Commit Graph

10 Commits

Author SHA1 Message Date
a7a0c6adee refactor(lua): 11 个适配器改用共享前导 —— 删掉约 20KB 逐字重复
按前导抽出三块共通实现(都是实测逐字节相同的):

| 钩子 | 原本重复 | 各 2.2KB |
|---|---|---|
| transform_stream_chunk | github/groq/kimicode/mistral | 2210B × 4 |
| transform_response     | openai/groq/kimicode/mistral/github | 2206B × 5 |
| transform_error        | deepseek/github/groq/kimicode/openai/sensenova | 292B × 6 |

适配器总行数 2409(收工前 groq 单文件 146 行 → 46 行)。

★ 两个必须记下的教训(都在本提交里踩到并修好):

1. **抽公共代码要抽最全的实现,不是最小的**。我先抽了 transform_error 的
   最小版(只认 {error:{message}}),打挂了 TestOpenAITransformErrorEnvelopes
   的 6 个子用例 —— 最小版丢掉了 HTML 错误页、裸文本、扁平 {code,message}、
   FastAPI {detail} 等形状。openai.lua 原有的 1580B 版本才是该进前导的那份。
   抽最小实现是**静默降级**:不报错,只是少认几种错误体。

2. **抽出共性时不能丢掉"非共性但重要"的细节**。openai_response 我漏了两处:
   reasoning_content 透传、以及 tool_calls 的 id 规整(safe_tool_id)。
   后者尤其危险 —— 本网关是扇出路由器,一个非法 id("bash:0")会被回放到
   每个 Claude 上游导致整条 AUTO 链失败;且**流式与非流式必须同规则**,
   否则同一请求两条路径产出不同 id(实测报错:
   流式 "bash_0_40248ca0" vs 非流式 "bash:0")。现两条路径都走
   __prelude.tool_id。

判据:TestPreludeRefactorIsByteIdentical —— 固定输入下逐字节比对
(含"arguments 必须解码成表""usage-only chunk 必须是 done:false"
"arguments 不得留原样 JSON 字符串"等反向断言),并覆盖错误体 6 种形状
与"非 OpenAI 形状必须返回 ok=false"。

另修 channels_pool_test:断言改为**关系**(关一个模型源里少一个)而非写死
数量 —— 兜底目录会随对齐进度变化(minimax 3→4),写死数量会把正常的目录
扩充变成测试失败。

回归:internal/{lua,provider,core,gateway} 全绿。
2026-10-13 22:51:50 +08:00
93eeba3762 feat(types): 统一格式落地为内核实际使用的类型 —— 私有字段泄漏归零
Phase 2 收尾(入向)。ChatRequest 从"客户端发什么就是什么"改为
UnifiedRequest(最大集合)的别名,内核真正用上了那个超集类型。

改掉的具体缺陷:

1. 私有字段泄漏归零(v1 的核心问题)
   v1 的 disable_thinking 是内核私有开关,却被序列化进交给适配器的 body,
   迫使 10 个适配器各自写 req.disable_thinking = nil 来清理。
   现在它是中立的 thinking 字段({enabled, budget_tokens}),客户端与
   适配器都认这个词:
   - gateway/chat.go:thinkingFromClient() 把 disable_thinking 翻译成
     thinking.enabled=false;冲突时中立字段优先(它能带预算,布尔不能)
   - deepseek.lua:thinking.enabled=false → extra_body.thinking.type=disabled
   - anthropic.lua:thinking.enabled=true + budget → 原生 {type,budget_tokens}
     (enabled=false 时不传该字段——Anthropic 没有 type=disabled 这个写法)
   - 其余 7 个适配器:把"清理内核私有字段"改成"清理中立字段"

2. Tools/ToolChoice 改 json.RawMessage
   原为 []interface{}(OpenAI 原始形状 = 内核在假设某个协议)。
   内核从不解释工具 schema——它原样交给适配器,所以原始形状不损失任何
   内核侧统一性。ToolDef 结构化类型保留在包内供新适配器选用。

3. 删除 MarshalJSON 后门
   v1 手工 splice ExtraBody 进 OpenAI 形状的 body;现在 ExtraBody 是
   UnifiedRequest 上一个正正经经声明的字段。

渐进迁移:ChatRequest 保留为别名(不是新类型),49 处引用零改动编译,
语义变更不被机械改名淹没。

测试:新增 thinking_test.go(翻译链 4 例 + 泄漏回归 + tools 透传);
vm_test.go 两处迁移到中立语义。golden 字节级未漂移。
2026-10-11 09:35:32 +08:00
dev
21ec8f59d8 feat: surface zero cache hits — distinguish 'missed' from 'not reported'
Live testing across the zen pool showed models report
prompt_tokens_details.cached_tokens even when the hit count is 0 (e.g.
nemotron-3-ultra-free returns cached_tokens:0, audio_tokens:0,
cache_write_tokens:0). The previous >0 guard dropped those objects, so a
cache-enabled upstream looked identical to one without cache support.

- types: PromptTokensDetails.CachedTokens always emitted (drop inner
  omitempty) so clients see cached_tokens:0 explicitly; dsh reads it as
  a 0% hit instead of 'no data'
- adapters (9): forward prompt_tokens_details whenever the upstream
  provides it (presence check instead of >0)
- Req: add cache_reported flag set when usage carried cache accounting;
  WebUI shows an amber 0% tag for reported-but-missed rows and keeps
  the em-dash only for sources that never report cache data
2026-08-25 10:04:44 +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
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
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
3069cfce4e fix(gateway): pass through upstream token usage in streams for all adapters
The prior usage-passthrough fix only covered openai/opencode; the same
empty-choices+usage drop bug remained in the 5 sibling OpenAI-compatible
adapters, and non-OpenAI providers (anthropic/gemini/ollama) never surfaced
streaming usage at all.

- deepseek/github/groq/kimicode/mistral: preserve usage on empty-choices
  chunks and attach it to normal chunks (same pattern as openai.lua)
- anthropic: emit usage from message_start (prompt) and message_delta
  (completion); gateway merges split usage additively
- gemini: read usageMetadata in the stream path
- ollama: fix non-streaming key (usage -> token_usage, matches
  UnifiedResponse json tag) and read prompt_eval_count/eval_count;
  surface counts from the done stream chunk
- gateway: mergeUsage combines usage across chunks (non-zero fields win,
  total recomputed from prompt+completion) so split usage doesn't lose
  the prompt half; single-chunk case (OpenAI) preserved exactly
- usage-only chunks: done=false (no redundant terminal stop), matching
  the Go fallback standardSSEChunk
2026-08-18 23:24:01 +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