", - "answer": "Privacy Policy", - "sql": "SELECT title FROM cms_page WHERE is_active = 0;", - "sql_execute_result": [ - [ - "Privacy Policy" - ] - ] - }, - { - "question": "What is the name of the website with code 'base'?", - "answer": "Main Website", - "sql": "SELECT name FROM store_website WHERE code = 'base';", - "sql_execute_result": [ - [ - "Main Website" - ] - ] - }, - { - "question": "What is the value associated with EAV attribute option ID 71?", - "answer": "Hydration Pocket", - "sql": "SELECT value FROM eav_attribute_option_value WHERE option_id = 71;", - "sql_execute_result": [ - [ - "Hydration Pocket" - ] - ] - }, - { - "question": "Find all orders for customer with ID 2.", - "answer": [ - "000000009", - "000000059", - "000000068", - "000000079", - "000000093", - "000000095", - "000000096", - "000000107", - "000000115", - "000000144", - "000000217", - "000000257", - "000000273" - ], - "sql": "SELECT increment_id FROM sales_order_grid WHERE customer_id = 2;", - "sql_execute_result": [ - [ - "000000009" - ], - [ - "000000059" - ], - [ - "000000068" - ], - [ - "000000079" - ], - [ - "000000093" - ], - [ - "000000095" - ], - [ - "000000096" - ], - [ - "000000107" - ], - [ - "000000115" - ], - [ - "000000144" - ], - [ - "000000217" - ], - [ - "000000257" - ], - [ - "000000273" - ] - ] - }, - { - "question": "What is the payment method used for the order by 'Bob Johnson'?", - "answer": "checkmo", - "sql": "SELECT payment_method FROM sales_order_grid WHERE customer_name = 'Bob Johnson';", - "sql_execute_result": [ - [ - "checkmo" - ], - [ - "checkmo" - ], - [ - "checkmo" - ], - [ - "checkmo" - ], - [ - "checkmo" - ], - [ - "checkmo" - ], - [ - "checkmo" - ], - [ - "checkmo" - ] - ] - }, - { - "question": "What is the layout of the CMS page titled '404 Not Found'?", - "answer": "2columns-right", - "sql": "SELECT page_layout FROM cms_page WHERE title = '404 Not Found';", - "sql_execute_result": [ - [ - "2columns-right" - ] - ] - }, - { - "question": "Find the shipping address associated with the order increment ID '000000289'.", - "answer": "333 S Broad St, Philadelphia, Pennsylvania, 19102", - "sql": "SELECT shipping_address FROM sales_order_grid WHERE increment_id = '000000289';", - "sql_execute_result": [ - [ - "333 S Broad St,Philadelphia,Pennsylvania,19102" - ] - ] - }, - { - "question": "What is the default name for the region with code 'CN-JX' in China?", - "answer": "Jiangxi Sheng", - "sql": "SELECT default_name FROM directory_country_region WHERE code = 'CN-JX' AND country_id = 'CN';", - "sql_execute_result": [ - [ - "Jiangxi Sheng" - ] - ] - }, - { - "question": "Find all completed orders on 2023-05-04 in store with ID 1.", - "answer": 1, - "sql": "SELECT orders_count FROM sales_order_aggregated_created WHERE period = '2023-05-04' AND store_id = 1 AND order_status = 'complete';", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "What is the title of the review by nickname 'Ezra'?", - "answer": "I slipped on a rock on these shoes and I", - "sql": "SELECT title FROM review_detail WHERE nickname = 'Ezra';", - "sql_execute_result": [ - [ - "I slipped on a rock on these shoes and I" - ] - ] - }, - { - "question": "How many orders were canceled on 2022-12-05 in store with ID 1?", - "answer": 1, - "sql": "SELECT orders_count FROM sales_order_aggregated_created WHERE period = '2022-12-05' AND store_id = 1 AND order_status = 'canceled';", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "What is the attribute code for the attribute with ID 152?", - "answer": "style_general", - "sql": "SELECT attribute_code FROM eav_attribute WHERE attribute_id = 152;", - "sql_execute_result": [ - [ - "style_general" - ] - ] - }, - { - "question": "What is the backend type for the 'image' attribute?", - "answer": "varchar", - "sql": "SELECT backend_type FROM eav_attribute WHERE attribute_code = 'image';", - "sql_execute_result": [ - [ - "varchar" - ], - [ - "varchar" - ] - ] - }, - { - "question": "What is the shipping amount for the complete order on 2022-06-18?", - "answer": "The shipping amount for the complete order on 2022-06-18 is 10.00.", - "sql": "SELECT total_shipping_amount FROM sales_order_aggregated_created WHERE period = '2022-06-18' AND order_status = 'complete';", - "sql_execute_result": [ - [ - "10.0000" - ], - [ - "10.0000" - ] - ] - }, - { - "question": "What is the name of the region with ID 880 in Italy?", - "answer": "Medio Campidano", - "sql": "SELECT default_name FROM directory_country_region WHERE region_id = 880 AND country_id = 'IT';", - "sql_execute_result": [ - [ - "Medio Campidano" - ] - ] - }, - { - "question": "List the sequence values in the shipment sequence table.", - "answer": [ - 1, - 2, - 3 - ], - "sql": "SELECT sequence_value FROM sequence_shipment_1;", - "sql_execute_result": [ - [ - 1 - ], - [ - 2 - ], - [ - 3 - ] - ] - }, - { - "question": "What is the frontend input type for the 'email' attribute?", - "answer": "text", - "sql": "SELECT frontend_input FROM eav_attribute WHERE attribute_code = 'email';", - "sql_execute_result": [ - [ - "text" - ] - ] - }, - { - "question": "Find the total quantity ordered for product with ID 315.", - "answer": 1.0, - "sql": "SELECT SUM(qty_ordered) FROM sales_order_item WHERE product_id = 315;", - "sql_execute_result": [ - [ - "1.0000" - ] - ] - }, - { - "question": "What is the total grand total of all canceled orders?", - "answer": "17408.07", - "sql": "SELECT SUM(base_grand_total) FROM sales_order WHERE status = 'canceled';", - "sql_execute_result": [ - [ - "17408.0700" - ] - ] - }, - { - "question": "How many orders have been canceled in store with ID 0?", - "answer": 125, - "sql": "SELECT COUNT(*) FROM sales_order_aggregated_created WHERE store_id = 0 AND order_status = 'canceled';", - "sql_execute_result": [ - [ - 125 - ] - ] - }, - { - "question": "What is the product name for product with ID 315 in the daily bestsellers?", - "answer": "Orion Two-Tone Fitted Jacket-XL-Black", - "sql": "SELECT product_name FROM sales_bestsellers_aggregated_daily WHERE product_id = 315;", - "sql_execute_result": [ - [ - "Orion Two-Tone Fitted Jacket-XL-Black" - ], - [ - "Orion Two-Tone Fitted Jacket-XL-Black" - ] - ] - }, - { - "question": "What is the order status of the order with ID 308?", - "answer": "pending", - "sql": "SELECT status FROM sales_order WHERE entity_id = 308;", - "sql_execute_result": [ - [ - "pending" - ] - ] - }, - { - "question": "Check if the product with SKU 'MT02-M-Gray' is in stock.", - "answer": "The product with SKU 'MT02-M-Gray' is in stock.", - "sql": "SELECT is_in_stock FROM cataloginventory_stock_item WHERE product_id = (SELECT entity_id FROM catalog_product_entity WHERE sku = 'MT02-M-Gray');", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "How many orders were placed by the customer with email 'customer5@example.com'?", - "answer": 0, - "sql": "SELECT COUNT(*) FROM sales_order WHERE customer_email = 'customer5@example.com';", - "sql_execute_result": [ - [ - 0 - ] - ] - }, - { - "question": "What is the total quantity ordered for order ID 308?", - "answer": "4.0000", - "sql": "SELECT total_qty_ordered FROM sales_order WHERE entity_id = 308;", - "sql_execute_result": [ - [ - "4.0000" - ] - ] - }, - { - "question": "What is the email address for the customer with ID 24?", - "answer": "musiclover99@hotmail.com", - "sql": "SELECT email FROM customer_entity WHERE entity_id = 24;", - "sql_execute_result": [ - [ - "musiclover99@hotmail.com" - ] - ] - }, - { - "question": "Find the total number of reviews for the product with ID 937.", - "answer": 3, - "sql": "SELECT COUNT(*) FROM review WHERE entity_pk_value = 937;", - "sql_execute_result": [ - [ - 3 - ] - ] - }, - { - "question": "What is the rating value given in the review with ID 256?", - "answer": 4, - "sql": "SELECT value FROM rating_option_vote WHERE review_id = 256;", - "sql_execute_result": [ - [ - 4 - ] - ] - }, - { - "question": "What is the first name of the customer with email 'jessica.wong@gmail.com'?", - "answer": "Jessica", - "sql": "SELECT firstname FROM customer_entity WHERE email = 'jessica.wong@gmail.com';", - "sql_execute_result": [ - [ - "Jessica" - ] - ] - }, - { - "question": "Find the sequence table associated with shipments for store ID 1.", - "answer": "sequence_shipment_1", - "sql": "SELECT sequence_table FROM sales_sequence_meta WHERE entity_type = 'shipment' AND store_id = 1;", - "sql_execute_result": [ - [ - "sequence_shipment_1" - ] - ] - }, - { - "question": "How many reviews are pending approval?", - "answer": 346, - "sql": "SELECT COUNT(*) FROM review WHERE status_id = 1;", - "sql_execute_result": [ - [ - 346 - ] - ] - }, - { - "question": "What is the customer group ID for the customer named 'Isaac Rodriguez'?", - "answer": 1, - "sql": "SELECT group_id FROM customer_entity WHERE firstname = 'Isaac' AND lastname = 'Rodriguez';", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "What percentage rating did the product with ID 1412 receive in its review?", - "answer": [ - 80, - 60, - 80 - ], - "sql": "SELECT percent FROM rating_option_vote WHERE entity_pk_value = 1412;", - "sql_execute_result": [ - [ - 80 - ], - [ - 60 - ], - [ - 80 - ] - ] - }, - { - "question": "Find the category ID for the product with ID 648.", - "answer": 17, - "sql": "SELECT category_id FROM catalog_category_product WHERE product_id = 648;", - "sql_execute_result": [ - [ - 17 - ] - ] - }, - { - "question": "What is the email address for the customer associated with order ID 233?", - "answer": "daniel.jackson@hotmail.com", - "sql": "SELECT email FROM sales_order_address WHERE parent_id = 233 AND address_type = 'billing';", - "sql_execute_result": [ - [ - "daniel.jackson@hotmail.com" - ] - ] - }, - { - "question": "Find all products with the attribute code 'pierce-gym-short-32-gray'.", - "answer": [ - 1017 - ], - "sql": "SELECT entity_id FROM catalog_product_entity_varchar WHERE value = 'pierce-gym-short-32-gray';", - "sql_execute_result": [ - [ - 1017 - ] - ] - }, - { - "question": "List the categories that include the keyword 'bags'.", - "answer": [ - "Bags", - "bags", - "gear/bags" - ], - "sql": "SELECT value FROM catalog_category_entity_varchar WHERE value LIKE '%bags%';", - "sql_execute_result": [ - [ - "Bags" - ], - [ - "bags" - ], - [ - "gear/bags" - ] - ] - }, - { - "question": "What is the rating value for the option code '5' from rating ID 2?", - "answer": 5, - "sql": "SELECT value FROM rating_option WHERE code = '5' AND rating_id = 2;", - "sql_execute_result": [ - [ - 5 - ] - ] - }, - { - "question": "How many shipments have been created for customer ID 1?", - "answer": 2, - "sql": "SELECT COUNT(*) FROM sales_shipment WHERE customer_id = 1;", - "sql_execute_result": [ - [ - 2 - ] - ] - }, - { - "question": "What is the total quantity shipped in shipment ID 3?", - "answer": "2.0000", - "sql": "SELECT total_qty FROM sales_shipment WHERE entity_id = 3;", - "sql_execute_result": [ - [ - "2.0000" - ] - ] - }, - { - "question": "Find the customer first name associated with order ID 86.", - "answer": "John", - "sql": "SELECT firstname FROM sales_order_address WHERE parent_id = 86 AND address_type = 'shipping';", - "sql_execute_result": [ - [ - "John" - ] - ] - }, - { - "question": "List all distinct regions from the sales order addresses.", - "answer": [ - "Michigan", - "Nevada", - "California", - "Massachusetts", - "New York", - "Arizona", - "Texas", - "Alabama", - "Georgia", - "Washington", - "Colorado", - "Florida", - "Illinois", - "Pennsylvania" - ], - "sql": "SELECT DISTINCT region FROM sales_order_address;", - "sql_execute_result": [ - [ - "Michigan" - ], - [ - "Nevada" - ], - [ - "California" - ], - [ - "Massachusetts" - ], - [ - "New York" - ], - [ - "Arizona" - ], - [ - "Texas" - ], - [ - "Alabama" - ], - [ - "Georgia" - ], - [ - "Washington" - ], - [ - "Colorado" - ], - [ - "Florida" - ], - [ - "Illinois" - ], - [ - "Pennsylvania" - ] - ] - }, - { - "question": "What is the email address of the customer with ID 5?", - "answer": "helloworld@yahoo.com", - "sql": "SELECT email FROM customer_entity WHERE entity_id = 5;", - "sql_execute_result": [ - [ - "helloworld@yahoo.com" - ] - ] - }, - { - "question": "List all orders for the customer with email david.lee@gmail.com.", - "answer": [ - 37, - 151, - 194 - ], - "sql": "SELECT entity_id FROM sales_order WHERE customer_email = 'david.lee@gmail.com';", - "sql_execute_result": [ - [ - 37 - ], - [ - 151 - ], - [ - 194 - ] - ] - }, - { - "question": "What is the total quantity ordered for the product 'Daria Bikram Pant-28-Black' in store with ID 1 on 2022-06-19?", - "answer": 1.0, - "sql": "SELECT qty_ordered FROM sales_bestsellers_aggregated_daily WHERE product_id = 1876 AND store_id = 1 AND period = '2022-06-19';", - "sql_execute_result": [ - [ - "1.0000" - ] - ] - }, - { - "question": "What is the status of the order with increment ID '000000151'?", - "answer": "canceled", - "sql": "SELECT status FROM sales_order_grid WHERE increment_id = '000000151';", - "sql_execute_result": [ - [ - "canceled" - ] - ] - }, - { - "question": "Find out the product name of the product with entity ID 1876.", - "answer": "Daria Bikram Pant-28-Black", - "sql": "SELECT product_name FROM sales_bestsellers_aggregated_daily WHERE product_id = 1876;", - "sql_execute_result": [ - [ - "Daria Bikram Pant-28-Black" - ], - [ - "Daria Bikram Pant-28-Black" - ], - [ - "Daria Bikram Pant-28-Black" - ], - [ - "Daria Bikram Pant-28-Black" - ] - ] - }, - { - "question": "Is the product with entity ID 2040 in stock?", - "answer": "Yes, the product with entity ID 2040 is in stock.", - "sql": "SELECT is_in_stock FROM cataloginventory_stock_item WHERE product_id = 2040;", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "Find the most popular product for store ID 1 on 2022-06-19.", - "answer": "Daria Bikram Pant-28-Black", - "sql": "SELECT product_name FROM sales_bestsellers_aggregated_daily WHERE store_id = 1 AND period = '2022-06-19' ORDER BY qty_ordered DESC LIMIT 1;", - "sql_execute_result": [ - [ - "Daria Bikram Pant-28-Black" - ] - ] - }, - { - "question": "What is the creation date of the order with entity ID 177?", - "answer": "2022-07-13 18:02:34", - "sql": "SELECT created_at FROM sales_order_grid WHERE entity_id = 177;", - "sql_execute_result": [ - [ - "2022-07-13 18:02:34" - ] - ] - }, - { - "question": "What is the status code for review status with ID 2?", - "answer": "Pending", - "sql": "SELECT status_code FROM review_status WHERE status_id = 2;", - "sql_execute_result": [ - [ - "Pending" - ] - ] - }, - { - "question": "Find the email address associated with the sales order address having entity ID 543.", - "answer": "michael.nguyen@yahoo.com", - "sql": "SELECT email FROM sales_order_address WHERE entity_id = 543;", - "sql_execute_result": [ - [ - "michael.nguyen@yahoo.com" - ] - ] - }, - { - "question": "What is the price of the product with entity ID 1997?", - "answer": "24.00", - "sql": "SELECT value FROM catalog_product_entity_decimal WHERE entity_id = 1997 AND attribute_id = 77;", - "sql_execute_result": [ - [ - "24.000000" - ] - ] - }, - { - "question": "Find the product name for the product with entity ID 928.", - "answer": "Hawkeye Yoga Short-33-Black", - "sql": "SELECT value FROM catalog_product_entity_varchar WHERE entity_id = 928 AND attribute_id = 73;", - "sql_execute_result": [ - [ - "Hawkeye Yoga Short-33-Black" - ] - ] - }, - { - "question": "Which attribute is visible and used in product listing according to the catalog_eav_attribute table?", - "answer": [ - 73, - 76, - 77, - 78, - 79, - 80, - 87, - 88, - 89, - 93, - 94, - 95, - 97, - 121, - 123, - 124, - 129, - 131, - 132, - 133, - 135, - 136, - 144 - ], - "sql": "SELECT attribute_id FROM catalog_eav_attribute WHERE is_visible = 1 AND used_in_product_listing = 1;", - "sql_execute_result": [ - [ - 73 - ], - [ - 76 - ], - [ - 77 - ], - [ - 78 - ], - [ - 79 - ], - [ - 80 - ], - [ - 87 - ], - [ - 88 - ], - [ - 89 - ], - [ - 93 - ], - [ - 94 - ], - [ - 95 - ], - [ - 97 - ], - [ - 121 - ], - [ - 123 - ], - [ - 124 - ], - [ - 129 - ], - [ - 131 - ], - [ - 132 - ], - [ - 133 - ], - [ - 135 - ], - [ - 136 - ], - [ - 144 - ] - ] - }, - { - "question": "Find the postal code for the billing address of the sales order with parent ID 294.", - "answer": "90028", - "sql": "SELECT postcode FROM sales_order_address WHERE parent_id = 294 AND address_type = 'billing';", - "sql_execute_result": [ - [ - "90028" - ] - ] - }, - { - "question": "What is the value of the attribute with ID 124 for the product with entity ID 1902?", - "answer": "0", - "sql": "SELECT value FROM catalog_product_entity_varchar WHERE entity_id = 1902 AND attribute_id = 124;", - "sql_execute_result": [ - [ - "0" - ] - ] - }, - { - "question": "What is the global visibility status for the attribute with ID 78?", - "answer": 1, - "sql": "SELECT is_global FROM catalog_eav_attribute WHERE attribute_id = 78;", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "Find the first name of the customer associated with the billing address having email 'alexander.thomas@hotmail.com'.", - "answer": "Alexander", - "sql": "SELECT firstname FROM sales_order_address WHERE email = 'alexander.thomas@hotmail.com' AND address_type = 'billing';", - "sql_execute_result": [ - [ - "Alexander" - ], - [ - "Alexander" - ], - [ - "Alexander" - ], - [ - "Alexander" - ], - [ - "Alexander" - ], - [ - "Alexander" - ], - [ - "Alexander" - ], - [ - "Alexander" - ], - [ - "Alexander" - ], - [ - "Alexander" - ] - ] - }, - { - "question": "What is the attribute ID of a catalog_eav_attribute that is searchable?", - "answer": [ - 73, - 74, - 75, - 76, - 77, - 83, - 97, - 119, - 121, - 136 - ], - "sql": "SELECT attribute_id FROM catalog_eav_attribute WHERE is_searchable = 1;", - "sql_execute_result": [ - [ - 73 - ], - [ - 74 - ], - [ - 75 - ], - [ - 76 - ], - [ - 77 - ], - [ - 83 - ], - [ - 97 - ], - [ - 119 - ], - [ - 121 - ], - [ - 136 - ] - ] - } -] \ No newline at end of file diff --git a/random_sample/generated_tasks.json.bak2 b/random_sample/generated_tasks.json.bak2 deleted file mode 100644 index 261116c..0000000 --- a/random_sample/generated_tasks.json.bak2 +++ /dev/null @@ -1,8182 +0,0 @@ -[ - { - "question": "What is the email address of the customer with ID 70?", - "answer": "emma.lopez@gmail.com", - "sql": "SELECT email FROM customer_entity WHERE entity_id = 70;", - "sql_execute_result": [ - [ - "emma.lopez@gmail.com" - ] - ] - }, - { - "question": "How many customers are in the 'General' group?", - "answer": 70, - "sql": "SELECT entity_id, firstname, lastname FROM customer_entity WHERE group_id = (SELECT customer_group_id FROM customer_group WHERE customer_group_code = 'General');", - "sql_execute_result": [ - [ - 1, - "Veronica", - "Costello" - ], - [ - 2, - "John", - "Smith" - ], - [ - 3, - "Jane", - "Doe" - ], - [ - 4, - "Bob", - "Jones" - ], - [ - 5, - "Sarah", - "Miller" - ], - [ - 6, - "Julia", - "Williams" - ], - [ - 7, - "Bob", - "Johnson" - ], - [ - 8, - "Mary", - "Martin" - ], - [ - 9, - "John", - "Lee" - ], - [ - 10, - "Jane", - "Smith" - ], - [ - 11, - "Daniel", - "Jackson" - ], - [ - 12, - "Lisa", - "Kim" - ], - [ - 13, - "Matt", - "Baker" - ], - [ - 14, - "John", - "Doe" - ], - [ - 15, - "Jane", - "Smith" - ], - [ - 16, - "Samantha", - "Jones" - ], - [ - 17, - "Lily", - "Potter" - ], - [ - 18, - "Grace", - "Nguyen" - ], - [ - 19, - "Lucy", - "Garcia" - ], - [ - 20, - "Olivia", - "Lee" - ], - [ - 21, - "Ava", - "Brown" - ], - [ - 22, - "Sophie", - "Taylor" - ], - [ - 23, - "Alex", - "Johnson" - ], - [ - 24, - "Emma", - "Davis" - ], - [ - 25, - "Adam", - "Garcia" - ], - [ - 26, - "Jennifer", - "White" - ], - [ - 27, - "Alex", - "Martin" - ], - [ - 28, - "Lisa", - "Green" - ], - [ - 29, - "Michael", - "Nguyen" - ], - [ - 30, - "David", - "Lee" - ], - [ - 31, - "Jason", - "Miller" - ], - [ - 32, - "Katie", - "Wong" - ], - [ - 33, - "Adam", - "Garcia" - ], - [ - 34, - "Brian", - "Smith" - ], - [ - 35, - "Samantha", - "Nguyen" - ], - [ - 36, - "Alexander", - "Thomas" - ], - [ - 37, - "Sam", - "Wilson" - ], - [ - 38, - "Kate", - "Jones" - ], - [ - 39, - "David", - "Smith" - ], - [ - 40, - "Jessica", - "Nguyen" - ], - [ - 41, - "Maxwell", - "Baker" - ], - [ - 42, - "Emily", - "Chen" - ], - [ - 43, - "Anna", - "Nguyen" - ], - [ - 44, - "Roberto", - "Lopez" - ], - [ - 45, - "Amanda", - "Kim" - ], - [ - 46, - "Jane", - "Doe" - ], - [ - 47, - "John", - "Smith" - ], - [ - 48, - "Jessica", - "Chang" - ], - [ - 49, - "James", - "Kim" - ], - [ - 50, - "Samantha", - "Wu" - ], - [ - 51, - "Robert", - "Johnson" - ], - [ - 52, - "Sophia", - "Kim" - ], - [ - 53, - "William", - "Chang" - ], - [ - 54, - "Jessica", - "Wong" - ], - [ - 55, - "Ethan", - "Garcia" - ], - [ - 56, - "Olivia", - "Jackson" - ], - [ - 57, - "Jacob", - "Rivera" - ], - [ - 58, - "Sophia", - "Young" - ], - [ - 59, - "Ryan", - "Tanaka" - ], - [ - 60, - "Julie", - "Nguyen" - ], - [ - 61, - "Matthew", - "Kim" - ], - [ - 62, - "Emily", - "Wilson" - ], - [ - 63, - "James", - "Baker" - ], - [ - 64, - "Isabella", - "Santos" - ], - [ - 65, - "Nathan", - "Chen" - ], - [ - 66, - "Hannah", - "Lim" - ], - [ - 67, - "Isaac", - "Rodriguez" - ], - [ - 68, - "Natalie", - "Kim" - ], - [ - 69, - "Sean", - "Miller" - ], - [ - 70, - "Emma", - "Lopez" - ] - ] - }, - { - "question": "How many items of product ID 1292 are currently in stock?", - "answer": 100, - "sql": "SELECT qty FROM cataloginventory_stock_item WHERE product_id = 1292;", - "sql_execute_result": [ - [ - "100.0000" - ] - ] - }, - { - "question": "What is the default name of the region with ID 880?", - "answer": "Medio Campidano", - "sql": "SELECT default_name FROM directory_country_region WHERE region_id = 880;", - "sql_execute_result": [ - [ - "Medio Campidano" - ] - ] - }, - { - "question": "What is the current sequence value for orders?", - "answer": 308, - "sql": "SELECT sequence_value FROM sequence_order_1 ORDER BY sequence_value DESC LIMIT 1;", - "sql_execute_result": [ - [ - 308 - ] - ] - }, - { - "question": "Find the product name for entity ID 1718 in the catalog.", - "answer": "Nona Fitness Tank-XS-Purple", - "sql": "SELECT value FROM catalog_product_entity_varchar WHERE entity_id = 1718 AND attribute_id = 73;", - "sql_execute_result": [ - [ - "Nona Fitness Tank-XS-Purple" - ] - ] - }, - { - "question": "What is the country code for the region named 'Olt'?", - "answer": "RO", - "sql": "SELECT country_id FROM directory_country_region WHERE default_name = 'Olt';", - "sql_execute_result": [ - [ - "RO" - ] - ] - }, - { - "question": "List all active sequence profiles.", - "answer": [ - { - "profile_id": 1, - "meta_id": 1 - }, - { - "profile_id": 2, - "meta_id": 2 - }, - { - "profile_id": 3, - "meta_id": 3 - }, - { - "profile_id": 4, - "meta_id": 4 - }, - { - "profile_id": 5, - "meta_id": 5 - }, - { - "profile_id": 6, - "meta_id": 6 - }, - { - "profile_id": 7, - "meta_id": 7 - }, - { - "profile_id": 8, - "meta_id": 8 - } - ], - "sql": "SELECT profile_id, meta_id FROM sales_sequence_profile WHERE is_active = 1;", - "sql_execute_result": [ - [ - 1, - 1 - ], - [ - 2, - 2 - ], - [ - 3, - 3 - ], - [ - 4, - 4 - ], - [ - 5, - 5 - ], - [ - 6, - 6 - ], - [ - 7, - 7 - ], - [ - 8, - 8 - ] - ] - }, - { - "question": "What is the sort order for the attribute option ID 95?", - "answer": 8, - "sql": "SELECT sort_order FROM eav_attribute_option WHERE option_id = 95;", - "sql_execute_result": [ - [ - 8 - ] - ] - }, - { - "question": "Find the SKU for the product with entity ID 1640.", - "answer": "Prima Compete Bra Top-S-Blue", - "sql": "SELECT value FROM catalog_product_entity_varchar WHERE entity_id = 1640 AND attribute_id = 73;", - "sql_execute_result": [ - [ - "Prima Compete Bra Top-S-Blue" - ] - ] - }, - { - "question": "Find the image path for the product with entity ID 8.", - "answer": "/w/b/wb01-black-0.jpg", - "sql": "SELECT value FROM catalog_product_entity_varchar WHERE entity_id = 8 AND attribute_id = 88;", - "sql_execute_result": [ - [ - "/w/b/wb01-black-0.jpg" - ] - ] - }, - { - "question": "What is the country ID for the region code 'GR-L'?", - "answer": "GR", - "sql": "SELECT country_id FROM directory_country_region WHERE code = 'GR-L';", - "sql_execute_result": [ - [ - "GR" - ] - ] - }, - { - "question": "What is the price of the product with SKU 'WS03-XS-Red'?", - "answer": "The price of the product with SKU 'WS03-XS-Red' is 29.00.", - "sql": "SELECT price FROM sales_invoice_item WHERE sku = 'WS03-XS-Red';", - "sql_execute_result": [ - [ - "29.0000" - ] - ] - }, - { - "question": "Find the tax amount for the product 'Minerva LumaTech\u2122 V-Tee'.", - "answer": "The tax amount for the product 'Minerva LumaTech\u2122 V-Tee' is 2.6400.", - "sql": "SELECT tax_amount FROM sales_invoice_item WHERE name = 'Minerva LumaTech™ V-Tee';", - "sql_execute_result": [ - [ - "2.6400" - ] - ] - }, - { - "question": "What is the ISO3 code for the country with ISO2 code 'TT'?", - "answer": "TTO", - "sql": "SELECT iso3_code FROM directory_country WHERE iso2_code = 'TT';", - "sql_execute_result": [ - [ - "TTO" - ] - ] - }, - { - "question": "How many products have a decimal attribute value of 1.000000?", - "answer": 1847, - "sql": "SELECT sku FROM catalog_product_entity WHERE entity_id IN (SELECT entity_id FROM catalog_product_entity_decimal WHERE value = '1.000000');", - "sql_execute_result": [ - [ - "MH01-L-Black" - ], - [ - "MH01-L-Gray" - ], - [ - "MH01-L-Orange" - ], - [ - "MH01-M-Black" - ], - [ - "MH01-M-Gray" - ], - [ - "MH01-M-Orange" - ], - [ - "MH01-S-Black" - ], - [ - "MH01-S-Gray" - ], - [ - "MH01-S-Orange" - ], - [ - "MH01-XL-Black" - ], - [ - "MH01-XL-Gray" - ], - [ - "MH01-XL-Orange" - ], - [ - "MH01-XS-Black" - ], - [ - "MH01-XS-Gray" - ], - [ - "MH01-XS-Orange" - ], - [ - "MH02-L-Black" - ], - [ - "MH02-L-Purple" - ], - [ - "MH02-L-Red" - 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[ - "WT09-XL-White" - ], - [ - "WT09-XL-Yellow" - ], - [ - "WT09-XS-Purple" - ], - [ - "WT09-XS-White" - ], - [ - "WT09-XS-Yellow" - ] - ] - }, - { - "question": "How many product IDs have an integer attribute ID 93 with a value of 60?", - "answer": 137, - "sql": "SELECT entity_id FROM catalog_product_entity_int WHERE attribute_id = 93 AND value = 60;", - "sql_execute_result": [ - [ - 97 - ], - [ - 100 - ], - [ - 103 - ], - [ - 106 - ], - [ - 109 - ], - [ - 257 - ], - [ - 260 - ], - [ - 263 - ], - [ - 266 - ], - [ - 269 - ], - [ - 305 - ], - [ - 308 - ], - [ - 311 - ], - [ - 314 - ], - [ - 317 - ], - [ - 337 - ], - [ - 340 - ], - [ - 343 - ], - [ - 346 - ], - [ - 349 - ], - [ - 481 - ], - [ - 484 - ], - [ - 487 - ], - [ - 490 - ], - [ - 493 - ], - [ - 529 - ], - [ - 532 - ], - [ - 535 - ], - [ - 538 - ], - [ - 541 - ], - [ - 545 - ], - [ - 548 - ], - [ - 551 - ], - [ - 554 - ], - [ - 557 - ], - [ - 657 - ], - [ - 660 - ], - [ - 663 - ], - [ - 666 - ], - [ - 669 - ], - [ - 707 - ], - [ - 708 - ], - [ - 709 - ], - [ - 710 - ], - [ - 711 - ], - [ - 914 - ], - [ - 917 - ], - [ - 920 - ], - [ - 923 - ], - [ - 1165 - ], - [ - 1168 - ], - [ - 1171 - ], - [ - 1174 - ], - [ - 1177 - ], - [ - 1213 - ], - [ - 1216 - ], - [ - 1219 - ], - [ - 1335 - ], - [ - 1338 - ], - [ - 1341 - ], - [ - 1344 - ], - [ - 1347 - ], - [ - 1463 - ], - [ - 1466 - ], - [ - 1469 - ], - [ - 1472 - ], - [ - 1475 - ], - [ - 1511 - ], - [ - 1514 - ], - [ - 1517 - ], - [ - 1520 - ], - [ - 1523 - ], - [ - 1527 - ], - [ - 1530 - ], - [ - 1533 - ], - [ - 1536 - ], - [ - 1539 - ], - [ - 1559 - ], - [ - 1562 - ], - [ - 1565 - ], - [ - 1568 - ], - [ - 1571 - ], - [ - 1575 - ], - [ - 1578 - ], - [ - 1581 - ], - [ - 1584 - ], - [ - 1587 - ], - [ - 1607 - ], - [ - 1610 - ], - [ - 1613 - ], - [ - 1616 - ], - [ - 1619 - ], - [ - 1623 - ], - [ - 1626 - ], - [ - 1629 - ], - [ - 1632 - ], - [ - 1635 - ], - [ - 1639 - ], - [ - 1642 - ], - [ - 1645 - ], - [ - 1648 - ], - [ - 1651 - ], - [ - 1687 - ], - [ - 1690 - ], - [ - 1693 - ], - [ - 1696 - ], - [ - 1699 - ], - [ - 1751 - ], - [ - 1754 - ], - [ - 1757 - ], - [ - 1760 - ], - [ - 1763 - ], - [ - 1767 - ], - [ - 1770 - ], - [ - 1773 - ], - [ - 1776 - ], - [ - 1779 - ], - [ - 1783 - ], - [ - 1786 - ], - [ - 1789 - ], - [ - 1792 - ], - [ - 1795 - ], - [ - 1799 - ], - [ - 1802 - ], - [ - 1805 - ], - [ - 1808 - ], - [ - 1811 - ], - [ - 1922 - ], - [ - 1925 - ], - [ - 1928 - ], - [ - 1931 - ], - [ - 1934 - ], - [ - 1970 - ], - [ - 1973 - ], - [ - 1976 - ], - [ - 1979 - ], - [ - 1982 - ] - ] - }, - { - "question": "What is the entity ID of the product with SKU 'WS12-M-Orange'?", - "answer": 1548, - "sql": "SELECT entity_id FROM catalog_product_entity WHERE sku = 'WS12-M-Orange';", - "sql_execute_result": [ - [ - 1548 - ] - ] - }, - { - "question": "Retrieve the base price including tax for the product with SKU 'WS08-XS-Blue'.", - "answer": "34.6400", - "sql": "SELECT base_price_incl_tax FROM sales_invoice_item WHERE sku = 'WS08-XS-Blue';", - "sql_execute_result": [ - [ - "34.6400" - ] - ] - }, - { - "question": "How many countries have their ISO2 code in the sample data?", - "answer": 5, - "sql": "SELECT COUNT(*) FROM directory_country WHERE iso2_code IN ('TT', 'QA', 'IM', 'NU', 'CH');", - "sql_execute_result": [ - [ - 5 - ] - ] - }, - { - "question": "What is the product type of the product with SKU 'MH06-L-Black'?", - "answer": "simple", - "sql": "SELECT type_id FROM catalog_product_entity WHERE sku = 'MH06-L-Black';", - "sql_execute_result": [ - [ - "simple" - ] - ] - }, - { - "question": "Determine the attribute set ID of the product with SKU 'MJ10-L-Orange'.", - "answer": 9, - "sql": "SELECT attribute_set_id FROM catalog_product_entity WHERE sku = 'MJ10-L-Orange';", - "sql_execute_result": [ - [ - 9 - ] - ] - }, - { - "question": "What is the price of the product with SKU 'aero-daily-fitness-tee-s-yellow'?", - "answer": "The price of the product with SKU 'aero-daily-fitness-tee-s-yellow' is 24.00.", - "sql": "SELECT value FROM catalog_product_entity_decimal WHERE entity_id = (SELECT entity_id FROM catalog_product_entity_varchar WHERE value = 'aero-daily-fitness-tee-s-yellow') AND attribute_id = 77;", - "sql_execute_result": [ - [ - "24.000000" - ] - ] - }, - { - "question": "Find the description of the product with ID 460.", - "answer": "

Pumping iron or dialing the track, you've got cool comfort on your side in our short-sleeve Helios EverCool™ Tee. The fabric is infused with moisture-wicking technology that pulls sweat off your skin for speedy evaporation. Stretchy fabric gussets encourage ventilation while increasing your range of motion.

\n

• Teal quick dry tee.
• Relaxed fit.
• Crew neckline.
• Machine wash/dry.

", - "sql": "SELECT value FROM catalog_product_entity_text WHERE entity_id = 460 AND attribute_id = 75;", - "sql_execute_result": [ - [ - "

Pumping iron or dialing the track, you've got cool comfort on your side in our short-sleeve Helios EverCool™ Tee. The fabric is infused with moisture-wicking technology that pulls sweat off your skin for speedy evaporation. Stretchy fabric gussets encourage ventilation while increasing your range of motion.

\n

• Teal quick dry tee.
• Relaxed fit.
• Crew neckline.
• Machine wash/dry.

" - ] - ] - }, - { - "question": "What is the total quantity ordered for the product 'Maxima Drawstring Short-29-Orange' in 2022?", - "answer": 2, - "sql": "SELECT qty_ordered FROM sales_bestsellers_aggregated_yearly WHERE product_name = 'Maxima Drawstring Short-29-Orange' AND period = '2022-01-01';", - "sql_execute_result": [ - [ - "1.0000" - ], - [ - "1.0000" - ] - ] - }, - { - "question": "What is the address of customer Sophia Young?", - "answer": "1 International Pl, Boston, Massachusetts, 02110", - "sql": "SELECT CONCAT(street, ', ', city, ', ', region, ', ', postcode) AS full_address FROM customer_address_entity WHERE firstname = 'Sophia' AND lastname = 'Young';", - "sql_execute_result": [ - [ - "1 International Pl, Boston, Massachusetts, 02110" - ] - ] - }, - { - "question": "What is the price of the product with entity ID 1532?", - "answer": "The price of the product with entity ID 1532 is 32.00.", - "sql": "SELECT value FROM catalog_product_entity_decimal WHERE entity_id = 1532 AND attribute_id = 77;", - "sql_execute_result": [ - [ - "32.000000" - ] - ] - }, - { - "question": "How many orders for products purchased in store with ID 1 during 2023 were found?", - "answer": 141, - "sql": "SELECT product_id, product_name, qty_ordered FROM sales_bestsellers_aggregated_yearly WHERE store_id = 1 AND period = '2023-01-01';", - "sql_execute_result": [ - [ - 3, - "Crown Summit Backpack", - "1.0000" - ], - [ - 7, - "Impulse Duffle", - "2.0000" - ], - [ - 11, - "Endeavor Daytrip Backpack", - "1.0000" - ], - [ - 13, - "Overnight Duffle", - "3.0000" - ], - [ - 16, - "Dual Handle Cardio Ball", - "1.0000" - ], - [ - 20, - "Quest Lumaflex™ Band", - "1.0000" - ], - [ - 23, - "Harmony Lumaflex™ Strength Band Kit ", - "1.0000" - ], - [ - 25, - "Sprite Stasis Ball 55 cm", - "1.0000" - ], - [ - 26, - "Sprite Stasis Ball 55 cm", - "1.0000" - ], - [ - 27, - "Sprite Stasis Ball 65 cm", - "1.0000" - ], - [ - 28, - "Sprite Stasis Ball 65 cm", - "2.0000" - ], - [ - 29, - "Sprite Stasis Ball 65 cm", - "2.0000" - ], - [ - 30, - "Sprite Stasis Ball 75 cm", - "1.0000" - ], - [ - 33, - "Sprite Yoga Strap 6 foot", - "4.0000" - ], - [ - 34, - "Sprite Yoga Strap 8 foot", - "2.0000" - ], - [ - 36, - "Aim Analog Watch", - "2.0000" - ], - [ - 37, - "Endurance Watch", - "1.0000" - ], - [ - 47, - "Chaz Kangeroo Hoodie-XS-Black", - "1.0000" - ], - [ - 50, - "Chaz Kangeroo Hoodie-S-Black", - "1.0000" - ], - [ - 88, - "Bruno Compete Hoodie-L-Black", - "1.0000" - ], - [ - 95, - "Frankie Sweatshirt-XS-Green", - "1.0000" - ], - [ - 127, - "Stark Fundamental Hoodie-XS-Black", - "1.0000" - ], - [ - 128, - "Stark Fundamental Hoodie-XS-Blue", - "1.0000" - ], - [ - 129, - "Stark Fundamental Hoodie-XS-Purple", - "1.0000" - ], - [ - 134, - "Stark Fundamental Hoodie-M-Blue", - "1.0000" - ], - [ - 204, - "Mach Street Sweatshirt -XL-Blue", - "2.0000" - ], - [ - 220, - "Grayson Crewneck Sweatshirt -XL-Red", - "1.0000" - ], - [ - 234, - "Ajax Full-Zip Sweatshirt -L-Red", - "1.0000" - ], - [ - 243, - "Marco Lightweight Active Hoodie-S-Green", - "1.0000" - ], - [ - 262, - "Beaumont Summit Kit-M-Red", - "1.0000" - ], - [ - 315, - "Orion Two-Tone Fitted Jacket-XL-Black", - "1.0000" - ], - [ - 336, - "Taurus Elements Shell-XS-White", - "1.0000" - ], - [ - 351, - "Mars HeatTech™ Pullover-XS-Black", - "1.0000" - ], - [ - 388, - "Jupiter All-Weather Trainer -S-Purple", - "1.0000" - ], - [ - 421, - "Proteus Fitness Jackshirt-M-Black", - "1.0000" - ], - [ - 422, - "Proteus Fitness Jackshirt-M-Blue", - "1.0000" - ], - [ - 432, - "Gobi HeatTec® Tee-XS-Orange", - "2.0000" - ], - [ - 546, - "Aero Daily Fitness Tee-S-Black", - "1.0000" - ], - [ - 586, - "Logan HeatTec® Tee-L-Red", - "1.0000" - ], - [ - 601, - "Deion Long-Sleeve EverCool™ Tee-L-Green", - "1.0000" - ], - [ - 603, - "Deion Long-Sleeve EverCool™ Tee-XL-Black", - "1.0000" - ], - [ - 645, - "Tristan Endurance Tank-M-Gray", - "1.0000" - ], - [ - 652, - "Tristan Endurance Tank-XL-Red", - "1.0000" - ], - [ - 662, - "Primo Endurance Tank-M-Red", - "1.0000" - ], - [ - 691, - "Argus All-Weather Tank-M-Gray", - "1.0000" - ], - [ - 699, - "Sparta Gym Tank-XL-Green", - "2.0000" - ], - [ - 709, - "Tiberius Gym Tank-M-Yellow", - "1.0000" - ], - [ - 716, - "Atlas Fitness Tank-L-Blue", - "1.0000" - ], - [ - 726, - "Caesar Warm-Up Pant-32-Gray", - "1.0000" - ], - [ - 736, - "Caesar Warm-Up Pant-36-Purple", - "1.0000" - ], - [ - 758, - "Geo Insulated Jogging Pant-34-Green", - "1.0000" - ], - [ - 764, - "Supernova Sport Pant-32-Black", - "1.0000" - ], - [ - 790, - "Mithra Warmup Pant-32-Gray", - "1.0000" - ], - [ - 796, - "Mithra Warmup Pant-34-Gray", - "1.0000" - ], - [ - 801, - "Mithra Warmup Pant-36-Orange", - "1.0000" - ], - [ - 804, - "Thorpe Track Pant-32-Blue", - "1.0000" - ], - [ - 805, - "Thorpe Track Pant-32-Purple", - "1.0000" - ], - [ - 824, - "Zeppelin Yoga Pant-34-Red", - "1.0000" - ], - [ - 850, - "Orestes Yoga Pant -34-Green", - "1.0000" - ], - [ - 859, - "Aether Gym Pant -33-Brown", - "1.0000" - ], - [ - 863, - "Aether Gym Pant -34-Green", - "1.0000" - ], - [ - 865, - "Aether Gym Pant -36-Brown", - "1.0000" - ], - [ - 883, - "Cobalt CoolTech™ Fitness Short-32-Red", - "1.0000" - ], - [ - 895, - "Apollo Running Short-33-Black", - "1.0000" - ], - [ - 926, - "Hawkeye Yoga Short-32-Blue", - "2.0000" - ], - [ - 936, - "Hawkeye Yoga Short-36-Gray", - "2.0000" - ], - [ - 948, - "Lono Yoga Short-36-Gray", - "1.0000" - ], - [ - 961, - "Rapha Sports Short-36-Blue", - "1.0000" - ], - [ - 977, - "Troy Yoga Short-32-Black", - "1.0000" - ], - [ - 986, - "Troy Yoga Short-36-Black", - "1.0000" - ], - [ - 1007, - "Arcadio Gym Short-33-Blue", - "1.0000" - ], - [ - 1014, - "Arcadio Gym Short-36-Red", - "1.0000" - ], - [ - 1033, - "Mona Pullover Hoodlie-S-Orange", - "1.0000" - ], - [ - 1040, - "Mona Pullover Hoodlie-L-Purple", - "1.0000" - ], - [ - 1063, - "Autumn Pullie-XS-Red", - "1.0000" - ], - [ - 1064, - "Autumn Pullie-S-Green", - "1.0000" - ], - [ - 1175, - "Helena Hooded Fleece-XL-Blue", - "1.0000" - ], - [ - 1182, - "Eos V-Neck Hoodie-S-Blue", - "1.0000" - ], - [ - 1202, - "Circe Hooded Ice Fleece-M-Green", - "1.0000" - ], - [ - 1219, - "Stellar Solar Jacket-L-Yellow", - "1.0000" - ], - [ - 1222, - "Josie Yoga Jacket-XS-Blue", - "1.0000" - ], - [ - 1225, - "Josie Yoga Jacket-S-Blue", - "1.0000" - ], - [ - 1240, - "Augusta Pullover Jacket-S-Blue", - "1.0000" - ], - [ - 1243, - "Augusta Pullover Jacket-M-Blue", - "1.0000" - ], - [ - 1254, - "Ingrid Running Jacket-XS-Red", - "1.0000" - ], - [ - 1255, - "Ingrid Running Jacket-XS-White", - "1.0000" - ], - [ - 1271, - "Riona Full Zip Jacket-XS-Red", - "1.0000" - ], - [ - 1283, - "Riona Full Zip Jacket-XL-Red", - "1.0000" - ], - [ - 1299, - "Inez Full Zip Jacket-XL-Red", - "1.0000" - ], - [ - 1329, - "Jade Yoga Jacket-XL-Blue", - "1.0000" - ], - [ - 1335, - "Nadia Elements Shell-XS-Yellow", - "1.0000" - ], - [ - 1336, - "Nadia Elements Shell-S-Black", - "1.0000" - ], - [ - 1340, - "Nadia Elements Shell-M-Orange", - "1.0000" - ], - [ - 1351, - "Neve Studio Dance Jacket-XS-Orange", - "1.0000" - ], - [ - 1354, - "Neve Studio Dance Jacket-S-Orange", - "1.0000" - ], - [ - 1355, - "Neve Studio Dance Jacket-M-Black", - "1.0000" - ], - [ - 1365, - "Juno Jacket-XS-Blue", - "1.0000" - ], - [ - 1407, - "Gabrielle Micro Sleeve Top-L-Green", - "1.0000" - ], - [ - 1424, - "Iris Workout Top-L-Red", - "1.0000" - ], - [ - 1430, - "Layla Tee-XS-Green", - "2.0000" - ], - [ - 1468, - "Juliana Short-Sleeve Tee-M-White", - "1.0000" - ], - [ - 1473, - "Juliana Short-Sleeve Tee-XL-Black", - "1.0000" - ], - [ - 1479, - "Minerva LumaTech™ V-Tee-XS-Red", - "2.0000" - ], - [ - 1483, - "Minerva LumaTech™ V-Tee-M-Black", - "1.0000" - ], - [ - 1500, - "Tiffany Fitness Tee-M-Red", - "1.0000" - ], - [ - 1505, - "Tiffany Fitness Tee-XL-Blue", - "1.0000" - ], - [ - 1568, - "Gwyn Endurance Tee-L-Yellow", - "1.0000" - ], - [ - 1607, - "Erica Evercool Sports Bra-XS-Yellow", - "1.0000" - ], - [ - 1631, - "Celeste Sports Bra-L-Red", - "1.0000" - ], - [ - 1644, - "Prima Compete Bra Top-M-Purple", - "1.0000" - ], - [ - 1681, - "Bella Tank-XL-Black", - "1.0000" - ], - [ - 1685, - "Zoe Tank-XS-Green", - "1.0000" - ], - [ - 1690, - "Zoe Tank-S-Yellow", - "1.0000" - ], - [ - 1695, - "Zoe Tank-L-Orange", - "1.0000" - ], - [ - 1699, - "Zoe Tank-XL-Yellow", - "1.0000" - ], - [ - 1757, - "Chloe Compete Tank-M-Yellow", - "1.0000" - ], - [ - 1818, - "Karmen Yoga Pant-29-White", - "1.0000" - ], - [ - 1828, - "Ida Workout Parachute Pant-28-Blue", - "1.0000" - ], - [ - 1832, - "Ida Workout Parachute Pant-29-Purple", - "3.0000" - ], - [ - 1836, - "Cora Parachute Pant-28-White", - "1.0000" - ], - [ - 1838, - "Cora Parachute Pant-29-Blue", - "1.0000" - ], - [ - 1849, - "Diana Tights-28-Blue", - "1.0000" - ], - [ - 1855, - "Aeon Capri-28-Black", - "1.0000" - ], - [ - 1859, - "Aeon Capri-29-Blue", - "1.0000" - ], - [ - 1885, - "Sylvia Capri-28-Red", - "1.0000" - ], - [ - 1905, - "Fiona Fitness Short-28-Green", - "1.0000" - ], - [ - 1906, - "Fiona Fitness Short-28-Red", - "1.0000" - ], - [ - 1910, - "Fiona Fitness Short-30-Black", - "1.0000" - ], - [ - 1913, - "Fiona Fitness Short-31-Black", - "1.0000" - ], - [ - 1922, - "Maxima Drawstring Short-28-Yellow", - "1.0000" - ], - [ - 1932, - "Maxima Drawstring Short-32-Gray", - "1.0000" - ], - [ - 1938, - "Gwen Drawstring Bike Short-28-Orange", - "1.0000" - ], - [ - 1941, - "Gwen Drawstring Bike Short-29-Orange", - "1.0000" - ], - [ - 1958, - "Artemis Running Short-30-Black", - "1.0000" - ], - [ - 1988, - "Angel Light Running Short-29-Orange", - "1.0000" - ], - [ - 1989, - "Angel Light Running Short-29-Purple", - "2.0000" - ], - [ - 1993, - "Echo Fit Compression Short-28-Purple", - "1.0000" - ], - [ - 1995, - "Echo Fit Compression Short-29-Blue", - "1.0000" - ], - [ - 2022, - "Ina Compression Short-29-Orange", - "1.0000" - ], - [ - 2023, - "Ina Compression Short-29-Red", - "1.0000" - ], - [ - 2038, - "Erika Running Short-32-Purple", - "1.0000" - ] - ] - }, - { - "question": "What is the full name and phone number of the customer living at 789 Main St, Dallas?", - "answer": { - "full_name": "John Smith", - "telephone": "2145551212" - }, - "sql": "SELECT CONCAT(firstname, ' ', lastname) AS full_name, telephone FROM customer_address_entity WHERE street = '789 Main St' AND city = 'Dallas';", - "sql_execute_result": [ - [ - "John Smith", - "2145551212" - ] - ] - }, - { - "question": "Find the entity ID for the product named 'Hollister Backyard Sweatshirt-XL-Green'.", - "answer": 123, - "sql": "SELECT entity_id FROM catalog_product_entity_varchar WHERE value = 'Hollister Backyard Sweatshirt-XL-Green';", - "sql_execute_result": [ - [ - 123 - ] - ] - }, - { - "question": "What is the attribute code for the attribute with ID 33?", - "answer": "postcode", - "sql": "SELECT attribute_code FROM eav_attribute WHERE attribute_id = 33;", - "sql_execute_result": [ - [ - "postcode" - ] - ] - }, - { - "question": "How many search results are there for the query 'Antonia Racer Tank'?", - "answer": 23, - "sql": "SELECT num_results FROM search_query WHERE query_text = 'Antonia Racer Tank';", - "sql_execute_result": [ - [ - 23 - ] - ] - }, - { - "question": "What is the name of the store group with ID 0?", - "answer": "Default", - "sql": "SELECT name FROM store_group WHERE group_id = 0;", - "sql_execute_result": [ - [ - "Default" - ] - ] - }, - { - "question": "What is the stock name for stock ID 1?", - "answer": "Default", - "sql": "SELECT stock_name FROM cataloginventory_stock WHERE stock_id = 1;", - "sql_execute_result": [ - [ - "Default" - ] - ] - }, - { - "question": "Is the search query 'nike' active?", - "answer": "Yes, the search query 'nike' is active.", - "sql": "SELECT is_active FROM search_query WHERE query_text = 'nike';", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "What is the website ID of the main website store group?", - "answer": 1, - "sql": "SELECT website_id FROM store_group WHERE name = 'Main Website Store';", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "What is the popularity of the search query 'hollister'?", - "answer": 19, - "sql": "SELECT popularity FROM search_query WHERE query_text = 'hollister';", - "sql_execute_result": [ - [ - 19 - ] - ] - }, - { - "question": "Find the default store ID for the store group named 'Main Website Store'.", - "answer": 1, - "sql": "SELECT default_store_id FROM store_group WHERE name = 'Main Website Store';", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "Find the email address for customer with the billing address '6146 Honey Bluff Parkway, Calder, Michigan, 49628-7978'.", - "answer": "The email address for the customer with the specified billing address is 'roni_cost@example.com'.", - "sql": "SELECT customer_email FROM sales_order_grid WHERE billing_address = '6146 Honey Bluff Parkway,Calder,Michigan,49628-7978';", - "sql_execute_result": [ - [ - "roni_cost@example.com" - ], - [ - "roni_cost@example.com" - ] - ] - }, - { - "question": "What is the SKU of the product with entity_id 2040?", - "answer": "The SKU of the product with entity_id 2040 is WSH12.", - "sql": "SELECT sku FROM catalog_product_entity WHERE entity_id = 2040;", - "sql_execute_result": [ - [ - "WSH12" - ] - ] - }, - { - "question": "How many children does the category with ID 29 have?", - "answer": 4, - "sql": "SELECT children_count FROM catalog_category_entity WHERE entity_id = 29;", - "sql_execute_result": [ - [ - 4 - ] - ] - }, - { - "question": "List all order IDs for the customer with email 'roni_cost@example.com'.", - "answer": [ - 1, - 2 - ], - "sql": "SELECT entity_id FROM sales_order WHERE customer_email = 'roni_cost@example.com';", - "sql_execute_result": [ - [ - 1 - ], - [ - 2 - ] - ] - }, - { - "question": "What is the total quantity of products ordered in order with increment ID '000000002'?", - "answer": "The total quantity of products ordered in order with increment ID '000000002' is 1.0000.", - "sql": "SELECT total_qty_ordered FROM sales_order WHERE increment_id = '000000002';", - "sql_execute_result": [ - [ - "1.0000" - ] - ] - }, - { - "question": "What is the grand total of the invoice with entity_id 2?", - "answer": "The grand total of the invoice with entity_id 2 is 39.6400.", - "sql": "SELECT grand_total FROM sales_invoice WHERE entity_id = 2;", - "sql_execute_result": [ - [ - "39.6400" - ] - ] - }, - { - "question": "How many orders were placed on '2023-04-19'?", - "answer": 9, - "sql": "SELECT COUNT(*) FROM sales_order WHERE DATE(created_at) = '2023-04-19';", - "sql_execute_result": [ - [ - 9 - ] - ] - }, - { - "question": "Which region does 'Burtnieku novads' belong to?", - "answer": "The region 'Burtnieku novads' belongs to the locale 'en_US'.", - "sql": "SELECT locale FROM directory_country_region_name WHERE name = 'Burtnieku novads';", - "sql_execute_result": [ - [ - "en_US" - ] - ] - }, - { - "question": "What is the subtotal of the order with entity_id 1?", - "answer": "The subtotal of the order with entity_id 1 is 29.0000.", - "sql": "SELECT subtotal FROM sales_order WHERE entity_id = 1;", - "sql_execute_result": [ - [ - "29.0000" - ] - ] - }, - { - "question": "What is the status label for the 'fraud' status?", - "answer": "Suspected Fraud", - "sql": "SELECT label FROM sales_order_status WHERE status = 'fraud';", - "sql_execute_result": [ - [ - "Suspected Fraud" - ] - ] - }, - { - "question": "Find the sequence value for the latest order in store 1.", - "answer": 308, - "sql": "SELECT MAX(sequence_value) FROM sequence_order_1;", - "sql_execute_result": [ - [ - 308 - ] - ] - }, - { - "question": "What is the name of the default stock available on website 0?", - "answer": "Default", - "sql": "SELECT stock_name FROM cataloginventory_stock WHERE website_id = 0 AND stock_id = 1;", - "sql_execute_result": [ - [ - "Default" - ] - ] - }, - { - "question": "How many orders have a status of 'pending_payment'?", - "answer": 0, - "sql": "SELECT COUNT(*) FROM sales_order WHERE status = 'pending_payment';", - "sql_execute_result": [ - [ - 0 - ] - ] - }, - { - "question": "Retrieve the sequence table associated with the creditmemo entity type in store 1.", - "answer": "The sequence table associated with the creditmemo entity type in store 1 is 'sequence_creditmemo_1'.", - "sql": "SELECT sequence_table FROM sales_sequence_meta WHERE entity_type = 'creditmemo' AND store_id = 1;", - "sql_execute_result": [ - [ - "sequence_creditmemo_1" - ] - ] - }, - { - "question": "What is the sequence value for the first invoice in store 1?", - "answer": 1, - "sql": "SELECT MIN(sequence_value) FROM sequence_invoice_1;", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "How many status labels for sales orders were found?", - "answer": 12, - "sql": "SELECT label FROM sales_order_status;", - "sql_execute_result": [ - [ - "Canceled" - ], - [ - "Closed" - ], - [ - "Complete" - ], - [ - "Suspected Fraud" - ], - [ - "On Hold" - ], - [ - "Payment Review" - ], - [ - "PayPal Canceled Reversal" - ], - [ - "PayPal Reversed" - ], - [ - "Pending" - ], - [ - "Pending Payment" - ], - [ - "Pending PayPal" - ], - [ - "Processing" - ] - ] - }, - { - "question": "Find the sequence table used for orders in store 0.", - "answer": "The sequence table used for orders in store 0 is 'sequence_order_0'.", - "sql": "SELECT sequence_table FROM sales_sequence_meta WHERE entity_type = 'order' AND store_id = 0;", - "sql_execute_result": [ - [ - "sequence_order_0" - ] - ] - }, - { - "question": "What is the sequence value for the last invoice in store 1?", - "answer": 2, - "sql": "SELECT MAX(sequence_value) FROM sequence_invoice_1;", - "sql_execute_result": [ - [ - 2 - ] - ] - }, - { - "question": "Which store uses the sequence table 'sequence_shipment_1'?", - "answer": 1, - "sql": "SELECT store_id FROM sales_sequence_meta WHERE sequence_table = 'sequence_shipment_1';", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "What is the product name and price for the best-selling product with ID 848 in 2022?", - "answer": [ - { - "product_name": "Orestes Yoga Pant -34-Black", - "product_price": "52.8000" - } - ], - "sql": "SELECT product_name, product_price FROM sales_bestsellers_aggregated_yearly WHERE product_id = 848 AND period = '2022-01-01';", - "sql_execute_result": [ - [ - "Orestes Yoga Pant -34-Black", - "52.8000" - ], - [ - "Orestes Yoga Pant -34-Black", - "52.8000" - ] - ] - }, - { - "question": "What is the total quantity ordered for the canceled order with increment ID 000000291?", - "answer": "The total quantity ordered for the canceled order with increment ID 000000291 is 1.0000.", - "sql": "SELECT total_qty_ordered FROM sales_order WHERE increment_id = '000000291' AND status = 'canceled';", - "sql_execute_result": [ - [ - "1.0000" - ] - ] - }, - { - "question": "Find the store code for the store group with group ID 1.", - "answer": "The store code for the store group with group ID 1 is 'main_website_store'.", - "sql": "SELECT code FROM store_group WHERE group_id = 1;", - "sql_execute_result": [ - [ - "main_website_store" - ] - ] - }, - { - "question": "What is the description of the product with entity ID 992?", - "answer": "The description of the product with entity ID 992 is:

You'll let your fear go and push your limits in your new Sol Active Short. Featuring ultra-breathable performance fabric and a flat comfort-fit waistband, the Sol Active Short is perfect for high-intensity circuits or high-heat bikram.

\n

• Light blue jersey shorts with mesh detail.
• 87% Spandex 13% Lycra.
• Machine wash cold, tumble dry low.
• Superior performance fabric.
• Flat-lock seams.

", - "sql": "SELECT value FROM catalog_product_entity_text WHERE entity_id = 992 AND attribute_id = 75;", - "sql_execute_result": [ - [ - "

You'll let your fear go and push your limits in your new Sol Active Short. Featuring ultra-breathable performance fabric and a flat comfort-fit waistband, the Sol Active Short is perfect for high-intensity circuits or high-heat bikram.

\n

• Light blue jersey shorts with mesh detail.
• 87% Spandex 13% Lycra.
• Machine wash cold, tumble dry low.
• Superior performance fabric.
• Flat-lock seams.

" - ] - ] - }, - { - "question": "How many orders were completed on April 5, 2023?", - "answer": 3, - "sql": "SELECT orders_count FROM sales_order_aggregated_created WHERE period = '2023-04-05' AND order_status = 'complete';", - "sql_execute_result": [ - [ - 3 - ], - [ - 3 - ] - ] - }, - { - "question": "How many total income amounts were found?", - "answer": 2, - "sql": "SELECT total_income_amount FROM sales_order_aggregated_created WHERE period = '2022-01-31' AND order_status = 'complete';", - "sql_execute_result": [ - [ - "176.6000" - ], - [ - "176.6000" - ] - ] - }, - { - "question": "Find the customer email and first name for the order with increment ID 000000065.", - "answer": { - "customer_email": "avidreader99@yahoo.com", - "customer_firstname": "Grace" - }, - "sql": "SELECT customer_email, customer_firstname FROM sales_order WHERE increment_id = '000000065';", - "sql_execute_result": [ - [ - "avidreader99@yahoo.com", - "Grace" - ] - ] - }, - { - "question": "What is the total shipping amount for canceled orders on February 18, 2022?", - "answer": "The total shipping amount for canceled orders on February 18, 2022, is 10.0000.", - "sql": "SELECT total_shipping_amount FROM sales_order_aggregated_created WHERE period = '2022-02-18' AND order_status = 'canceled';", - "sql_execute_result": [ - [ - "5.0000" - ], - [ - "5.0000" - ] - ] - }, - { - "question": "Find the total quantity ordered for product with ID 39 in 2022.", - "answer": "The total quantity ordered for product with ID 39 in 2022 is 8.", - "sql": "SELECT qty_ordered FROM sales_bestsellers_aggregated_yearly WHERE product_id = 39 AND period = '2022-01-01';", - "sql_execute_result": [ - [ - "4.0000" - ], - [ - "4.0000" - ] - ] - }, - { - "question": "What are the applied rule IDs for the complete order with increment ID 000000028?", - "answer": "The applied rule IDs for the complete order with increment ID 000000028 are 1, 2, and 3.", - "sql": "SELECT applied_rule_ids FROM sales_order WHERE increment_id = '000000028' AND status = 'complete';", - "sql_execute_result": [ - [ - "1,2,3" - ] - ] - }, - { - "question": "What is the payment method used for the order with parent_id 203?", - "answer": "Check / Money order", - "sql": "SELECT additional_information FROM sales_order_payment WHERE parent_id = 203;", - "sql_execute_result": [ - [ - "{\"method_title\":\"Check \\/ Money order\"}" - ] - ] - }, - { - "question": "What is the SKU of the product that has entity_id 948?", - "answer": "The SKU of the product with entity_id 948 is MSH06-36-Gray.", - "sql": "SELECT sku FROM catalog_product_entity WHERE entity_id = 948;", - "sql_execute_result": [ - [ - "MSH06-36-Gray" - ] - ] - }, - { - "question": "Find the product name with product_id 865 from the bestsellers aggregated yearly table for the period 2023-01-01.", - "answer": "The product name with product_id 865 for the period 2023-01-01 is 'Aether Gym Pant -36-Brown'.", - "sql": "SELECT product_name FROM sales_bestsellers_aggregated_yearly WHERE product_id = 865 AND period = '2023-01-01';", - "sql_execute_result": [ - [ - "Aether Gym Pant -36-Brown" - ], - [ - "Aether Gym Pant -36-Brown" - ] - ] - }, - { - "question": "What is the current stock quantity for the product with product_id 2040?", - "answer": "The current stock quantity for the product with product_id 2040 is 0.", - "sql": "SELECT qty FROM cataloginventory_stock_item WHERE product_id = 2040;", - "sql_execute_result": [ - [ - "0.0000" - ] - ] - }, - { - "question": "Get the total number of orders with status 'pending'.", - "answer": 10, - "sql": "SELECT COUNT(*) FROM sales_order WHERE status = 'pending';", - "sql_execute_result": [ - [ - 10 - ] - ] - }, - { - "question": "What is the current stock quantity for the product with ID 2040?", - "answer": "The current stock quantity for the product with ID 2040 is 0.", - "sql": "SELECT qty FROM cataloginventory_stock_item WHERE product_id = 2040;", - "sql_execute_result": [ - [ - "0.0000" - ] - ] - }, - { - "question": "What is the SKU for the product with entity_id 2040?", - "answer": "The SKU for the product with entity_id 2040 is WSH12.", - "sql": "SELECT sku FROM catalog_product_entity WHERE entity_id = 2040;", - "sql_execute_result": [ - [ - "WSH12" - ] - ] - }, - { - "question": "How many products are associated with category ID 258?", - "answer": 0, - "sql": "SELECT COUNT(*) FROM catalog_category_product WHERE category_id = 258;", - "sql_execute_result": [ - [ - 0 - ] - ] - }, - { - "question": "What is the average product price in the sales_bestsellers_aggregated_monthly table for store_id 0?", - "answer": 41.31, - "sql": "SELECT AVG(product_price) FROM sales_bestsellers_aggregated_monthly WHERE store_id = 0;", - "sql_execute_result": [ - [ - "41.31182556" - ] - ] - }, - { - "question": "What is the SKU of the product with entity ID 1013?", - "answer": "MSH11-36-Blue", - "sql": "SELECT sku FROM catalog_product_entity WHERE entity_id = 1013;", - "sql_execute_result": [ - [ - "MSH11-36-Blue" - ] - ] - }, - { - "question": "How many results were returned for the search query 'hollister'?", - "answer": 1, - "sql": "SELECT num_results FROM search_query WHERE query_text = 'hollister';", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "What is the name of the customer with customer ID 5?", - "answer": "Sarah Miller", - "sql": "SELECT CONCAT(firstname, ' ', lastname) AS name FROM customer_entity WHERE entity_id = 5;", - "sql_execute_result": [ - [ - "Sarah Miller" - ] - ] - }, - { - "question": "What is the total stock quantity for product ID 143?", - "answer": "The total stock quantity for product ID 143 is 100.", - "sql": "SELECT qty FROM cataloginventory_stock_item WHERE product_id = 143;", - "sql_execute_result": [ - [ - "100.0000" - ] - ] - }, - { - "question": "What is the category path for the category with entity ID 29?", - "answer": "The category path for the category with entity ID 29 is '1/2/29'.", - "sql": "SELECT path FROM catalog_category_entity WHERE entity_id = 29;", - "sql_execute_result": [ - [ - "1/2/29" - ] - ] - }, - { - "question": "What is the base price of the product with entity ID 918?", - "answer": "The base price of the product with entity ID 918 is 32.50.", - "sql": "SELECT value FROM catalog_product_entity_decimal WHERE entity_id = 918 AND attribute_id = 77;", - "sql_execute_result": [ - [ - "32.500000" - ] - ] - }, - { - "question": "What is the email address of the customer with entity ID 3?", - "answer": "jane.doe@hotmail.com", - "sql": "SELECT email FROM customer_entity WHERE entity_id = 3;", - "sql_execute_result": [ - [ - "jane.doe@hotmail.com" - ] - ] - }, - { - "question": "What is the SKU of the product with entity_id 1536?", - "answer": "The SKU of the product with entity_id 1536 is WS11-L-Yellow.", - "sql": "SELECT sku FROM catalog_product_entity WHERE entity_id = 1536;", - "sql_execute_result": [ - [ - "WS11-L-Yellow" - ] - ] - }, - { - "question": "Find the email address associated with the order having address entity_id 389.", - "answer": "lisa.kim@gmail.com", - "sql": "SELECT email FROM sales_order_address WHERE entity_id = 389;", - "sql_execute_result": [ - [ - "lisa.kim@gmail.com" - ] - ] - }, - { - "question": "What is the product name of the bestseller item with product_id 1980 in the year 2022?", - "answer": "Bess Yoga Short-32-Blue", - "sql": "SELECT product_name FROM sales_bestsellers_aggregated_yearly WHERE product_id = 1980 AND period = '2022-01-01';", - "sql_execute_result": [ - [ - "Bess Yoga Short-32-Blue" - ], - [ - "Bess Yoga Short-32-Blue" - ] - ] - }, - { - "question": "What is the value associated with the option having option_id 114?", - "answer": "Track Pants", - "sql": "SELECT value FROM eav_attribute_option_value WHERE option_id = 114;", - "sql_execute_result": [ - [ - "Track Pants" - ] - ] - }, - { - "question": "What is the street address for the shipping address with entity_id 299?", - "answer": "789 Rodeo Drive", - "sql": "SELECT street FROM sales_order_address WHERE entity_id = 299;", - "sql_execute_result": [ - [ - "789 Rodeo Drive" - ] - ] - }, - { - "question": "What is the rating position of the product 'Kratos Gym Pant-32-Green' for the year 2022?", - "answer": [ - 93, - 214 - ], - "sql": "SELECT rating_pos FROM sales_bestsellers_aggregated_yearly WHERE product_name = 'Kratos Gym Pant-32-Green' AND period = '2022-01-01';", - "sql_execute_result": [ - [ - 93 - ], - [ - 214 - ] - ] - }, - { - "question": "What is the first name associated with the billing address having entity_id 322?", - "answer": "Samantha", - "sql": "SELECT firstname FROM sales_order_address WHERE entity_id = 322;", - "sql_execute_result": [ - [ - "Samantha" - ] - ] - }, - { - "question": "Find the SKU of the product with entity_id 1268.", - "answer": "WJ04", - "sql": "SELECT sku FROM catalog_product_entity WHERE entity_id = 1268;", - "sql_execute_result": [ - [ - "WJ04" - ] - ] - }, - { - "question": "What is the created timestamp for the product with SKU 'MJ07-XS-Black'?", - "answer": "The created timestamp for the product with SKU 'MJ07-XS-Black' is 2023-04-19 16:13:28.", - "sql": "SELECT created_at FROM catalog_product_entity WHERE sku = 'MJ07-XS-Black';", - "sql_execute_result": [ - [ - "2023-04-19 16:13:28" - ] - ] - }, - { - "question": "What is the postal code for the address with entity_id 346?", - "answer": "10001", - "sql": "SELECT postcode FROM sales_order_address WHERE entity_id = 346;", - "sql_execute_result": [ - [ - "10001" - ] - ] - }, - { - "question": "What is the status code for the review status with ID 3?", - "answer": "Not Approved", - "sql": "SELECT status_code FROM review_status WHERE status_id = 3;", - "sql_execute_result": [ - [ - "Not Approved" - ] - ] - }, - { - "question": "How many orders were canceled on 2022-11-22?", - "answer": 2, - "sql": "SELECT orders_count FROM sales_order_aggregated_created WHERE period = '2022-11-22' AND order_status = 'canceled';", - "sql_execute_result": [ - [ - 1 - ], - [ - 1 - ] - ] - }, - { - "question": "Find the default name for the region with the code 'VE-D'.", - "answer": "Aragua", - "sql": "SELECT default_name FROM directory_country_region WHERE code = 'VE-D';", - "sql_execute_result": [ - [ - "Aragua" - ] - ] - }, - { - "question": "What is the total income amount for store ID 1 on 2022-07-01?", - "answer": "The total income amount for store ID 1 on 2022-07-01 is 168.8000.", - "sql": "SELECT total_income_amount FROM sales_order_aggregated_created WHERE period = '2022-07-01' AND store_id = 1;", - "sql_execute_result": [ - [ - "168.8000" - ] - ] - }, - { - "question": "What is the frontend label for the attribute with the code 'performance_fabric'?", - "answer": "Performance Fabric", - "sql": "SELECT frontend_label FROM eav_attribute WHERE attribute_code = 'performance_fabric';", - "sql_execute_result": [ - [ - "Performance Fabric" - ] - ] - }, - { - "question": "What is the price value for the product with entity ID 1489?", - "answer": "The price value for the product with entity ID 1489 is 32.00.", - "sql": "SELECT value FROM catalog_product_entity_decimal WHERE entity_id = 1489 AND attribute_id = 77;", - "sql_execute_result": [ - [ - "32.000000" - ] - ] - }, - { - "question": "What is the attribute code for the attribute with ID 46?", - "answer": "The attribute code for the attribute with ID 46 is 'is_active'.", - "sql": "SELECT attribute_code FROM eav_attribute WHERE attribute_id = 46;", - "sql_execute_result": [ - [ - "is_active" - ] - ] - }, - { - "question": "Which country has the region code 'BG-23'?", - "answer": "The country with the region code 'BG-23' is BG.", - "sql": "SELECT country_id FROM directory_country_region WHERE code = 'BG-23';", - "sql_execute_result": [ - [ - "BG" - ] - ] - }, - { - "question": "What is the frontend input type for the attribute with the code 'price_type'?", - "answer": "The frontend input type for the attribute with the code 'price_type' is 'boolean'.", - "sql": "SELECT frontend_input FROM eav_attribute WHERE attribute_code = 'price_type';", - "sql_execute_result": [ - [ - "boolean" - ] - ] - }, - { - "question": "Find the total shipping amount for orders on 2023-02-22.", - "answer": "The total shipping amounts for orders on 2023-02-22 are 25.0000 and 25.0000.", - "sql": "SELECT total_shipping_amount FROM sales_order_aggregated_created WHERE period = '2023-02-22';", - "sql_execute_result": [ - [ - "25.0000" - ], - [ - "25.0000" - ] - ] - } -] \ No newline at end of file diff --git a/random_sample/generated_tasks.json.bak3 b/random_sample/generated_tasks.json.bak3 deleted file mode 100644 index c6e30ca..0000000 --- a/random_sample/generated_tasks.json.bak3 +++ /dev/null @@ -1,9329 +0,0 @@ -[ - { - "question": "What is the product name for the SKU 'miko-pullover-hoodie-xs-orange'?", - "answer": [ - "miko-pullover-hoodie-xs-orange" - ], - "sql": "SELECT value FROM catalog_product_entity_varchar WHERE entity_id = 1078 AND attribute_id = 121;", - "sql_execute_result": [ - [ - "miko-pullover-hoodie-xs-orange" - ] - ] - }, - { - "question": "What is the description for the product with ID 937?", - "answer": [ - "

What more do you need than a sporty yoga short made with organic cotton and a little spandex for mobility? The Hawkeye Yoga Short brings a stylish, standard fit you can sport with confidence outside the studio.

\n

• Dark gray shorts with red accents.
• 92% Organic Cotton 8% Spandex.
• Breathable stretch organic cotton.
• Medium=8.0\" (21.0cm) inseam.

" - ], - "sql": "SELECT value FROM catalog_product_entity_text WHERE entity_id = 937 AND attribute_id = 75;", - "sql_execute_result": [ - [ - "

What more do you need than a sporty yoga short made with organic cotton and a little spandex for mobility? The Hawkeye Yoga Short brings a stylish, standard fit you can sport with confidence outside the studio.

\n

• Dark gray shorts with red accents.
• 92% Organic Cotton 8% Spandex.
• Breathable stretch organic cotton.
• Medium=8.0\" (21.0cm) inseam.

" - ] - ] - }, - { - "question": "How many orders were completed on 2022-06-21 in store ID 1?", - "answer": [ - "1" - ], - "sql": "SELECT orders_count FROM sales_order_aggregated_created WHERE store_id = 1 AND period = '2022-06-21' AND order_status = 'complete';", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "What is the value of the attribute with ID 137 for the product with ID 43?", - "answer": [ - "9,16,5,11" - ], - "sql": "SELECT value FROM catalog_product_entity_text WHERE entity_id = 43 AND attribute_id = 137;", - "sql_execute_result": [ - [ - "9,16,5,11" - ] - ] - }, - { - "question": "How many total income amounts were found for orders completed on 2022-11-03?", - "answer": [ - "2" - ], - "sql": "SELECT total_income_amount FROM sales_order_aggregated_created WHERE period = '2022-11-03' AND order_status = 'complete';", - "sql_execute_result": [ - [ - "198.6400" - ], - [ - "198.6400" - ] - ] - }, - { - "question": "What is the attribute code for attribute ID 93 in the catalog EAV attribute?", - "answer": [ - "visual" - ], - "sql": "SELECT JSON_UNQUOTE(JSON_EXTRACT(additional_data, '$.swatch_input_type')) AS attribute_code FROM catalog_eav_attribute WHERE attribute_id = 93;", - "sql_execute_result": [ - [ - "visual" - ] - ] - }, - { - "question": "How many product names have the attribute ID 106 and value 'container2'?", - "answer": [ - "2051" - ], - "sql": "SELECT value FROM catalog_product_entity_varchar WHERE attribute_id = 106 AND value = 'container2';", - "sql_execute_result": [ - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - 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], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ], - [ - "container2" - ] - ] - }, - { - "question": "Is the sequence profile with ID 8 active?", - "answer": [ - "1" - ], - "sql": "SELECT is_active FROM sales_sequence_profile WHERE profile_id = 8;", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "What is the discount amount for orders pending on 2023-05-31?", - "answer": [ - "48.6000", - "48.6000" - ], - "sql": "SELECT total_discount_amount FROM sales_order_aggregated_created WHERE period = '2023-05-31' AND order_status = 'pending';", - "sql_execute_result": [ - [ - "48.6000" - ], - [ - "48.6000" - ] - ] - }, - { - "question": "What is the name of the store with store ID 1?", - "answer": [ - "Default Store View" - ], - "sql": "SELECT name FROM store WHERE store_id = 1;", - "sql_execute_result": [ - [ - "Default Store View" - ] - ] - }, - { - "question": "Find the stock quantity for product with ID 526.", - "answer": [ - "0.0000" - ], - "sql": "SELECT qty FROM cataloginventory_stock_item WHERE product_id = 526;", - "sql_execute_result": [ - [ - "0.0000" - ] - ] - }, - { - "question": "List all shipment items for shipment with parent ID 3.", - "answer": [ - "Troy Yoga Short", - "MSH09-36-Black", - "24.0000", - "Eos V-Neck Hoodie", - "WH11-S-Blue", - "54.0000" - ], - "sql": "SELECT name, sku, price FROM sales_shipment_item WHERE parent_id = 3;", - "sql_execute_result": [ - [ - "Troy Yoga Short", - "MSH09-36-Black", - "24.0000" - ], - [ - "Eos V-Neck Hoodie", - "WH11-S-Blue", - "54.0000" - ] - ] - }, - { - "question": "What is the name of the region with region ID 767?", - "answer": [ - "Sj\u00e6lland" - ], - "sql": "SELECT name FROM directory_country_region_name WHERE region_id = 767 AND locale = 'en_US';", - "sql_execute_result": [ - [ - "Sj\u00e6lland" - ] - ] - }, - { - "question": "Is the store with code 'admin' active?", - "answer": [ - "1" - ], - "sql": "SELECT is_active FROM store WHERE code = 'admin';", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "Find the value of the rating option with option ID 13.", - "answer": [ - "3" - ], - "sql": "SELECT value FROM rating_option WHERE option_id = 13;", - "sql_execute_result": [ - [ - 3 - ] - ] - }, - { - "question": "Get the stock status for product ID 1918.", - "answer": [ - "1" - ], - "sql": "SELECT is_in_stock FROM cataloginventory_stock_item WHERE product_id = 1918;", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "What locale is used for the region with name 'Madrid'?", - "answer": [ - "en_US" - ], - "sql": "SELECT locale FROM directory_country_region_name WHERE name = 'Madrid';", - "sql_execute_result": [ - [ - "en_US" - ] - ] - }, - { - "question": "What is the status of the review with ID 240?", - "answer": [ - "Approved" - ], - "sql": "SELECT status_code FROM review JOIN review_status ON review.status_id = review_status.status_id WHERE review.review_id = 240;", - "sql_execute_result": [ - [ - "Approved" - ] - ] - }, - { - "question": "Find the full address for customer with address ID 51.", - "answer": [ - "444 Beacon St, Boston, Massachusetts, 02108, US" - ], - "sql": "SELECT CONCAT(street, ', ', city, ', ', region, ', ', postcode, ', ', country_id) AS full_address FROM customer_address_entity WHERE entity_id = 51;", - "sql_execute_result": [ - [ - "444 Beacon St, Boston, Massachusetts, 02108, US" - ] - ] - }, - { - "question": "What is the SKU of the product with entity ID 533?", - "answer": [ - "MS06-M-Blue" - ], - "sql": "SELECT sku FROM catalog_product_entity WHERE entity_id = 533;", - "sql_execute_result": [ - [ - "MS06-M-Blue" - ] - ] - }, - { - "question": "Find all categories under the path '1/2/7'.", - "answer": [ - "34", - "35", - "36", - "39", - "40", - "8" - ], - "sql": "SELECT entity_id, path FROM catalog_category_entity WHERE path LIKE '1/2/7/%';", - "sql_execute_result": [ - [ - 34, - "1/2/7/34" - ], - [ - 35, - "1/2/7/35" - ], - [ - 36, - "1/2/7/36" - ], - [ - 39, - "1/2/7/39" - ], - [ - 40, - "1/2/7/40" - ], - [ - 8, - "1/2/7/8" - ] - ] - }, - { - "question": "What is the position of category with entity ID 37?", - "answer": [ - "6" - ], - "sql": "SELECT position FROM catalog_category_entity WHERE entity_id = 37;", - "sql_execute_result": [ - [ - 6 - ] - ] - }, - { - "question": "Who is the customer with the telephone number 3035551212?", - "answer": [ - "Jason Miller", - "Olivia Jackson", - "Nathan Chen" - ], - "sql": "SELECT CONCAT(firstname, ' ', lastname) AS name FROM customer_address_entity WHERE telephone = '3035551212';", - "sql_execute_result": [ - [ - "Jason Miller" - ], - [ - "Olivia Jackson" - ], - [ - "Nathan Chen" - ] - ] - }, - { - "question": "Check if the customer address ID 2 is active.", - "answer": [ - "1" - ], - "sql": "SELECT is_active FROM customer_address_entity WHERE entity_id = 2;", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "What is the name of the region with region ID 32?", - "answer": [ - "Massachusetts" - ], - "sql": "SELECT default_name FROM directory_country_region WHERE region_id = 32;", - "sql_execute_result": [ - [ - "Massachusetts" - ] - ] - }, - { - "question": "How many products were found?", - "answer": [ - "67" - ], - "sql": "SELECT entity_id, sku FROM catalog_product_entity WHERE created_at = '2023-04-19 16:13:41';", - "sql_execute_result": [ - [ - 1079, - "WH04-XS-Purple" - ], - [ - 1080, - "WH04-S-Blue" - ], - [ - 1081, - "WH04-S-Orange" - ], - [ - 1082, - "WH04-S-Purple" - ], - [ - 1083, - "WH04-M-Blue" - ], - [ - 1084, - "WH04-M-Orange" - ], - [ - 1085, - "WH04-M-Purple" - ], - [ - 1086, - "WH04-L-Blue" - ], - [ - 1087, - "WH04-L-Orange" - ], - [ - 1088, - "WH04-L-Purple" - ], - [ - 1089, - "WH04-XL-Blue" - ], - [ - 1090, - "WH04-XL-Orange" - ], - [ - 1091, - "WH04-XL-Purple" - ], - [ - 1092, - "WH04" - ], - [ - 1093, - "WH05-XS-Orange" - ], - [ - 1094, - "WH05-XS-Purple" - ], - [ - 1095, - "WH05-XS-White" - ], - [ - 1096, - "WH05-S-Orange" - ], - [ - 1097, - "WH05-S-Purple" - ], - [ - 1098, - "WH05-S-White" - ], - [ - 1099, - "WH05-M-Orange" - ], - [ - 1100, - "WH05-M-Purple" - ], - [ - 1101, - "WH05-M-White" - ], - [ - 1102, - "WH05-L-Orange" - ], - [ - 1103, - "WH05-L-Purple" - ], - [ - 1104, - "WH05-L-White" - ], - [ - 1105, - "WH05-XL-Orange" - ], - [ - 1106, - "WH05-XL-Purple" - ], - [ - 1107, - "WH05-XL-White" - ], - [ - 1108, - "WH05" - ], - [ - 1109, - "WH06-XS-Purple" - ], - [ - 1110, - "WH06-S-Purple" - ], - [ - 1111, - "WH06-M-Purple" - ], - [ - 1112, - "WH06-L-Purple" - ], - [ - 1113, - "WH06-XL-Purple" - ], - [ - 1114, - "WH06" - ], - [ - 1115, - "WH07-XS-Gray" - ], - [ - 1116, - "WH07-XS-Purple" - ], - [ - 1117, - "WH07-XS-White" - ], - [ - 1118, - "WH07-S-Gray" - ], - [ - 1119, - "WH07-S-Purple" - ], - [ - 1120, - "WH07-S-White" - ], - [ - 1121, - "WH07-M-Gray" - ], - [ - 1122, - "WH07-M-Purple" - ], - [ - 1123, - "WH07-M-White" - ], - [ - 1124, - "WH07-L-Gray" - ], - [ - 1125, - "WH07-L-Purple" - ], - [ - 1126, - "WH07-L-White" - ], - [ - 1127, - "WH07-XL-Gray" - ], - [ - 1128, - "WH07-XL-Purple" - ], - [ - 1129, - "WH07-XL-White" - ], - [ - 1130, - "WH07" - ], - [ - 1131, - "WH08-XS-Orange" - ], - [ - 1132, - "WH08-XS-Purple" - ], - [ - 1133, - "WH08-XS-White" - ], - [ - 1134, - "WH08-S-Orange" - ], - [ - 1135, - "WH08-S-Purple" - ], - [ - 1136, - "WH08-S-White" - ], - [ - 1137, - "WH08-M-Orange" - ], - [ - 1138, - "WH08-M-Purple" - ], - [ - 1139, - "WH08-M-White" - ], - [ - 1140, - "WH08-L-Orange" - ], - [ - 1141, - "WH08-L-Purple" - ], - [ - 1142, - "WH08-L-White" - ], - [ - 1143, - "WH08-XL-Orange" - ], - [ - 1144, - "WH08-XL-Purple" - ], - [ - 1145, - "WH08-XL-White" - ] - ] - }, - { - "question": "What is the default store ID for the 'Main Website Store' group?", - "answer": [ - "1" - ], - "sql": "SELECT default_store_id FROM store_group WHERE name = 'Main Website Store';", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "Find the product with the highest rating position in store 1 for the year 2022.", - "answer": [ - "Quest Lumaflex™ Band" - ], - "sql": "SELECT product_name FROM sales_bestsellers_aggregated_yearly WHERE store_id = 1 AND period = '2022-01-01' ORDER BY rating_pos ASC LIMIT 1;", - "sql_execute_result": [ - [ - "Quest Lumaflex™ Band" - ] - ] - }, - { - "question": "What is the entity type code for entity type ID 8?", - "answer": [ - "shipment" - ], - "sql": "SELECT entity_type_code FROM eav_entity_type WHERE entity_type_id = 8;", - "sql_execute_result": [ - [ - "shipment" - ] - ] - }, - { - "question": "List all the sequence values for invoices.", - "answer": [ - "1", - "2" - ], - "sql": "SELECT sequence_value FROM sequence_invoice_1;", - "sql_execute_result": [ - [ - 1 - ], - [ - 2 - ] - ] - }, - { - "question": "How many items were found?", - "answer": [ - "2" - ], - "sql": "SELECT product_price FROM sales_bestsellers_aggregated_yearly WHERE product_name = 'Aether Gym Pant -34-Brown';", - 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"answer": [ - "1.0000" - ], - "sql": "SELECT qty_ordered FROM sales_bestsellers_aggregated_yearly WHERE product_name = 'Antonia Racer Tank-L-Purple' AND store_id = 0;", - "sql_execute_result": [ - [ - "1.0000" - ] - ] - }, - { - "question": "What is the code for the 'Main Website Store' group?", - "answer": [ - "main_website_store" - ], - "sql": "SELECT code FROM store_group WHERE name = 'Main Website Store';", - "sql_execute_result": [ - [ - "main_website_store" - ] - ] - }, - { - "question": "What is the total grand total for completed orders for customer Grace Nguyen?", - "answer": [ - "143.8000", - "215.0000", - "196.2000", - "65.0000", - "198.6400", - "251.2400" - ], - "sql": "SELECT grand_total FROM sales_order WHERE customer_id = 18 AND state = 'complete';", - "sql_execute_result": [ - [ - "143.8000" - ], - [ - "215.0000" - ], - [ - "196.2000" - ], - [ - "65.0000" - ], - [ - "198.6400" - ], - [ - "251.2400" - ] - ] - }, - { - "question": "How many orders with status 'canceled' were found?", - "answer": [ - "142" - ], - "sql": "SELECT increment_id, CONCAT(customer_firstname, ' ', customer_lastname) AS customer_name, grand_total FROM sales_order WHERE status = 'canceled';", - "sql_execute_result": [ - [ - "000000001", - "Veronica Costello", - "36.3900" - ], - [ - "000000003", - "Brian Smith", - "160.2500" - ], - [ - "000000005", - "Grace Nguyen", - "137.0000" - ], - [ - "000000006", - "John Doe", - "53.0000" - ], - [ - "000000007", - "Adam Garcia", - "108.2500" - ], - [ - "000000008", - "Bob Johnson", - "146.0000" - ], - [ - "000000010", - "Matt Baker", - "107.6000" - ], - [ - "000000012", - "Jane Smith", - "113.8000" - ], - [ - "000000014", - "Bob Jones", - "85.2000" - ], - [ - "000000015", - "Sarah Miller", - "61.0000" - ], - [ - "000000018", - "Sophie Taylor", - "105.0000" - ], - [ - "000000019", - "Julia Williams", - "214.6000" - ], - [ - "000000025", - "John Doe", - "204.2500" - ], - [ - "000000026", - "Lucy Garcia", - "216.0000" - ], - [ - "000000029", - "Lucy Garcia", - "98.4000" - ], - [ - "000000030", - "Alex Martin", - "67.0000" - ], - [ - "000000038", - "Jason Miller", - "52.2000" - ], - [ - "000000039", - "Sarah Miller", - "218.8500" - ], - [ - "000000040", - "Lisa Kim", - "74.0000" - ], - [ - "000000041", - "Mary Martin", - "161.2500" - ], - [ - "000000042", - "Michael Nguyen", - "211.6000" - ], - [ - "000000044", - "Lisa Green", - "155.0000" - ], - [ - "000000046", - "Samantha Jones", - "139.0000" - ], - [ - "000000049", - "Alexander Thomas", - "205.0000" - ], - [ - "000000052", - "Brian Smith", - "48.0000" - ], - [ - "000000056", - "Adam Garcia", - "198.6000" - ], - [ - "000000058", - "John Lee", - "199.1000" - ], - [ - "000000059", - "John Smith", - "95.4000" - ], - [ - "000000060", - "Jennifer White", - "60.0000" - ], - [ - "000000063", - "Alex Martin", - "196.0000" - ], - [ - "000000066", - "Samantha Jones", - "162.2000" - ], - [ - "000000067", - "Lily Potter", - "194.7600" - ], - [ - "000000068", - "John Smith", - "211.8000" - ], - [ - "000000072", - "Jennifer White", - "178.0000" - ], - [ - "000000074", - "Adam Garcia", - "67.8000" - ], - [ - "000000076", - "Jane Smith", - "72.0000" - ], - [ - "000000077", - "Alex Johnson", - "104.0000" - ], - [ - "000000080", - "Grace Nguyen", - "37.5000" - ], - [ - "000000081", - "Samantha Jones", - "183.5900" - ], - [ - "000000085", - "Grace Nguyen", - "203.0000" - ], - [ - "000000086", - "John Doe", - "105.4000" - ], - [ - "000000088", - "Samantha Jones", - "168.8000" - ], - [ - "000000094", - "Alex Johnson", - "64.0000" - ], - [ - "000000095", - "John Smith", - "64.0000" - ], - [ - "000000098", - "Julia Williams", - "223.6000" - ], - [ - "000000101", - "Sarah Miller", - "199.8000" - ], - [ - "000000103", - "Jane Smith", - "71.5000" - ], - [ - "000000106", - "John Lee", - "99.0000" - ], - [ - "000000107", - "John Smith", - "45.0000" - ], - [ - "000000108", - "Alex Johnson", - "75.0000" - ], - [ - "000000109", - "Jason Miller", - "136.4000" - ], - [ - "000000110", - "Julia Williams", - "104.0000" - ], - [ - "000000111", - "Lily Potter", - "217.1200" - ], - [ - "000000117", - "John Lee", - "196.8000" - ], - [ - "000000118", - "Brian Smith", - "29.0000" - ], - [ - "000000120", - "Matt Baker", - "112.0000" - ], - [ - "000000122", - "Alexander Thomas", - "123.2000" - ], - [ - "000000123", - "Olivia Lee", - "209.0000" - ], - [ - "000000124", - "Sophie Taylor", - "193.6400" - ], - [ - "000000126", - "Grace Nguyen", - "207.0000" - ], - [ - "000000129", - "Olivia Lee", - "151.0000" - ], - [ - "000000132", - "Samantha Jones", - "161.8000" - ], - [ - "000000134", - "Daniel Jackson", - "64.0000" - ], - [ - "000000135", - "Jennifer White", - "141.0000" - ], - [ - "000000136", - "Lily Potter", - "208.2000" - ], - [ - "000000141", - "Matt Baker", - "167.0000" - ], - [ - "000000142", - "Julia Williams", - "87.0000" - ], - [ - "000000143", - "Brian Smith", - "95.0000" - ], - [ - "000000144", - "John Smith", - "171.0000" - ], - [ - "000000149", - "Ava Brown", - "34.0000" - ], - [ - "000000151", - "David Lee", - "217.2000" - ], - [ - "000000152", - "Alex Johnson", - "223.0000" - ], - [ - "000000153", - "Jane Doe", - "180.8000" - ], - [ - "000000157", - "Sarah Miller", - "44.0000" - ], - [ - "000000159", - "Daniel Jackson", - "29.0000" - ], - [ - "000000162", - "Olivia Lee", - "152.2000" - ], - [ - "000000165", - "Alex Martin", - "202.6000" - ], - [ - "000000167", - "Julia Williams", - "38.6000" - ], - [ - "000000168", - "Bob Jones", - "146.0000" - ], - [ - "000000170", - "Olivia Lee", - "66.0000" - ], - [ - "000000171", - "Samantha Nguyen", - "220.0000" - ], - [ - "000000172", - "Adam Garcia", - "64.0000" - ], - [ - "000000173", - "Alex Martin", - "94.2000" - ], - [ - "000000174", - "Emma Davis", - "45.8000" - ], - [ - "000000175", - "Katie Wong", - "205.6400" - ], - [ - "000000176", - "Lisa Kim", - "37.0000" - ], - [ - "000000177", - "Alex Martin", - "76.0000" - ], - [ - "000000178", - "Lisa Kim", - "64.0000" - ], - [ - "000000180", - "Samantha Jones", - "135.2000" - ], - [ - "000000183", - "Grace Nguyen", - "201.6000" - ], - [ - "000000185", - "Sarah Miller", - "37.0000" - ], - [ - "000000191", - "Alex Martin", - "95.0000" - ], - [ - "000000193", - "Bob Jones", - "224.4000" - ], - [ - "000000194", - "David Lee", - "94.0000" - ], - [ - "000000195", - "Lisa Kim", - "133.0000" - ], - [ - "000000198", - "Katie Wong", - "96.0000" - ], - [ - "000000204", - "Lucy Garcia", - "44.0000" - ], - [ - "000000206", - "Alexander Thomas", - "62.0000" - ], - [ - "000000209", - "Daniel Jackson", - "39.0000" - ], - [ - "000000210", - "John Lee", - "228.9900" - ], - [ - "000000211", - "Sophie Taylor", - "107.4000" - ], - [ - "000000212", - "Sophie Taylor", - "68.0000" - ], - [ - "000000219", - "Alexander Thomas", - "223.0000" - ], - [ - "000000220", - "Jane Doe", - "153.4000" - ], - [ - "000000221", - "Brian Smith", - "76.0000" - ], - [ - "000000222", - "Jane Smith", - "185.0000" - ], - [ - "000000224", - "Matt Baker", - "73.0000" - ], - [ - "000000226", - "Jane Smith", - "54.0000" - ], - [ - "000000227", - "Bob Johnson", - "27.0000" - ], - [ - "000000229", - "Sarah Miller", - "55.0000" - ], - [ - "000000232", - "Samantha Jones", - "143.0000" - ], - [ - "000000234", - "Lily Potter", - "50.0000" - ], - [ - "000000241", - "Alex Johnson", - "44.0000" - ], - [ - "000000242", - "Olivia Lee", - "183.0000" - ], - [ - "000000244", - "Alex Johnson", - "89.0000" - ], - [ - "000000245", - "Jane Doe", - "37.5000" - ], - [ - "000000246", - "Jane Smith", - "74.0000" - ], - [ - "000000248", - "Alexander Thomas", - "192.0000" - ], - [ - "000000249", - "Samantha Jones", - "125.0000" - ], - [ - "000000252", - "Ava Brown", - "65.0000" - ], - [ - "000000254", - "Michael Nguyen", - "145.5000" - ], - [ - "000000255", - "Lisa Kim", - "34.0000" - ], - [ - "000000259", - "Jane Doe", - "189.6000" - ], - [ - "000000261", - "Lily Potter", - "192.0000" - ], - [ - "000000265", - "Daniel Jackson", - "94.0000" - ], - [ - "000000266", - "Samantha Jones", - "183.1900" - ], - [ - "000000267", - "Julia Williams", - "117.0000" - ], - [ - "000000271", - "Jane Smith", - "77.0000" - ], - [ - "000000272", - "Michael Nguyen", - "82.0000" - ], - [ - "000000273", - "John Smith", - "190.0000" - ], - [ - "000000275", - "Alexander Thomas", - "195.4000" - ], - [ - "000000278", - "Jason Miller", - "37.0000" - ], - [ - "000000279", - "Samantha Nguyen", - "44.0000" - ], - [ - "000000280", - "Daniel Jackson", - "71.5000" - ], - [ - "000000283", - "Bob Jones", - "154.8000" - ], - [ - "000000289", - "Daniel Jackson", - "194.5000" - ], - [ - "000000290", - "Julia Williams", - "53.0000" - ], - [ - "000000291", - "Adam Garcia", - "43.4000" - ], - [ - "000000292", - "Jason Miller", - "121.0000" - ], - [ - "000000293", - "John Doe", - "208.0000" - ], - [ - "000000294", - "Alexander Thomas", - "171.0000" - ], - [ - "000000296", - "Lily Potter", - "23.0000" - ] - ] - }, - { - "question": "What is the label for the order status 'pending_payment'?", - "answer": [ - "Pending Payment" - ], - "sql": "SELECT label FROM sales_order_status WHERE status = 'pending_payment';", - "sql_execute_result": [ - [ - "Pending Payment" - ] - ] - }, - { - "question": "Determine the country ISO-3 code for the country with ISO-2 code 'PG'.", - "answer": [ - "PNG" - ], - "sql": "SELECT iso3_code FROM directory_country WHERE iso2_code = 'PG';", - "sql_execute_result": [ - [ - "PNG" - ] - ] - }, - { - "question": "Find the total number of items ordered in order with increment ID '000000287'.", - "answer": [ - "1.0000" - ], - "sql": "SELECT total_qty_ordered FROM sales_order WHERE increment_id = '000000287';", - "sql_execute_result": [ - [ - "1.0000" - ] - ] - }, - { - "question": "What is the value description for attribute option with ID 77?", - "answer": [ - "Reflective" - ], - "sql": "SELECT value FROM eav_attribute_option_value WHERE option_id = 77;", - "sql_execute_result": [ - [ - "Reflective" - ] - ] - }, - { - "question": "Which customer has the billing address in Beverly Hills?", - "answer": [ - "Jane Smith", - "Alex Johnson", - "Julie Nguyen" - ], - "sql": "SELECT CONCAT(firstname, ' ', lastname) FROM customer_address_entity WHERE city = 'Beverly Hills';", - "sql_execute_result": [ - [ - "Jane Smith" - ], - [ - "Alex Johnson" - ], - [ - "Julie Nguyen" - ] - ] - }, - { - "question": "What is the shipping method used for order with increment ID '000000114'?", - "answer": [ - "flatrate_flatrate" - ], - "sql": "SELECT shipping_method FROM sales_order WHERE increment_id = '000000114';", - "sql_execute_result": [ - [ - "flatrate_flatrate" - ] - ] - }, - { - "question": "Identify the name of the customer who placed the order with increment ID '000000023'.", - "answer": [ - "Alex Martin" - ], - "sql": "SELECT CONCAT(customer_firstname, ' ', customer_lastname) AS customer_name FROM sales_order WHERE increment_id = '000000023';", - "sql_execute_result": [ - [ - "Alex Martin" - ] - ] - }, - { - "question": "What is the stock quantity for the product with ID 1819?", - "answer": [ - "0.0000" - ], - "sql": "SELECT qty FROM cataloginventory_stock_item WHERE product_id = 1819;", - "sql_execute_result": [ - [ - "0.0000" - ] - ] - }, - { - "question": "Find the name of the store with store ID 0.", - "answer": [ - "Admin" - ], - "sql": "SELECT name FROM store WHERE store_id = 0;", - "sql_execute_result": [ - [ - "Admin" - ] - ] - }, - { - "question": "What is the default name for the region with region ID 273?", - "answer": [ - "Essonne" - ], - "sql": "SELECT default_name FROM directory_country_region WHERE region_id = 273;", - "sql_execute_result": [ - [ - "Essonne" - ] - ] - }, - { - "question": "What is the sort order for the attribute option with option ID 134?", - "answer": [ - "18" - ], - "sql": "SELECT sort_order FROM eav_attribute_option WHERE option_id = 134;", - "sql_execute_result": [ - [ - 18 - ] - ] - }, - { - "question": "Find the attribute ID for the integer value with value ID 9019.", - "answer": [ - "93" - ], - "sql": "SELECT attribute_id FROM catalog_product_entity_int WHERE value_id = 9019;", - "sql_execute_result": [ - [ - 93 - ] - ] - }, - { - "question": "Is the store with code 'default' active?", - "answer": [ - "1" - ], - "sql": "SELECT is_active FROM store WHERE code = 'default';", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "What is the value for the product attribute with entity ID 1263 and attribute ID 97?", - "answer": [ - "1" - ], - "sql": "SELECT value FROM catalog_product_entity_int WHERE entity_id = 1263 AND attribute_id = 97;", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "Find the region code for the region named 'Cremona'.", - "answer": [ - "CR" - ], - "sql": "SELECT code FROM directory_country_region WHERE default_name = 'Cremona';", - "sql_execute_result": [ - [ - "CR" - ] - ] - }, - { - "question": "What is the website ID associated with the store named 'Default Store View'?", - "answer": [ - "1" - ], - "sql": "SELECT website_id FROM store WHERE name = 'Default Store View';", - "sql_execute_result": [ - [ - 1 - ] - ] - }, - { - "question": "Find the product ID of the product with stock ID 1 and item ID 412.", - "answer": [ - "412" - ], - "sql": "SELECT product_id FROM cataloginventory_stock_item WHERE item_id = 412 AND stock_id = 1;", - "sql_execute_result": [ - [ - 412 - ] - ] - }, - { - "question": "What is the email address for customer with ID 45?", - "answer": [ - "amanda.kim@gmail.com" - ], - "sql": "SELECT email FROM customer_grid_flat WHERE entity_id = 45;", - "sql_execute_result": [ - [ - "amanda.kim@gmail.com" - ] - ] - }, - { - "question": "Find the full billing address for customer 'Bob Johnson'.", - "answer": [ - "123 Main St Richardson Texas 75080" - ], - "sql": "SELECT billing_full FROM customer_grid_flat WHERE name = 'Bob Johnson';", - "sql_execute_result": [ - [ - "123 Main St Richardson Texas 75080" - ] - ] - }, - { - "question": "What is the ISO-3 code for the country with ISO-2 code 'TM'?", - "answer": [ - "TKM" - ], - "sql": "SELECT iso3_code FROM directory_country WHERE iso2_code = 'TM';", - "sql_execute_result": [ - [ - "TKM" - ] - ] - }, - { - "question": "List all rating options for rating ID 4.", - "answer": [ - "16", - "1", - "1", - "17", - "2", - "2", - "18", - "3", - "3", - "19", - "4", - "4", - "20", - "5", - "5" - ], - "sql": "SELECT option_id, code, value FROM rating_option WHERE rating_id = 4;", - "sql_execute_result": [ - [ - 16, - "1", - 1 - ], - [ - 17, - "2", - 2 - ], - [ - 18, - "3", - 3 - ], - [ - 19, - "4", - 4 - ], - [ - 20, - "5", - 5 - ] - ] - }, - { - "question": "How many units of 'Impulse Duffle' were ordered in January 2023?", - "answer": [ - "2.0000", - "2.0000" - ], - "sql": "SELECT qty_ordered FROM sales_bestsellers_aggregated_monthly WHERE product_name = 'Impulse Duffle' AND period = '2023-01-01';", - "sql_execute_result": [ - [ - "2.0000" - ], - [ - "2.0000" - ] - ] - }, - { - "question": "Find the sort order for the attribute option with ID 179.", - "answer": [ - "19" - ], - "sql": "SELECT sort_order FROM eav_attribute_option WHERE option_id = 179;", - "sql_execute_result": [ - [ - 19 - ] - ] - }, - { - "question": "What is the billing telephone number for customer 'John Smith'?", - "answer": [ - "2058812302", - "2145551212" - ], - "sql": "SELECT billing_telephone FROM customer_grid_flat WHERE name = 'John Smith';", - "sql_execute_result": [ - [ - "2058812302" - ], - [ - "2145551212" - ] - ] - }, - { - "question": "Find the store ID associated with the 'Autumn Pullie-S-Green' product in April 2023.", - "answer": [ - "0", - "1" - ], - "sql": "SELECT store_id FROM sales_bestsellers_aggregated_monthly WHERE product_name = 'Autumn Pullie-S-Green' AND period = '2023-04-01';", - "sql_execute_result": [ - [ - 0 - ], - [ - 1 - ] - ] - }, - { - "question": "Which country has the ISO-3 code 'CIV'?", - "answer": [ - "CI" - ], - "sql": "SELECT country_id FROM directory_country WHERE iso3_code = 'CIV';", - "sql_execute_result": [ - [ - "CI" - ] - ] - }, - { - "question": "Get the billing city for customer with email 'isabella.santos@gmail.com'.", - "answer": [ - "Miami" - ], - "sql": "SELECT billing_city FROM customer_grid_flat WHERE email = 'isabella.santos@gmail.com';", - "sql_execute_result": [ - [ - "Miami" - ] - ] - }, - { - "question": "What is the email address of the customer for the shipping address on order with ID 123?", - "answer": [ - "soccerfanatic22@gmail.com" - ], - "sql": "SELECT email FROM sales_order_address WHERE parent_id = 123 AND address_type = 'shipping';", - "sql_execute_result": [ - [ - "soccerfanatic22@gmail.com" - ] - ] - }, - { - "question": "Find the SKU and name of the product with entity ID 392.", - "answer": [ - "Jupiter All-Weather Trainer -L-Blue" - ], - "sql": "SELECT value FROM catalog_product_entity_varchar WHERE entity_id = 392 AND attribute_id = 73;", - "sql_execute_result": [ - [ - "Jupiter All-Weather Trainer -L-Blue" - ] - ] - }, - { - "question": "What is the status label for the status 'pending' in the sales order status table?", - "answer": [ - "Pending" - ], - "sql": "SELECT label FROM sales_order_status WHERE status = 'pending';", - "sql_execute_result": [ - [ - "Pending" - ] - ] - }, - { - "question": "What is the option value for the option ID 52 in the attribute option value table?", - "answer": [ - "Gray" - ], - "sql": "SELECT value FROM eav_attribute_option_value WHERE option_id = 52;", - "sql_execute_result": [ - [ - "Gray" - ] - ] - }, - { - "question": "Find the sequence value for the most recent invoice.", - "answer": [ - "2" - ], - "sql": "SELECT sequence_value FROM sequence_invoice_1 ORDER BY sequence_value DESC LIMIT 1;", - "sql_execute_result": [ - [ - 2 - ] - ] - }, - { - "question": "What is the name of the product with attribute ID 106 and store ID 0 in the catalog product entity varchar table?", - "answer": [ - "container2" - ], - "sql": "SELECT value FROM catalog_product_entity_varchar WHERE attribute_id = 106 AND store_id = 0 LIMIT 1;", - "sql_execute_result": [ - [ - "container2" - ] - ] - }, - { - "question": "Retrieve the postcode for the billing address of order with ID 127.", - "answer": [ - "60606" - ], - "sql": "SELECT postcode FROM sales_order_address WHERE parent_id = 127 AND address_type = 'billing';", - "sql_execute_result": [ - [ - "60606" - ] - ] - }, - { - "question": "What is the name associated with the attribute option value ID 66?", - "answer": [ - "Shoulder" - ], - "sql": "SELECT value FROM eav_attribute_option_value WHERE value_id = 66;", - "sql_execute_result": [ - [ - "Shoulder" - ] - ] - }, - { - "question": "Find the total number of quotes for the customer with email 'john.smith.xyz@gmail.com'.", - "answer": [ - "26" - ], - "sql": "SELECT COUNT(*) FROM sales_order_address WHERE email = 'john.smith.xyz@gmail.com';", - "sql_execute_result": [ - [ - 26 - ] - ] - }, - { - "question": "What is the label for the sales order status 'paypal_canceled_reversal'?", - "answer": [ - "PayPal Canceled Reversal" - ], - "sql": "SELECT label FROM sales_order_status WHERE status = 'paypal_canceled_reversal';", - "sql_execute_result": [ - [ - "PayPal Canceled Reversal" - ] - ] - }, - { - "question": "What is the name of the region with code 'VD' in Switzerland?", - "answer": [ - "Vaud" - ], - "sql": "SELECT default_name FROM directory_country_region WHERE code = 'VD' AND country_id = 'CH';", - "sql_execute_result": [ - [ - "Vaud" - ] - ] - }, - { - "question": "Find the entity type associated with meta ID 5 in the sales sequence meta table.", - "answer": [ - "order" - ], - "sql": "SELECT entity_type FROM sales_sequence_meta WHERE meta_id = 5;", - "sql_execute_result": [ - [ - "order" - ] - ] - }, - { - "question": "Retrieve the description for the product with entity ID 508.", - "answer": [ - "

Like it's v-neck counterpart, the crew-neck Atomic Tee will get you to your goal and beyond with its many load-bearing features: ultra-lightweight, moisture-wicking Cocona® fabric, chafe-free flatlock seams and an ergonomic cut that moves with your body.

\n

• Red polyester tee.
• Crew neckline.
• Cocona® performance fabric.
• Machine wash/dry.

" - ], - "sql": "SELECT value FROM catalog_product_entity_text WHERE entity_id = 508 AND attribute_id = 75;", - "sql_execute_result": [ - [ - "

Like it's v-neck counterpart, the crew-neck Atomic Tee will get you to your goal and beyond with its many load-bearing features: ultra-lightweight, moisture-wicking Cocona® fabric, chafe-free flatlock seams and an ergonomic cut that moves with your body.

\n

• Red polyester tee.
• Crew neckline.
• Cocona® performance fabric.
• Machine wash/dry.

" - ] - ] - }, - { - "question": "What is the ISO3 code for the country with ISO2 code 'IT'?", - "answer": [ - "ITA" - ], - "sql": "SELECT iso3_code FROM directory_country WHERE iso2_code = 'IT';", - "sql_execute_result": [ - [ - "ITA" - ] - ] - }, - { - "question": "Find the sequence table associated with the meta ID 8 for shipments.", - "answer": [ - "sequence_shipment_1" - ], - "sql": "SELECT sequence_table FROM sales_sequence_meta WHERE meta_id = 8 AND entity_type = 'shipment';", - "sql_execute_result": [ - [ - "sequence_shipment_1" - ] - ] - }, - { - "question": "What is the next sequence value for invoice in store ID 1?", - "answer": [ - "2" - ], - "sql": "SELECT sequence_value FROM sequence_invoice_1 ORDER BY sequence_value DESC LIMIT 1;", - "sql_execute_result": [ - [ - 2 - ] - ] - }, - { - "question": "How many regions are in the country with ID 'EC'?", - "answer": [ - "24" - ], - "sql": "SELECT default_name FROM directory_country_region WHERE country_id = 'EC';", - "sql_execute_result": [ - [ - "Azuay" - ], - [ - "Bol\u00edvar" - ], - [ - "Ca\u00f1ar" - ], - [ - "Carchi" - ], - [ - "Chimborazo" - ], - [ - "Cotopaxi" - ], - [ - "El Oro" - ], - [ - "Esmeraldas" - ], - [ - "Gal\u00e1pagos" - ], - [ - "Guayas" - ], - [ - "Imbabura" - ], - [ - "Loja" - ], - [ - "Los R\u00edos" - ], - [ - "Manab\u00ed" - ], - [ - "Morona Santiago" - ], - [ - "Napo" - ], - [ - "Orellana" - ], - [ - "Pastaza" - ], - [ - "Pichincha" - ], - [ - "Santa Elena" - ], - [ - "Santo Domingo de los Ts\u00e1chilas" - ], - [ - "Sucumb\u00edos" - ], - [ - "Tungurahua" - ], - [ - "Zamora Chinchipe" - ] - ] - }, - { - "question": "What are the attribute IDs linked to the product entity ID 654 in the catalog product entity text table?", - "answer": [ - "75", - "139", - "152", - "155", - "156" - ], - "sql": "SELECT attribute_id FROM catalog_product_entity_text WHERE entity_id = 654;", - "sql_execute_result": [ - [ - 75 - ], - [ - 139 - ], - [ - 152 - ], - [ - 155 - ], - [ - 156 - ] - ] - }, - { - "question": "Retrieve the full description text for the product with entity ID 517.", - "answer": [ - "

The Balboa Persistence Tee is a must-have for any athlete, Philadelphia or elsewhere. We took the best of performance apparel, cut the fluff and boiled it down to the basics for a lightweight, quick-drying t-shirt.

\n

• Crew neckline.
• Semi-fitted.
• Cocona® performance fabric.
• Machine wash/dry.

" - ], - "sql": "SELECT value FROM catalog_product_entity_text WHERE entity_id = 517 AND attribute_id = 75;", - "sql_execute_result": [ - [ - "

The Balboa Persistence Tee is a must-have for any athlete, Philadelphia or elsewhere. We took the best of performance apparel, cut the fluff and boiled it down to the basics for a lightweight, quick-drying t-shirt.

\n

• Crew neckline.
• Semi-fitted.
• Cocona® performance fabric.
• Machine wash/dry.

" - ] - ] - }, - { - "question": "What is the sequence table for creditmemo in store ID 0?", - "answer": [ - "sequence_creditmemo_0" - ], - "sql": "SELECT sequence_table FROM sales_sequence_meta WHERE meta_id = 3 AND store_id = 0;", - "sql_execute_result": [ - [ - "sequence_creditmemo_0" - ] - ] - }, - { - "question": "What is the email address for customer with ID 5?", - "answer": [ - "helloworld@yahoo.com" - ], - "sql": "SELECT customer_email FROM sales_order WHERE customer_id = 5 LIMIT 1;", - "sql_execute_result": [ - [ - "helloworld@yahoo.com" - ] - ] - }, - { - "question": "How many orders were found for customer 'Grace Nguyen'?", - "answer": [ - "15" - ], - "sql": "SELECT entity_id FROM sales_order WHERE customer_firstname = 'Grace' AND customer_lastname = 'Nguyen';", - "sql_execute_result": [ - [ - 5 - ], - [ - 11 - ], - [ - 16 - ], - [ - 32 - ], - [ - 65 - ], - [ - 80 - ], - [ - 85 - ], - [ - 114 - ], - [ - 126 - ], - [ - 166 - ], - [ - 183 - ], - [ - 189 - ], - [ - 300 - ], - [ - 307 - ], - [ - 308 - ] - ] - }, - { - "question": "What is the status of the order with increment ID '000000248'?", - "answer": [ - "canceled" - ], - "sql": "SELECT status FROM sales_order WHERE increment_id = '000000248';", - "sql_execute_result": [ - [ - "canceled" - ] - ] - }, - { - "question": "What is the total grand total for orders with the status 'complete'?", - "answer": [ - "20625.5000" - ], - "sql": "SELECT SUM(grand_total) AS total_grand_total FROM sales_order WHERE status = 'complete';", - "sql_execute_result": [ - [ - "20625.5000" - ] - ] - }, - { - "question": "Find the text description for product with entity ID 1599.", - "answer": [ - "

A heavenly soft and stylish eco garment, the Electra Bra Top is perfect for wearing on its own as a hot yoga top or layered under a tank.

\n

• Gray rouched bra top.
• Attractive back straps feature contrasting motif fabric.
• Interior bra top is lined with breathable mesh.
• Elastic underband for superior support.
• Removable cup inserts.
• Chafe-free flat lock seams provide added comfort.

" - ], - "sql": "SELECT value FROM catalog_product_entity_text WHERE entity_id = 1599 AND attribute_id = 75;", - "sql_execute_result": [ - [ - "

A heavenly soft and stylish eco garment, the Electra Bra Top is perfect for wearing on its own as a hot yoga top or layered under a tank.

\n

• Gray rouched bra top.
• Attractive back straps feature contrasting motif fabric.
• Interior bra top is lined with breathable mesh.
• Elastic underband for superior support.
• Removable cup inserts.
• Chafe-free flat lock seams provide added comfort.

" - ] - ] - }, - { - "question": "Find the total quantity ordered for customer with email 'avidreader99@yahoo.com'.", - "answer": [ - "3.0000" - ], - "sql": "SELECT total_qty_ordered FROM sales_order WHERE customer_email = 'avidreader99@yahoo.com' LIMIT 1;", - "sql_execute_result": [ - [ - "3.0000" - ] - ] - }, - { - "question": "How many order statuses were found?", - "answer": [ - "12" - ], - "sql": "SELECT label FROM sales_order_status;", - "sql_execute_result": [ - [ - "Canceled" - ], - [ - "Closed" - ], - [ - "Complete" - ], - [ - "Suspected Fraud" - ], - [ - "On Hold" - ], - [ - "Payment Review" - ], - [ - "PayPal Canceled Reversal" - ], - [ - "PayPal Reversed" - ], - [ - "Pending" - ], - [ - "Pending Payment" - ], - [ - "Pending PayPal" - ], - [ - "Processing" - ] - ] - }, - { - "question": "What is the shipping method for order ID 199?", - "answer": [ - "flatrate_flatrate" - ], - "sql": "SELECT shipping_method FROM sales_order WHERE entity_id = 199;", - "sql_execute_result": [ - [ - "flatrate_flatrate" - ] - ] - }, - { - "question": "What is the email address for the customer with ID 31?", - "answer": [ - "jason.miller@yahoo.com" - ], - "sql": "SELECT customer_email FROM sales_order_grid WHERE customer_id = 31;", - "sql_execute_result": [ - [ - "jason.miller@yahoo.com" - ], - [ - "jason.miller@yahoo.com" - ], - [ - "jason.miller@yahoo.com" - ], - [ - "jason.miller@yahoo.com" - ], - [ - "jason.miller@yahoo.com" - ], - [ - "jason.miller@yahoo.com" - ], - [ - "jason.miller@yahoo.com" - ], - [ - "jason.miller@yahoo.com" - ], - [ - "jason.miller@yahoo.com" - ] - ] - }, - { - "question": "How many orders were found for the customer with email 'john.smith.xyz@gmail.com'?", - "answer": [ - "13" - ], - "sql": "SELECT increment_id FROM sales_order_grid WHERE customer_email = 'john.smith.xyz@gmail.com';", - "sql_execute_result": [ - [ - "000000009" - ], - [ - "000000059" - ], - [ - "000000068" - ], - [ - "000000079" - ], - [ - "000000093" - ], - [ - "000000095" - ], - [ - "000000096" - ], - [ - "000000107" - ], - [ - "000000115" - ], - [ - "000000144" - ], - [ - "000000217" - ], - [ - "000000257" - ], - [ - "000000273" - ] - ] - }, - { - "question": "Find the status of the order with increment ID '000000152'.", - "answer": [ - "canceled" - ], - "sql": "SELECT status FROM sales_order_grid WHERE increment_id = '000000152';", - "sql_execute_result": [ - [ - "canceled" - ] - ] - }, - { - "question": "What is the total quantity shipped for the shipment with increment ID '000000003'?", - "answer": [ - "2.0000" - ], - "sql": "SELECT total_qty FROM sales_shipment WHERE increment_id = '000000003';", - "sql_execute_result": [ - [ - "2.0000" - ] - ] - }, - { - "question": "List all reviews for the product with ID 1990.", - "answer": [ - "296", - "297" - ], - "sql": "SELECT review_id FROM review WHERE entity_pk_value = 1990;", - "sql_execute_result": [ - [ - 296 - ], - [ - 297 - ] - ] - }, - { - "question": "What is the rating value for the review with ID 59?", - "answer": [ - "4" - ], - "sql": "SELECT value FROM rating_option_vote WHERE review_id = 59;", - "sql_execute_result": [ - [ - 4 - ] - ] - }, - { - "question": "Find the store name for the order with increment ID '000000013'.", - "answer": [ - "Main Website\nMain Website Store\nDefault Store View" - ], - "sql": "SELECT store_name FROM sales_order_grid WHERE increment_id = '000000013';", - "sql_execute_result": [ - [ - "Main Website\nMain Website Store\nDefault Store View" - ] - ] - }, - { - "question": "What is the shipping method for the order with ID 68?", - "answer": [ - "Flat Rate - Fixed" - ], - "sql": "SELECT shipping_information FROM sales_order_grid WHERE entity_id = 68;", - "sql_execute_result": [ - [ - "Flat Rate - Fixed" - ] - ] - }, - { - "question": "Find the status for the review with ID 263.", - "answer": [ - "1" - ], - "sql": "SELECT status_id FROM review WHERE review_id = 263;", - "sql_execute_result": [ - [ - 1 - ] - ] - } -] \ No newline at end of file diff --git a/random_sample/requirements.txt b/random_sample/requirements.txt new file mode 100644 index 0000000..78204fa --- /dev/null +++ b/random_sample/requirements.txt @@ -0,0 +1,3 @@ +mysql-connector==2.2.9 +openai +dotenv \ No newline at end of file diff --git a/random_sample/tes1.json b/random_sample/tes1.json new file mode 100644 index 0000000..a3ac9d7 --- /dev/null +++ b/random_sample/tes1.json @@ -0,0 +1,1088 @@ +[ + { + "question": "What is the email address for the customer with ID 8?", + "sql": "SELECT email FROM customer_entity WHERE entity_id = 8;", + "answer": [ + "marym@gmail.com" + ], + "sql_execute_result": [ + [ + "marym@gmail.com" + ] + ] + }, + { + "question": "How many products are in the category with ID 32?", + "sql": "SELECT product_id FROM catalog_category_product WHERE category_id = 32;", + "answer": [ + "247" + ], + "sql_execute_result": [ + [ + 725 + ], + [ + 726 + ], + [ + 727 + ], + [ + 728 + ], + [ + 729 + ], + [ + 730 + ], + [ + 731 + ], + [ + 732 + ], + [ + 733 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Doe'?", + "sql": "SELECT COUNT(*) FROM sales_order WHERE customer_id = 46;", + "answer": [ + "0" + ], + "sql_execute_result": [ + [ + 0 + ] + ] + }, + { + "question": "What is the total quantity of product with SKU 'BRT-001' currently in stock?", + "sql": "SELECT qty FROM cataloginventory_stock_item WHERE product_id = 1805;", + "answer": [ + "100.0000" + ], + "sql_execute_result": [ + [ + "100.0000" + ] + ] + }, + { + "question": "Find the description of the product with entity ID 764.", + "sql": "SELECT value FROM catalog_product_entity_text WHERE entity_id = 764 AND attribute_id = 75;", + "answer": [ + "

Turn the corner and open it up -- your driveway is two blocks away. The Supernova Sport Pant gets you there with key features like moisture-wicking LumaTech™ fabric and mesh ventilation. Side seam pockets ensure total convenience during rest periods.

\n

• Dark heather gray straight leg cotton pants.
• Relaxed fit.
• Internal drawstring.
• Machine wash/dry.

" + ], + "sql_execute_result": [ + [ + "

Turn the corner and open it up -- your driveway is two blocks away. The Supernova Sport Pant gets you there with key features like moisture-wicking LumaTech™ fabric and mesh ventilation. Side seam pockets ensure total convenience during rest periods.

\n

• Dark heather gray straight leg cotton pants.
• Relaxed fit.
• Internal drawstring.
• Machine wash/dry.

" + ] + ] + }, + { + "question": "What is the billing address for the order with parent ID 279?", + "sql": "SELECT CONCAT(street, ', ', city, ', ', region, ', ', postcode, ', ', country_id) FROM sales_order_address WHERE parent_id = 279 AND address_type = 'billing';", + "answer": [ + "123 Main St, Dallas, Texas, 75201, US" + ], + "sql_execute_result": [ + [ + "123 Main St, Dallas, Texas, 75201, US" + ] + ] + }, + { + "question": "How many units of the product with SKU 'WS08-XS-Blue' are in stock?", + "sql": "SELECT qty FROM cataloginventory_stock_item JOIN catalog_product_entity ON cataloginventory_stock_item.product_id = catalog_product_entity.entity_id WHERE catalog_product_entity.sku = 'WS08-XS-Blue';", + "answer": [ + "3" + ], + "sql_execute_result": [ + [ + "3.0000" + ] + ] + }, + { + "question": "Which customer groups are available?", + "sql": "SELECT customer_group_code FROM customer_group;", + "answer": [ + "NOT LOGGED IN", + "General", + "Wholesale", + "Retailer" + ], + "sql_execute_result": [ + [ + "NOT LOGGED IN" + ], + [ + "General" + ], + [ + "Wholesale" + ], + [ + "Retailer" + ] + ] + }, + { + "question": "What is the price including tax for 'Iris Workout Top'?", + "sql": "SELECT price_incl_tax FROM sales_invoice_item WHERE name = 'Iris Workout Top';", + "answer": [ + "31.3900" + ], + "sql_execute_result": [ + [ + "31.3900" + ] + ] + }, + { + "question": "Find the region name for region ID 1023 in the US locale.", + "sql": "SELECT name FROM directory_country_region_name WHERE region_id = 1023 AND locale = 'en_US';", + "answer": [ + "\u015bwi\u0119tokrzyskie" + ], + "sql_execute_result": [ + [ + "\u015bwi\u0119tokrzyskie" + ] + ] + }, + { + "question": "How many unique billing addresses for orders shipped to California were found?", + "sql": "SELECT CONCAT(street, ', ', city, ', ', region, ', ', postcode, ', ', country_id) FROM sales_order_address WHERE region = 'California' AND address_type = 'billing';", + "answer": [ + "7" + ], + 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"789 Rodeo Drive, Beverly Hills, California, 90212, US" + ], + [ + "456 Hollywood Blvd, Los Angeles, California, 90028, US" + ], + [ + "321 Maple Avenue, Oakland, California, 94602, US" + ], + [ + "321 Maple Avenue, Oakland, California, 94602, US" + ], + [ + "789 Rodeo Drive, Beverly Hills, California, 90212, US" + ], + [ + "456 Hollywood Blvd, Los Angeles, California, 90028, US" + ] + ] + } +] \ No newline at end of file diff --git a/random_sample/test1.json b/random_sample/test1.json new file mode 100644 index 0000000..3bd8edb --- /dev/null +++ b/random_sample/test1.json @@ -0,0 +1,282 @@ +[ + { + "question": "What is the total income amount for orders completed on February 14, 2022, in store 1?", + "sql": "SELECT total_income_amount FROM sales_order_aggregated_created WHERE period = '2022-02-14' AND store_id = 1 AND order_status = 'complete';", + "answer": [ + "240.0000" + ], + "sql_execute_result": [ + [ + "240.0000" + ] + ] + }, + { + "question": "Find the email address for the shipping address with entity ID 197.", + "sql": "SELECT email FROM sales_order_address WHERE entity_id = 197;", + "answer": [ + "janesmith456@yahoo.com" + ], + "sql_execute_result": [ + [ + "janesmith456@yahoo.com" + ] + ] + }, + { + "question": "What is the name of the product with ID 16 that was a bestseller in March 2023?", + "sql": "SELECT product_name FROM sales_bestsellers_aggregated_monthly WHERE product_id = 16 AND period = '2023-03-01';", + "answer": [ + "Dual Handle Cardio Ball" + ], + "sql_execute_result": [ + [ + "Dual Handle Cardio Ball" + ], + [ + "Dual Handle Cardio Ball" + ] + ] + }, + { + "question": "What is the value associated with the attribute option ID 80?", + "sql": "SELECT value FROM eav_attribute_option_value WHERE option_id = 80;", + "answer": [ + "Men" + ], + "sql_execute_result": [ + [ + "Men" + ] + ] + }, + { + "question": "Find the percentage rating for the review with ID 219.", + "sql": "SELECT percent FROM rating_option_vote WHERE review_id = 219;", + "answer": [ + "100" + ], + "sql_execute_result": [ + [ + 100 + ] + ] + }, + { + "question": "What is the total shipping amount for orders completed on July 1, 2022, in store 0?", + "sql": "SELECT total_shipping_amount FROM sales_order_aggregated_created WHERE period = '2022-07-01' AND store_id = 0 AND order_status = 'complete';", + "answer": [ + "15.0000" + ], + "sql_execute_result": [ + [ + "15.0000" + ] + ] + }, + { + "question": "What is the product price for the 'Zoe Tank-S-Yellow' that was a bestseller in January 2023?", + "sql": "SELECT product_price FROM sales_bestsellers_aggregated_monthly WHERE product_name = 'Zoe Tank-S-Yellow' AND period = '2023-01-01';", + "answer": [ + "29.0000" + ], + "sql_execute_result": [ + [ + "29.0000" + ], + [ + "29.0000" + ] + ] + }, + { + "question": "Find the total quantity ordered for orders that were canceled on February 24, 2023, in store 1.", + "sql": "SELECT total_qty_ordered FROM sales_order_aggregated_created WHERE period = '2023-02-24' AND store_id = 1 AND order_status = 'canceled';", + "answer": [ + "5.0000" + ], + "sql_execute_result": [ + [ + "5.0000" + ] + ] + }, + { + "question": "What is the region associated with the sales order address with entity ID 228?", + "sql": "SELECT region FROM sales_order_address WHERE entity_id = 228;", + "answer": [ + "Massachusetts" + ], + "sql_execute_result": [ + [ + "Massachusetts" + ] + ] + }, + { + "question": "Find the rating position for the product 'Sinbad Fitness Tank-M-Blue' in October 2022.", + "sql": "SELECT rating_pos FROM sales_bestsellers_aggregated_monthly WHERE product_name = 'Sinbad Fitness Tank-M-Blue' AND period = '2022-10-01';", + "answer": [ + "5", + "2" + ], + "sql_execute_result": [ + [ + 5 + ], + [ + 2 + ] + ] + }, + { + "question": "What is the ISO-3 code for the country with ISO-2 code 'VC'?", + "sql": "SELECT iso3_code FROM directory_country WHERE iso2_code = 'VC';", + "answer": [ + "VCT" + ], + "sql_execute_result": [ + [ + "VCT" + ] + ] + }, + { + "question": "How many orders were completed on 2022-01-17 in the default store?", + "sql": "SELECT orders_count FROM sales_order_aggregated_created WHERE period = '2022-01-17' AND store_id = 0 AND order_status = 'complete';", + "answer": [ + "2" + ], + "sql_execute_result": [ + [ + 2 + ] + ] + }, + { + "question": "Find the total quantity ordered for the product 'Gobi HeatTec\u00ae Tee-XS-Orange' in April 2023.", + "sql": "SELECT qty_ordered FROM sales_bestsellers_aggregated_monthly WHERE product_name = 'Gobi HeatTec® Tee-XS-Orange' AND period = '2023-04-01';", + "answer": [ + "4.0000" + ], + "sql_execute_result": [ + [ + "2.0000" + ], + [ + "2.0000" + ] + ] + }, + { + "question": "What is the product price for 'Cora Parachute Pant-29-Blue' in April 2023?", + "sql": "SELECT product_price FROM sales_bestsellers_aggregated_monthly WHERE product_name = 'Cora Parachute Pant-29-Blue' AND period = '2023-04-01';", + "answer": [ + "60.0000" + ], + "sql_execute_result": [ + [ + "60.0000" + ], + [ + "60.0000" + ] + ] + }, + { + "question": "List all countries that have an ISO-2 code starting with 'F'.", + "sql": "SELECT country_id FROM directory_country WHERE iso2_code LIKE 'F%';", + "answer": [ + "FI", + "FJ", + "FK", + "FM", + "FO", + "FR" + ], + "sql_execute_result": [ + [ + "FI" + ], + [ + "FJ" + ], + [ + "FK" + ], + [ + "FM" + ], + [ + "FO" + ], + [ + "FR" + ] + ] + }, + { + "question": "What is the rating position of 'Lando Gym Jacket-XS-Green' in May 2022?", + "sql": "SELECT rating_pos FROM sales_bestsellers_aggregated_monthly WHERE product_name = 'Lando Gym Jacket-XS-Green' AND period = '2022-05-01';", + "answer": [ + "5", + "18" + ], + "sql_execute_result": [ + [ + 5 + ], + [ + 18 + ] + ] + }, + { + "question": "How many products have a value of '2' for attribute ID 136 in the default store?", + "sql": "SELECT COUNT(*) FROM catalog_product_entity_int WHERE attribute_id = 136 AND store_id = 0 AND value = 2;", + "answer": [ + "2038" + ], + "sql_execute_result": [ + [ + 2038 + ] + ] + }, + { + "question": "Find the period for the order with ID 1003.", + "sql": "SELECT period FROM sales_order_aggregated_created WHERE id = 1003;", + "answer": [ + "2023-01-13" + ], + "sql_execute_result": [ + [ + "2023-01-13" + ] + ] + }, + { + "question": "What is the total income amount for orders on 2022-09-23 in store ID 1?", + "sql": "SELECT total_income_amount FROM sales_order_aggregated_created WHERE period = '2022-09-23' AND store_id = 1;", + "answer": [ + "210.0000" + ], + "sql_execute_result": [ + [ + "210.0000" + ] + ] + }, + { + "question": "Find the sequence value for the latest shipment entry.", + "sql": "SELECT sequence_value FROM sequence_shipment_1 ORDER BY sequence_value DESC LIMIT 1;", + "answer": [ + "3" + ], + "sql_execute_result": [ + [ + 3 + ] + ] + } +] \ No newline at end of file diff --git a/scripts/portforward.sh b/scripts/portforward.sh index 7ab957b..032c8e0 100644 --- a/scripts/portforward.sh +++ b/scripts/portforward.sh @@ -1,5 +1,5 @@ # 用于将网站的mysql端口转发到本地，保证稳定性 autossh -M 0 -f -N -o "ServerAliveInterval 30" \ -o "ServerAliveCountMax 3" \ - -L 23306:localhost:23306 yuyr@g14_jump2 + -L 23306:localhost:23306 yuyr@g14