Replace temperature with reasoning effort and retry in-stream errors

- Providers get a default reasoning effort (low..max, empty = not sent);
  chat can override it per message. OpenAI sends reasoning_effort,
  Claude sends output_config.effort with adaptive thinking, and thinking
  blocks are replayed during the tool loop.
- Show a thinking indicator while waiting for model output.
- Surface in-stream upstream errors (e.g. overloaded) instead of
  breaking the SSE stream, and retry them up to 3 times when nothing
  has been emitted yet.

backend/alembic/versions/20261014_5c2e8f1a7b3d_provider_reasoning_effort.py

@@ -0,0 +1,31 @@
+"""provider reasoning effort replaces temperature
+
+Revision ID: 5c2e8f1a7b3d
+Revises: 8349ae014ddd
+Create Date: 2026-10-14 14:00:00.000000
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '5c2e8f1a7b3d'
+down_revision: Union[str, Sequence[str], None] = '8349ae014ddd'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # 用户决定不再设置 temperature（2026-10-14），改为思考强度
+    op.drop_column('ai_providers', 'temperature')
+    op.add_column('ai_providers', sa.Column('reasoning_effort', sa.String(length=8), nullable=True))
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    op.drop_column('ai_providers', 'reasoning_effort')
+    op.add_column('ai_providers', sa.Column('temperature', sa.Float(), nullable=True))

backend/src/alphapulse/ai/llm.py

@@ -2,14 +2,16 @@
 
 消息使用与协议无关的格式（与 ChatMessage.content 一致，外加 role）：
   {"role": "user", "text"}
-  {"role": "assistant", "text", "tool_calls": [{"id", "name", "arguments"}]}
+  {"role": "assistant", "text", "tool_calls": [{"id", "name", "arguments"}], "thinking_blocks"?: [...]}
   {"role": "tool", "tool_call_id", "name", "result"}
 """
 
+import asyncio
 import json
+import logging
 from collections.abc import AsyncIterator
 from dataclasses import dataclass, field
-from typing import Any
+from typing import Any, Literal
 
 import anthropic
 import openai
@@ -17,9 +19,17 @@ import openai
 from alphapulse.core.crypto import decrypt
 from alphapulse.models import AiProvider
 
-TIMEOUT_SECONDS = 120
+# 高思考强度时 OpenAI 格式在推理期间不推送任何内容，读超时要留足
+TIMEOUT_SECONDS = 600
+# 思考强度：两种协议共有的取值。OpenAI 另有 none/minimal，但实测经 Sub2API 会卡住后 502，不提供
+Effort = Literal["low", "medium", "high", "xhigh", "max"]
 # SDK 自带重试只针对连接错误、429 和 5xx
 MAX_RETRIES = 2
+# 流内错误（上游过载等）SDK 不重试，由这里重试；只在还没有输出内容时重试
+STREAM_RETRIES = 3
+STREAM_RETRY_DELAY_SECONDS = 2
+
+logger = logging.getLogger("alphapulse.ai.llm")
 
 
 class LlmError(Exception):
@@ -39,6 +49,8 @@ class Completion:
     text: str = ""
     tool_calls: list[dict] = field(default_factory=list)
     usage: dict = field(default_factory=dict)
+    # Anthropic 的 thinking / redacted_thinking 块：工具调用循环中必须原样回传，否则接口报错
+    thinking_blocks: list[dict] = field(default_factory=list)
 
 
 @dataclass
@@ -46,27 +58,60 @@ class TextDelta:
     text: str
 
 
+@dataclass
+class ThinkingStarted:
+    """模型开始思考（只有 Claude 格式会推送）；前端据此显示「思考中」"""
+
+
 def api_root(base_url: str) -> str:
     """用户可能填 https://host 或 https://host/v1，统一成不带 /v1 的根地址"""
     root = base_url.rstrip("/")
     return root.removesuffix("/v1")
 
 
-async def stream(provider: AiProvider, system: str, messages: list[dict], tools: list[ToolSpec]) -> AsyncIterator[TextDelta | Completion]:
-    """先逐段产出 TextDelta，最后产出一个 Completion"""
+async def stream(
+    provider: AiProvider, system: str, messages: list[dict], tools: list[ToolSpec], effort: Effort | None = None,
+) -> AsyncIterator[TextDelta | ThinkingStarted | Completion]:
+    """先逐段产出 TextDelta / ThinkingStarted，最后产出一个 Completion。effort 为空表示不传，由模型决定"""
+    for attempt in range(STREAM_RETRIES + 1):
+        output = False
         try:
             if provider.protocol == "openai":
-            async for event in _stream_openai(provider, system, messages, tools):
-                yield event
+                events = _stream_openai(provider, system, messages, tools, effort)
             else:
-            async for event in _stream_anthropic(provider, system, messages, tools):
+                events = _stream_anthropic(provider, system, messages, tools, effort)
+            async for event in events:
+                output = True
                 yield event
-    except (openai.APIStatusError, anthropic.APIStatusError) as e:
-        raise LlmError(f"模型接口返回 {e.status_code}：{e.message}") from e
-    except (openai.APITimeoutError, anthropic.APITimeoutError) as e:
-        raise LlmError(f"模型接口超时（{TIMEOUT_SECONDS}s）") from e
-    except (openai.APIConnectionError, anthropic.APIConnectionError) as e:
-        raise LlmError(f"无法连接模型接口：{e}") from e
+            return
+        except (openai.APIError, anthropic.APIError) as e:
+            # 已经推送过内容再重试会重复输出，只重试还没有输出时的流内错误
+            if output or attempt == STREAM_RETRIES or not _in_stream_error(e):
+                raise _llm_error(e) from e
+            logger.warning("模型流内出错，%ss 后第 %s 次重试：%s", STREAM_RETRY_DELAY_SECONDS, attempt + 1, _llm_error(e))
+            await asyncio.sleep(STREAM_RETRY_DELAY_SECONDS)
+
+
+def _in_stream_error(e: Exception) -> bool:
+    """连接建立后上游在流内返回的错误事件（如 overloaded）。
+    OpenAI SDK 抛 APIError 基类；Anthropic SDK 抛 APIStatusError，状态码是流本身的 200"""
+    if isinstance(e, openai.APIError):
+        return not isinstance(e, (openai.APIStatusError, openai.APIConnectionError))
+    return isinstance(e, anthropic.APIStatusError) and e.status_code == 200
+
+
+def _llm_error(e: Exception) -> LlmError:
+    if _in_stream_error(e):
+        error = e.body.get("error") if isinstance(e.body, dict) else None
+        message = error.get("message") if isinstance(error, dict) else None
+        return LlmError(f"模型接口出错：{message or e.message}")
+    if isinstance(e, (openai.APIStatusError, anthropic.APIStatusError)):
+        return LlmError(f"模型接口返回 {e.status_code}：{e.message}")
+    if isinstance(e, (openai.APITimeoutError, anthropic.APITimeoutError)):
+        return LlmError(f"模型接口超时（{TIMEOUT_SECONDS}s）")
+    if isinstance(e, (openai.APIConnectionError, anthropic.APIConnectionError)):
+        return LlmError(f"无法连接模型接口：{e}")
+    return LlmError(f"模型接口出错：{e}")
 
 
 # ---------- OpenAI ----------
@@ -92,7 +137,7 @@ def _openai_messages(system: str, messages: list[dict]) -> list[dict]:
     return out
 
 
-async def _stream_openai(provider: AiProvider, system: str, messages: list[dict], tools: list[ToolSpec]) -> AsyncIterator[TextDelta | Completion]:
+async def _stream_openai(provider: AiProvider, system: str, messages: list[dict], tools: list[ToolSpec], effort: Effort | None) -> AsyncIterator[TextDelta | Completion]:
     client = openai.AsyncOpenAI(
         api_key=decrypt(provider.api_key_encrypted), base_url=f"{api_root(provider.base_url)}/v1",
         timeout=TIMEOUT_SECONDS, max_retries=MAX_RETRIES,
@@ -100,8 +145,8 @@ async def _stream_openai(provider: AiProvider, system: str, messages: list[dict]
     kwargs: dict[str, Any] = {}
     if tools:
         kwargs["tools"] = [{"type": "function", "function": {"name": t.name, "description": t.description, "parameters": t.parameters}} for t in tools]
-    if provider.temperature is not None:
-        kwargs["temperature"] = provider.temperature
+    if effort:
+        kwargs["reasoning_effort"] = effort
     result = Completion()
     # 按 index 拼接分片到达的工具调用
     calls: dict[int, dict] = {}
@@ -139,7 +184,10 @@ def _anthropic_messages(messages: list[dict]) -> list[dict]:
         if m["role"] == "user":
             out.append({"role": "user", "content": m["text"]})
         elif m["role"] == "assistant":
-            blocks: list[dict] = [{"type": "text", "text": m["text"]}] if m["text"] else []
+            # 思考块必须排在最前，原样回传
+            blocks: list[dict] = list(m.get("thinking_blocks") or [])
+            if m["text"]:
+                blocks.append({"type": "text", "text": m["text"]})
             blocks += [{"type": "tool_use", "id": c["id"], "name": c["name"], "input": c["arguments"]} for c in m.get("tool_calls") or []]
             # 空的 assistant 消息会被接口拒绝
             if blocks:
@@ -154,7 +202,7 @@ def _anthropic_messages(messages: list[dict]) -> list[dict]:
     return out
 
 
-async def _stream_anthropic(provider: AiProvider, system: str, messages: list[dict], tools: list[ToolSpec]) -> AsyncIterator[TextDelta | Completion]:
+async def _stream_anthropic(provider: AiProvider, system: str, messages: list[dict], tools: list[ToolSpec], effort: Effort | None) -> AsyncIterator[TextDelta | ThinkingStarted | Completion]:
     client = anthropic.AsyncAnthropic(
         api_key=decrypt(provider.api_key_encrypted), base_url=api_root(provider.base_url),
         timeout=TIMEOUT_SECONDS, max_retries=MAX_RETRIES,
@@ -162,11 +210,13 @@ async def _stream_anthropic(provider: AiProvider, system: str, messages: list[di
     kwargs: dict[str, Any] = {}
     if tools:
         kwargs["tools"] = [{"name": t.name, "description": t.description, "input_schema": t.parameters} for t in tools]
-    if provider.temperature is not None:
-        # 当前 SDK 不再提供 temperature 参数，按原始字段透传
-        kwargs["extra_body"] = {"temperature": provider.temperature}
+    if effort:
+        # 思考强度通过 output_config.effort 控制，配合自适应思考（由模型决定是否、思考多久）
+        kwargs["output_config"] = {"effort": effort}
+        kwargs["thinking"] = {"type": "adaptive"}
     result = Completion()
     blocks: dict[int, dict] = {}
+    thinking: dict[int, dict] = {}
     async with client:
         events = await client.messages.create(
             model=provider.model, system=system, messages=_anthropic_messages(messages), max_tokens=provider.max_tokens,
@@ -177,15 +227,26 @@ async def _stream_anthropic(provider: AiProvider, system: str, messages: list[di
                 result.usage["input_tokens"] = event.message.usage.input_tokens
             elif event.type == "content_block_start" and event.content_block.type == "tool_use":
                 blocks[event.index] = {"id": event.content_block.id, "name": event.content_block.name, "arguments": ""}
+            elif event.type == "content_block_start" and event.content_block.type == "thinking":
+                thinking[event.index] = {"type": "thinking", "thinking": event.content_block.thinking, "signature": event.content_block.signature}
+                yield ThinkingStarted()
+            elif event.type == "content_block_start" and event.content_block.type == "redacted_thinking":
+                thinking[event.index] = {"type": "redacted_thinking", "data": event.content_block.data}
+                yield ThinkingStarted()
             elif event.type == "content_block_delta":
                 if event.delta.type == "text_delta":
                     result.text += event.delta.text
                     yield TextDelta(event.delta.text)
                 elif event.delta.type == "input_json_delta":
                     blocks[event.index]["arguments"] += event.delta.partial_json
+                elif event.delta.type == "thinking_delta":
+                    thinking[event.index]["thinking"] += event.delta.thinking
+                elif event.delta.type == "signature_delta":
+                    thinking[event.index]["signature"] += event.delta.signature
             elif event.type == "message_delta":
                 result.usage["output_tokens"] = event.usage.output_tokens
     result.tool_calls = [_parse_call(b["id"], b["name"], b["arguments"]) for _, b in sorted(blocks.items())]
+    result.thinking_blocks = [b for _, b in sorted(thinking.items())]
     yield result
 
 

backend/src/alphapulse/api/chat.py

@@ -53,6 +53,8 @@ class SessionOut(BaseModel):
 class SendIn(BaseModel):
     text: Annotated[str, Field(min_length=1, max_length=20000)]
     provider_id: int | None = None
+    # 本轮思考强度；为空时用模型配置里的默认值
+    reasoning_effort: llm.Effort | None = None
 
 
 class ResetIn(BaseModel):
@@ -121,7 +123,7 @@ async def get_chat_session(session: Annotated[AsyncSession, Depends(get_session)
 
 @router.post("/messages", response_class=EventSourceResponse)
 async def send_message(body: SendIn) -> AsyncIterator[ServerSentEvent]:
-    """事件：prompt、delta、tool_call、tool_result、skill_draft、context_updated、usage、error、done"""
+    """事件：prompt、thinking、delta、tool_call、tool_result、skill_draft、context_updated、usage、error、done"""
     if generating.locked():
         yield event("error", {"message": "上一轮还在生成，请等待完成或先停止"})
         return
@@ -148,14 +150,17 @@ async def send_message(body: SendIn) -> AsyncIterator[ServerSentEvent]:
         await save_messages(ChatMessage(session_id=current.id, role="user", content={"text": body.text}))
 
         specs = tool_specs()
+        effort = body.reasoning_effort or provider.reasoning_effort
         for round_no in range(MAX_TOOL_ROUNDS + 1):
             text = ""
             completion: llm.Completion | None = None
             try:
-                async for item in llm.stream(provider, system, messages, specs if round_no < MAX_TOOL_ROUNDS else []):
+                async for item in llm.stream(provider, system, messages, specs if round_no < MAX_TOOL_ROUNDS else [], effort):
                     if isinstance(item, llm.TextDelta):
                         text += item.text
                         yield event("delta", {"text": item.text})
+                    elif isinstance(item, llm.ThinkingStarted):
+                        yield event("thinking", {})
                     else:
                         completion = item
             except llm.LlmError as e:
@@ -170,6 +175,8 @@ async def send_message(body: SendIn) -> AsyncIterator[ServerSentEvent]:
                 raise
 
             assistant = {"text": completion.text, "tool_calls": completion.tool_calls}
+            if completion.thinking_blocks:
+                assistant["thinking_blocks"] = completion.thinking_blocks
             await save_messages(ChatMessage(session_id=current.id, role="assistant", content=assistant, provider_id=provider.id, usage=completion.usage))
             messages.append({"role": "assistant", **assistant})
             yield event("usage", completion.usage)

backend/src/alphapulse/api/settings.py

@@ -30,7 +30,7 @@ class ProviderOut(BaseModel):
     model: str
     max_tokens: int
     context_window: int
-    temperature: float | None
+    reasoning_effort: llm.Effort | None
     is_default: bool
     updated_at: datetime
 
@@ -43,7 +43,7 @@ class ProviderIn(BaseModel):
     model: Annotated[str, Field(min_length=1, max_length=128)]
     max_tokens: Annotated[int, Field(ge=256, le=128000)] = 8192
     context_window: Annotated[int, Field(ge=4096, le=2000000)] = 200000
-    temperature: Annotated[float | None, Field(ge=0, le=2)] = None
+    reasoning_effort: llm.Effort | None = None
     is_default: bool = False
 
 
@@ -56,7 +56,8 @@ class ProviderPatch(BaseModel):
     model: Annotated[str | None, Field(min_length=1, max_length=128)] = None
     max_tokens: Annotated[int | None, Field(ge=256, le=128000)] = None
     context_window: Annotated[int | None, Field(ge=4096, le=2000000)] = None
-    temperature: Annotated[float | None, Field(ge=0, le=2)] = None
+    # 显式传 null 表示不传思考强度
+    reasoning_effort: llm.Effort | None = None
     is_default: bool | None = None
 
 
@@ -111,9 +112,9 @@ async def create_provider(body: ProviderIn, session: Annotated[AsyncSession, Dep
 async def patch_provider(provider_id: int, body: ProviderPatch, session: Annotated[AsyncSession, Depends(get_session)]) -> ProviderOut:
     provider = await get_provider(session, provider_id)
     values = body.model_dump(exclude_unset=True, exclude={"api_key", "base_url", "is_default"})
-    # temperature 显式传 null 表示清除，其他字段为 null 时忽略
+    # reasoning_effort 显式传 null 表示清除，其他字段为 null 时忽略
     for field, value in values.items():
-        if value is not None or field == "temperature":
+        if value is not None or field == "reasoning_effort":
             setattr(provider, field, value)
     if body.base_url is not None:
         provider.base_url = str(body.base_url)
@@ -147,7 +148,7 @@ async def test_provider(provider_id: int, session: Annotated[AsyncSession, Depen
     started = time.monotonic()
     try:
         completion = None
-        async for event in llm.stream(provider, "只回复 OK。", [{"role": "user", "text": "ping"}], []):
+        async for event in llm.stream(provider, "只回复 OK。", [{"role": "user", "text": "ping"}], [], provider.reasoning_effort):
             if isinstance(event, llm.Completion):
                 completion = event
     except llm.LlmError as e:

backend/src/alphapulse/models/ai.py

@@ -1,6 +1,6 @@
 from datetime import datetime
 
-from sqlalchemy import BigInteger, Boolean, DateTime, Float, ForeignKey, Integer, LargeBinary, String, Text, UniqueConstraint, func
+from sqlalchemy import BigInteger, Boolean, DateTime, ForeignKey, Integer, LargeBinary, String, Text, UniqueConstraint, func
 from sqlalchemy.dialects.postgresql import JSONB
 from sqlalchemy.orm import Mapped, mapped_column
 
@@ -23,7 +23,8 @@ class AiProvider(Base):
     max_tokens: Mapped[int] = mapped_column(Integer, default=8192)
     # 上下文上限（token），超过时拒绝发送而不是静默截断
     context_window: Mapped[int] = mapped_column(Integer, default=200000)
-    temperature: Mapped[float | None] = mapped_column(Float)
+    # 默认思考强度；为空表示不传，由模型决定。Chat 可按轮覆盖
+    reasoning_effort: Mapped[str | None] = mapped_column(String(8))
     is_default: Mapped[bool] = mapped_column(Boolean, default=False)
     created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
     updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())

web/src/apis/settings.ts

@@ -2,6 +2,11 @@ import { http } from "@/http"
 
 export type Protocol = "openai" | "anthropic"
 
+/** 思考强度，与后端 llm.Effort 一致 */
+export const EFFORTS = ["low", "medium", "high", "xhigh", "max"] as const
+
+export type Effort = typeof EFFORTS[number]
+
 export interface Provider {
   id: number
   name: string
@@ -11,7 +16,8 @@ export interface Provider {
   model: string
   max_tokens: number
   context_window: number
-  temperature: number | null
+  /** 为空表示不传，由模型决定 */
+  reasoning_effort: Effort | null
   is_default: boolean
   updated_at: string
 }
@@ -25,7 +31,8 @@ export interface ProviderForm {
   model: string
   max_tokens: number
   context_window: number
-  temperature: number | null
+  /** 为空表示不传，由模型决定 */
+  reasoning_effort: Effort | null
   is_default: boolean
 }
 
@@ -53,6 +60,6 @@ export function deleteProvider(id: number): Promise<void> {
 }
 
 export function testProvider(id: number): Promise<TestResult> {
-  // 模型接口超时 120s 且 SDK 会重试，前端放宽等待时间
-  return http.post(`/settings/providers/${id}/test`, null, { timeout: 400000 })
+  // 模型接口读超时 600s 且 SDK 会重试 2 次，前端放宽等待时间
+  return http.post(`/settings/providers/${id}/test`, null, { timeout: 1900000 })
 }

web/src/pages/chat/components/MessageList.vue

@@ -18,6 +18,8 @@ export interface DisplayItem {
   error?: string
   /** 生成中：这次模型调用已结束，后续文字另起一条 */
   closed?: boolean
+  /** 生成中：模型正在思考，还没有输出文字 */
+  thinking?: boolean
 }
 
 defineProps<{ items: DisplayItem[] }>()
@@ -53,6 +55,9 @@ function prettyResult(result: string) {
       </div>
       <div v-else class="flex flex-col gap-2">
         <!-- markdown-it 已关闭 html，模型输出中的标签按文本显示 -->
+        <div v-if="item.thinking" class="text-xs text-[var(--el-text-color-secondary)]">
+          思考中…
+        </div>
         <div v-if="item.text" class="markdown-body" v-html="renderMarkdown(item.text)" />
         <el-collapse v-if="item.toolCalls.length" class="tool-cards">
           <el-collapse-item v-for="call in item.toolCalls" :key="call.id" :name="call.id">

web/src/pages/chat/index.vue

@@ -1,11 +1,11 @@
 <script lang="ts" setup>
 import type { ChatMessage } from "@/apis/chat"
-import type { Provider } from "@/apis/settings"
+import type { Effort, Provider } from "@/apis/settings"
 import type { SkillNode } from "@/apis/skills"
 import type { DisplayItem } from "@/pages/chat/components/MessageList.vue"
 import { ElNotification } from "element-plus"
 import { getChatSession, resetChat } from "@/apis/chat"
-import { listProviders } from "@/apis/settings"
+import { EFFORTS, listProviders } from "@/apis/settings"
 import { listSkillTree } from "@/apis/skills"
 import SkillTree from "@/components/SkillTree.vue"
 import SkillVersionDrawer from "@/components/SkillVersionDrawer.vue"
@@ -22,6 +22,8 @@ const contextTab = ref<InstanceType<typeof ContextTab>>()
 const providers = ref<Provider[]>([])
 const providerId = usePersisted<number | null>("chat.providerId", null)
 const provider = computed(() => providers.value.find(p => p.id === providerId.value))
+// 为空表示跟随模型配置里的默认值
+const effort = usePersisted<Effort | null>("chat.effort", null)
 
 const messages = ref<ChatMessage[]>([])
 const estimatedTokens = ref(0)
@@ -94,11 +96,19 @@ function currentAssistant(): DisplayItem {
 function onEvent(name: string, data: any) {
   if (name === "prompt") {
     estimatedTokens.value = data.estimated_tokens
+    // OpenAI 格式推理期间没有任何推送，先显示等待状态直到出字
+    currentAssistant().thinking = true
+  } else if (name === "thinking") {
+    currentAssistant().thinking = true
   } else if (name === "delta") {
-    currentAssistant().text += data.text
+    const item = currentAssistant()
+    item.thinking = false
+    item.text += data.text
   } else if (name === "usage") {
     // 一次模型调用结束；之后的工具调用挂在这条回复上，新的文字另起一条
-    currentAssistant().closed = true
+    const item = currentAssistant()
+    item.closed = true
+    item.thinking = false
   } else if (name === "tool_call") {
     const last = [...liveItems.value].reverse().find(i => i.kind === "assistant") ?? currentAssistant()
     last.toolCalls.push({ id: data.id, name: data.name, arguments: data.arguments, result: null })
@@ -108,6 +118,10 @@ function onEvent(name: string, data: any) {
       if (call)
         call.result = data.preview
     }
+    // 工具都返回后进入下一次模型调用
+    const last = liveItems.value.at(-1)
+    if (last?.closed && last.toolCalls.every(c => c.result !== null))
+      currentAssistant().thinking = true
   } else if (name === "skill_draft") {
     highlightIds.value = [...new Set([...highlightIds.value, data.skill_id])]
     loadSkills()
@@ -115,7 +129,9 @@ function onEvent(name: string, data: any) {
   } else if (name === "context_updated") {
     contextTab.value?.refreshFromChat()
   } else if (name === "error") {
-    currentAssistant().error = data.message
+    const item = currentAssistant()
+    item.thinking = false
+    item.error = data.message
   }
   scrollToBottom()
 }
@@ -131,11 +147,11 @@ async function submit() {
   input.value = ""
   liveItems.value = [{ key: `live-user${Date.now()}`, kind: "user", text, toolCalls: [] }]
   scrollToBottom()
-  await send({ text, provider_id: provider.value.id }, onEvent)
+  await send({ text, provider_id: provider.value.id, reasoning_effort: effort.value }, onEvent)
   // 无论完成、出错还是停止，都以服务端保存的会话为准；保留本地的错误提示
   const errors = liveItems.value.filter(i => i.error)
   await loadSession()
-  liveItems.value = errors.map(e => ({ ...e, text: "", toolCalls: [] }))
+  liveItems.value = errors.map(e => ({ ...e, text: "", toolCalls: [], thinking: false }))
 }
 
 function onKeydown(e: Event | KeyboardEvent) {
@@ -222,6 +238,9 @@ loadSkills()
               <el-select v-model="providerId" placeholder="选择模型配置" class="w-60" :disabled="streaming">
                 <el-option v-for="p in providers" :key="p.id" :value="p.id" :label="`${p.name}（${p.model}）`" />
               </el-select>
+              <el-select v-model="effort" class="w-44" :disabled="streaming" clearable :value-on-clear="null" :placeholder="`思考强度：跟随配置（${provider?.reasoning_effort ?? '不设置'}）`">
+                <el-option v-for="e in EFFORTS" :key="e" :value="e" :label="`思考强度：${e}`" />
+              </el-select>
               <el-tooltip content="下一轮发给模型的 system prompt（含已发布 skills、CONTEXT.md）与历史消息的估算大小" placement="bottom">
                 <span class="text-xs" :class="tokenRatio > 0.8 ? 'text-red-500 font-bold' : 'text-[var(--el-text-color-secondary)]'">
                   上下文约 {{ estimatedTokens.toLocaleString() }}{{ provider ? ` / ${provider.context_window.toLocaleString()}` : "" }} token

web/src/pages/settings/index.vue

@@ -1,6 +1,6 @@
 <script lang="ts" setup>
 import type { Provider, ProviderForm } from "@/apis/settings"
-import { createProvider, deleteProvider, listProviders, patchProvider, testProvider } from "@/apis/settings"
+import { createProvider, deleteProvider, EFFORTS, listProviders, patchProvider, testProvider } from "@/apis/settings"
 import { formatTime } from "@/utils/text"
 
 const PROTOCOL_LABELS = { openai: "OpenAI 格式（/v1/chat/completions）", anthropic: "Claude 格式（/v1/messages）" } as const
@@ -12,7 +12,7 @@ const saving = ref(false)
 const testing = ref<number | null>(null)
 
 function emptyForm(): ProviderForm {
-  return { name: "", protocol: "anthropic", base_url: "", api_key: "", model: "", max_tokens: 8192, context_window: 200000, temperature: null, is_default: false }
+  return { name: "", protocol: "anthropic", base_url: "", api_key: "", model: "", max_tokens: 8192, context_window: 200000, reasoning_effort: null, is_default: false }
 }
 const form = ref<ProviderForm>(emptyForm())
 
@@ -106,6 +106,11 @@ load()
       </el-table-column>
       <el-table-column prop="base_url" label="Base URL" min-width="200" />
       <el-table-column prop="model" label="模型" min-width="150" />
+      <el-table-column label="思考强度" width="90">
+        <template #default="{ row }">
+          {{ (row as Provider).reasoning_effort ?? "-" }}
+        </template>
+      </el-table-column>
       <el-table-column label="API Key" width="100">
         <template #default="{ row }">
           ****{{ row.api_key_hint }}
@@ -169,9 +174,11 @@ load()
           <el-input-number v-model="form.context_window" :min="4096" :max="2000000" :step="10000" controls-position="right" />
           <span class="ml-2 text-xs">token，超过时拒绝发送</span>
         </el-form-item>
-        <el-form-item label="Temperature">
-          <el-input-number v-model="form.temperature" :min="0" :max="2" :step="0.1" :value-on-clear="null" controls-position="right" placeholder="默认" />
-          <span class="ml-2 text-xs">留空使用模型默认值</span>
+        <el-form-item label="思考强度">
+          <el-select v-model="form.reasoning_effort" clearable placeholder="不设置" class="w-40" :value-on-clear="null">
+            <el-option v-for="e in EFFORTS" :key="e" :value="e" :label="e" />
+          </el-select>
+          <span class="ml-2 text-xs">Chat 默认值，可在 Chat 页按轮调整；留空则不传，由模型决定。越高越慢、越贵</span>
         </el-form-item>
         <el-form-item label="设为默认">
           <el-switch v-model="form.is_default" />