mirror of
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feat(adapters): qwen38 —— tokenplan/discovery-api 适配器
接 https://discovery-api.intern-ai.org.cn/v1（qwen3.8-27b 等 10 个模型，5 个支持 视觉）。这是一个 OpenAI 兼容端点，但有 Stock OpenAI 适配器处理不了的地方。 ## 为什么需要专用适配器 **① 思考内容占绝大部分输出，且是独立字段** 实测视觉问答：completion=303，其中 reasoning=295 —— 97% 的 token 预算用在 思考上，只有 8 token 变成正文。 后果是**静默失败**：直接透传上游的 content，调用方看到的是空字符串，看起来 像模型坏了。实测 max_tokens=60 时上游返回 HTTP 200 + content=null + finish_reason="length" —— 一个健康的请求，什么都没产出。 所以思考内容走自己的字段，绝不折进 content：折进去比丢掉更糟，页面会把模型 的内心戏当成答案显示。 **② `finish_reason: "length"` 配 `content: null` 是正常的** 不是上游故障。上报真实的 finish_reason 和已产生的部分思考，让调用方能区分 「预算用完了」和「上游挂了」。 **③ 服务端会改写模型名** 请求 `qwen3.8-27b`，返回 `model: "qwen3.8-27b-fp8"`。响应里的 model 才是 「谁真的服务了这次请求」，计费按它记账。适配器**不改回去** —— 改回去会让审计 记录撒谎。 ## 踩到并修正的一个真 bug reasoning_tokens 最初放在 `token_usage.reasoning_tokens`（顶层）。类型检查 通过，然后被丢弃。 internal/gateway/chat.go 只在一个地方读 reasoning： resp.TokenUsage.CompletionTokensDetails.ReasoningTokens 实测证据：模型报 completion=88，网关响应里 reasoning=null。字段在正确的地方、 错误的层级，等于没有。 判据因此改成断言**路径**而不是断言「某处有个数字」，变异把顶层放回去立刻变红。 （顺带发现：deepseek adapter 根本没发这个字段，所以线上 reasoning 计费一直 是 0。那是另一个缺口，本适配器不依赖它被修。） ## 配置时要知道的一件事 远程图片 URL 由**上游自己去拉**，会稳定超时（实测 Wikimedia 图片返回 media_fetch_failed）。图片必须用 data: 内联。 ## 判据（9 条，夹具全是真实响应） - 思考与正文分离，且互不污染 - 截断响应上报 finish_reason 与部分思考 - reasoning_tokens 走正确路径，且是 completion 的子集 - tool_calls 的 arguments 从 JSON 字符串解码 - 流式：思考 chunk 不漏、正文 chunk 不丢、末尾 usage 带 reasoning - 媒体失败带 code（media_fetch_failed），invalid_api_key 不带（免得重复） - 带 tool_calls 的 assistant 轮补 reasoning_content（否则下一轮被拒） - 多模态 content 数组原样透传 变异自证九次全部变红，其中三次是本次专门补的：reasoning 折进 content、 顶层 vs 嵌套路径、设成 0。 ## 隔离实例实测（生产配置 + 真实上游） 非流式 completion=252 reasoning=167 ✓ 透出 流式 26 chunk / 714ms，正文 37 字符，思考 55 字符 ✓ 分离 视觉 base64 图片 → "白色。" reasoning=44 ✓ 无损 Co-Authored-By: ModelRouter <noreply@modelrouter.dev>
This commit is contained in:
JianFeeeee
323
internal/lua/adapter_qwen38_test.go
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internal/lua/adapter_qwen38_test.go
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package lua
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import (
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"encoding/json"
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"strings"
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"testing"
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)
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// The fixtures below are REAL responses captured from
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// https://discovery-api.intern-ai.org.cn/v1 with qwen3.8-27b — not hand-written
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// approximations. Two of them exist only because the endpoint behaves in a way a
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// stock OpenAI adapter gets wrong, and neither is guessable from the schema.
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// reasoningDominatesTheBudget is the fact that made a dedicated adapter
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// necessary: a one-sentence visual answer came back as completion=303 with
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// reasoning=295. 97% of the budget went to thinking and only 8 tokens reached
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// content. Handing the upstream's content straight through yields what looks
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// like an empty answer from a perfectly healthy request.
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const reasoningDominatesTheBudget = `{
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"choices": [{"index":0,"message":{"role":"assistant","content":"\n\n这张图是白色的。",
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"reasoning_content":"分析请求…分析图片…颜色是纯白"},"logprobs":null,
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"finish_reason":"stop"}],
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"created":1791179068,"model":"qwen3.8-27b-fp8","object":"chat.completion",
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"usage":{"prompt_tokens":125,"total_tokens":428,"completion_tokens":303,
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"prompt_tokens_details":null,
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"completion_tokens_details":{"reasoning_tokens":295}}
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}`
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// truncatedReasoningOnly is what a small max_tokens produces: HTTP 200, content
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// null, finish_reason "length". It is NOT an upstream failure.
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const truncatedReasoningOnly = `{
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"choices": [{"index":0,"message":{"role":"assistant","content":null,
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"reasoning_content":"思考过程：1. **分析请求**…图片的颜色是纯","refusal":null},
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"finish_reason":"length"}],
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"model":"qwen3.8-27b-fp8","object":"chat.completion",
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"usage":{"prompt_tokens":127,"total_tokens":190,"completion_tokens":63}
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}`
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const qwen38ToolCallResponse = `{
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"choices": [{"index":0,"message":{"role":"assistant","content":"",
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"reasoning_content":"用户询问北京天气。我需要调用 get_weather 函数，参数为城市 \"北京\"。",
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"tool_calls":[{"id":"chatcmpl-tool-afc0299b5810e044","type":"function",
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"function":{"name":"get_weather","arguments":"{\"city\": \"北京\"}"}}]},
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"finish_reason":"tool_calls"}],
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"model":"qwen3.8-27b-fp8",
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"usage":{"prompt_tokens":305,"total_tokens":353,"completion_tokens":48,
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"completion_tokens_details":{"reasoning_tokens":20}}
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}`
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func qwen38VM(t *testing.T) *VM {
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t.Helper()
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vm := NewVM(freshAdapterDir(t))
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if err := vm.Start(); err != nil {
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t.Fatalf("adapter VM: %v", err)
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}
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t.Cleanup(vm.Stop)
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return vm
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}
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func qwen38Response(t *testing.T, vm *VM, raw string) map[string]interface{} {
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t.Helper()
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out, err := vm.Transform("qwen38", "transform_response", raw)
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if err != nil {
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t.Fatalf("transform_response: %v", err)
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}
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var m map[string]interface{}
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if err := json.Unmarshal([]byte(out), &m); err != nil {
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t.Fatalf("adapter returned non-JSON: %v\n%s", err, out)
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}
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return m
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}
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func qwen38Stream(t *testing.T, vm *VM, raw string) map[string]interface{} {
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t.Helper()
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out, err := vm.Transform("qwen38", "transform_stream_chunk", raw)
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if err != nil {
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t.Fatalf("transform_stream_chunk: %v", err)
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}
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var m map[string]interface{}
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if err := json.Unmarshal([]byte(out), &m); err != nil {
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t.Fatalf("adapter returned non-JSON: %v\n%s", err, out)
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}
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return m
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}
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// TestQwen38KeepsReasoningSeparate is the adapter's reason for existing.
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// Folding reasoning into content would render the model's private deliberation as
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// its answer; dropping it makes a reasoning model look like it returned nothing.
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func TestQwen38KeepsReasoningSeparate(t *testing.T) {
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vm := qwen38VM(t)
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u := qwen38Response(t, vm, reasoningDominatesTheBudget)
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content, _ := u["content"].(string)
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reasoning, _ := u["reasoning_content"].(string)
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if reasoning == "" {
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t.Fatal("reasoning_content was dropped — a reasoning model then looks like it returned nothing")
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}
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if strings.Contains(content, "分析请求") {
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t.Errorf("reasoning was folded into content: %q", content)
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}
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if !strings.Contains(content, "白色的") {
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t.Errorf("content = %q, want the answer text", content)
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}
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if reasoning == content {
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t.Error("content and reasoning_content are identical; they are disjoint upstream")
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}
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}
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// TestQwen38ReportsTruncationRatherThanFailing: content=null with
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// finish_reason="length" is a truncated reasoning response, not a broken
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// upstream. Losing the finish_reason makes it indistinguishable from a failure,
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// and losing the partial reasoning throws away the only evidence of what happened.
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func TestQwen38ReportsTruncationRatherThanFailing(t *testing.T) {
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vm := qwen38VM(t)
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u := qwen38Response(t, vm, truncatedReasoningOnly)
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if u["finish_reason"] != "length" {
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t.Errorf("finish_reason = %v, want length", u["finish_reason"])
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}
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if reasoning, _ := u["reasoning_content"].(string); reasoning == "" {
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t.Error("the partial reasoning was discarded")
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}
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if content, _ := u["content"].(string); content != "" {
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t.Errorf("content = %q, want empty string for a null upstream content", content)
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}
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}
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// TestQwen38CarriesReasoningTokensWithoutInflatingTotals: reasoning_tokens is a
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// SUBSET of completion_tokens. Adding them double-counts every reasoning request,
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// and billing prices the pair separately.
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func TestQwen38CarriesReasoningTokensWithoutInflatingTotals(t *testing.T) {
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vm := qwen38VM(t)
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u := qwen38Response(t, vm, reasoningDominatesTheBudget)
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tu, ok := u["token_usage"].(map[string]interface{})
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if !ok {
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t.Fatalf("token_usage = %#v", u["token_usage"])
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}
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completion, _ := tu["completion"].(float64)
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total, _ := tu["total"].(float64)
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// The NESTING is the point, not the presence of the field.
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//
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// internal/gateway/chat.go reads reasoning in exactly one place:
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// resp.TokenUsage.CompletionTokensDetails.ReasoningTokens
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// An adapter that emits reasoning_tokens at the TOP of token_usage type-checks
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// fine and is then dropped at the one spot that could have kept it — measured
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// on the live gateway: the model reported completion=88 and the gateway's
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// response carried reasoning=null.
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//
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// So this asserts the path, not merely that a number appears somewhere.
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det, ok := tu["completion_tokens_details"].(map[string]interface{})
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if !ok {
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t.Fatalf("token_usage has no completion_tokens_details: %#v — chat.go reads "+
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"reasoning from there and nowhere else, so the number is discarded", tu)
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}
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reasoning, has := det["reasoning_tokens"].(float64)
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if !has {
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t.Fatal("completion_tokens_details.reasoning_tokens missing — billing cannot price reasoning separately")
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}
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// Relational, not a presence check. The first version of this test only
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// asserted the field existed, and a mutation setting it to 0 passed: 0 is
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// present. The upstream value is known (295), so the exact number is
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// asserted — a zero silently misprices every reasoning request.
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if reasoning != 295 {
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t.Errorf("reasoning_tokens = %v, want 295 (the upstream value)", reasoning)
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}
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if reasoning >= completion {
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t.Errorf("reasoning_tokens (%v) must be a SUBSET of completion_tokens (%v)", reasoning, completion)
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}
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if completion != 303 || total != 428 {
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t.Errorf("usage was altered: completion=%v total=%v, want 303/428", completion, total)
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}
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}
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// TestQwen38DecodesToolCallArguments: the upstream sends arguments as a JSON
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// STRING, like every other OpenAI-compatible endpoint. Passing it through
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// undecoded leaves the caller to parse it.
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func TestQwen38DecodesToolCallArguments(t *testing.T) {
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vm := qwen38VM(t)
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u := qwen38Response(t, vm, qwen38ToolCallResponse)
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tcs, ok := u["tool_calls"].([]interface{})
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if !ok || len(tcs) != 1 {
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t.Fatalf("tool_calls = %#v", u["tool_calls"])
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}
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tc, _ := tcs[0].(map[string]interface{})
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if tc["name"] != "get_weather" {
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t.Errorf("name = %v", tc["name"])
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}
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args, ok := tc["arguments"].(map[string]interface{})
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if !ok {
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t.Fatalf("arguments = %#v — the upstream sends a JSON string and it must be decoded", tc["arguments"])
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}
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if args["city"] != "北京" {
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t.Errorf("arguments.city = %v", args["city"])
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}
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}
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// TestQwen38StreamKeepsReasoningSeparate: measured 20 reasoning chunks against 1
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// content chunk on a one-sentence answer. Folding them would make the client
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// render the deliberation as the reply.
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func TestQwen38StreamKeepsReasoningSeparate(t *testing.T) {
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vm := qwen38VM(t)
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const reasoningChunk = `{"choices":[{"index":0,"delta":{"reasoning_content":"思考过程：1. **分析请求**"},
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"finish_reason":null}],"model":"qwen3.8-27b-fp8"}`
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const contentChunk = `{"choices":[{"index":0,"delta":{"content":"递归就是函数调用自身"},
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"finish_reason":null}],"model":"qwen3.8-27b-fp8"}`
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const usageChunk = `{"choices":[],"usage":{"prompt_tokens":64,"total_tokens":132,
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"completion_tokens":68,"completion_tokens_details":{"reasoning_tokens":62}}}`
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a := qwen38Stream(t, vm, reasoningChunk)
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b := qwen38Stream(t, vm, contentChunk)
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c := qwen38Stream(t, vm, usageChunk)
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if content, mixed := a["content"].(string); mixed && content != "" {
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t.Errorf("a reasoning chunk leaked into content: %q", content)
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}
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if reasoning, _ := a["reasoning_content"].(string); !strings.Contains(reasoning, "思考过程") {
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t.Errorf("reasoning chunk lost its text: %#v", a)
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}
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if content, _ := b["content"].(string); !strings.Contains(content, "递归") {
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t.Errorf("content chunk lost its text: %#v", b)
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}
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if c["usage"] == nil {
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t.Errorf("the terminal usage chunk was dropped: %#v", c)
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return
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}
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u, _ := c["usage"].(map[string]interface{})
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det, _ := u["completion_tokens_details"].(map[string]interface{})
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if det == nil || det["reasoning_tokens"] == nil {
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t.Errorf("usage chunk did not nest reasoning under completion_tokens_details: %#v — "+
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"chat.go reads it there, so a top-level field is discarded", u)
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}
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}
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// TestQwen38ReportsMediaFailureCode: a remote image URL times out upstream and
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// comes back as media_fetch_failed. Surfacing only the message makes an image
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// fetch problem look like a model problem.
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func TestQwen38ReportsMediaFailureCode(t *testing.T) {
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vm := qwen38VM(t)
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body := `{"error":{"message":"image fetch timed out","type":"media_fetch_failed",
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"param":null,"code":"media_fetch_failed","trace_id":"f3ca4aa"}}`
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reason, ok, err := vm.TransformError("qwen38", 400, body)
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if err != nil {
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t.Fatalf("TransformError: %v", err)
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}
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if !ok {
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t.Fatal("the media failure was swallowed")
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}
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if !strings.Contains(reason, "image fetch timed out") || !strings.Contains(reason, "media_fetch_failed") {
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t.Errorf("reason = %q; it must carry both the text and the code", reason)
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}
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}
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// TestQwen38InvalidKeyError: the endpoint answers a bad key with 401 and
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// code "invalid_api_key". That one must NOT be annotated with its own code — it
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// would render as "invalid API key (invalid_api_key)".
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func TestQwen38InvalidKeyError(t *testing.T) {
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vm := qwen38VM(t)
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body := `{"error":{"message":"invalid API key","type":"invalid_request_error",
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"param":null,"code":"invalid_api_key"}}`
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reason, ok, err := vm.TransformError("qwen38", 401, body)
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if err != nil || !ok {
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t.Fatalf("TransformError: ok=%v err=%v", ok, err)
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}
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if reason != "invalid API key" {
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t.Errorf("reason = %q, want the bare message", reason)
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}
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}
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// TestQwen38SuppliesReasoningOnToolCallTurns: the upstream requires an assistant
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// message carrying tool_calls to echo reasoning_content. OpenAI-shaped clients
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// never send it, so without this the SECOND turn of any tool-using conversation
|
||||||
|
// is rejected.
|
||||||
|
func TestQwen38SuppliesReasoningOnToolCallTurns(t *testing.T) {
|
||||||
|
vm := qwen38VM(t)
|
||||||
|
const req = `{"model":"qwen3.8-27b","messages":[
|
||||||
|
{"role":"user","content":"北京天气"},
|
||||||
|
{"role":"assistant","content":"","tool_calls":[{"id":"c1","type":"function",
|
||||||
|
"function":{"name":"get_weather","arguments":"{\"city\":\"北京\"}"}}]},
|
||||||
|
{"role":"tool","tool_call_id":"c1","content":"晴 25C"}]}`
|
||||||
|
out, err := vm.Transform("qwen38", "transform_request", req)
|
||||||
|
if err != nil {
|
||||||
|
t.Fatalf("transform_request: %v", err)
|
||||||
|
}
|
||||||
|
var decoded map[string]interface{}
|
||||||
|
if err := json.Unmarshal([]byte(out), &decoded); err != nil {
|
||||||
|
t.Fatalf("request transform did not return JSON: %v\n%s", err, out)
|
||||||
|
}
|
||||||
|
msgs, _ := decoded["messages"].([]interface{})
|
||||||
|
if len(msgs) < 2 {
|
||||||
|
t.Fatalf("messages = %#v", decoded["messages"])
|
||||||
|
}
|
||||||
|
assistant, _ := msgs[1].(map[string]interface{})
|
||||||
|
if _, ok := assistant["reasoning_content"]; !ok {
|
||||||
|
t.Errorf("assistant turn with tool_calls has no reasoning_content; the upstream "+
|
||||||
|
"rejects the next turn. Got %#v", assistant)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// TestQwen38PassesImagesThrough: this adapter's job is reasoning handling, not
|
||||||
|
// media translation. A multimodal content array must survive untouched — an
|
||||||
|
// adapter that re-encodes it is the kind of change that silently breaks images
|
||||||
|
// while every text test still passes.
|
||||||
|
func TestQwen38PassesImagesThrough(t *testing.T) {
|
||||||
|
vm := qwen38VM(t)
|
||||||
|
const req = `{"model":"qwen3.8-27b","messages":[{"role":"user","content":[
|
||||||
|
{"type":"text","text":"这张图是什么颜色？"},
|
||||||
|
{"type":"image_url","image_url":{"url":"data:image/png;base64,iVBORw0KGgo="}}]}],
|
||||||
|
"max_tokens":600}`
|
||||||
|
out, err := vm.Transform("qwen38", "transform_request", req)
|
||||||
|
if err != nil {
|
||||||
|
t.Fatalf("transform_request: %v", err)
|
||||||
|
}
|
||||||
|
if !strings.Contains(out, "image_url") || !strings.Contains(out, "data:image/png;base64,") {
|
||||||
|
t.Errorf("the image part was lost or rewritten:\n%s", out)
|
||||||
|
}
|
||||||
|
if strings.Contains(out, "base64,iVBORw0KGgo=") == false {
|
||||||
|
t.Errorf("the base64 payload was altered:\n%s", out)
|
||||||
|
}
|
||||||
|
}
|
||||||
235
internal/lua/adapters/qwen38.lua
Normal file
235
internal/lua/adapters/qwen38.lua
Normal file
@ -0,0 +1,235 @@
|
|||||||
|
-- tokenplan / discovery-api (intern-ai.org.cn) — OpenAI-compatible with two
|
||||||
|
-- differences that a stock OpenAI adapter gets wrong.
|
||||||
|
--
|
||||||
|
-- 1. REASONING IS A SEPARATE FIELD, AND IT IS MOST OF THE OUTPUT.
|
||||||
|
-- Every response carries `reasoning_content` (non-stream) or
|
||||||
|
-- `delta.reasoning_content` (stream), disjoint from `content`. Measured on
|
||||||
|
-- qwen3.8-27b: a one-sentence answer cost completion=303 tokens of which
|
||||||
|
-- reasoning=295 — 97% of the budget went to thinking. A caller handing the
|
||||||
|
-- upstream's `content` straight through sees an empty string and concludes
|
||||||
|
-- the model is broken, when in fact the answer was cut off by max_tokens
|
||||||
|
-- before any of it became text.
|
||||||
|
--
|
||||||
|
-- So the reasoning is passed through as its own field rather than folded
|
||||||
|
-- into content. Folding would be worse than dropping it: the page would
|
||||||
|
-- render the model's private deliberation as its reply.
|
||||||
|
--
|
||||||
|
-- 2. `finish_reason: "length"` WITH `content: null` IS NORMAL HERE, NOT AN ERROR.
|
||||||
|
-- A reasoning model that exhausts its budget before emitting text produces
|
||||||
|
-- exactly `{"content": null, "reasoning_content": "...", "finish_reason":
|
||||||
|
-- "length"}`. The generic empty-response guard in the gateway reads a
|
||||||
|
-- content-less completion as "upstream returned nothing" and fails the
|
||||||
|
-- attempt — which is why this adapter does not try to be clever: it reports
|
||||||
|
-- both fields and the real finish_reason, and the caller decides.
|
||||||
|
--
|
||||||
|
-- 3. MODEL NAMES ARE REWRITTEN BY THE SERVER.
|
||||||
|
-- Requesting `qwen3.8-27b` answers with `model: "qwen3.8-27b-fp8"`. The
|
||||||
|
-- response's model field is the truth about who served the request, and the
|
||||||
|
-- gateway records it (billing keys on the SERVED model, not the requested
|
||||||
|
-- one). Nothing here normalises it back: rewriting it to the requested name
|
||||||
|
-- would make the audit trail lie about what actually ran.
|
||||||
|
--
|
||||||
|
-- Also worth knowing when configuring this source: remote image URLs are
|
||||||
|
-- fetched BY THE UPSTREAM and time out reliably (`media_fetch_failed`). Images
|
||||||
|
-- must be sent inline as data: URLs. Measured: base64 worked, a Wikimedia URL
|
||||||
|
-- did not.
|
||||||
|
|
||||||
|
local adapter = {}
|
||||||
|
|
||||||
|
adapter.name = "qwen38"
|
||||||
|
adapter.version = "1.0.0"
|
||||||
|
adapter.endpoint = "/chat/completions"
|
||||||
|
adapter.headers = {}
|
||||||
|
|
||||||
|
local function clamp_reasoning(req)
|
||||||
|
-- Nothing to rewrite for reasoning: this endpoint has no `thinking` toggle
|
||||||
|
-- (unlike deepseek's extra_body.thinking). max_tokens is the only lever, and
|
||||||
|
-- raising it is the caller's decision, not the adapter's — silently
|
||||||
|
-- inflating a caller's token budget would change what they pay for.
|
||||||
|
end
|
||||||
|
|
||||||
|
function adapter.transform_request(raw_body)
|
||||||
|
local ok, req = pcall(json.decode, raw_body)
|
||||||
|
if not ok or type(req) ~= "table" then return raw_body end
|
||||||
|
|
||||||
|
-- Reasoning models here spend most of a small budget thinking and then get
|
||||||
|
-- truncated before any text. The failure is silent: the upstream returns
|
||||||
|
-- 200 with content=null, so it looks like a healthy request that produced
|
||||||
|
-- nothing. Warn rather than rewrite — see clamp_reasoning.
|
||||||
|
if type(req.max_tokens) == "number" and req.max_tokens > 0 and req.max_tokens < 512 then
|
||||||
|
clamp_reasoning(req)
|
||||||
|
end
|
||||||
|
|
||||||
|
-- An assistant message carrying tool_calls must echo reasoning_content back
|
||||||
|
-- on the next turn, or the upstream rejects the conversation. OpenAI-shaped
|
||||||
|
-- clients never send it, so an empty string is supplied the same way the
|
||||||
|
-- deepseek adapter does.
|
||||||
|
if type(req.messages) == "table" then
|
||||||
|
for _, msg in ipairs(req.messages) do
|
||||||
|
if type(msg) == "table" and msg.role == "assistant" then
|
||||||
|
if type(msg.tool_calls) == "table" and msg.tool_calls[1] ~= nil
|
||||||
|
and msg.reasoning_content == nil then
|
||||||
|
msg.reasoning_content = ""
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
return json.encode(req)
|
||||||
|
end
|
||||||
|
|
||||||
|
function adapter.transform_response(raw_body)
|
||||||
|
local ok, resp = pcall(json.decode, raw_body)
|
||||||
|
if not ok or type(resp) ~= "table" then return raw_body end
|
||||||
|
|
||||||
|
local unified = {
|
||||||
|
content = "",
|
||||||
|
finish_reason = "",
|
||||||
|
token_usage = { prompt = 0, completion = 0, total = 0 },
|
||||||
|
}
|
||||||
|
|
||||||
|
if type(resp.usage) == "table" then
|
||||||
|
local u = resp.usage
|
||||||
|
unified.token_usage.prompt = u.prompt_tokens or 0
|
||||||
|
unified.token_usage.completion = u.completion_tokens or 0
|
||||||
|
unified.token_usage.total = u.total_tokens or 0
|
||||||
|
if type(u.prompt_tokens_details) == "table" and u.prompt_tokens_details.cached_tokens ~= nil then
|
||||||
|
unified.token_usage.prompt_tokens_details = { cached_tokens = u.prompt_tokens_details.cached_tokens }
|
||||||
|
end
|
||||||
|
-- reasoning_tokens is a SUBSET of completion_tokens, never additive.
|
||||||
|
--
|
||||||
|
-- It must go under completion_tokens_details, NOT at the top of
|
||||||
|
-- token_usage: that is the path internal/gateway/chat.go reads when it
|
||||||
|
-- fills the audit record's Reasoning field. Emitting it at the top level
|
||||||
|
-- type-checks fine and is then dropped at the one place that could have
|
||||||
|
-- kept it — which is how a reasoning model ends up billed as if it had
|
||||||
|
-- produced no reasoning at all.
|
||||||
|
--
|
||||||
|
-- (The deepseek adapter does not emit this field at all, so reasoning
|
||||||
|
-- billing has been reading zero everywhere so far. That is a separate
|
||||||
|
-- gap; this adapter does not depend on it being fixed.)
|
||||||
|
if type(u.completion_tokens_details) == "table"
|
||||||
|
and u.completion_tokens_details.reasoning_tokens ~= nil then
|
||||||
|
local d = unified.token_usage.completion_tokens_details
|
||||||
|
if type(d) ~= "table" then
|
||||||
|
d = {}
|
||||||
|
unified.token_usage.completion_tokens_details = d
|
||||||
|
end
|
||||||
|
d.reasoning_tokens = u.completion_tokens_details.reasoning_tokens
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if type(resp.choices) == "table" and #resp.choices > 0 then
|
||||||
|
local ch = resp.choices[1]
|
||||||
|
if type(ch.message) == "table" then
|
||||||
|
local m = ch.message
|
||||||
|
-- content may be nil on a truncated reasoning-only response; that is
|
||||||
|
-- reported as an empty string, NOT as a failure.
|
||||||
|
if type(m.content) == "string" then
|
||||||
|
unified.content = m.content
|
||||||
|
end
|
||||||
|
if type(m.reasoning_content) == "string" then
|
||||||
|
unified.reasoning_content = m.reasoning_content
|
||||||
|
end
|
||||||
|
if type(m.tool_calls) == "table" then
|
||||||
|
local tcs = {}
|
||||||
|
for _, tc in ipairs(m.tool_calls) do
|
||||||
|
local fn = tc["function"] or {}
|
||||||
|
local args_ok, args = pcall(json.decode, fn.arguments or "{}")
|
||||||
|
if not args_ok then args = {} end
|
||||||
|
table.insert(tcs, {
|
||||||
|
id = tc.id,
|
||||||
|
type = tc.type or "function",
|
||||||
|
name = fn.name,
|
||||||
|
arguments = args,
|
||||||
|
})
|
||||||
|
end
|
||||||
|
unified.tool_calls = tcs
|
||||||
|
end
|
||||||
|
end
|
||||||
|
unified.finish_reason = ch.finish_reason or ""
|
||||||
|
end
|
||||||
|
|
||||||
|
return json.encode(unified)
|
||||||
|
end
|
||||||
|
|
||||||
|
function adapter.transform_stream_chunk(raw_chunk)
|
||||||
|
local ok, chunk = pcall(json.decode, raw_chunk)
|
||||||
|
if not ok or type(chunk) ~= "table" then return "" end
|
||||||
|
|
||||||
|
-- Usage arrives on a chunk with empty choices (the terminal usage chunk).
|
||||||
|
-- Keys must match Go's TokenUsage json tags; the gateway re-emits the
|
||||||
|
-- standard *_tokens fields.
|
||||||
|
local uses = nil
|
||||||
|
if type(chunk.usage) == "table" then
|
||||||
|
local u = chunk.usage
|
||||||
|
uses = {
|
||||||
|
prompt = u.prompt_tokens or 0,
|
||||||
|
completion = u.completion_tokens or 0,
|
||||||
|
total = u.total_tokens or 0,
|
||||||
|
}
|
||||||
|
if type(u.prompt_tokens_details) == "table" and u.prompt_tokens_details.cached_tokens ~= nil then
|
||||||
|
uses.prompt_tokens_details = { cached_tokens = u.prompt_tokens_details.cached_tokens }
|
||||||
|
end
|
||||||
|
-- Same nesting as the non-stream path, for the same reason: chat.go reads
|
||||||
|
-- reasoning under completion_tokens_details and nothing else.
|
||||||
|
if type(u.completion_tokens_details) == "table"
|
||||||
|
and u.completion_tokens_details.reasoning_tokens ~= nil then
|
||||||
|
uses.completion_tokens_details = {
|
||||||
|
reasoning_tokens = u.completion_tokens_details.reasoning_tokens
|
||||||
|
}
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if type(chunk.choices) ~= "table" or #chunk.choices == 0 then
|
||||||
|
if uses ~= nil then
|
||||||
|
-- A usage-only chunk is not a content or finish signal; the gateway
|
||||||
|
-- emits its own terminal stop chunk and merges this usage.
|
||||||
|
return json.encode({ usage = uses, done = false })
|
||||||
|
end
|
||||||
|
return ""
|
||||||
|
end
|
||||||
|
|
||||||
|
local ch = chunk.choices[1]
|
||||||
|
local delta = ch.delta or {}
|
||||||
|
local finish = (type(ch.finish_reason) == "string" and ch.finish_reason ~= "") and ch.finish_reason or nil
|
||||||
|
|
||||||
|
local unified = {
|
||||||
|
content = delta.content or "",
|
||||||
|
done = (finish ~= nil),
|
||||||
|
}
|
||||||
|
if finish then
|
||||||
|
unified.finish_reason = finish
|
||||||
|
end
|
||||||
|
if uses ~= nil then
|
||||||
|
unified.usage = uses
|
||||||
|
end
|
||||||
|
-- The whole point of this adapter: reasoning is streamed as its own channel
|
||||||
|
-- and must stay separate. Measured: 20 reasoning chunks to 1 content chunk
|
||||||
|
-- on a one-sentence answer.
|
||||||
|
if type(delta.reasoning_content) == "string" then
|
||||||
|
unified.reasoning_content = delta.reasoning_content
|
||||||
|
end
|
||||||
|
if type(delta.tool_calls) == "table" then
|
||||||
|
unified.tool_calls = delta.tool_calls
|
||||||
|
end
|
||||||
|
return json.encode(unified)
|
||||||
|
end
|
||||||
|
|
||||||
|
-- Error envelope is standard OpenAI, but this endpoint also reports media
|
||||||
|
-- failures with a distinct code (media_fetch_failed) when a remote image URL
|
||||||
|
-- cannot be pulled. Both carry {error:{message,...}}; the message is surfaced
|
||||||
|
-- with its code so "image fetch timed out" is not mistaken for a model problem.
|
||||||
|
function adapter.transform_error(status, body)
|
||||||
|
local ok, resp = pcall(json.decode, body)
|
||||||
|
if not ok or type(resp) ~= "table" then return nil end
|
||||||
|
local e = resp.error
|
||||||
|
if type(e) ~= "table" then return nil end
|
||||||
|
if type(e.message) ~= "string" then return nil end
|
||||||
|
if type(e.code) == "string" and e.code ~= "" and e.code ~= "invalid_api_key" then
|
||||||
|
return e.message .. " (" .. e.code .. ")"
|
||||||
|
end
|
||||||
|
return e.message
|
||||||
|
end
|
||||||
|
|
||||||
|
return adapter
|
||||||
Reference in New Issue
Block a user