Commit Graph

36 Commits

Author SHA1 Message Date
4174310421 fix(channels): 渠道鉴权头三修 + 源名契约 + 夹具形状判据
P1-1 anthropic 基座补 build_headers(x-api-key + anthropic-version)
  根因:基座只有静态 anthropic-version、没有 build_headers ⇒ 内核兜底发
  Authorization: Bearer,而 Anthropic 协议要 x-api-key。
  ★ 证据冲突(计划 P1-1 原文一半有误):zcode 的
  /zcode-plan/anthropic/v1/messages 虽然**说 Anthropic 协议**,鉴权却是
  Bearer <zcode_jwt>(参考 zcode-transport.ts:121-125;全仓 grep x-api-key
  = 0 次命中)。故 zcode 必须**显式覆盖** build_headers 回到 Bearer,
  否则基座的正向修复会把它从"对"改成"错"。
  内核改动:internal/provider/provider.go —— 兜底 Bearer 让位给 x-api-key,
  避免同一请求带两套鉴权(部分 Anthropic 兼容上游对歧义鉴权直接 401)。

P1-2 channels-cline 的 build_headers 补 workos: 前缀
  根因:参考 cline.ts 发 `Bearer workos:<jwt>`,而池里存的是第二步 register
  换回的裸 token ⇒ 之前无 build_headers,内核兜底发裸 Bearer。
  幂等:已带前缀(含大小写变体)不重复拼。

P1-3 channels-gemini 只确认不改
  参考 gemini.ts:353-366 同样是 OAuth Bearer + 身份五头、刻意不发
  x-goog-api-key ⇒ 我们现状本来就对。加判据把这个结论钉住,防下轮照
  P1-1 的模子把它改坏。

P2 源名契约:仅 [A-Za-z0-9_-],禁 : 与 /
  根因:: 与 / 是限定名分隔符,出现在源名里会让 `源名:模型名` 有两种都
  自圆其说的切法,同一请求串在不同部署下可能路由到不同源,且**不报错**。
  校验加在写入口 core.normalizeSource(硬拒,API 400);启动时只告警,
  避免存量带 : 的源名让升级 brick 掉整个网关。
  解析入口 splitPinned 不做校验(解析不该拒绝已有数据)。

P3 夹具形状判据(本轮最重要的一条)
  根因:trae 的 Result 信封缺陷在扁平夹具下所有判据全绿,真实登录却报
  「缺少 Token」—— 夹具形状错 ⇒ 判据必然虚假通过。
  通用形状层:shapeFamilies 登记表,每族给 real/naive/fact/eval/assert,
  断言 fact(real) 且 !fact(naive) 且 assert(eval(real))。
  刻意**不比较 eval 输出形状**:trae 余额实现合法保留顶层兼容路径,
  拿输出比对会把合法兼容判成错误;能区分真假的是 fact(额度在哪一层)。
  另有仓库扫描:对特征明确的退化形状静态反查,需退化夹具时写 SHAPE-OK 豁免。

判据:11 个文件 / 30 用例。全部变异已验证会报红(9 处)。
2026-10-18 00:38:49 +08:00
fc5326402f fix(channels,provider): 渠道模型不可用 + 额度异常的三个根因
## 背景
用户报三问题:① channel 注册的模型无法使用;② 额度显示依旧异常;
③ 其他渠道应也存在类似问题。三条全部核实并给出结论(③ 见文档)。

## ① 网关解不开 `源名:模型名` 限定名(internal/provider/registry.go)

`ResolvePinned` 用 `strings.IndexAny(model, "-:/")` 找**第一个**分隔符。
而渠道源名形如 `channels-trae-b8dd6b3df30e`(**含 `-`**),于是
`channels-trae-b8dd6b3df30e:Doubao-Seed-2.1-Pro` 被切成

    src="channels"、model="trae-b8dd6b3df30e:Doubao-Seed-2.1-Pro"

⇒ 源名永不匹配 ⇒ `Resolve()` 返回 nil
⇒ 客户端看到 `model "..." is not configured`。

**为什么这个写法必须支持**:本实例有 **44 个模型被多源同时提供**
(`glm-5.2` 由 6 个源提供),WebUI 的模型下拉框用 `源名:模型名` 作为
option value 是**有意设计** —— 用户必须能指定"从哪个源调用"。

修法:抽 `splitPinned()`,**按已知源名做最长前缀匹配**(源名是已知集合,
这是唯一可靠办法),保留旧 `-:/` 兜底以兼容非源名形态。
`EffectiveModel` 同步修(同一拆分规则,避免两处漂移)。

## ② trae 适配器三连错(internal/lua/plugins/channels.lua)

三个错误逐层叠加,每个都被上一层掩盖:

**(a) 没有 `build_headers`** ⇒ 内核兜底发 `Authorization: Bearer`,
而 trae 推理端点要 `Cloud-IDE-JWT` + 设备身份头 ⇒
401「We're sorry, but we are not able to authenticate you」。
→ 与上一轮「额度不可用」是**同一根因的两个出口**(额度端点修了、推理端点漏了)。
教训:修"某一族请求的鉴权"时,必须把该族的**所有出口**列出来逐个对齐。

**(b) header 常量的值是错的**(照参考 trae-product.ts 的 TRAE 配置逐项核对):
  · `X-App-Id` 抄成 `6eefa6c9`,真实是 `6eefa01c-1036-4c7e-9ca5-d891f63bfcd8`
  · `app_version_code` 用了 `0.1.52`,真实是**日期式** `20260811`
⇒ 上游 `4001 bad request: binding: expr_path=app_version_code,
   cause=parameter type does not match binding data`

**(c) 流式转换完全不对**:TRAE 是 **SOLO 自定义 SSE**:
    event:output → data:{"response":"###","reasoning_content":null,...}
正文在 `response`,推理在 `reasoning_content`。
  · 套 `__prelude.openai_stream_chunk` ⇒ 无 `choices` ⇒ `empty stream`
  · 改成 OpenAI 外壳 `{choices:[{delta}]}` **仍然空** —— 内核
    `json.Unmarshal(unified, &UnifiedChunk)` 解的是**扁平结构**,外壳字段全被丢弃
⇒ 必须输出扁平 `{content, reasoning_content, usage, done, finish_reason}`。

⚠️ 内核只把 `data:` 行交给适配器(`event:` 行在 provider.go 被丢弃),
   因此判别帧类型只能靠**内容特征**(`response` / `reasoning_content` /
   `finish_reason` / `prompt_tokens`),拿不到 event 名。

## ③ 余额恒为 0(同文件 balance.total_fn)

读的是 `p.credits_limit`(pack **顶层**)—— 该字段**根本不存在**。
真实路径是 `pack.entitlement_base_info.quota.credits_limit`
(参考 trae-credits.ts:341)+ `pack.usage.credits_amount`。
实测上游:`免费`(无该字段) / `每月登录赠送`(500) / `签到奖励`(100) ⇒ 实际 600。
修法:读嵌套路径 + 保留顶层兼容 + `usage_summary.{total,consumed}_amount` 兜底。
修后余额 600 → 实测 598.516(真实扣减)。

## ④ 夹具与生产形状不一致(internal/core/channels_trae_test.go)

夹具原先返回**扁平** `{"Token":...}`,与生产 `{"Result":{"Token":...}}` 不符
⇒ 「代码只读顶层」这个缺陷在夹具里**恰好是对的**,所有 trae 判据都绿。
**夹具形状错 = 判据必然虚假通过**。这是 ExchangeToken 缺陷藏了两轮的原因。

## 判据(新增,变异 4/4 咬住)
- `internal/provider/registry_pinned_name_test.go`(4 用例)
- `internal/core/channels_trae_chat_headers_test.go`(3 条)
- `internal/core/channels_trae_solo_sse_test.go`(2 条)
- `internal/core/channels_trae_credits_hdr_test.go`(1 条)
- `internal/core/channels_trae_exchange_shape_test.go`(2 条)

## 文档
`docs/channel-repair-plan.md`:现状快照(生产实测)+ 各渠道缺陷矩阵 +
后续计划(P0-P4)。**如实记录**:其余 4 个渠道(zcode/cline/gemini/qoder)
有同类缺陷风险,但**账号数均为 0、无法做生产验证**。

## 生产已实测
- `channels-trae-...:Doubao-Seed-2.1-Pro` → 80 帧,正文 27 + 推理 299
- `channels-trae-...:glm-5.2` / `channels-buddy-...:hy4-preview` → OK
- `credits/status` → active:true, dailyCredit:100, todayCheckedIn:true
- `credits/balances` → total:598.516 credits
2026-10-17 17:38:58 +08:00
aea1a2c48d fix(provider): 上游错误文案别再丢 —— 补网关/腾讯错误体形状 + 修误导性日志
## 发现路径:读生产日志(不是测试报红)
```
unhandled upstream error body (adapter "openai" lacks transform_error):
  status=404 body={"error_msg":"404 Route Not Found"}
unhandled upstream error body (adapter "buddy" lacks transform_error):
  status=400 body={"code":11101,"msg":"Non-stream chat request is currently not supported",...}
```

### ★ 缺陷 A:错误体形状漏了「网关」与「腾讯系」两类
`__prelude.openai_error()` 只认 `error.message` / `error`(裸串) / `{code,message}`
/ `detail` / HTML `<title>` / 裸文本 六种,**没有** `error_msg`(华为/APISIX 网关)
与 `msg`(腾讯系用 msg 而非 message)⇒ 返回 nil
⇒ 客户端只看到 `api error 404: unknown error`,**而 body 里明明有可用文案**。

修法照参考(llm-adapter.ts:524 `errorDetail()` 显式认 `error_code`/`error_msg`;
buddy-adapter.ts:576 认 `msg` + `displayMsg.{zh,en}`):
- 新增 `{error_msg}` / `{msg}` / `{error_code,+error_msg}` 三种形状;
- 新增 `displayMsg.{zh,en}` 本地化文案,**排在最前**(参考注释明说"本地化文案
  最可读";其早期实现只读 `error.*`/`message` ⇒ 全部落空 ⇒ 把 291 字符原始
  报文糊进错误提示,而服务端早已备好中文说明)。

覆盖 7 个用共享前导的适配器:buddy/deepseek/github/groq/kimicode/openai/sensenova。

### ★ 缺陷 B:内核那句日志会把人带偏
措辞是 `adapter %q lacks transform_error`,但 **openai.lua:139 与 buddy.lua:166
都明明定义了该钩子** —— 真实原因只是它**返回了 nil**(认不出那个形状)。
我照着这句话去适配器里找"有没有这个函数",查了几轮才发现函数在、是返回值空。
修法:内核直接查事实(`p.vm.HasFn(adapter, "transform_error")`),
两种原因**分开措辞**,不再猜。

## 判据
`internal/lua/adapter_prelude_error_shapes_test.go`:
- 12 个形状子用例(4 个新增 + 6 个回归 + 2 个 displayMsg),
  **装载仓库里真实的 openai.lua**(含共享前导),不是自造桩 ——
  第一版用空 VM 跑,12 个全 FAIL(`adapter openai not loaded`),
  那等于什么也没验。
- 配对判据 `...StillReportsUnknownShapes`:真正认不出的形状仍须 `ok=false`,
  防止"为了让上面通过就把『抽不出』和『没这个钩子』混为一谈"。
- 变异:删掉新增的 error_msg/msg 分支 ⇒ APISIX 用例报红。

## 备注
`anthropic/gemini/mistral/ollama/opencodezen/qwen38/trae` 有自己写的
`transform_error`,不走共享前导,**未获得**本次新增形状(各自协议的错误体
本就不同)。此处只修共享前导的 7 个。
2026-10-17 00:37:52 +08:00
1c6a2821e4 feat(provider): 端点机制去厂商化 —— 适配器声明 endpoint_stream
内核不再知道 Gemini 的流式动词。之前 provider.ChatURL 对
":generateContent" 做字符串替换(注释自认 "Gemini is the only such
adapter"),把厂商协议知识放进了调度器。

现在适配器自己声明:

    adapter.endpoint_stream = "/v1beta/models/{model}:streamGenerateContent"

内核只比较两个不透明字符串,四级优先级:

    source.endpoint_stream > source.endpoint > adapter.endpoint_stream
                                          > adapter.endpoint

- staticInfo.endpointStream + VM.StreamEndpoint() 编译期提取
- config.Source.StreamEndpoint(yaml: endpoint_stream)
- gemini.lua 声明 endpoint_stream
- 内核代码 generateContent 计数归零(仅注释/适配器持有)

首版实现踩了一个回归并当场修掉:源自定义 endpoint 时适配器的 stream
endpoint 会错误覆盖用户的显式路径。TestEndpointPrecedence 把四级优先级
钉死,防止再犯。

验证:go test -tags luajit ./... 全绿;golden 字节级未漂移。
2026-10-11 08:30:39 +08:00
6113924655 chore(adapters): 移除非官方调用方式的适配器,保留官方适配器与模板
移除 4 个靠伪造客户端身份/绕过 WAF 才能工作的适配器,内核不再发行任何
非官方调用方式:

- opencodezen / opencode / opencodego:靠 x-opencode-* 客户端指纹冒充官方
  客户端;zen 免费池明确要求「free tier can only be used in OpenCode」
- agentrouter:伪造 Origin: qwen.ai + Referer + X-Requested-With 绕过阿里云 WAF

保留 12 个官方适配器(openai/deepseek/anthropic/gemini/github/groq/mistral/
ollama/kimicode/qwen38/sensenova/trae),其中 sensenova/qwen38 实测零伪装特征。

本地保留两处:adapters/_local_archive/(gitignored 非递归子目录,不加载)
与线上 /etc/llmsproxy/adapters/(adapter_dir 权威副本,cp -f 不删文件)。

测试同步:删除仅服务于被移除适配器的 11 个测试函数与 usage_cost_test.go;
改写 2 处借 opencode 之名的 provider 测试为用 openai 的 HTML 错误页压缩能力
验证同一契约(适配器钩子拥有错误格式、原文不外泄)。

验证:go test -tags luajit ./... 全绿;二进制内 x-opencode-client /
QwenCode/0.2.0 / qwen.ai 等指纹字符串计数归零。
2026-10-11 08:17:56 +08:00
438629256d feat(ui): 生图二级页面 + 图像模型不再混进对话页
## ① 图像模型曾与对话模型不可区分

根因是结构性的,不是漏了个过滤:

    Provider.Models()   → []string   ← 只有 ID,Kind 被丢掉
    SourceStatus.Models → []string   ← 扁平列表

于是 /api/status 给前端的 34 个模型(29 对话 + 5 图像)长得一模一样。选了
gpt-image-2 就 POST 到 /api/chat —— 那个端点服务不了图像模型,必然失败,
而且报错完全不提「这是图像模型」。

修法:新增 ModelKinds []ModelKindInfo([{id,kind}]),纯增量、omitempty,
老 UI 不受影响;前端按 optgroup 分组,且这些 option 是 **disabled** 的。

分组还不够 —— 分组只是视觉分隔,operator 照样能选,选了照样失败。
disabled 的 option 会自解释(「请到生图页」),可选的只会失败。

★ 写这段时踩了一次 String.replace 的坑:`imgOpts.join("").replace(...)`
只替换**第一个**匹配,于是 6 个图像模型里只有第 1 个被禁用。浏览器实测
逐条读 e.disabled 才抓到(siliconflow 禁用了、vela 的 5 个没有)。
判据只看 value 里有没有 "image" 的话,第一次跑就会永远报警——那是判据
自己错了,比没判据更糟。

## ② 生图二级页面

侧栏新增 tab,与「对话」并列。网格展示 + 提示词输入 + 数量/尺寸,
调用 /v1/images/generations(不是 /api/chat)。

按钮显示已用秒数:实测该上游 15.7s~112.5s,波动 7.6 倍,不显示进度
看起来就像卡死了。

浏览器实测(几何测量,非仅 DOM 断言):
    tab-image 容器   986×523  visible
    img-grid         840×374  visible
    提示词输入框      634×64   visible
    生图页 6 个模型,全部 kind=image,全部可选
    对话页 6 个图像模型,全部 disabled;97 个对话模型全部可选

## ③ base_url 引导不足

文案直接给出截断位置和具体后果,而不是泛泛地说「填 URL」:

    填主机地址,通常不带 /v1 —— 网关会自动拼接路径。但各家约定不同:
    若填 https://x.com/v1,对话路径会正确,而图像生成会变成 /v1/v1/… 而 404。
    不确定就用上方「自动探测模型」。

/v1/v1 不是假设:ImageURL 是 BaseURL + ImageEndpoint(),而图像默认路径
是**绝对**的 /v1/images/generations,chat 的默认是相对的 /chat/completions。
两者对「版本段归谁管」的假设相反。今天在 vela 上为此排查了两次。

## 判据

新增 6 个契约测试:
- 对话页必须读 model_kinds(不读就会退回扁平列表)
- 禁用图像模型不能用 joined-HTML 的 String.replace(只改第一个)
- 生图页存在且注册进 TABS(漏了则 pane 永不可见,症状是「页面空白」)
- sendImage 必须打 /v1/images/generations
- urlHint 必须同时提到 /v1 和 404
- TestImageEndpointComposesUnderRealConditions 用真实形态验证:
  相对路径拼出 /v1/images/generations 得 200,默认路径拼出 /v1/v1/… 得 404
2026-10-06 15:02:42 +08:00
a2e1adc2d8 fix(gemini): endpoint 自相矛盾导致预置模板必失败
gemini.lua 里 adapter.endpoint = "/v1/models",而它自己的注释写的是
  POST /v1/models/{model}:generateContent
两者矛盾,而 Go 侧是静态拼接(provider.URL = base_url + endpoint),拼不出
模型名。预置模板 "Google Gemini"(base_url=.../v1beta)于是会 POST 到
  https://generativelanguage.googleapis.com/v1beta/v1/models
既多一段 /v1,又缺 :generateContent——那是 Gemini 的模型**列表**端点,
对 POST 返 405。所以任何用户从模板建这个源,拿到的都是必定失败的源。

实测确认影响范围:线上 21 个源里没有 gemini(openai×15 / trae / sensenova /
opencodezen / deepseek / anthropic / agentrouter),所以是潜伏缺陷。

修法:endpoint 改成模板 `/v1beta/models/{model}:generateContent`,新增
provider.ChatURL(model, stream):
  - 用 **PathEscape** 替换 {model}——模型 id 进的是 URL 路径,不转义的话
    一个 "/" 就会静默指向另一个资源(判据里用 RequestURI 而非 URL.Path
    断言,因为后者是解码后的,看不出 %2F);
  - 流式把 ":generateContent" 换成 ":streamGenerateContent"(同一个路径、
    不同动词,也在路径里)。替换刻意只认这个精确后缀,免得别的适配器
    仅仅提到这个词就被改写;
  - source 自己设的 endpoint: 仍然优先,模板被整体跳过。
Chat / ChatStream / probeChat 三处调用点改为传本次请求真实的 model——AUTO
按槽位把 req.Model 钉死,所以 URL 必须跟随**请求**的模型,用源默认模型会让
多模型源每次都打同一个(还记到别的模型的账上)。

判定静态 endpoint 的其他 10 个适配器零影响(TestNonGeminiEndpointsAreUntouched)。

顺带:Stats 的 mutex 不是可重入的,导出方法自己加锁、*Locked 后缀要求调用
方持锁。持锁调导出方法会死锁——我的探针真卡死过一次(直到 10 分钟超时)。
补上 LOCKING 注释,并加判据把这条规则钉住(含一个 20 秒上限的行为判据,
让未来的重构撞死锁时快速失败而不是拖满整个套件)。
2026-10-01 23:37:17 +08:00
97bb9c6ef4 feat(opencode): 透传 completion_tokens_details.reasoning_tokens 与上游 cost
回答「opencodego 的用量与费用透传呢」时逐字段核对上游产出,发现 usage 漏了
一项、费用整项丢失。

## 上游实际发什么(实测 opencode.ai/zen/go/v1)

  {
    "choices": [...],
    "usage": { "prompt_tokens": 37, "completion_tokens": 40, "total_tokens": 77,
               "prompt_cache_hit_tokens": 0, "prompt_cache_miss_tokens": 37,
               "prompt_tokens_details": {"cached_tokens": 0},
               "completion_tokens_details": {"reasoning_tokens": 40} },
    "cost": "0"
  }

cost 在**顶层**且是**字符串**。流式时还会单独发一帧:
{"choices":[],"cost":"0"}

## 此前丢了两样

1. completion_tokens_details.reasoning_tokens —— 输出里有多少是思考 token。
   没有它,客户端无法判断 completion_tokens 里多少是可见回答、多少是思考,
   而两者都按输出计费。
2. cost —— 唯一的费用信号,网关整个丢弃。Go 订阅是包月制恒为 "0",
   但 Zen 按量付费模型(以及未来的其它源)有信息量。

顺带修掉一处流式/非流式不一致:命中缓存时上游同时给
prompt_tokens_details.cached_tokens 和独立的 hit/miss,流式路径写成了 elseif,
只留 details,与非流式产出不同(只认独立字段的老客户端会看不到缓存)。

## 实现

- types.TokenUsage += CompletionTokensDetails;UnifiedResponse / UnifiedChunk += Cost
- opencodego/opencodezen 适配器映射两个字段;空 choices 帧改成 usage 与 cost
  都可带(早退只带 usage 会把同帧的 cost 丢干净 —— 新测试先抓到的就是这个)
- Gateway ChatCompletion / ChatChunk += cost,随终帧发(对齐上游的
  {"choices":[],"cost":"0"} 形态)
- Go 兜底 standardSSEChunk 同步支持(openai 系适配器不再漏 reasoning_tokens;
  纯 cost 帧不再被整体丢弃),新增 rawCostString 兼容字符串/数字两种形态

费用只做**搬运**:不解析、不换算、不汇总 —— 它是上游事实,且只有部分上游提供。

## 验证

经网关实测 gozen:deepseek-v4.1-flash,流式与非流式产出逐字段一致:
  prompt_tokens_details.cached_tokens=6784
  prompt_cache_hit_tokens=6784 / miss=148
  completion_tokens_details.reasoning_tokens=16
  cost="0"

测试:TestOpenCodeCostAndReasoningPassthrough(含「无数据不得凭空造字段」反例)、
TestOpenCodeStreamCacheFieldsMatchNonStream、TestTokenUsageMarshalsCompletionTokensDetails。

(cherry picked from commit c744ee151e)
2026-09-27 17:12:03 +08:00
d81074621a fix(opencode): 采纳客户端真实会话 id + 超窗消息不再被限流措辞封杀
两处都源于同一次排查:pi 到底有没有带会话标识、超窗为什么触发不了压缩。

## 1) 客户端会话 id:pi 一直在发,只是被配置关掉了

之前结论是「通用客户端不发会话 id」——只对了一半。pi 有会话 id,且能发:
pi-ai 的 createClient 在 compat.sendSessionAffinityHeaders 为真时,会把
平台会话 id(uuidv7,整个会话恒定)放到 x-session-affinity /
x-client-request-id / session_id 上。该开关默认 false,而 llmsproxy 的
provider 配置里没开,所以此前一直收不到。

现在网关按优先级采纳:x-session-affinity → x-session-id → session_id →
body 的 prompt_cache_key,并把值经 types.ChatRequest.ClientSession 传到
适配器 meta.client_session。适配器的会号种子优先级变为:
客户端会话 id > 首条 user 消息指纹 > 按源固定。

刻意不采纳 x-client-request-id:名字含 request,部分客户端每请求都换,
拿它当会话会让上游前缀缓存永不命中(pi 总会同时发 x-session-affinity,够用)。

实测:抓 127.0.0.1:8081 的真实 pi 请求,配置打开后收到
x-session-affinity = session_id = x-client-request-id = <子会话 uuid>。
上游缓存确为会话级隔离(同前缀、不同会号:A 冷→命中,B 首次仍为 0),
两个不同 header 值互不命中,反证网关确实采纳了客户端会话 id。

## 2) 超窗消息必须「干净」,否则被同链的限流措辞反向封杀

pi 的 isContextOverflow 先查 NON_OVERFLOW_PATTERNS(/rate limit/、
/too many requests/、Bedrock 前缀),命中就直接判为「非超窗」——**即使
消息里已经有 context_length_exceeded**,pi 也不会压缩重试。

而 AUTO 链的失败消息天生是多 tier 原因的拼接,超窗 tier(gozen 400
maximum context length)常与配额/限流 tier(429 token plan exhausted、
cooling、no free slot)同时出现。此前把 tier 明细原样拼在归一化标记后面,
等于让一条限流 tier 的措辞反过来封杀超窗识别。

现在超窗走独立的干净消息:
  context_length_exceeded: context window is full; reduce the length of
  the messages (gozen/deepseek-v4.1-flash)
只留超窗措辞 + 超窗源名,不带任何其它 tier 的文本。

测试:TestOverflowMessageSurvivesRateLimitedSiblingTier 用 pi 的完整判定
顺序(先 NON_OVERFLOW 后 OVERFLOW)断言同链限流 tier 不再封杀超窗识别;
TestClientSessionFromRequestHeaders / TestClientRequestIDIsNotUsedAsSession /
TestOpenCodePrefersClientSessionID 覆盖会话采纳与优先级。

(cherry picked from commit c9c09b2ba2)
2026-09-27 17:12:03 +08:00
868ac59692 feat(provider): support per-source proxy_url for geo-blocked upstreams
Upstreams like justwoker/tabitoken sit behind Cloudflare geo/IP blocks and
only respond through a proxy. A global env proxy is wrong (intranet sources
trae/localzen must stay direct), so add an explicit per-source proxy_url
that overrides http.ProxyFromEnvironment for just that source. Sources
without proxy_url keep the existing env/direct behavior.

Also accepts a User-Agent header per source (config already supported
headers) so CF-fronted resellers can be reached.

Verified e2e: tabitoken and justwoker now return tool_calls through
127.0.0.1:7890 (clash); trae stays direct. Full go test -tags luajit
passes.
2026-09-05 09:46:36 +08:00
5b628b4d6f fix(scheduler): rebalance the pref score so metered sources aren't written off silently
Report: an allowance-metered source (sensenova) had its deepseek model sitting at
pref -10 while the WebUI showed zero failures. All three numbers were accurate,
and they exposed three compounding problems.

1. Quota exhaustion cost the SAME score as a real failure. RecordQuotaExhausted
   intentionally avoids failCount (an exhausted allowance is not a fault), so the
   UI showed fails=0 / cooling=false — yet it deducted the full prefFailStep (5).
   For a metered source, running out of budget is an everyday event, so the score
   drifted deep negative with no visible cause. Now quota and 429 events cost
   prefQuotaStep (1): the cooldown already keeps the slot out of rotation until
   the window resets, the score only needs a mild preference for slots with budget.

2. Recovery was 5:1 asymmetric. A failure cost -5 but a success only +1, so a
   slot at -10 needed ten consecutive successes just to reach neutral — which it
   could never get, because a low score makes the scheduler not pick it in the
   first place (starvation). Success now rewards prefSuccessStep (2): recovery
   from -10 needs five successes, while a real failure still outweighs one.

3. No idle decay. A penalised slot kept its negative score forever once it stopped
   being selected. Pref() now applies lazy decay: after prefDecayAfter (2 min) of
   no outcome, the score drifts one step back toward 0 per interval (never past
   0, never touches positive scores). Applied in Pref() and TryProbe(), so a
   naturally-recovered idle slot is schedulable again without needing a probe.

The failure penalty itself is unchanged (prefFailStep=5), so genuinely broken
upstreams are still marked as clearly worse than healthy ones.

Tests: quota penalty lighter than failure; 5 quota resets stay well above the
floor with failCount untouched; 429 is quota-class; recovery from -10 needs <=5;
idle decay rehabilitates a written-off slot, stops at 0, and never drags a
positive score; decay repeatedly lifts a slot off the prefMin floor. Updated the
two pre-existing tests that asserted the old -5/129 +1 values.
2026-08-31 11:50:47 +08:00
21cd0429a2 feat(provider): half-cooldown probe so recovered slots return immediately
A slot that failed ten times landed in a 30-minute cooldown and was a hard
skip for the whole window: ModelAvailable() said no, the scheduler dropped the
candidate, and the ONLY way back was a success that could never happen. In
practice an upstream that blipped for a minute — or whose quota reset in
seconds — stayed unusable for half an hour.

Cooldown is now a window with a probe gate instead of a wall:

  * backoffCap 30min -> 5min. 401/403 no longer implicitly "jump to the cap"
    but get an explicit authCooldown (10min); 429 keeps its 30s fixed window.
  * ModelState records cooldownFrom alongside cooldownUntil, so the window has
    a measurable midpoint, plus a single-token probe permit (probeInFlight).
  * TryProbe() hands out that permit only in the SECOND half of the window: a
    freshly failed slot stays completely silent, and past the midpoint exactly
    one request may go through as a probe.
  * ProbeSlots() = clamp(max_concurrent/10, 1, 2), so a source configured for
    max_concurrent=10 lets exactly one request probe.
  * A probe is a REAL request: success runs RecordSuccess(), which clears the
    cooldown and failCount on the spot and returns the slot to full rotation.
    Failure reopens the window, pushing the next probe to the NEW midpoint
    instead of retrying immediately.

Quota exhaustion is modelled separately (RecordQuotaExhausted): running out of
allowance is not the upstream being broken, so it must not feed the exponential
ladder or inflate failCount. It cools until the allowance window can plausibly
have reset (capped at 30min so a manual top-up is noticed) and is recognised
from the adapter-normalized error text via ReportStatusReason.

Scheduler side: collectCands() splits a tier into normal + probe candidates
with probes strictly LAST, so probe traffic is what a tier falls back to, never
what it prefers, and the round-robin cursor rotates over the normal head only.
releaseProbes() returns every permit on all exits (success, hard failure, ctx
cancel, tier fallthrough); the 90-line inlined busy-poll loop is extracted into
pollBusyTier() to keep those exits auditable. Direct Chat/ChatStream/Image take
the same gate.

Also fixes a real blacklist bug found on the way: a slot whose pref sank to
prefMin was refused by ModelAvailable forever, long after every cooldown had
expired. Such a slot is now probeable, and one success lifts it off the floor.

Verified live against a controllable upstream (tier1 flaky, tier2 healthy):
failure ladder 5s/9s/20s/40s/79s; after healing the upstream, 10 AUTO requests
across the silent half produced ZERO upstream calls; past the midpoint a single
AUTO request produced exactly ONE upstream call, cleared the remaining 39s of
cooldown, and the next three requests were served by the recovered slot.
2026-08-30 08:04:50 +08:00
624fd74b45 fix: anthropic tool-call round-trip, cache zero-hit parity, round-robin load balancing
anthropic.lua v3.0.0:
- Issue 1: tool_result/tool_use round-trip
- Issue 3: thinking default OFF (opt-in via extra_body.thinking)
- Issue 4: tool_choice mapping
- Issue 5: collect_blocks preserves unknown part types
- message_stop no longer emits done=true (was overwriting tool_calls finish_reason)
- cache_read_input_tokens normalized even at 0

gemini.lua:
- transform_response was missing cachedContentTokenCount

openai.lua (Issue 6):
- transform_error handles flat envelopes, nginx HTML, bare text

chat.go mergeUsage:
- Keep PromptTokensDetails even when CachedTokens=0

scheduler.go:
- Remove sort.SliceStable by Pref; round-robin cursor is the only LB mechanism

provider.go ModelAvailable:
- Also check Pref() > prefMin, persistently failing slots exit cands

presets.go:
- 17 built-in source templates

Tests: 6 new test functions, 2 updated for new semantics
2026-08-28 12:02:46 +08:00
747dff5b76 fix(gateway): record actual served model for image requests
handleImage recorded the raw request model id, so AUTO image
generations showed up as model=AUTO in the request records and
by-model aggregates instead of the image model actually served
(e.g. Kwai-Kolors/Kolors).

UnifiedResponse gains an optional Model field; Provider.Image fills
it with the resolved id (AUTO resolves to the source's best image
model), and handleImage prefers it when writing the audit record.
2026-08-26 20:28:20 +08:00
dev
18c2385c61 feat: add per-source TTFB and tokens/s metrics to status page
- Req: add FirstByteMs field (ms to first byte, tracked for streaming)
- Stat: add FirstByteSum for aggregation
- SourceAverages(): new method computing per-source avg TTFB and tokens/s
  from the in-memory ring (300s window)
- SourceStatus: add AvgFirstByteMs and AvgTokPerS fields
- pumpStream: record FirstByteMs after first SSE chunk sent to client
- singleChat/singleChatAuto: set FirstByteMs = LatMs (non-streaming)
- handleStatusAPI: populate the new SourceStatus fields from SourceAverages()
- WebUI source table: two new columns showing TTFB (s) and Tokens/s
2026-08-25 08:24:08 +08:00
dev
045ecf47bc feat(types): pass through upstream cache tokens in TokenUsage
dsh displays cache-hit %, but llmsproxy dropped every upstream's cache
fields — deepseek prompt_cache_hit_tokens, OpenAI prompt_tokens_details.
cached_tokens, anthropic cache_read_input_tokens, gemini cachedContentTokenCount.

Changes:
- TokenUsage: add PromptTokensDetails (with CachedTokens) + PromptCacheHit/Miss
- MarshalJSON: emit prompt_tokens_details.cached_tokens (OpenAI v2 standard)
  and prompt_cache_hit/miss_tokens (DeepSeek legacy) — dsh reads the former
  first, falls back to the latter
- mergeUsage: preserve cache fields across stream chunks
- standardSSEChunk: parse the upstream raw prompt_tokens_details too
- deepseek.lua: forward prompt_cache_hit/miss_tokens + create
  prompt_tokens_details from them
- openai.lua: forward prompt_tokens_details.cached_tokens and legacy
  prompt_cache_hit/miss_tokens; normalize legacy hits into the standard
  object so dsh sees them regardless of upstream format
- anthropic.lua: map cache_read_input_tokens → prompt_tokens_details
- gemini.lua: map cachedContentTokenCount → prompt_tokens_details
2026-08-25 07:57:32 +08:00
dev
5bb94db08c refactor: unify oneLineStr/oneLine into types.OneLine
provider.oneLineStr and gateway.oneLine had byte-identical bodies (flatten
whitespace + cap length). Move the single implementation into the types
package, which both layers already depend on, and delete both locals.
2026-08-24 22:26:51 +08:00
dev
ef396f9b47 chore: remove dead code found in redundancy audit
- provider.truncate: zero callers (oneLineStr is the used superset)
- gateway.normalizeModel: zero callers
- config.resolvedAPIKey: zero callers (core.resolveSourceKey is the live equivalent)
- Stats.Records: zero callers (CSV export uses AuditRecords)
- store.containsString: zero callers
- Config.MaxConcurrent: global inflight-cap field never read; per-source
  MaxConcurrent is what actually drives semaphores. Legacy configs carrying a
  top-level max_concurrent key still load (yaml.v3 ignores unknown fields —
  verified by test).
- gui renderer esc(): zero callers; renderer uses textContent, and the embedded
  WebUI has its own esc()
2026-08-24 22:25:21 +08:00
b2183df1e8 feat(adapter): move per-source error condensing into transform_error hooks
Every upstream formats errors differently, which is adapter territory:
the protocol gains an optional transform_error(status, body) hook and all
built-in adapters implement their own envelope parsing (zen free-pool
labels, anthropic/gemini/ollama/mistral shapes, sensenova quota notes,
agentrouter WAF pages). The core keeps a single uniform fallback: when no
hook yields a reason clients get "api error <status>: unknown error" and
the raw body goes to server logs only.
2026-08-24 19:17:36 +08:00
6eac80bc6c fix(provider): condense upstream error bodies before they reach clients
api error strings embedded raw upstream response bodies, so JSON quota
payloads and WAF HTML pages leaked through to clients (and through the
per-tier chain summary). shortAPIError extracts the envelope reason
(error.message / message / msg), collapses HTML blocklist pages to a
marker, and caps everything at one line.
2026-08-24 18:56:06 +08:00
e19cff248f fix(provider): fail the candidate on error-only streams instead of serving empty replies
Some upstreams (zen free pool) answer HTTP 200 with a single-chunk stream
whose only payload is finish_reason:"network_error" and an empty delta.
ChatStream used to hand that channel up as a success, so clients received
a laundered empty reply (agents ended their turn mid-loop) and the broken
slot kept its scheduling preference. ChatStream now holds back the first
chunk: an error-only done chunk fails the candidate before any byte
reaches the gateway, letting the scheduler fall through to the next
source. Standard finish reasons (including instant-empty "stop") are never
classified as errors.
2026-08-24 15:41:45 +08:00
bb3af3bdb3 feat(gateway): pass through upstream finish_reason end-to-end
The gateway hardcoded "stop" on every terminating stream chunk, so
tool-call rounds reported finish_reason=stop and length caps were
invisible to clients. UnifiedChunk now carries finish_reason; adapters
emit it (with empty-string finish reasons like sensenova treated as
non-terminal), standardSSEChunk passes it through for un-adapted
upstreams, [DONE] no longer emits a duplicate reason-less done chunk,
and both streaming paths emit the real reason with "stop" as fallback.

Also vendor sensenova/agentrouter adapters into the repo: they were
WebUI-only uploads and a deploy sync silently removed them while live
AUTO-chain slots still referenced them.
2026-08-24 15:05:50 +08:00
690f55c6f1 feat: proactive rate limiting (RPM) + 429 short cooldown for sources
- config.go: Source add RPM field (requests-per-minute cap, 0=unlimited)
- provider.go: RecordRateLimit() — 429 uses fixed 30s cooldown, not exponential
- provider.go: Throttle() — token-bucket proactive rate limiter, spaces requests
  at 60s/RPM interval, respects context cancellation
- provider.go: ReportStatus() — 429 -> RecordRateLimit, 5xx -> RecordFailure
- provider.go: Chat/ChatStream — wire Throttle after TryAcquire
- api.go: sourcePayload + RPM, buildSource passes RPM through
- ui/index.html: add RPM input field in source editor, bilingual i18n labels
- deploy.sh: backup old binary + rollback on healthcheck failure
- provider_test.go: TestModelStateRateLimitShortCooldown, TestThrottleSpacingAndCancel
- config.yaml: sensenova rpm: 12
2026-08-24 02:00:25 +08:00
83e6d88813 feat(gateway): pass through exact upstream token usage in streams
Streaming responses now carry the upstream's real token usage instead of
gateway estimates:
- UnifiedChunk gains an optional Usage field; adapters (opencode, openai)
  extract usage from upstream stream chunks (including the final chunk with
  empty choices) and pass it through.
- standardSSEChunk preserves usage for passthrough adapters.
- Gateway emits the exact usage in the final stream chunk when available,
  falling back to estimates only when the upstream provided none.

Non-streaming usage was already fixed to emit OpenAI-standard keys.
2026-08-18 19:04:44 +08:00
8c5279b416 fix(status): source column reflects real traffic + theme-aware tray menu
- api/status sources now carry recent_ok/recent_err (last 300s real gateway
  requests via Stats.SourceRecent) so a source actually serving traffic is
  never shown as down just because probe /models got rate-limited
- WebUI source status column repaints every 5s (no more frozen-at-first-
  render) with a manual refresh button; shows success rate + probe + cooldown
- tray menu status rows were enabled:false (GTK fixed light-grey, invisible
  on light themes) — now enabled with no-op click and nativeTheme listener
  rebuilds the menu on dark/light switches
- ignore local ops scripts (scripts/, machine-specific)
2026-08-17 17:37:04 +08:00
2bc1d0e67a feat: opencode zen adapter + first-run config generation, fix stats/stream bugs
- adapters/opencode.lua: opencode.ai zen free pool adapter — sends the
  opencode client User-Agent (zen fingerprints clients by UA; non-official
  clients hit FreeUsageLimitError); pairs with api_key: public
- config: no config file ships in the repo; first run generates a default
  config at the -config path with a random admin key, loopback listen and a
  keyless zen source (config.EnsureDefault); remove config.example.yaml
- lua: seed bundled adapters from the embedded FS instead of a hardcoded
  name list
- ui: widen model kind select (chat was clipped to 'cha')
- phase 5 bugfixes: stats ms/s bucket mixing, cleanScopes nil, ctx.Err
  guards, direct-path ModelAvailable, empty stream body failure,
  bestImageModel rewrite, transform failure recording, Core.mu, timer,
  effective model for tool-calls
2026-08-13 12:25:07 +08:00
d06210204b feat: config.yaml only, OpenRouter free models, remove runtime.json config
- Move all config (auto rules, keys) from runtime.json to config.yaml
- Store now only holds runtime sources (WebUI-created)
- Add OpenRouter free models to config.yaml
- One-time migration from legacy runtime.json on startup
- Fix gateway tests for new config structure
- Update core.go with migrateFromRuntime, saveConfig, seedKeys/seedAuto
- Remove SaveKey/KeyByValue/AutoRules from Store
- Add Config.Save() with YAML marshaling
- Update WebUI admin keys visibility (show all keys including admin)
- Bump binary to 11MB with luajit
2026-08-13 10:18:19 +08:00
20d2268546 fix: close P10 audit items — P10-1 sources API admin guard, P10-2 scope prefix strip, P10-3 runtime source timeout w/ stream-safe clients
- api.go: handleSourcesAPI now requires admin role (GET leaks upstream api_keys, POST/DELETE mutate routing)
- chat.go: hasScopeModel made a Gateway method that strips source-model/:// prefix strictly via Registry.EffectiveModel (only when the prefix names a real source serving the bare model) so dash-bearing ids like deepseek-v4-flash-free are never corrupted; +TestHasScopeModelWithSourcePrefix
- config.go: DefaultSourceTimeout/QueueTimeout/Concurrency constants shared by YAML ApplyDefaults and runtime sources
- core.go: mergedSources applies the same defaults to runtime sources (JSON never persisted timeout fields); a dead upstream can no longer hold a concurrency slot forever
- provider.go: split non-streaming client{Timeout} vs stream client{} sharing a Transport with ResponseHeaderTimeout, so long SSE bodies are not cut by client.Timeout; ChatStream uses doRawStream
- plan.md: mark P4-4/5/6 done, record P4-7/8 (tier-order, audit export, UI key view, zen upstream diagnosis)
- online verified: user key -> /api/sources 403 (GET+POST), admin 200, AUTO stream/non-stream healthy
2026-08-11 15:22:20 +08:00
88802f9ef6 feat: AUTO chain rewrite — silent failover+busy skip+pref round-robin+503 tier summary; chain edits reset slot cooldowns (P0/P1); stats by_status + audit jsonl rotation; UI priority-page health badges & status-code card; ctx-menu capture-phase close (outside-press guard); main.go ops warnings; local bundled-Lua verified tests (3 latent bugs fixed); plan.md 2026-08-11 00:03:11 +08:00
41c9b0e14a feat: source-model routing disambiguation (source-model/:/ prefix); same-tier round-robin load balancing; public_base_url for copy config; fmtTok(B/M/K) unit scaling; status column reorder (reachability first); drop emoji from seed-warn modal; fix tests for first-run adapter seeding; install lua5.1 dev lib 2026-08-10 12:55:22 +08:00
839f33ba01 feat(secrets): encrypt sensitive store fields (AES-GCM, master.key 0600, api_key_env) + fast models-endpoint probing (fix zen backlog + source status) + UI cleanup (drop redundant parens labels, grid models 4/row) 2026-08-10 10:33:03 +08:00
d48b993010 feat(auto): AUTO-only priority chain with tiered slots + live source probing; CSV export w/ key names; audit persistence; fix prompt token accounting & deepseek thinking 2026-08-10 00:10:19 +08:00
859d310ad3 feat(auto): quota-aware AUTO slot scheduling (model tiers, hour/week/month resets) + key scope periods; fix key/slot create/delete UX 2026-08-09 11:07:38 +08:00
5d50b69153 fix: tool call anchor & wire format, streaming chunk passthrough, WebUI narrow-screen, docs bilingual 2026-08-08 11:26:58 +08:00
ccbcede581 feat: LuaJIT worker-pool VM, multimodal/disable-thinking passthrough, WebUI redesign, DeepSeek V4 2026-08-07 13:34:16 +08:00
f7f76e097d feat: ModelRouter — unified OpenAI-compatible multi-source LLM gateway
- Lua adapters per upstream (transform_request/response/stream_chunk, build_headers signing hooks)
- AUTO priority routing with per-model kind (chat/image), explicit source/model routing
- Per-source concurrency caps with queueing, exponential backoff, AUTO failover
- OpenAI-compatible API: chat completions, SSE streaming, image generations, models
- Gateway key auth, web UI for adapter/source management, runtime persistence
- e2e test running the real binary against mocked upstreams
2026-08-05 15:25:47 +08:00