进阶用法

{
    "name": "get_current_weather",
    "description": "Get the current weather in a given location",
    "parameters": {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, CA"
            },
            "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
        },
        "required": ["location"]
    }
}

{"location": "Paris", "temperature": "22"}

# https://platform.openai.com/docs/guides/function-calling
 
from openai import OpenAI
import json
 
client = OpenAI(
    base_url="http://127.0.0.1:8000", # 根据 RWKV Runner 的配置修改端口号
    api_key="test",
)
 
# 这是硬编码的虚拟函数示例，返回天气情况
# 在生产环境中，需要改成你的后台 API 或外部 API
def get_current_weather(location, unit="fahrenheit"):
    """Get the current weather in a given location"""
    if "tokyo" in location.lower():
        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": unit})
    elif "san francisco" in location.lower():
        return json.dumps(
            {"location": "San Francisco", "temperature": "72", "unit": unit}
        )
    elif "paris" in location.lower():
        return json.dumps({"location": "Paris", "temperature": "22", "unit": unit})
    else:
        return json.dumps({"location": location, "temperature": "unknown"})
 
 
def run_conversation():
    # 第 1 步：将对话和可用函数发送给模型
    messages = [
        {
            "role": "user",
            "content": "What's the weather like in Paris?",
        }
    ]
    tools = [
        {
            "type": "function",
            "function": {
                "name": "get_current_weather",
                "description": "Get the current weather in a given location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        },
                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                    },
                    "required": ["location"],
                },
            },
        }
    ]
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        tools=tools,
        tool_choice="auto",  # 不存在任何工具时，默认值 none。如果存在工具，则 auto 是默认值
    )
    response_message = response.choices[0].message
    tool_calls = response_message.tool_calls
    # 第 2 步：检查模型是否想要调用函数
    if tool_calls:
        # 第 3 步：调用函数
        # 注意：JSON 响应不一定生效；请务必处理错误情况
        available_functions = {
            "get_current_weather": get_current_weather,
        }  # 本示例中只有一个函数，实际可以有多个
        messages.append(response_message)  # 用 assistant 的回答扩展对话
        # 第 4 步：向模型发送每个函数调用和函数响应的信息
        for tool_call in tool_calls:
            function_name = tool_call.function.name
            function_to_call = available_functions[function_name]
            function_args = json.loads(tool_call.function.arguments)
            function_response = function_to_call(
                location=function_args.get("location"),
                unit=function_args.get("unit"),
            )
            messages.append(
                {
                    "tool_call_id": tool_call.id,
                    "role": "tool",
                    "name": function_name,
                    "content": function_response,
                }
            )  # 通过函数响应扩展模型的对话内容
        second_response = client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
        )  # 从模型中获取新的响应，在模型中可以看到函数响应
        return second_response.choices[0].message.content
 
 
print(run_conversation())

git clone https://github.com/josStorer/RWKV-Runner

cd RWKV-Runner 
sudo apt install python3-dev # 以 root 权限安装 python3-dev
chmod +x ./RWKV-Runner # 更改文件模式（权限）为可执行文件
./RWKV-Runner # 运行 RWKV Runner 
cd backend-python 
pip3 install -r requirements.txt # 也可以运行 pip3 install -r requirements_without_cyac.txt

mkdir models # 创建 models 文件夹
wget -N https://huggingface.co/BlinkDL/rwkv-6-world/resolve/main/RWKV-x060-World-1B6-v2.1-20240328-ctx4096.pth -P models/

python ./backend-python/main.py

python ./backend-python/main.py --rwkv.cpp # 以 rwkv.cpp 模式推理
python ./backend-python/main.py --webgpu 

cd RWKV-Runner/frontend
npm ci
npm run build #编译前端
cd ..
python ./backend-python/webui_server.py #单独启动前端服务

python ./backend-python/main.py --webui #同时启动前后端服务

python ./backend-python/main.py -h