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
108 lines
3.8 KiB
Python
108 lines
3.8 KiB
Python
"""Direct OpenAI-compatible provider — bypasses LiteLLM."""
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from __future__ import annotations
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from typing import Any
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from .base import LLMProvider, LLMResponse, ToolCallRequest
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try: # pragma: no cover - optional dependency
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import json_repair
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except ModuleNotFoundError: # pragma: no cover
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json_repair = None # type: ignore[assignment]
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try: # pragma: no cover - optional dependency
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from openai import AsyncOpenAI
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except ModuleNotFoundError: # pragma: no cover
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AsyncOpenAI = None # type: ignore[assignment]
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class CustomProvider(LLMProvider):
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"""直接连接任意 OpenAI-compatible endpoint。"""
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def __init__(
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self,
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api_key: str = "no-key",
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api_base: str = "http://localhost:8000/v1",
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default_model: str = "default",
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request_timeout_seconds: float | None = None,
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) -> None:
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super().__init__(api_key, api_base, request_timeout_seconds=request_timeout_seconds)
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self.default_model = default_model
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self._client = None
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def _client_or_raise(self):
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if AsyncOpenAI is None:
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raise RuntimeError("openai package is not installed")
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if self._client is None:
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self._client = AsyncOpenAI(
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api_key=self.api_key,
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base_url=self.api_base,
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timeout=self.request_timeout_seconds,
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)
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return self._client
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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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client = self._client_or_raise()
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kwargs: dict[str, Any] = {
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"model": model or self.default_model,
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"messages": self.sanitize_empty_content(messages),
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"max_tokens": max(1, max_tokens),
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"temperature": temperature,
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}
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if tools:
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kwargs.update(tools=tools, tool_choice="auto")
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try:
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response = await client.chat.completions.create(**kwargs)
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except Exception as exc:
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return LLMResponse(content=f"Error: {exc}", finish_reason="error", provider_name="custom")
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choice = response.choices[0]
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message = choice.message
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parsed_tool_calls: list[ToolCallRequest] = []
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for tool_call in message.tool_calls or []:
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raw_arguments = tool_call.function.arguments
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if isinstance(raw_arguments, str):
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if json_repair is not None:
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arguments = json_repair.loads(raw_arguments)
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else:
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import json
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arguments = json.loads(raw_arguments)
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else:
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arguments = raw_arguments
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parsed_tool_calls.append(
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ToolCallRequest(
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id=tool_call.id,
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name=tool_call.function.name,
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arguments=arguments,
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)
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)
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usage = getattr(response, "usage", None)
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usage_payload = {}
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if usage is not None:
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usage_payload = {
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"prompt_tokens": getattr(usage, "prompt_tokens", 0),
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"completion_tokens": getattr(usage, "completion_tokens", 0),
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"total_tokens": getattr(usage, "total_tokens", 0),
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}
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return LLMResponse(
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content=message.content,
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tool_calls=parsed_tool_calls,
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finish_reason=choice.finish_reason or "stop",
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usage=usage_payload,
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reasoning_content=getattr(message, "reasoning_content", None),
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provider_name="custom",
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model=model or self.default_model,
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)
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def get_default_model(self) -> str:
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return self.default_model
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