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
278 lines
12 KiB
Python
278 lines
12 KiB
Python
"""LiteLLM provider implementation for multi-provider support."""
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from __future__ import annotations
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from contextlib import contextmanager
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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 .base import LLMProvider, LLMResponse, ToolCallRequest
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from .registry import find_by_model, find_by_name, find_gateway
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from .runtime import ProviderRoutingConfig
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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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import litellm
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from litellm import acompletion
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except ModuleNotFoundError: # pragma: no cover
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litellm = None # type: ignore[assignment]
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acompletion = None # type: ignore[assignment]
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_ALLOWED_MSG_KEYS = frozenset({"role", "content", "tool_calls", "tool_call_id", "name"})
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class LiteLLMProvider(LLMProvider):
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"""通过 LiteLLM 统一访问大多数 provider。"""
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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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default_model: str = "anthropic/claude-opus-4-5",
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extra_headers: dict[str, str] | None = None,
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provider_name: str | None = None,
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request_timeout_seconds: float | None = None,
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routing: ProviderRoutingConfig | 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.extra_headers = extra_headers or {}
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self.routing = routing
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self.provider_name = provider_name
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self._gateway = find_gateway(provider_name, api_key, api_base)
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if litellm is not None:
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litellm.suppress_debug_info = True
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litellm.drop_params = True
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def _build_env_overrides(self, api_key: str | None, api_base: str | None, model: str) -> dict[str, str]:
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"""为当前请求生成 LiteLLM 依赖的临时环境变量。
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LiteLLM 对部分 provider 仍然优先读取环境变量。为了避免不同 runtime
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之间互相污染，这里只生成“本次请求需要的 env 覆盖”，真正调用时再临时注入。
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"""
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if not api_key:
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return {}
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spec = self._gateway
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if spec is None and self.provider_name:
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spec = find_by_name(self.provider_name)
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if spec is None:
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spec = find_by_model(model)
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if spec is None or not spec.env_key:
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return {}
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overrides: dict[str, str] = {spec.env_key: api_key}
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effective_base = api_base or spec.default_api_base
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for env_name, env_value in spec.env_extras:
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resolved = env_value.replace("{api_key}", api_key).replace("{api_base}", effective_base)
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overrides[env_name] = resolved
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return overrides
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@contextmanager
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def _temporary_env(self, overrides: dict[str, str]):
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"""只在当前请求期间注入 provider 需要的环境变量。"""
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if not overrides:
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yield
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return
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sentinel = object()
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previous: dict[str, object] = {}
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for key, value in overrides.items():
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previous[key] = os.environ.get(key, sentinel)
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os.environ[key] = value
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try:
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yield
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finally:
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for key, old_value in previous.items():
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if old_value is sentinel:
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os.environ.pop(key, None)
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else:
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os.environ[key] = str(old_value)
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def _resolve_model(self, model: str) -> str:
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if self._gateway:
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prefix = self._gateway.litellm_prefix
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resolved = model.split("/")[-1] if self._gateway.strip_model_prefix else model
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if prefix and not resolved.startswith(f"{prefix}/"):
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resolved = f"{prefix}/{resolved}"
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return resolved
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if self.provider_name:
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spec = find_by_name(self.provider_name)
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if spec is not None and not spec.is_gateway and not spec.is_local:
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resolved = model
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if spec.litellm_prefix and not any(resolved.startswith(prefix) for prefix in spec.skip_prefixes):
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resolved = f"{spec.litellm_prefix}/{resolved}"
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elif spec.name == "openai" and "/" not in resolved:
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resolved = f"openai/{resolved}"
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return resolved
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spec = find_by_model(model)
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if spec and spec.litellm_prefix:
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if not any(model.startswith(prefix) for prefix in spec.skip_prefixes):
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model = f"{spec.litellm_prefix}/{model}"
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return model
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@staticmethod
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def _sanitize_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
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sanitized = []
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for message in messages:
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clean = {key: value for key, value in message.items() if key in _ALLOWED_MSG_KEYS}
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if clean.get("role") == "assistant" and "content" not in clean:
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clean["content"] = None
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if isinstance(clean.get("tool_calls"), list):
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clean["tool_calls"] = LiteLLMProvider._sanitize_tool_calls(clean["tool_calls"])
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sanitized.append(clean)
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return sanitized
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@staticmethod
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def _sanitize_tool_calls(tool_calls: list[Any]) -> list[dict[str, Any]]:
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sanitized: list[dict[str, Any]] = []
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for tool_call in tool_calls:
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if not isinstance(tool_call, dict):
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continue
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clean = dict(tool_call)
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function = clean.get("function")
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if isinstance(function, dict):
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clean_function = dict(function)
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arguments = clean_function.get("arguments")
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if not isinstance(arguments, str):
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clean_function["arguments"] = json.dumps(arguments or {}, ensure_ascii=False, default=str)
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clean["function"] = clean_function
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sanitized.append(clean)
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return sanitized
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def _apply_model_overrides(self, original_model: str, kwargs: dict[str, Any]) -> None:
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spec = find_by_model(original_model)
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if spec is None:
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return
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model_lower = original_model.lower()
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for pattern, overrides in spec.model_overrides:
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if pattern in model_lower:
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kwargs.update(overrides)
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return
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def _apply_openrouter_routing(self, kwargs: dict[str, Any]) -> None:
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if self.provider_name != "openrouter" or self.routing is None:
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return
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provider_payload: dict[str, Any] = {}
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if self.routing.sort:
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provider_payload["sort"] = self.routing.sort
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if self.routing.only:
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provider_payload["only"] = self.routing.only
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if self.routing.ignore:
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provider_payload["ignore"] = self.routing.ignore
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if self.routing.order:
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provider_payload["order"] = self.routing.order
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if self.routing.require_parameters:
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provider_payload["require_parameters"] = True
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if self.routing.data_collection:
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provider_payload["data_collection"] = self.routing.data_collection
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if provider_payload:
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kwargs["provider"] = provider_payload
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def _apply_thinking_mode(self, original_model: str, resolved_model: str, kwargs: dict[str, Any], enabled: bool | None) -> None:
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if enabled is None:
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return
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model_key = f"{original_model} {resolved_model}".lower()
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if "qwen" not in model_key:
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return
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extra_body = dict(kwargs.get("extra_body") or {})
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chat_template_kwargs = dict(extra_body.get("chat_template_kwargs") or {})
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chat_template_kwargs["enable_thinking"] = bool(enabled)
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extra_body["chat_template_kwargs"] = chat_template_kwargs
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kwargs["extra_body"] = extra_body
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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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if acompletion is None:
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return LLMResponse(content="Error: litellm is not installed", finish_reason="error", provider_name=self.provider_name)
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original_model = model or self.default_model
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resolved_model = self._resolve_model(original_model)
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sanitized_messages = self._sanitize_messages(self.sanitize_empty_content(messages))
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kwargs: dict[str, Any] = {
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"model": resolved_model,
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"messages": sanitized_messages,
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"max_tokens": max(1, max_tokens),
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"temperature": temperature,
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"timeout": self.request_timeout_seconds or 45.0,
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}
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if self.api_key:
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kwargs["api_key"] = self.api_key
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if self.api_base:
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kwargs["api_base"] = self.api_base
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if self.extra_headers:
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kwargs["extra_headers"] = self.extra_headers
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if tools:
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kwargs["tools"] = tools
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kwargs["tool_choice"] = "auto"
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self._apply_model_overrides(original_model, kwargs)
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self._apply_openrouter_routing(kwargs)
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self._apply_thinking_mode(original_model, resolved_model, kwargs, thinking_enabled)
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env_overrides = self._build_env_overrides(self.api_key, self.api_base, original_model)
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try:
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with self._temporary_env(env_overrides):
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response = await acompletion(**kwargs)
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except Exception as exc:
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return LLMResponse(content=f"Error: {exc}", finish_reason="error", provider_name=self.provider_name, model=resolved_model)
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choice = response.choices[0]
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message = choice.message
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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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try:
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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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arguments = json.loads(raw_arguments)
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except Exception as exc:
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# 这里不要因为单个 tool_call 参数坏掉而直接炸掉整轮请求。
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# 后面的 ToolExecutor 会把这个标记转换成一条标准 tool failure。
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arguments = {
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"__beaver_tool_argument_parse_error__": str(exc),
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"__raw_arguments__": raw_arguments,
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}
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else:
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arguments = raw_arguments
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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=getattr(message, "content", None),
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tool_calls=tool_calls,
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finish_reason=getattr(choice, "finish_reason", "stop") 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=self.provider_name or "litellm",
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model=resolved_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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