Building Multilingual Apps with Qwen-2.5: A Practical API Guide

import openai

client = openai.OpenAI(
    api_key="your-itapi-key",
    base_url="https://api.itapi.ai/v1"
)

MODEL = "qwen-2.5-72b"

def support_reply(user_message: str) -> dict:
    response = client.chat.completions.create(
        model=MODEL,
        messages=[
            {
                "role": "system",
                "content": (
                    "You are a helpful customer support agent. "
                    "Detect the user's language and respond in the same language. "
                    "Be polite, concise, and accurate. "
                    "If you need to escalate, set escalation=true in the JSON output."
                )
            },
            {"role": "user", "content": user_message}
        ],
        response_format={"type": "json_object"},
        temperature=0.3
    )

    import json
    return json.loads(response.choices[0].message.content)

# Test cases
print(support_reply("How do I reset my API key?"))
print(support_reply("我的API密钥怎么重置？"))
print(support_reply("APIキーのリセット方法を教えてください"))

EXTRACTION_PROMPT = """
Extract the following information from the user's message and return valid JSON.
Fields: intent, product_name, urgency (low/medium/high), language_detected.

Rules:
- intent must be one of: pricing_question, technical_issue, feature_request, complaint
- product_name should be null if not mentioned
- urgency is "high" if words like urgent, asap, broken, down are present (in any language)
- language_detected is the ISO 639-1 code
"""

def extract_intent(message: str) -> dict:
    response = client.chat.completions.create(
        model=MODEL,
        messages=[
            {"role": "system", "content": EXTRACTION_PROMPT},
            {"role": "user", "content": message}
        ],
        response_format={"type": "json_object"},
        temperature=0.1
    )
    import json
    return json.loads(response.choices[0].message.content)

# Test
print(extract_intent("Prices are too high, fix this now"))
print(extract_intent("價格太高了，請馬上處理"))
print(extract_intent("el precio es muy alto, solución inmediata"))

def translate_document(text: str, target_lang: str) -> str:
    response = client.chat.completions.create(
        model=MODEL,
        messages=[
            {
                "role": "system",
                "content": (
                    f"Translate the following document into {target_lang}. "
                    "Preserve formatting, markdown, and technical terms. "
                    "Do not add commentary. Output only the translation."
                )
            },
            {"role": "user", "content": text}
        ],
        temperature=0.2
    )
    return response.choices[0].message.content

# Translate a 10K token technical spec
translated = translate_document(technical_spec_md, "zh-CN")

def route_by_language(message: str) -> str:
    """Returns the optimal model name for the detected language."""
    # Fast language detection (you can also use a dedicated library)
    chinese_chars = sum(1 for c in message if '\u4e00' <= c <= '\u9fff')
    ratio = chinese_chars / max(len(message), 1)

    if ratio > 0.3:
        return "qwen-2.5-72b"
    elif any(c in message for c in 'ãéüのは') and ratio < 0.1:
        return "qwen-2.5-72b"  # Also strong for Japanese/Spanish/German
    else:
        return "gpt-4o"  # Default for English-heavy content