聚客大模型：重新定义企业级智能交互与业务增长引擎

import requests
import json

# 聚客大模型开放API示例 - 自动生成工单摘要并提交
def auto_create_ticket(conversation_text, customer_id):
    url = "https://api.juke-ai.com/v1/ticket/create"
    headers = {"Authorization": "Bearer YOUR_API_KEY", "Content-Type": "application/json"}
    
    # 调用模型提取关键信息
    summarization_payload = {
        "model": "juke-summarizer",
        "input": conversation_text,
        "extract_fields": ["issue_type", "priority", "product_sku", "customer_phone"]
    }
    summary_resp = requests.post("https://api.juke-ai.com/v1/extract", json=summarization_payload, headers=headers)
    info = summary_resp.json()["data"]
    
    # 构造工单数据
    ticket_data = {
        "customer_id": customer_id,
        "title": info.get("issue_type", "未分类问题"),
        "description": conversation_text[:500],
        "priority": info.get("priority", "medium"),
        "sku": info.get("product_sku"),
        "contact": info.get("customer_phone"),
        "source": "chat_bot"
    }
    create_resp = requests.post(url, json=ticket_data, headers=headers)
    return create_resp.json()

# 示例调用
print(auto_create_ticket("我买的冰箱三天不制冷，订单号SD123456，快给我处理！", "C10086"))

from juke_sdk import JukeClient

client = JukeClient(api_key="YOUR_KEY", endpoint="https://private.juke-ai.com")

# 开启业务插件（查询库存）
response = client.chat(
    messages=[{"role": "user", "content": "这款蓝色XL卫衣还有库存吗？"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "query_inventory",
            "parameters": {"color": "blue", "size": "XL", "category": "hoodie"}
        }
    }],
    tool_choice="auto"
)
print(response["choices"][0]["message"]["content"])
# 输出示例："当前蓝色XL卫衣在华东仓有12件，华南仓有5件，预计下单后2天内送达。"

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, get_peft_model, TaskType

# 加载聚客基础模型（以开源版本为例）
base_model = AutoModelForCausalLM.from_pretrained("juke-ai/base-7b")
tokenizer = AutoTokenizer.from_pretrained("juke-ai/base-7b")

# 配置LoRA（仅训练少量参数）
lora_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    r=8,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
)
peft_model = get_peft_model(base_model, lora_config)
peft_model.print_trainable_parameters()  # 可训练参数仅占0.1%

# 使用自有业务数据集训练（示例省略数据加载）
# trainer = Trainer(model=peft_model, ...)
# trainer.train()

# 使用cURL直接调用聚客对话接口
curl -X POST https://api.juke-ai.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "juke-assistant",
    "messages": [{"role": "user", "content": "我上次买的手机充电器坏了，可以换新吗？"}],
    "business_context": {"order_id": "OD7890", "purchase_date": "2026-07-15"}
  }'