steven_li
30ab74ffb2
- 集成MCP连接管理器，支持MCP服务器连接 - 添加多种内置工具：ClarifyTool、CronTool、DelegateTool、ExecuteCodeTool、 PatchFileTool、ProcessTool、SendMessageTool、SpawnTool、TerminalTool、 TodoTool、WebFetchTool、WebSearchTool、WriteFileTool等 - 实现工具注册和装配功能 - 添加技能选择上下文参数 - 支持思考模式控制参数thinking_enabled feat(coordinator): 重构任务执行计划器参数命名 - 将learning_candidate_enabled重命名为allow_candidate_generation - 更新TeamGraphScheduler中的参数传递 - 修改LocalAgentRunner中的相关参数处理 - 更新README文档中的相应描述 refactor(context): 标准化工具调用参数格式 - 添加_json导入用于参数序列化 - 实现_provider_tool_calls方法标准化OpenAI兼容的工具调用载荷 - 修复工具调用中参数非字符串类型的序列化问题 refactor(session): 优化消息历史记录过滤逻辑 - 修改get_messages_as_conversation为基于运行状态过滤消息 - 排除未完成、失败或错误结束的运行记录 - 改进对话历史的可见性控制机制 fix(store): 修复FTS索引重建逻辑 - 添加异常处理防止FTS索引创建失败 - 实现_rebuild_fts_index方法重新构建全文搜索索引 - 优化索引触发器和表的维护流程
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 = 4096,
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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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