initial commit for prototype
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import json
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import os
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from typing import Any
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from mini_agent.tools.tavily_search import TavilySearch
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from openai import OpenAI
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from mini_agent.tools.tool_search import ToolSearch
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from mini_agent.tools.tool_manager import ToolManager
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from dotenv import load_dotenv
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load_dotenv()
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class ReActAgent:
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SYSTEM_PROMPT = """
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你是一个能够调用外部工具的智能助手。
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工作规则：
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1. 首先理解用户的问题，判断是否需要外部工具。
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2. 如果需要工具，调用 search_tools 搜索合适的工具。
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3. 根据搜索结果选择合适的工具。
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4. 工具执行后，根据返回结果判断下一步行动。
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5. 如果需要其他工具，可以再次调用 search_tools。
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6. 当已经获得足够的信息，能够完整回答用户问题时，直接给出最终答案。
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注意事项：
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- 不允许编造工具执行结果。
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- 不允许调用尚未通过 search_tools 发现的业务工具。
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- 工具搜索没有结果时，可以尝试调整关键词重新搜索。
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- 如果工具无法完成任务，明确告知用户。
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- 不要为了调用工具而调用工具。
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- 只有在任务已经完成或明确无法继续时才结束回答。
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"""
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def __init__(
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self,
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tool_manager: ToolManager,
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model: str | None = None,
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api_key: str | None = None,
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base_url: str | None = None,
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timeout: int | None = None,
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max_steps: int = 10,
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max_tool_calls: int = 10,
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) -> None:
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self.model = model or os.getenv("LLM_MODEL_ID")
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self.base_url = base_url or os.getenv("LLM_BASE_URL")
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self.api_key = api_key or os.getenv("LLM_API_KEY")
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self.timeout = timeout or int(os.getenv("LLM_TIMEOUT", 60))
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if not all([self.model, self.base_url, self.api_key]):
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raise ValueError(
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"模型ID、API密钥和服务地址必须被提供或在.env文件中定义。"
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)
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self.client = OpenAI(
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api_key=self.api_key,
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base_url=self.base_url,
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timeout=self.timeout,
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)
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self.tool_manager = tool_manager
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self.max_steps = max_steps
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self.max_tool_calls = max_tool_calls
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self.search_tool = ToolSearch(self.tool_manager)
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self.tool_manager.register(self.search_tool)
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web_search_tool = TavilySearch()
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web_search_tool.config(**{"api_key":os.getenv("TAVILY_KEY","")})
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self.tool_manager.register(web_search_tool)
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def run(self, question: str) -> str:
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# 已发现的业务工具
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# 初始只开放工具发现能力
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available_tools = [self.search_tool]
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tool_calls_count = 0
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messages = [
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{"role":"user","content":question}
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]
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for step in range(self.max_steps):
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print(f"\n========== Step {step + 1} ==========")
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tools = [self.tool_manager.tool_to_openai(tool) for tool in available_tools]
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response = self.client.chat.completions.create(
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model=self.model,
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messages=messages,
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tools=tools,
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tool_choice="auto",
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)
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message = response.choices[0].message
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# 模型没有调用工具，说明它选择直接回答
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if not message.tool_calls:
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if message.content:
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print(f"最终回答: {message.content}")
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return message.content
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raise RuntimeError("模型没有返回有效内容")
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# 保存模型的工具调用消息
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messages.append(
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message.model_dump(exclude_none=True)
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)
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for tool_call in message.tool_calls:
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tool_name = tool_call.function.name
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try:
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arguments = json.loads(
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tool_call.function.arguments or "{}"
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)
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except json.JSONDecodeError:
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observation = json.dumps(
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{"error": "工具参数不是合法的 JSON"},
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ensure_ascii=False,
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)
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call.id,
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"content": observation,
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})
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continue
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print(f"调用工具: {tool_name}")
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print(f"参数: {arguments}")
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observation = self.tool_manager.call_tool(tool_name,arguments)
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# 更新当前可用工具
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if tool_name == self.search_tool.name:
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new_tools = []
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for item in observation:
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if not any(tool.name == item.name for tool in available_tools):
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available_tools.append(item)
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new_tools.append(item)
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observation = [self.tool_manager.tool_to_openai(tool) for tool in new_tools]
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call.id,
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"content": str(observation),
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})
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raise RuntimeError(
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f"Agent 已达到最大执行步数 ({self.max_steps})，任务未完成"
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)
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@@ -0,0 +1,60 @@
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from mini_agent.tools.tool import Configuration, Tool,Parameter
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from pydantic import BaseModel, Field
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from tavily import TavilyClient
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class ResultItem(BaseModel):
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url:str
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title:str
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content:str
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score:float
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raw_content:str | None = None
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id:str
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class SearchResult(BaseModel):
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query:str
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answer:str | None
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images:list[str] = []
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results: list[ResultItem]
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class TavilySearch(Tool[SearchResult]):
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@property
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def name(self)->str:
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return "Tavily网络搜索工具 "
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@property
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def description(self) -> str:
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return """
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使用 Tavily 搜索引擎进行互联网搜索，获取最新网页信息。
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适用于查询时效性信息、新闻、技术文档、产品资料以及需要外部信息验证的问题。
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"""
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@property
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def catalog(self) -> str:
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return "web.search"
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@property
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def configurations(self) -> list[Configuration]:
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return [
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Configuration(name="api_key",type="str",required=True)
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]
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@property
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def parameters(self) -> list[Parameter]:
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return [
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Parameter(name="query",type="str",description="query keyword",required=True)
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]
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def config(self,**kwargs) -> None:
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api_key = kwargs.get("api_key",None)
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if not api_key:
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raise ValueError("API Key is required")
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self.client = TavilyClient(api_key=api_key)
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def run(self, **kwargs) -> SearchResult:
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query = kwargs.get("query",None)
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if not query:
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raise ValueError("查询参数不能为空")
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response = self.client.search(query=query,search_depth="advanced",include_answer="advanced")
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return SearchResult.model_validate(response)
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from pydantic import BaseModel
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from abc import ABC,abstractmethod
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class Parameter(BaseModel):
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name:str
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type:str
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description:str
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required:bool =True
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class Configuration(BaseModel):
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name:str
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type:str
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required:bool = True
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class Tool[T](ABC):
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@property
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@abstractmethod
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def name(self)->str:
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pass
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@property
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@abstractmethod
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def description(self)->str:
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pass
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@property
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@abstractmethod
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def catalog(self)->str:
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pass
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@property
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@abstractmethod
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def parameters(self)->list[Parameter]:
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pass
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@property
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@abstractmethod
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def configurations(self)->list[Configuration]:
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pass
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@abstractmethod
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def config(self,**kwargs)->None:
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pass
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@abstractmethod
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def run(self,**kwargs)->T:
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pass
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from typing import Any
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from mini_agent.tools.tool import Tool
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class ToolManager:
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_tools:dict[str,Tool] = {}
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def register[T](self,tool:Tool[T])->None:
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if tool.name in self._tools:
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raise ValueError(f"{tool.name} is already registered")
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self._tools[tool.name] = tool
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def get_tools(self,keyword:str,catalog:str | None)->list[Tool]:
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return list(self._tools.values())
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def get_tools_for_openai(self,keyword:str,catalog:str|None)->list[dict[str,Any]]:
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tools = self.get_tools(keyword,catalog)
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result = []
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for tool in tools:
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result.append(self.tool_to_openai(tool))
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return result
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def call_tool(self, name: str, arguments: dict[str, Any]) -> Any:
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tool = self._tools.get(name, None)
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if tool is None:
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raise ValueError(f"Tool not found: {name}")
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return tool.run(**arguments)
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def tool_to_openai[T](self,tool:Tool[T])->dict[str,Any]:
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type_mapping = {
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"str": "string",
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"string": "string",
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"int": "integer",
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"integer": "integer",
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"float": "number",
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"number": "number",
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"bool": "boolean",
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"boolean": "boolean",
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"list": "array",
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"dict": "object",
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}
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properties = {}
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required = []
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for parameter in tool.parameters:
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json_type = type_mapping.get(parameter.type)
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if json_type is None:
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raise ValueError(
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f"不支持的参数类型: {parameter.type}"
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)
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properties[parameter.name] = {
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"type": json_type,
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}
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if parameter.description:
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properties[parameter.name]["description"] = (
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parameter.description
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)
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if parameter.required:
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required.append(parameter.name)
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return {
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"type": "function",
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"function": {
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"name": tool.name,
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"description": tool.description.strip(),
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"parameters": {
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"type": "object",
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"properties": properties,
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"required": required,
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"additionalProperties": False,
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},
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},
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}
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@@ -0,0 +1,43 @@
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from mini_agent.tools.tool import Tool,Parameter,Configuration
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from mini_agent.tools.tool_manager import ToolManager
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class ToolSearch(Tool[list[Tool]]):
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def __init__(self,tool_manager:ToolManager) -> None:
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self.tool_manager = tool_manager
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@property
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def name(self)->str:
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return """search_tools"""
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@property
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def description(self) -> str:
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return """
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"根据用户需求搜索可用工具。 当需要外部数据、查询业务信息或执行特定操作时，首先使用该工具查找合适的业务工具。可以根据需要多次搜索不同的工具。
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"""
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@property
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def catalog(self) -> str:
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return "system"
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def config(self, **kwargs) -> None:
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pass
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@property
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def configurations(self) -> list[Configuration]:
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return []
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@property
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def parameters(self) -> list[Parameter]:
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return [
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Parameter(name="keyword",type="str",description="关键词",required=True),
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Parameter(name="catalog",type="str",description="工具分类",required=False)
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]
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def run(self, **kwargs) -> list[Tool]:
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keyword = kwargs.get("keyword", None)
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catalog = kwargs.get("catalog", None)
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if not keyword:
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raise ValueError("keyword is required for query")
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return self.tool_manager.get_tools(keyword,catalog)
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