yuyr/webrlvr
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

clean code

Browse Source
This commit is contained in:
yuyr 2025-06-17 09:53:40 +00:00
parent a5b060d4f8
commit 17f1b1f8c7
13 changed files with 1377 additions and 22877 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

364
random_sample/generate_tasks copy 2.py
View File

@ -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()

370
random_sample/generate_tasks copy 3.py
View File

@ -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()

408
random_sample/generate_tasks copy 4.py
View File

@ -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()

437
random_sample/generate_tasks copy 5.py
View File

@ -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()

246
random_sample/generate_tasks copy.py
View File

@ -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()

4
random_sample/generate_tasks.py
View File

@ -9,8 +9,10 @@ from dotenv import load_dotenv
# --- Configuration --- # --- Configuration ---
load_dotenv() load_dotenv()
server_address = "localhost"
MYSQL_CONFIG = { MYSQL_CONFIG = {
"host": "localhost", "host": server_address,
"port": "23306", "port": "23306",
"user": "mcpuser", "user": "mcpuser",
"password": "StrongPass123!", "password": "StrongPass123!",

3539
random_sample/generated_tasks.json.bak
View File

File diff suppressed because it is too large Load Diff

8182
random_sample/generated_tasks.json.bak2
View File

File diff suppressed because it is too large Load Diff

9329
random_sample/generated_tasks.json.bak3
View File

File diff suppressed because it is too large Load Diff

3
random_sample/requirements.txt Normal file
View File

@ -0,0 +1,3 @@
mysql-connector==2.2.9
openai
dotenv

1088
random_sample/tes1.json Normal file
View File

File diff suppressed because it is too large Load Diff

282
random_sample/test1.json Normal file
View File

@ -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&reg; 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
]
]
}
]

2
scripts/portforward.sh
View File

@ -1,5 +1,5 @@
# 用于将网站的mysql端口转发到本地，保证稳定性 # 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" \ autossh -M 0 -f -N -o "ServerAliveInterval 30" \
-o "ServerAliveCountMax 3" \ -o "ServerAliveCountMax 3" \
-L 23306:localhost:23306 yuyr@g14_jump2 -L 23306:localhost:23306 yuyr@g14