233 lines
9.7 KiB
Python
233 lines
9.7 KiB
Python
import time
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from prompt_toolkit import prompt
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from openai import OpenAI
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from get_tianqi import get_tianqi_data
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from cha_xianglaing import search1
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from weixin import weix
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import json
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tool_list = [
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{"name":"tainqi","description":"查询天气"},
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{"name":"sousuo","description":"网页搜索"},
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{"name":"export_video","description":"导出视频片段"},
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]
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toole_details = {
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"tianqi":{
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"type":"function",
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"function":{
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"name":"tianqi",
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"description":"查询天气状况(温度、湿度、风力、天气....)时使用此tool",
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"strict": True,
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"parameters": {
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"type":"object",
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"properties":{
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"city":{"type": "string", "description": "要获取天气的城市名称"}
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},
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"required": ["city"],
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"additionalProperties": False
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}
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}
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},
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}
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def get_tools_details():
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# 获取工具详情
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pass
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class send_message:
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def __init__(self, system_content: str ,tool_choice="auto"):
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self.ai_return = []
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self.tool_choice1 = tool_choice
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self.messages_1 = [
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{"role": "system", "content": system_content},
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]
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def test_openai(self):
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# print("111",self.messages_1)
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client = OpenAI(api_key="tp-cj0x379me5rqk198qnnt4n1spcdbx986jjr163gu2iiw1s7w",
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base_url="https://token-plan-cn.xiaomimimo.com/v1")
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response = client.chat.completions.create(
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model="mimo-v2.5-pro",
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messages=self.messages_1,
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top_p=0.5,
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tools=[
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{"type": "function",
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"function": {
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"name": "get_tianqi",
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"description": "获取天气信息。当你需要回答任何与天气、气温、冷暖有关的问题时调用此工具；判断用户是否是询问温度而不是出现冷热就调用此工具，例如用户说：你这个笑话好冷，则无需调用。**必须**使用此工具来获取实时、真实的天气数据。",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {"type": "string", "description": "城市名称"}
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},
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"required": ["city"],
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"additionalProperties": False
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}
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}
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},{"type": "function",
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"function": {
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"name": "get_donghua_images",
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"description": "用户需要查询具体动画场景时调用此工具。例如:用户说这个场景不错帮我找下，调用此工具；",
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"parameters": {
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"type": "object",
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"properties": {
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"donghua": {"type": "string", "description": "动画场景描述,必须为英文;例如:Tom is tired"},
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"xiangsidu": {"type": "integer", "description": "图片与场景的相似度要求,例如:'给我相似度大于50%的图片 则传值0.5'"},
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"tupianshuliang": {"type": "integer", "description": "返回图片数量,例如:'给我返回10张图片 则传值10'"},
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},
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"required": ["donghua"],
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"additionalProperties": False
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}}
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},
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{"type": "function",
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"function": {
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"name": "export_video",
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"description": "当用户确认某张图片并要求导出视频时调用此工具。例如：'这张图不错，给我前后30秒的视频'、'导出这个场景后面5分钟'、'我要整个视频'、'帮我截取这个片段'",
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"parameters": {
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"type": "object",
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"properties": {
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"image_name": {
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"type": "string",
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"description": "用户确认的图片文件名，例如 '001 Puss Gets the Boot [1940]_05m23s_456.jpg'"
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},
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"mode": {
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"type": "string",
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"enum": ["around", "after", "before", "total"],
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"description": "导出模式：around=前后各N秒，after=后N秒，before=前N秒，total=整个视频"
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},
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"seconds": {
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"type": "integer",
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"description": "秒数，total模式下可省略，默认300秒。例如用户说'前后30秒'则传30，'前后5分钟'则传300",
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"default": 300
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}
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},
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"required": ["image_name", "mode","seconds"],
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"additionalProperties": False
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}}
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}
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],
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tool_choice=self.tool_choice1,
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)
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return response.choices[0].message
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def test_json_format(json_string):
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try:
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return json.loads(json_string)
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except json.JSONDecodeError:
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return False
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def get_tianqi(ai_json):
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print(f"正在获取{ai_json['city']}的天气信息...")
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return f"{get_tianqi_data(ai_json['city'])}"
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# m = send_message('')
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cont = 1
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# m = send_message('你是一个个人助手，会适当使用工具')
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class get_ai_message:
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def __init__(self):
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self.system_content = open("猫和老鼠分镜.md", "r", encoding="utf-8").read()
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self.m = send_message(self.system_content)
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self.wx = weix()
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async def _handle_donghua(self, args):
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"""处理动画图片搜索工具"""
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donghua = args["donghua"]
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xiangsidu = args.get("xiangsidu", 0)
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tupianshuliang = args.get("tupianshuliang", 5)
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re_donghua_image = search1(query_text=donghua, top_k=tupianshuliang)
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result_str = ""
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for result in re_donghua_image:
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if result.score < xiangsidu:
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continue
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result_str += f"相似度:{result.score},图片路径:{result.payload}\n"
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print(result)
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await self.wx.send_images(result.payload["image_path"])
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return result_str if result_str else "未找到符合条件的图片"
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async def _handle_export_video(self, args):
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"""处理视频导出工具"""
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from video_cutter import export_video_clip
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export_result = export_video_clip(
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image_name=args["image_name"],
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mode=args["mode"],
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seconds=args.get("seconds", 300)
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)
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if export_result["success"]:
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await self.wx.send_video(export_result["video_path"])
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return (
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f"✅ 视频已导出并发送！\n"
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f"⏱️ 时间范围：{export_result['start_time']} -> {export_result['end_time']}\n"
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f"🎬 时长：{export_result['duration']}"
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)
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else:
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return f"❌ 视频导出失败：{export_result['error']}"
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async def sen_mess(self, s_input_text):
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# 拼接用户消息
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self.m.messages_1.append({"role": "user", "content": s_input_text})
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while True:
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out_text = self.m.test_openai()
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if out_text.tool_calls:
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# 将 assistant 的 tool_calls 消息整体追加到 messages（转为 dict）
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self.m.messages_1.append({
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"role": "assistant",
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"content": out_text.content,
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"tool_calls": [
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{
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"id": tc.id,
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"type": "function",
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"function": {
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"name": tc.function.name,
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"arguments": tc.function.arguments
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}
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}
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for tc in out_text.tool_calls
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]
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})
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for tool_call in out_text.tool_calls:
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args = test_json_format(tool_call.function.arguments)
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tool_name = tool_call.function.name
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print("M调用了工具:", tool_name)
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tool_result = ""
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if tool_name == "get_tianqi":
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tool_result = get_tianqi(args)
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elif tool_name == "get_donghua_images":
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tool_result = await self._handle_donghua(args)
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elif tool_name == "export_video":
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tool_result = await self._handle_export_video(args)
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# 工具结果以 tool role 追加到 messages
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self.m.messages_1.append({
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"role": "tool",
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"tool_call_id": tool_call.id,
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"content": tool_result
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})
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print(f"工具 {tool_name} 返回: {tool_result[:100]}...")
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# 循环继续，让 LLM 看到工具结果后继续推理
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elif out_text.content:
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# 没有工具调用，发送最终文本给用户
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self.m.messages_1.append({"role": "assistant", "content": out_text.content})
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await self.wx.send_text(out_text.content)
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print(f"小爱:{out_text.content}")
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return out_text.content
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gm = get_ai_message()
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bot = gm.wx.bot
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@bot.on_message
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async def echo_handler(msg):
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print(f"收到来自 {msg.user_id} 的消息: {msg.text}")
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await gm.sen_mess(msg.text)
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# await bot.reply(msg, f"你说了: {msg.text}")
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bot.run()
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