Platform For AI:Model Gallery quick start

Este código de exemplo chama o serviço de modelo de grande porte no Alibaba Cloud Model Studio. Você precisará de uma Model Studio API key. O código utiliza o qwen-plus-latest para gerar dados de negócios e o qwen3-235b-a22b para criar rótulos estruturados para esses dados.

# -*- coding: utf-8 -*-
import os
import asyncio
import random
import json
import sys
from typing import List
import platform
from openai import AsyncOpenAI

# Create an asynchronous client instance.
# NOTE: This script uses the DashScope-compatible API endpoint.
# If you are using a different OpenAI-compatible service, change the base_url.
client = AsyncOpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
)

# List of US States and Territories.
us_states = [
    "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Delaware",
    "Florida", "Georgia", "Hawaii", "Idaho", "Illinois", "Indiana", "Iowa", "Kansas", "Kentucky",
    "Louisiana", "Maine", "Maryland", "Massachusetts", "Michigan", "Minnesota", "Mississippi",
    "Missouri", "Montana", "Nebraska", "Nevada", "New Hampshire", "New Jersey", "New Mexico",
    "New York", "North Carolina", "North Dakota", "Ohio", "Oklahoma", "Oregon", "Pennsylvania",
    "Rhode Island", "South Carolina", "South Dakota", "Tennessee", "Texas", "Utah", "Vermont",
    "Virginia", "Washington", "West Virginia", "Wisconsin", "Wyoming", "District of Columbia",
    "Puerto Rico", "Guam", "American Samoa", "U.S. Virgin Islands", "Northern Mariana Islands"
]

# Recipient templates.
recipient_templates = [
    "To: {name}", "Recipient: {name}", "Deliver to {name}", "For: {name}",
    "ATTN: {name}", "{name}", "Name: {name}", "Contact: {name}", "Receiver: {name}"
]

# Phone number templates.
phone_templates = [
    "Tel: {phone}", "Tel. {phone}", "Mobile: {phone}", "Phone: {phone}",
    "Contact number: {phone}", "Phone number {phone}", "TEL: {phone}", "MOBILE: {phone}",
    "Contact: {phone}", "P: {phone}", "{phone}", "Call: {phone}",
]

# Generate a plausible US-style phone number.
def generate_us_phone():
    """Generate a random 10-digit US phone number in (XXX) XXX-XXXX format."""
    area_code = random.randint(201, 999)  # Avoid 0xx, 1xx area codes.
    exchange = random.randint(200, 999)
    line = random.randint(1000, 9999)
    return f"({area_code}) {exchange}-{line}"

# Use an LLM to generate recipient and address information.
async def generate_recipient_and_address_by_llm(state: str):
    """Use an LLM to generate a recipient's name and address details for a given state."""
    prompt = f"""Please generate recipient information for a location in {state}, USA, including:
1. A realistic full English name (can be common or less common, aim for diversity).
2. A real city name within that state.
3. A specific street address (e.g., street number + name, apartment number, etc., should be realistic).
4. A corresponding 5-digit ZIP code for that city/area.

Please return only the JSON object in the following format:
{{"name": "Recipient Name", "city": "City Name", "street_address": "Specific Street Address", "zip_code": "ZIP Code"}}

Do not include any other text, just the JSON. Ensure names are diverse, not just John Doe.
"""

    try:
        response = await client.chat.completions.create(
            messages=[{"role": "user", "content": prompt}],
            model="qwen-plus-latest",
            temperature=1.5,  # Increase temperature for more diverse names and addresses.
        )

        result = response.choices[0].message.content.strip()
        # Clean up potential markdown code block markers.
        if result.startswith('```'):
            result = result.split('\n', 1)[1]
        if result.endswith('```'):
            result = result.rsplit('\n', 1)[0]

        # Try to parse JSON.
        info = json.loads(result)
        print(info)
        return info
    except Exception as e:
        print(f"Failed to generate recipient and address: {e}, using fallback.")
        # Fallback mechanism.
        backup_names = ["Michael Johnson", "Emily Williams", "David Brown", "Jessica Jones", "Christopher Davis",
                        "Sarah Miller"]
        return {
            "name": random.choice(backup_names),
            "city": "Anytown",
            "street_address": f"{random.randint(100, 9999)} Main St",
            "zip_code": f"{random.randint(10000, 99999)}"
        }

# Generate a single raw data record.
async def generate_record():
    """Generate one messy, combined string of US address information."""
    # Randomly select a state.
    state = random.choice(us_states)

    # Use an LLM to generate recipient and address info.
    info = await generate_recipient_and_address_by_llm(state)

    # Format recipient name.
    recipient = random.choice(recipient_templates).format(name=info['name'])

    # Generate a phone number.
    phone = generate_us_phone()
    phone_info = random.choice(phone_templates).format(phone=phone)

    # Assemble the full address line.
    full_address = f"{info['street_address']}, {info['city']}, {state} {info['zip_code']}"

    # Combine all components.
    components = [recipient, phone_info, full_address]

    # Randomize the order of components.
    random.shuffle(components)

    # Choose a random separator.
    separators = [' ', ', ', '; ', ' | ', '\t', ' - ', ' // ', '', '  ']
    separator = random.choice(separators)

    # Join the components.
    combined_data = separator.join(components)
    return combined_data.strip()

# Generate a batch of data.
async def generate_batch_data(count: int) -> List[str]:
    """Generate a specified number of data records."""
    print(f"Starting to generate {count} records...")

    # Use a semaphore to control concurrency (e.g., up to 20 concurrent requests).
    semaphore = asyncio.Semaphore(20)

    async def generate_single_record(index):
        async with semaphore:
            try:
                record = await generate_record()
                print(f"Generated record #{index + 1}: {record}")
                return record
            except Exception as e:
                print(f"Failed to generate record #{index + 1}: {e}")
                return None

    # Concurrently generate data.
    tasks = [generate_single_record(i) for i in range(count)]

    data = await asyncio.gather(*tasks)

    successful_data = [record for record in data if record is not None]

    return successful_data

# Save data to a file.
def save_data(data: List[str], filename: str = "us_recipient_data.json"):
    """Save the generated data to a JSON file."""
    with open(filename, 'w', encoding='utf-8') as f:
        json.dump(data, f, ensure_ascii=False, indent=2)
    print(f"Data has been saved to {filename}")

# Phase 1: Data Production.
async def produce_data_phase():
    """Handle the generation of raw recipient data."""
    print("=== Phase 1: Starting Raw Recipient Data Generation ===")

    # Generate 2,000 records.
    batch_size = 2000
    data = await generate_batch_data(batch_size)

    # Save the data.
    save_data(data, "us_recipient_data.json")

    print(f"\nTotal records generated: {len(data)}")
    print("\nSample Data:")
    for i, record in enumerate(data[:3]):  # Show first 3 as examples.
        print(f"{i + 1}. Raw Data: {record}\n")

    print("=== Phase 1 Complete ===\n")
    return True

# Define the system prompt for the extraction model.
def get_system_prompt_for_extraction():
    """Return the system prompt for the information extraction task."""
    return """You are a professional information extraction assistant specializing in parsing US shipping addresses from unstructured text.

## Task Description
Based on the given input text, accurately extract and generate a JSON object containing the following six fields:
- name: The full name of the recipient.
- street_address: The complete street address, including number, street name, and any apartment or suite number.
- city: The city name.
- state: The full state name (e.g., "California", not "CA").
- zip_code: The 5 or 9-digit ZIP code.
- phone: The complete contact phone number.

## Extraction Rules
1.  **Address Handling**:
    -   Accurately identify the components: street, city, state, and ZIP code.
    -   The `state` field must be the full official name (e.g., "New York", not "NY").
    -   The `street_address` should contain all details before the city, such as "123 Apple Lane, Apt 4B".
2.  **Name Identification**:
    -   Extract the full recipient name.
3.  **Phone Number Handling**:
    -   Extract the complete phone number, preserving its original format.
4.  **ZIP Code**:
    -   Extract the 5-digit or 9-digit (ZIP+4) code.

## Output Format
Strictly adhere to the following JSON format. Do not add any explanatory text or markdown.
{
  "name": "Recipient's Full Name",
  "street_address": "Complete Street Address",
  "city": "City Name",
  "state": "Full State Name",
  "zip_code": "ZIP Code",
  "phone": "Contact Phone Number"
}
"""

# Use an LLM to predict structured data from raw text.
async def predict_structured_data(raw_data: str):
    """Use an LLM to predict structured data from a raw string."""
    system_prompt = get_system_prompt_for_extraction()

    try:
        response = await client.chat.completions.create(
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": raw_data}
            ],
            model="qwen3-235b-a22b",  # A powerful model is recommended for this task.
            temperature=0.0,  # Lower temperature for higher accuracy in extraction.
            response_format={"type": "json_object"},
            extra_body={"enable_thinking": False}
        )

        result = response.choices[0].message.content.strip()

        # Clean up potential markdown code block markers.
        if result.startswith('```'):
            lines = result.split('\n')
            for i, line in enumerate(lines):
                if line.strip().startswith('{'):
                    result = '\n'.join(lines[i:])
                    break
        if result.endswith('```'):
            result = result.rsplit('\n```', 1)[0]

        structured_data = json.loads(result)
        return structured_data

    except Exception as e:
        print(f"Failed to predict structured data: {e}, Raw data: {raw_data}")
        # Return an empty structure on failure.
        return {
            "name": "",
            "street_address": "",
            "city": "",
            "state": "",
            "zip_code": "",
            "phone": ""
        }

# Phase 2: Data Conversion.
async def convert_data_phase():
    """Read raw data, predict structured data, and save it in SFT format."""
    print("=== Phase 2: Starting Data Conversion to SFT Format ===")

    try:
        print("Reading us_recipient_data.json file...")
        with open('us_recipient_data.json', 'r', encoding='utf-8') as f:
            raw_data_list = json.load(f)

        print(f"Successfully read {len(raw_data_list)} records.")
        print("Starting to predict structured data using the extraction model...")

        # A simple and clear system message can improve training and inference speed.
        system_prompt = "You are an expert assistant for extracting structured JSON from US shipping information. The JSON keys are name, street_address, city, state, zip_code, and phone."
        output_file = 'us_recipient_sft_data.json'

        # Use a semaphore to control concurrency.
        semaphore = asyncio.Semaphore(10)

        async def process_single_item(index, raw_data):
            async with semaphore:
                structured_data = await predict_structured_data(raw_data)
                print(f"Processing record #{index + 1}: {raw_data}")

                conversation = {
                        "instruction": system_prompt + '  ' + raw_data,
                        "output": json.dumps(structured_data, ensure_ascii=False)
                }

                return conversation

        print(f"Starting conversion to {output_file}...")

        tasks = [process_single_item(i, raw_data) for i, raw_data in enumerate(raw_data_list)]
        conversations = await asyncio.gather(*tasks)

        with open(output_file, 'w', encoding='utf-8') as outfile:
            json.dump(conversations, outfile, ensure_ascii=False, indent=4)

        print(f"Conversion complete! Processed {len(raw_data_list)} records.")
        print(f"Output file: {output_file}")
        print("=== Phase 2 Complete ===")

    except FileNotFoundError:
        print("Error: us_recipient_data.json not found.")
        sys.exit(1)
    except json.JSONDecodeError as e:
        print(f"JSON decoding error: {e}")
        sys.exit(1)
    except Exception as e:
        print(f"An error occurred during conversion: {e}")
        sys.exit(1)

# Main function.
async def main():
    print("Starting the data processing pipeline...")
    print("This program will execute two phases in sequence:")
    print("1. Generate raw US recipient data.")
    print("2. Predict structured data and convert it to SFT format.")
    print("-" * 50)

    # Phase 1: Generate data.
    success = await produce_data_phase()

    if success:
        # Phase 2: Convert data.
        await convert_data_phase()

        print("\n" + "=" * 50)
        print("All processes completed successfully!")
        print("Generated files:")
        print("- us_recipient_data.json: Raw, unstructured data list.")
        print("- us_recipient_sft_data.json: SFT-formatted training data.")
        print("=" * 50)
    else:
        print("Data generation phase failed. Terminating.")

if __name__ == '__main__':
    # Set event loop policy for Windows if needed.
    if platform.system() == 'Windows':
        asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())

    # Run the main coroutine.
    asyncio.run(main(), debug=False)