feat(agent): 增加简易 auto 推理强度模式
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@ -136,7 +136,7 @@ Eval 与生产 core 解耦，通过现有 `/v1` API 创建专用任务、监听
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默认 `deepseek_v4.flash`;复杂 bug / 终稿升 pro + reasoning_effort=max;fallback 手动切 Claude。成本量级:修 bug flash ~$0.01 / 完整申报书 flash ~$0.30(pro-max ~$1.5,Opus ~$10+)。99% 任务 flash 够用。
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模型思考参数由 profile 统一表达：`thinking_enabled` 只表示开关，`thinking_transport` 只表示已验证的传输协议，`reasoning_effort` 只表示开启后的推理强度，`thinking_clear` 表示 provider 是否清除历史思考，`reasoning_replay` 表示状态生命周期（`none` / `tool_turn` / `conversation` / `provider_managed`）；`core/llm_params.py` 是请求参数构造唯一入口，`core/context.py` 是历史消息清洗唯一入口。原始 assistant 响应完整落库，provider-bound 副本只向相同生产模型回放未改写的 reasoning：DeepSeek V4 仅保留当前用户轮次的工具链状态，GLM-5.3 Flash 在同模型会话内保留完整状态，未验证网关明确用 `none`，未来签名/加密 block 走 `provider_managed`。模型切换、上下文折叠和普通压缩都在同一入口应用隔离；上下文统计使用裁剪后的请求视图，原生图片 token 不反向污染 chars/token 校准。
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模型思考参数由 profile 统一表达：`thinking_enabled` 只表示开关，`thinking_transport` 只表示已验证的传输协议，`reasoning_effort` 只表示开启后的推理强度，`thinking_clear` 表示 provider 是否清除历史思考，`reasoning_replay` 表示状态生命周期（`none` / `tool_turn` / `conversation` / `provider_managed`）；`core/llm_params.py` 是请求参数构造唯一入口，`core/context.py` 是历史消息清洗唯一入口。档案的 `default_reasoning_effort=auto` 是平台编排值，不进入 provider：当前用户轮次首次调用和失败工具步后用 `high`，成功工具步后用 `low`，固定档保持原样，auto 不选择 `max`。纯 reasoning 从首个推理片段起超过 90 秒且尚无正文/工具调用时关闭当前流并发送 `reasoning_reset`，不持久化半截 assistant；随后以 `low` 和仅本次 provider 请求可见的简短约束重试一次，第二次仍超时则明确停止，用户取消始终优先。原始 assistant 响应完整落库，provider-bound 副本只向相同生产模型回放未改写的 reasoning：DeepSeek V4 仅保留当前用户轮次的工具链状态，GLM-5.3 Flash 在同模型会话内保留完整状态，未验证网关明确用 `none`，未来签名/加密 block 走 `provider_managed`。模型切换、上下文折叠和普通压缩都在同一入口应用隔离；上下文统计使用裁剪后的请求视图，原生图片 token 不反向污染 chars/token 校准。
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---
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@ -2,7 +2,7 @@
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> 配合 `DESIGN.md`。本文件只记 phase 状态、决策偏差、文件量、下一步。每条 1-2 句:做了啥 + 关键判断;细节查 `git log` / `git diff` / `DESIGN §7.9`。
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最后更新:2026-09-03（Unreleased：工具健康统一事件口径）
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最后更新:2026-09-04（Unreleased：简易 auto 推理强度模式）
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---
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@ -20,6 +20,8 @@
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---
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## 已完成关键能力
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- **09-04 / Unreleased / 简易 auto 推理强度模式**：模型档案可保留平台值 `auto`，DeepSeek V4 Flash 首次调用和失败工具步后使用 high、成功工具步后使用 low，Pro 的 medium 与其他固定档保持原样；纯 reasoning 超过 90 秒会清理直播推理并以 low 和请求内临时约束重试一次，第二次仍超时明确停止，取消优先且半截 assistant 不入库。正常 chat usage 留存配置值、实际档位、决策原因和保护重试标记，熔断复用 `agent_guard`；无 schema、migration、API、前端或版本变化。
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- **09-03 / 0.71.0 / 工具链可靠性收敛**：tool arguments salvage 新增“至少两个完整且完全一致副本 + 尾部截断副本”的保守恢复，语义不一致仍拒绝。修复 artifacts 部分唯一索引谓词被参数化后 PostgreSQL 无法匹配 `ON CONFLICT` 的问题，并让输出中任意行首 `[GATE FAIL]` 均按质量门记录，避免 SVG/PPT 质检占用真实故障大数。无 schema/migration/API 变化，生产失败历史未改写。
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- **09-03 / 0.71.0 / 工具健康统一写入 usage_events**：普通工具失败在 tool result 落消息后结构化写 `tool_failure`，并新增 `run_stopped`、`agent_guard`、`context_fold_failure`、`quality_gate` 四类任务异常事件；工具健康聚合彻底移除 messages JSONB 回扫，只用一次 usage_events 查询，切换前的普通失败历史不迁移。Admin 大数只计真实 failure，代理主动停止/重复保护/上下文异常改为独立控制区，质量门继续单列。新增 0040 migration，为健康 kind 时间窗和普通失败 message 去重补部分索引。
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@ -15,7 +15,7 @@ variants:
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thinking_enabled: true
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thinking_transport: extra_body
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reasoning_effort_levels: [low, high, max]
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default_reasoning_effort: high
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default_reasoning_effort: auto
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reasoning_replay: tool_turn # 只在当前用户轮次的工具链内原样回传 reasoning
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code_quality: good
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enable_run_python: true
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@ -15,6 +15,7 @@ REASONING_REPLAY_POLICIES = {
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"conversation",
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"provider_managed",
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}
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REASONING_EFFORT_AUTO = "auto"
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def model_profile_of(caps: object) -> str:
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@ -134,12 +135,20 @@ class ModelCapabilities:
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)
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if (
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caps.default_reasoning_effort
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and caps.default_reasoning_effort != REASONING_EFFORT_AUTO
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and caps.default_reasoning_effort not in caps.reasoning_effort_levels
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):
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raise ValueError(
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f"档案 {path} 的 default_reasoning_effort="
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f"{caps.default_reasoning_effort!r} 不在 reasoning_effort_levels 中"
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)
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if (
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caps.default_reasoning_effort == REASONING_EFFORT_AUTO
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and not {"low", "high"}.issubset(caps.reasoning_effort_levels)
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):
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raise ValueError(
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f"档案 {path} 使用 auto 时 reasoning_effort_levels 必须包含 low/high"
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)
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return caps
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@property
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@ -19,6 +19,8 @@ def build_thinking_kwargs(
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``extra_body`` 对应当前 DeepSeek、GLM 与方舟 ChatCompletions 的共同协议；
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effort 仅在开启且档案提供非空值时发送。
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"""
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if reasoning_effort == "auto":
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raise ValueError("reasoning_effort=auto 必须在调用编排层解析后才能发送")
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if transport == "none":
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return {}
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if transport != "extra_body":
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216
core/loop.py
216
core/loop.py
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@ -23,7 +23,11 @@ import litellm
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from . import pptx_guard
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from .artifacts import MAX_ARTIFACTS_PER_MESSAGE
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from .attachments import materialize_native_images
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from .capabilities import ModelCapabilities, model_profile_of
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from .capabilities import (
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REASONING_EFFORT_AUTO,
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ModelCapabilities,
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model_profile_of,
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)
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from .context import (
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CHARS_PER_TOKEN,
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COMPACT_CONTEXT_RATIO,
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@ -55,12 +59,49 @@ from .storage import (
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record_tool_failure,
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)
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from .task_actions import DeferredTaskActions
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from .tool_failure import structured_failure
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# 产物机检只挂能落盘的执行类工具(fs 写工具不适合造 pptx,机检无意义)
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_PPTX_GUARD_TOOLS = ("shell", "run_python")
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_CANCELLED_TOOL_PLACEHOLDER = "[cancelled by user]"
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_REASONING_RETRY_INSTRUCTION = (
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"本次重试请压缩内部推理，尽快给出正文或发起必要的工具调用。"
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)
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class ReasoningPhaseTimeout(RuntimeError):
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"""当前流只输出 reasoning，超过保护时限后已被关闭。"""
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class ReasoningGuardExhausted(RuntimeError):
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"""纯 reasoning 保护重试仍超时，本轮应明确停止。"""
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def resolve_reasoning_effort(
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configured: str,
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*,
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first_call: bool,
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previous_tools_succeeded: Optional[bool],
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) -> Tuple[Optional[str], str]:
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"""把档案配置解析为本次 provider 档位；``auto`` 永不直接出站。"""
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if configured != REASONING_EFFORT_AUTO:
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return configured or None, "configured"
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if first_call:
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return "high", "first_call"
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if previous_tools_succeeded is False:
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return "high", "previous_tools_failed"
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return "low", "previous_tools_succeeded"
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def tool_result_succeeded_for_reasoning(
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name: str, arguments: Any, result: str, *, productive: bool
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) -> bool:
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"""auto 使用的工具成功口径：净产出且没有错误或质量门失败。"""
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if not productive or "[产物机检 ERROR]" in result:
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return False
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return structured_failure(name, result, arguments=arguments) is None
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# 错误签名归一:同一类工具报错在不同参数/路径/数字下抹平,让「反复撞同一堵墙」
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@ -265,6 +306,11 @@ class AgentLoop:
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# Structured deliverables accumulated across tool steps in the current user turn.
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# They are persisted on the final assistant message, not mixed into provider payloads.
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self._pending_artifact_refs: list[dict] = []
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# auto reasoning 只在当前用户轮次内决策；档案值本身不直接发给 provider。
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self._llm_call_count = 0
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self._previous_tool_step_succeeded: Optional[bool] = None
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self._active_reasoning_effort: Optional[str] = None
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self._reasoning_usage: dict[str, Any] = {}
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def _emit(self, event: dict) -> None:
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if self.sink is not None:
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@ -296,6 +342,10 @@ class AgentLoop:
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def _run(self, user_message: Optional[str]) -> str:
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self._pending_artifact_refs = []
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self._llm_call_count = 0
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self._previous_tool_step_succeeded = None
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self._active_reasoning_effort = None
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self._reasoning_usage = {}
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self._maybe_fold_context()
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if user_message is not None:
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self.session.append({"role": "user", "content": user_message})
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@ -306,7 +356,22 @@ class AgentLoop:
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return "[cancelled]"
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start = time.monotonic()
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try:
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response, cancelled_mid_stream = self._stream_llm()
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except ReasoningGuardExhausted:
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# 用户取消优先于自动保护的终态；两者竞态时保持停止按钮语义。
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if self._is_cancelled():
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self._emit({"type": "cancelled"})
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return "[cancelled]"
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self._emit({
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"type": "warn",
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"msg": (
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"模型连续两次仅输出推理且超过 90 秒，已停止本轮以免继续空耗。"
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"回复「继续」可重新尝试。"
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),
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})
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self._emit({"type": "done"})
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return "[stopped: reasoning timeout]"
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elapsed = time.monotonic() - start
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if cancelled_mid_stream:
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@ -347,9 +412,13 @@ class AgentLoop:
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cache_hit_cny_per_mtoken=self.caps.cache_hit_cny_per_mtoken,
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pricing=self.caps.pricing,
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extra_units={
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k: v for k, v in usage_details.items()
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**{
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k: v
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for k, v in usage_details.items()
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if k not in ("tokens_in", "tokens_out") and v
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},
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**self._reasoning_usage,
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},
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response=response,
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)
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except Exception as e:
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@ -381,6 +450,7 @@ class AgentLoop:
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return content
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step_productive = False
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step_reasoning_success = True
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for i, tc in enumerate(tool_calls):
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if self._is_cancelled():
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self._fill_cancelled_tool_results(tool_calls[i:])
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@ -389,6 +459,15 @@ class AgentLoop:
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result, productive, artifacts = self._execute_tool_call(tc)
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self._remember_artifacts(artifacts)
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step_productive = step_productive or productive
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succeeded_for_reasoning = tool_result_succeeded_for_reasoning(
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tc.function.name,
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tc.function.arguments,
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result,
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productive=productive,
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)
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step_reasoning_success = (
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step_reasoning_success and succeeded_for_reasoning
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)
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message_id = self.session.append(
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{
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"role": "tool",
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@ -412,6 +491,10 @@ class AgentLoop:
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# 可观测性留痕失败不能打断主对话。
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pass
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# 下一次 auto 决策只看刚完成的整步：所有工具均有净产出且未报错/未过
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# 质量门才视为成功；任一错误、门失败或整步无净产出都回到 high。
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self._previous_tool_step_succeeded = step_reasoning_success
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# ask_user:本步调用了人工选择工具 → 提前结束本轮,等用户点选项 / 文字讨论,
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# 不回灌 LLM。选项已随该 tool_call 的 arguments 流给前端渲染成选项卡;tool 结果
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# 只是占位,下轮用户回复(点选项 = 发选项 label 文本)后模型自然接上。
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@ -471,6 +554,7 @@ class AgentLoop:
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# 一整段 2k+ token 生成),首败即降级非流式(provider 服务端拼 tool_calls,绕开
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# 流式 delta 错位),历史数据里非流式兜底从未再畸形。
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_MAX_MALFORMED_ATTEMPTS = 3
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_REASONING_PHASE_TIMEOUT_S = 90.0
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# DeepSeek 的长 write/edit arguments 在流式 delta 中偶发错位。function.name 首包
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# 到达时立即关流并非流式重发；正文和其他工具仍走流式。只限定已实证的模型族，
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返回 (response, cancelled_mid_stream);语义见 robust_stream docstring。
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"""
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configured = str(getattr(self.caps, "default_reasoning_effort", "") or "")
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call_count = int(getattr(self, "_llm_call_count", 0))
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effort, reason = resolve_reasoning_effort(
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configured,
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first_call=call_count == 0,
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previous_tools_succeeded=getattr(
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self, "_previous_tool_step_succeeded", None
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),
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)
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self._llm_call_count = call_count + 1
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self._active_reasoning_effort = effort
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self._reasoning_usage = {
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"reasoning_config": configured,
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"reasoning_effort": effort or "",
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"reasoning_reason": reason,
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"reasoning_guard_retry": False,
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}
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# 上下文压力门槛按当前模型 reliable_context 折算:体量未到阈值前不压缩(缓存全暖 + 不丢信息)。
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# 换算比值走校准态(实报 usage 优先,回退 2.5)—— 门槛语义是 token 口径,chars 只是载体。
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ratio = self._context_ratio()
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@ -591,6 +693,47 @@ class AgentLoop:
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sc["function"]["name"]: (sc["function"].get("parameters") or {}).get("required") or []
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for sc in self.executor.schemas()
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}
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try:
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return self._run_robust_stream(
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llm_messages=llm_messages,
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required_by_tool=required_by_tool,
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llm_start_event=llm_start_event,
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)
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except ReasoningPhaseTimeout:
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if self._is_cancelled():
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return None, True
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self._record_reasoning_guard("reasoning_timeout_retry", count=1)
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self._emit({
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"type": "warn",
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"level": "info",
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"msg": "推理阶段超过 90 秒，已降为 low 并压缩推理重试一次",
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})
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self._active_reasoning_effort = "low"
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self._reasoning_usage.update({
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"reasoning_effort": "low",
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"reasoning_reason": "reasoning_guard_retry",
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"reasoning_guard_retry": True,
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})
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retry_messages = self._with_reasoning_retry_instruction(llm_messages)
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try:
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return self._run_robust_stream(
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llm_messages=retry_messages,
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required_by_tool=required_by_tool,
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llm_start_event=llm_start_event,
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)
|
||||
except ReasoningPhaseTimeout as exc:
|
||||
if self._is_cancelled():
|
||||
return None, True
|
||||
self._record_reasoning_guard("reasoning_timeout_stop", count=2)
|
||||
raise ReasoningGuardExhausted from exc
|
||||
|
||||
def _run_robust_stream(
|
||||
self,
|
||||
*,
|
||||
llm_messages: List[dict],
|
||||
required_by_tool: Dict[str, List[str]],
|
||||
llm_start_event: dict,
|
||||
) -> Tuple[Optional[Any], bool]:
|
||||
return robust_stream(
|
||||
collect_stream=self._collect_stream_once,
|
||||
nonstream=self._nonstream_once,
|
||||
|
|
@ -605,6 +748,34 @@ class AgentLoop:
|
|||
max_attempts=self._MAX_MALFORMED_ATTEMPTS,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _with_reasoning_retry_instruction(llm_messages: List[dict]) -> List[dict]:
|
||||
"""只改本次 provider 请求副本，不进入 Session/DB。"""
|
||||
retry_messages = list(llm_messages)
|
||||
insert_at = len(retry_messages)
|
||||
for i in range(len(retry_messages) - 1, -1, -1):
|
||||
if retry_messages[i].get("role") == "user":
|
||||
insert_at = i
|
||||
break
|
||||
retry_messages.insert(insert_at, {
|
||||
"role": "system",
|
||||
"content": _REASONING_RETRY_INSTRUCTION,
|
||||
})
|
||||
return retry_messages
|
||||
|
||||
def _record_reasoning_guard(self, guard: str, *, count: int) -> None:
|
||||
try:
|
||||
record_agent_guard(
|
||||
task_id=self.session.task_id,
|
||||
user_id=self.user_id,
|
||||
model_profile=model_profile_of(self.caps),
|
||||
tool="(llm)",
|
||||
guard=guard,
|
||||
count=count,
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
def _try_salvage_response(self, response: Any) -> bool:
|
||||
"""尝试就地抢救本轮所有畸形 tool_call 的 arguments;成功才改写并返回 True。
|
||||
|
||||
|
|
@ -679,11 +850,32 @@ class AgentLoop:
|
|||
"""跑一次流式:攒 chunk + content delta 即时 emit,拼回完整 response。
|
||||
返回 (response, cancelled_mid_stream)。"""
|
||||
chunks: List[Any] = []
|
||||
reasoning_started_at: Optional[float] = None
|
||||
reasoning_seen = False
|
||||
output_seen = False
|
||||
reasoning_timed_out = False
|
||||
|
||||
def _stop_stream() -> bool:
|
||||
nonlocal reasoning_timed_out
|
||||
# 取消始终优先，竞态时不把用户主动停止误记成自动保护。
|
||||
if self._is_cancelled():
|
||||
return True
|
||||
if (
|
||||
reasoning_seen
|
||||
and not output_seen
|
||||
and reasoning_started_at is not None
|
||||
and time.monotonic() - reasoning_started_at
|
||||
>= self._REASONING_PHASE_TIMEOUT_S
|
||||
):
|
||||
reasoning_timed_out = True
|
||||
return True
|
||||
return False
|
||||
|
||||
stream = self.llm.chat_stream(
|
||||
messages=llm_messages,
|
||||
tools=self.executor.schemas(),
|
||||
reasoning_effort=self.caps.default_reasoning_effort or None,
|
||||
cancel_check=self._is_cancelled,
|
||||
reasoning_effort=getattr(self, "_active_reasoning_effort", None),
|
||||
cancel_check=_stop_stream,
|
||||
)
|
||||
cancelled = False
|
||||
may_reroute = self.caps.family == "deepseek_v4"
|
||||
|
|
@ -695,6 +887,12 @@ class AgentLoop:
|
|||
break
|
||||
chunks.append(chunk)
|
||||
tool_names = extract_delta_tool_names(chunk)
|
||||
try:
|
||||
has_tool_delta = bool(chunk.choices[0].delta.tool_calls)
|
||||
except (AttributeError, IndexError, TypeError):
|
||||
has_tool_delta = False
|
||||
if tool_names or has_tool_delta:
|
||||
output_seen = True
|
||||
if (
|
||||
may_reroute
|
||||
and any(name in self._DEEPSEEK_NONSTREAM_TOOLS for name in tool_names)
|
||||
|
|
@ -712,11 +910,15 @@ class AgentLoop:
|
|||
# _execute_tool_call 时机发更直观)。
|
||||
delta_text = extract_delta_content(chunk)
|
||||
if delta_text:
|
||||
output_seen = True
|
||||
self._emit({"type": "text", "delta": delta_text})
|
||||
# thinking 模型的推理 delta 也实时流出(reasoning 事件):深度推理可达
|
||||
# 分钟级,不发的话前端全程静止"思考中",用户以为卡死。
|
||||
delta_reasoning = extract_delta_reasoning(chunk)
|
||||
if delta_reasoning:
|
||||
if reasoning_started_at is None:
|
||||
reasoning_started_at = time.monotonic()
|
||||
reasoning_seen = True
|
||||
self._emit({"type": "reasoning", "delta": delta_reasoning})
|
||||
reasoning_emitted = True
|
||||
# interruptible stream 会在无新 chunk 的等待期直接因 cancel 结束迭代；
|
||||
|
|
@ -731,6 +933,10 @@ class AgentLoop:
|
|||
|
||||
if cancelled:
|
||||
return None, True
|
||||
if reasoning_timed_out:
|
||||
if reasoning_emitted:
|
||||
self._emit({"type": "reasoning_reset"})
|
||||
raise ReasoningPhaseTimeout
|
||||
|
||||
# 用 litellm 官方 helper 拼回完整 response(包括 tool_calls 拼接 + usage)。
|
||||
# messages 参数仅用于失败时回填 prompt token 估算,正常路径 stream_options.include_usage
|
||||
|
|
@ -756,7 +962,7 @@ class AgentLoop:
|
|||
box["resp"] = self.llm.chat(
|
||||
messages=llm_messages,
|
||||
tools=self.executor.schemas(),
|
||||
reasoning_effort=self.caps.default_reasoning_effort or None,
|
||||
reasoning_effort=getattr(self, "_active_reasoning_effort", None),
|
||||
)
|
||||
except BaseException as e: # noqa: BLE001 — 原样转抛回主线程
|
||||
box["exc"] = e
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ from __future__ import annotations
|
|||
from dataclasses import dataclass, field
|
||||
from typing import Any, List, Optional
|
||||
|
||||
from .capabilities import ModelCapabilities
|
||||
from .capabilities import REASONING_EFFORT_AUTO, ModelCapabilities
|
||||
from .llm import LLM
|
||||
|
||||
|
||||
|
|
@ -147,6 +147,8 @@ def probe_thinking(llm: LLM, caps: ModelCapabilities) -> ProbeResult:
|
|||
caps.default_reasoning_effort
|
||||
or (caps.reasoning_effort_levels[0] if caps.reasoning_effort_levels else None)
|
||||
)
|
||||
if effort == REASONING_EFFORT_AUTO:
|
||||
effort = "high"
|
||||
try:
|
||||
resp = llm.chat(
|
||||
messages=[{"role": "user", "content": "Briefly: what is 17 * 23?"}],
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
import os
|
||||
import tempfile
|
||||
import unittest
|
||||
from pathlib import Path
|
||||
from unittest.mock import patch
|
||||
|
|
@ -101,6 +102,15 @@ class LLMKwargsTests(unittest.TestCase):
|
|||
[{"role": "user", "content": "hello"}], None, None, "high"
|
||||
)
|
||||
|
||||
def test_auto_is_rejected_at_provider_boundary(self) -> None:
|
||||
llm = self._llm(
|
||||
family="deepseek_v4", thinking_enabled=True, thinking_transport="extra_body"
|
||||
)
|
||||
with self.assertRaisesRegex(ValueError, "auto"):
|
||||
llm._build_kwargs(
|
||||
[{"role": "user", "content": "hello"}], None, None, "auto"
|
||||
)
|
||||
|
||||
def test_flash_profile_matches_0731_capabilities(self) -> None:
|
||||
caps = ModelCapabilities.load(
|
||||
"deepseek_v4.flash", Path(__file__).resolve().parents[1] / "config" / "models"
|
||||
|
|
@ -108,7 +118,7 @@ class LLMKwargsTests(unittest.TestCase):
|
|||
|
||||
self.assertTrue(caps.thinking_enabled)
|
||||
self.assertEqual(caps.reasoning_effort_levels, ["low", "high", "max"])
|
||||
self.assertEqual(caps.default_reasoning_effort, "high")
|
||||
self.assertEqual(caps.default_reasoning_effort, "auto")
|
||||
self.assertEqual(caps.max_output, 8192)
|
||||
self.assertEqual(caps.output_cny_per_mtoken, 4.752)
|
||||
self.assertEqual(caps.cache_hit_cny_per_mtoken, 0.0504)
|
||||
|
|
@ -118,6 +128,25 @@ class LLMKwargsTests(unittest.TestCase):
|
|||
self.assertEqual(caps.thinking_transport, "extra_body")
|
||||
self.assertEqual(caps.reasoning_replay, "tool_turn")
|
||||
|
||||
pro = ModelCapabilities.load(
|
||||
"deepseek_v4.pro", Path(__file__).resolve().parents[1] / "config" / "models"
|
||||
)
|
||||
self.assertEqual(pro.default_reasoning_effort, "medium")
|
||||
|
||||
def test_auto_profile_requires_low_and_high_levels(self) -> None:
|
||||
with tempfile.TemporaryDirectory() as tmp:
|
||||
path = Path(tmp) / "test.yaml"
|
||||
path.write_text(
|
||||
"family: test\nvariants:\n bad:\n"
|
||||
" thinking_enabled: true\n"
|
||||
" thinking_transport: extra_body\n"
|
||||
" reasoning_effort_levels: [low, max]\n"
|
||||
" default_reasoning_effort: auto\n",
|
||||
encoding="utf-8",
|
||||
)
|
||||
with self.assertRaisesRegex(ValueError, "low/high"):
|
||||
ModelCapabilities.load("test.bad", Path(tmp))
|
||||
|
||||
def test_other_controllable_profiles_declare_transport(self) -> None:
|
||||
models_dir = Path(__file__).resolve().parents[1] / "config" / "models"
|
||||
|
||||
|
|
|
|||
|
|
@ -37,6 +37,7 @@ def _make_loop(stream_results, nonstream_results):
|
|||
loop.caps = SimpleNamespace(reliable_context=64_000, family="test", variant="t")
|
||||
loop.session = SimpleNamespace(messages=[], task_id="test-task")
|
||||
loop.user_id = "test-user" # 无 DB:_log_empty_response 落库路径静默跳过
|
||||
loop.user_root = None
|
||||
loop.executor = SimpleNamespace(schemas=lambda: []) # required_by_tool 取值用,空即可
|
||||
loop.events = []
|
||||
loop._emit = loop.events.append
|
||||
|
|
|
|||
|
|
@ -0,0 +1,269 @@
|
|||
from __future__ import annotations
|
||||
|
||||
import unittest
|
||||
from pathlib import Path
|
||||
from types import SimpleNamespace
|
||||
from unittest.mock import MagicMock, patch
|
||||
from uuid import uuid4
|
||||
|
||||
from core.loop import (
|
||||
AgentLoop,
|
||||
ReasoningGuardExhausted,
|
||||
ReasoningPhaseTimeout,
|
||||
resolve_reasoning_effort,
|
||||
tool_result_succeeded_for_reasoning,
|
||||
)
|
||||
from core.probe import probe_thinking
|
||||
|
||||
|
||||
def _text_response(text: str = "ok"):
|
||||
return SimpleNamespace(
|
||||
choices=[SimpleNamespace(message=SimpleNamespace(
|
||||
content=text, tool_calls=None,
|
||||
))],
|
||||
usage=None,
|
||||
)
|
||||
|
||||
|
||||
def _reasoning_chunk(text: str = "thinking"):
|
||||
return SimpleNamespace(choices=[SimpleNamespace(delta=SimpleNamespace(
|
||||
reasoning_content=text, content=None, tool_calls=None,
|
||||
))])
|
||||
|
||||
|
||||
def _stream_loop(*, configured: str = "auto", cancelled=False) -> AgentLoop:
|
||||
loop = object.__new__(AgentLoop)
|
||||
loop.caps = SimpleNamespace(
|
||||
default_reasoning_effort=configured,
|
||||
reliable_context=64_000,
|
||||
family="deepseek_v4",
|
||||
variant="flash",
|
||||
reasoning_replay="tool_turn",
|
||||
native_image_input=False,
|
||||
)
|
||||
loop.session = SimpleNamespace(
|
||||
messages=[{"role": "user", "content": "hello"}], task_id="task",
|
||||
)
|
||||
loop.user_id = "user"
|
||||
loop.user_root = None
|
||||
loop.working_dir = Path(".")
|
||||
loop.executor = SimpleNamespace(schemas=lambda: [])
|
||||
loop.cancel_check = (lambda: cancelled)
|
||||
loop.events = []
|
||||
loop._emit = loop.events.append
|
||||
loop._ctx_chars_per_token = 2.5
|
||||
loop._last_sent_chars = 0
|
||||
loop._last_had_native_images = False
|
||||
loop._llm_call_count = 0
|
||||
loop._previous_tool_step_succeeded = None
|
||||
loop._reasoning_usage = {}
|
||||
return loop
|
||||
|
||||
|
||||
class ReasoningDecisionTests(unittest.TestCase):
|
||||
def test_auto_first_call_is_high(self) -> None:
|
||||
self.assertEqual(
|
||||
resolve_reasoning_effort(
|
||||
"auto", first_call=True, previous_tools_succeeded=None
|
||||
),
|
||||
("high", "first_call"),
|
||||
)
|
||||
|
||||
def test_auto_success_is_low_and_failure_is_high(self) -> None:
|
||||
self.assertEqual(
|
||||
resolve_reasoning_effort(
|
||||
"auto", first_call=False, previous_tools_succeeded=True
|
||||
),
|
||||
("low", "previous_tools_succeeded"),
|
||||
)
|
||||
self.assertEqual(
|
||||
resolve_reasoning_effort(
|
||||
"auto", first_call=False, previous_tools_succeeded=False
|
||||
),
|
||||
("high", "previous_tools_failed"),
|
||||
)
|
||||
|
||||
def test_fixed_effort_is_unchanged(self) -> None:
|
||||
self.assertEqual(
|
||||
resolve_reasoning_effort(
|
||||
"max", first_call=False, previous_tools_succeeded=True
|
||||
),
|
||||
("max", "configured"),
|
||||
)
|
||||
|
||||
def test_tool_success_error_and_quality_gate_signals(self) -> None:
|
||||
self.assertTrue(tool_result_succeeded_for_reasoning(
|
||||
"read", '{}', "file content", productive=True,
|
||||
))
|
||||
self.assertFalse(tool_result_succeeded_for_reasoning(
|
||||
"read", '{}', "[Error] missing", productive=False,
|
||||
))
|
||||
self.assertFalse(tool_result_succeeded_for_reasoning(
|
||||
"shell", '{"command":"build"}',
|
||||
"created\n[产物机检 ERROR] 发现整页贴图", productive=True,
|
||||
))
|
||||
|
||||
def test_probe_resolves_auto_before_provider_call(self) -> None:
|
||||
efforts = []
|
||||
|
||||
def chat(**kwargs):
|
||||
efforts.append(kwargs["reasoning_effort"])
|
||||
return SimpleNamespace(choices=[SimpleNamespace(message=SimpleNamespace(
|
||||
reasoning_content="brief reasoning", content="391",
|
||||
))])
|
||||
|
||||
caps = SimpleNamespace(
|
||||
thinking_enabled=True,
|
||||
default_reasoning_effort="auto",
|
||||
reasoning_effort_levels=["low", "high", "max"],
|
||||
)
|
||||
result = probe_thinking(SimpleNamespace(chat=chat), caps)
|
||||
self.assertEqual(efforts, ["high"])
|
||||
self.assertEqual(result.status, "ok")
|
||||
|
||||
|
||||
class ReasoningGuardTests(unittest.TestCase):
|
||||
def test_pure_reasoning_timeout_resets_stream(self) -> None:
|
||||
loop = _stream_loop()
|
||||
|
||||
def chat_stream(**kwargs):
|
||||
yield _reasoning_chunk()
|
||||
self.assertTrue(kwargs["cancel_check"]())
|
||||
|
||||
loop.llm = SimpleNamespace(chat_stream=chat_stream)
|
||||
loop._active_reasoning_effort = "high"
|
||||
loop._REASONING_PHASE_TIMEOUT_S = 0
|
||||
|
||||
with self.assertRaises(ReasoningPhaseTimeout):
|
||||
loop._collect_stream_once(loop.session.messages)
|
||||
self.assertEqual(loop.events, [
|
||||
{"type": "reasoning", "delta": "thinking"},
|
||||
{"type": "reasoning_reset"},
|
||||
])
|
||||
|
||||
@patch("core.loop.record_agent_guard")
|
||||
def test_timeout_retries_once_with_low_and_ephemeral_instruction(self, guard) -> None:
|
||||
loop = _stream_loop()
|
||||
calls = []
|
||||
|
||||
def run_robust(**kwargs):
|
||||
calls.append((loop._active_reasoning_effort, kwargs["llm_messages"]))
|
||||
if len(calls) == 1:
|
||||
raise ReasoningPhaseTimeout
|
||||
return _text_response(), False
|
||||
|
||||
loop._run_robust_stream = run_robust
|
||||
response, cancelled = loop._stream_llm()
|
||||
|
||||
self.assertFalse(cancelled)
|
||||
self.assertEqual(response.choices[0].message.content, "ok")
|
||||
self.assertEqual([call[0] for call in calls], ["high", "low"])
|
||||
self.assertEqual(len(calls[0][1]), 1)
|
||||
self.assertEqual(len(calls[1][1]), 2)
|
||||
self.assertEqual(calls[1][1][0]["role"], "system")
|
||||
self.assertEqual(loop.session.messages, [{"role": "user", "content": "hello"}])
|
||||
self.assertEqual(loop._reasoning_usage, {
|
||||
"reasoning_config": "auto",
|
||||
"reasoning_effort": "low",
|
||||
"reasoning_reason": "reasoning_guard_retry",
|
||||
"reasoning_guard_retry": True,
|
||||
})
|
||||
guard.assert_called_once()
|
||||
|
||||
@patch("core.loop.record_agent_guard")
|
||||
def test_second_timeout_stops(self, guard) -> None:
|
||||
loop = _stream_loop()
|
||||
loop._run_robust_stream = MagicMock(side_effect=[
|
||||
ReasoningPhaseTimeout(), ReasoningPhaseTimeout(),
|
||||
])
|
||||
with self.assertRaises(ReasoningGuardExhausted):
|
||||
loop._stream_llm()
|
||||
self.assertEqual(guard.call_count, 2)
|
||||
|
||||
@patch("core.loop.record_agent_guard")
|
||||
def test_user_cancel_wins_over_guard_retry(self, guard) -> None:
|
||||
loop = _stream_loop(cancelled=True)
|
||||
loop._run_robust_stream = MagicMock(side_effect=ReasoningPhaseTimeout())
|
||||
response, cancelled = loop._stream_llm()
|
||||
self.assertIsNone(response)
|
||||
self.assertTrue(cancelled)
|
||||
guard.assert_not_called()
|
||||
|
||||
def test_nonstream_fallback_uses_resolved_effort(self) -> None:
|
||||
loop = _stream_loop()
|
||||
efforts = []
|
||||
|
||||
def chat(**kwargs):
|
||||
efforts.append(kwargs["reasoning_effort"])
|
||||
return _text_response()
|
||||
|
||||
loop.llm = SimpleNamespace(chat=chat)
|
||||
loop._active_reasoning_effort = "low"
|
||||
response = loop._nonstream_once(loop.session.messages)
|
||||
self.assertEqual(response.choices[0].message.content, "ok")
|
||||
self.assertEqual(efforts, ["low"])
|
||||
|
||||
|
||||
class _Session:
|
||||
def __init__(self):
|
||||
self.task_id = uuid4()
|
||||
self.messages = [{"role": "user", "content": "hello"}]
|
||||
self.appended = []
|
||||
|
||||
def append(self, message, **_kwargs):
|
||||
self.messages.append(message)
|
||||
self.appended.append(message)
|
||||
return uuid4()
|
||||
|
||||
|
||||
class ReasoningPersistenceTests(unittest.TestCase):
|
||||
def test_exhausted_guard_does_not_persist_partial_assistant(self) -> None:
|
||||
session = _Session()
|
||||
loop = AgentLoop(
|
||||
llm=MagicMock(), executor=MagicMock(), session=session,
|
||||
capabilities=SimpleNamespace(max_iterations=1),
|
||||
user_id=uuid4(), working_dir=Path("."),
|
||||
)
|
||||
loop._maybe_fold_context = MagicMock()
|
||||
loop._stream_llm = MagicMock(side_effect=ReasoningGuardExhausted())
|
||||
|
||||
result = loop.run_persisted_turn()
|
||||
|
||||
self.assertEqual(result, "[stopped: reasoning timeout]")
|
||||
self.assertEqual(session.appended, [])
|
||||
self.assertEqual(loop.events if hasattr(loop, "events") else [], [])
|
||||
|
||||
@patch("core.loop.record_chat_usage")
|
||||
def test_successful_chat_records_reasoning_metadata(self, record_usage) -> None:
|
||||
session = _Session()
|
||||
caps = SimpleNamespace(
|
||||
max_iterations=1, family="deepseek_v4", variant="flash",
|
||||
input_cny_per_mtoken=0, output_cny_per_mtoken=0,
|
||||
cache_hit_cny_per_mtoken=0, pricing={},
|
||||
)
|
||||
loop = AgentLoop(
|
||||
llm=MagicMock(), executor=MagicMock(), session=session,
|
||||
capabilities=caps, user_id=uuid4(), working_dir=Path("."),
|
||||
)
|
||||
loop._maybe_fold_context = MagicMock()
|
||||
|
||||
def stream():
|
||||
loop._reasoning_usage = {
|
||||
"reasoning_config": "auto",
|
||||
"reasoning_effort": "high",
|
||||
"reasoning_reason": "first_call",
|
||||
"reasoning_guard_retry": False,
|
||||
}
|
||||
return _text_response("done"), False
|
||||
|
||||
loop._stream_llm = MagicMock(side_effect=stream)
|
||||
self.assertEqual(loop.run_persisted_turn(), "done")
|
||||
units = record_usage.call_args.kwargs["extra_units"]
|
||||
self.assertEqual(units["reasoning_config"], "auto")
|
||||
self.assertEqual(units["reasoning_effort"], "high")
|
||||
self.assertEqual(units["reasoning_reason"], "first_call")
|
||||
self.assertFalse(units["reasoning_guard_retry"])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Loading…
Reference in New Issue