steven_li
33a9845566
feat(engine): 添加技能查看工具并优化异步任务管理 - 添加SkillViewTool到引擎加载器中，增强技能管理功能 - 在AgentLoop中引入_active_direct_task来跟踪活跃任务 - 实现直接任务执行时的同步处理逻辑 - 更新工具实例化方式以支持依赖注入 feat(config): 增加智能体运行时参数配置支持 - 扩展AgentDefaultsConfig添加max_tokens和temperature字段 - 实现配置解析函数_first_config_value处理多个配置源 - 支持通过Web API动态更新智能体运行时参数 - 添加前端页面配置表单和验证逻辑 refactor(provider): 统一最大令牌数参数类型为可选整型 - 将所有LLM提供者的max_tokens参数改为int | None类型 - 为AnthropicProvider实现模型特定的最大令牌数默认值 - 调整参数传递逻辑，优先级：调用参数 > 配置文件 > 模型默认值 - 移除硬编码的默认值，改用条件判断 feat(process): 增强事件投影功能 - 添加工具调用开始/结束事件的映射逻辑 - 实现技能激活事件的识别和展示 - 添加辅助函数处理工具调用名称和参数提取 - 优化运行记录关联逻辑，提升事件匹配准确性 fix(web): 更新网络请求客户端信任环境设置 - 将WebFetchTool和WebSearchTool的trust_env参数设为True - 确保HTTP客户端能够正确使用系统代理配置 - 修复可能的网络连接问题 test: 添加配置加载和事件投影相关测试 - 新增智能体默认参数配置测试用例 - 实现API配置持久化和重载测试 - 添加技能卡片和工具事件的投影测试 ```
100 lines
3.1 KiB
Python
100 lines
3.1 KiB
Python
"""Beaver provider 子系统的统一契约。"""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from typing import Any
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@dataclass(slots=True)
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class ToolCallRequest:
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"""模型返回的一次工具调用请求。"""
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id: str
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name: str
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arguments: dict[str, Any]
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@dataclass(slots=True)
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class LLMResponse:
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"""统一的模型响应结构。"""
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content: str | None
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tool_calls: list[ToolCallRequest] = field(default_factory=list)
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finish_reason: str = "stop"
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usage: dict[str, Any] = field(default_factory=dict)
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reasoning_content: str | None = None
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provider_name: str | None = None
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model: str | None = None
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@property
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def has_tool_calls(self) -> bool:
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return bool(self.tool_calls)
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class LLMProvider(ABC):
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"""所有 provider 实现必须遵守的统一接口。"""
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def __init__(
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self,
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api_key: str | None = None,
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api_base: str | None = None,
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request_timeout_seconds: float | None = None,
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) -> None:
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self.api_key = api_key
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self.api_base = api_base
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self.request_timeout_seconds = (
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max(1.0, float(request_timeout_seconds))
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if request_timeout_seconds is not None
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else None
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)
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@staticmethod
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def sanitize_empty_content(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""清理 provider 普遍不接受的空 content。"""
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result: list[dict[str, Any]] = []
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for message in messages:
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content = message.get("content")
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if isinstance(content, str) and content == "":
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clean = dict(message)
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clean["content"] = None if (message.get("role") == "assistant" and message.get("tool_calls")) else "(empty)"
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result.append(clean)
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continue
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if isinstance(content, list):
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filtered = [
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item
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for item in content
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if not (
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isinstance(item, dict)
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and item.get("type") in ("text", "input_text", "output_text")
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and not item.get("text")
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)
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]
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if len(filtered) != len(content):
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clean = dict(message)
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clean["content"] = filtered or "(empty)"
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if message.get("role") == "assistant" and message.get("tool_calls") and not filtered:
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clean["content"] = None
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result.append(clean)
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continue
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result.append(message)
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return result
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@abstractmethod
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async def chat(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int | None = None,
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temperature: float = 0.7,
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thinking_enabled: bool | None = None,
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) -> LLMResponse:
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"""统一聊天接口。"""
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@abstractmethod
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def get_default_model(self) -> str:
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"""返回 provider 的默认模型名。"""
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