orangeqiu/k2think2api repository overview

pip install -r requirements.txt

cp config.example .env
# 编辑 .env 文件，配置你的API密钥和其他选项

# 复制token示例文件并编辑
cp tokens.example.txt tokens.txt
# 编辑tokens.txt文件，添加你的实际K2Think tokens

python k2think_proxy.py

# 构建镜像 
docker build -t k2think-api .

# 先创建 .env 文件和tokens.txt，然后编辑配置
cp config.example .env
cp tokens.example.txt tokens.txt
# 编辑tokens.txt添加实际的token

# 运行容器
docker run -d \
  --name k2think-api \
  -p 8001:8001 \
  -v $(pwd)/tokens.txt:/app/tokens.txt:ro \
  -v $(pwd)/.env:/app/.env:ro \
  k2think-api

# 先创建 .env 文件和tokens.txt
cp config.example .env
cp tokens.example.txt tokens.txt

# 编辑 .env 文件配置API密钥等
# 编辑 tokens.txt 添加实际的K2Think tokens

# 启动服务
docker-compose up -d

# 检查服务状态
docker-compose logs -f k2think-api

curl -X POST http://localhost:8001/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-k2think" \
  -d '{
    "model": "MBZUAI-IFM/K2-Think",
    "messages": [
      {"role": "user", "content": "你擅长什么？"}
    ],
    "stream": false
  }'

curl http://localhost:8001/v1/models \
  -H "Authorization: Bearer sk-k2think"

curl http://localhost:8001/admin/tokens/stats

curl -X POST http://localhost:8001/admin/tokens/reset/0

curl -X POST http://localhost:8001/admin/tokens/reset-all

curl -X POST http://localhost:8001/admin/tokens/reload

import openai

# 配置客户端
client = openai.OpenAI(
    base_url="http://localhost:8001/v1",
    api_key="sk-k2think"
)

# 发送聊天请求
response = client.chat.completions.create(
    model="MBZUAI-IFM/K2-Think",
    messages=[
        {"role": "user", "content": "解释一下量子计算的基本原理"}
    ],
    stream=False
)

print(response.choices[0].message.content)

# 流式聊天
stream = client.chat.completions.create(
    model="MBZUAI-IFM/K2-Think",
    messages=[
        {"role": "user", "content": "写一首关于人工智能的诗"}
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

# Docker 容器日志
docker logs k2think-api

# docker-compose日志
docker-compose logs -f k2think-api

# 本地运行日志
# 日志会直接输出到控制台

# 检查当前配置
python check_config_simple.py

# 查看配置示例
python check_config_simple.py --example

import openai

client = openai.OpenAI(
    base_url="http://localhost:8001/v1",
    api_key="sk-k2think"
)

# 定义工具
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "获取指定城市的天气信息",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "城市名称，例如：北京、上海"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "温度单位"
                    }
                },
                "required": ["city"]
            }
        }
    }
]

# 发送工具调用请求
response = client.chat.completions.create(
    model="MBZUAI-IFM/K2-Think",
    messages=[
        {"role": "user", "content": "北京今天天气怎么样？"}
    ],
    tools=tools,
    tool_choice="auto"  # auto, none, required 或指定特定工具
)

# 处理响应
if response.choices[0].message.tool_calls:
    for tool_call in response.choices[0].message.tool_calls:
        function_name = tool_call.function.name
        function_args = tool_call.function.arguments
        print(f"调用工具: {function_name}")
        print(f"参数: {function_args}")
  
        # 在这里执行实际的工具调用
        # tool_result = execute_tool(function_name, function_args)
  
        # 继续对话，将工具结果返回给模型
        # ...

# 运行所有测试
cd test
python run_all_tests.py

# 运行特定测试
python run_all_tests.py debug_test      # 基础功能测试
python run_all_tests.py test_tools      # 完整工具调用测试  
python run_all_tests.py test_contentpart # ContentPart序列化测试
python run_all_tests.py test_message_accumulation # 消息累积测试

# 查看工具调用示例
python tool_example.py