mirror of
https://gitcode.com/JianFeeeee/ModelRouter.git
synced 2026-09-23 02:18:01 +00:00
d1a72cd23a
v1.5.4 stopped stripping reasoning_content, which fixes clients that send it — but most agent clients (pi included) never store or replay their reasoning, keeping only the tool call. OpenCode Go validates the field on any assistant turn that carries tool_calls and rejects the whole request: 400 invalid_request_error: The `reasoning_content` in the thinking mode must be passed back to the API. Verified against the live endpoint that an EMPTY string satisfies the check, so the adapter now fills in "" when a tool-calling assistant turn has no reasoning_content. Nothing is fabricated: the reasoning shown to the client is still exactly what the upstream returned for that turn. Measured: with a tool_call + tool_result history and no reasoning_content, all 25 configured Go models returned 400 before and all 25 answer correctly now. Test: TestOpenCodeGoVsZenReasoning also pins that a plain assistant turn (no tool calls) must NOT gain the field.
260 lines
11 KiB
Lua
260 lines
11 KiB
Lua
local adapter = {}
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adapter.name = "opencodego"
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adapter.version = "1.0.0"
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adapter.endpoint = "/chat/completions"
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-- OpenCode Go（包月订阅端点，https://opencode.ai/zen/go/v1）
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--
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-- 与 OpenCode Zen（opencodezen.lua，https://opencode.ai/zen/v1）是两个不同的
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-- 服务，行为要求并不相同，因此各有专用适配器：
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--
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-- * 本适配器（Go）服务包月订阅池。上游**强制要求**
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-- `x-opencode-session` 头，缺失直接 400 "MissingSessionID"。
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-- * Go 的 thinking 模型（deepseek-v4.1-flash 等）**必须回传** assistant 轮的
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-- `reasoning_content`，否则 400：
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-- The `reasoning_content` in the thinking mode must be passed back to the API.
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-- 这是本适配器与 Zen 适配器最关键的差异 —— Zen 会剥掉它，Go 必须原样保留。
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-- * Go 上游对协议更严格：非流式请求带 `stream_options` 会被拒
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-- （"stream_options should be set along with stream"）。
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--
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-- 同样需要 opencode 客户端指纹（UA + x-opencode-*）才能被正确识别与路由。
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adapter.headers = {
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["User-Agent"] = "opencode/1.18.21 ai-sdk/provider-utils/4.0.23 runtime/bun/1.3.14",
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}
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-- 每请求生成身份头。沙箱无 os/math，用 meta.timestamp + 请求体哈希派生：
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-- 同秒内重复请求 id 相同可接受（zen 只校验存在性，不校验格式）。
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local function rand_id(prefix, seed)
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return prefix .. string.sub(sha256_hex(seed), 1, 24)
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end
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function adapter.build_headers(meta)
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local ts = tostring(meta.timestamp or "")
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return {
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["User-Agent"] = adapter.headers["User-Agent"],
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["x-opencode-client"] = "cli",
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-- project 固定：同网关实例共享一个工作区身份
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["x-opencode-project"] = string.sub(sha256_hex("llmsproxy|" .. (meta.source and meta.source.name or "")), 1, 32),
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["x-opencode-session"] = rand_id("ses_", "session|" .. ts),
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["x-opencode-request"] = rand_id("msg_", "request|" .. ts .. "|" .. tostring(meta.body or "")),
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}
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end
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-- OpenAI /chat/completions format (pass-through, strip provider-specific fields)
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-- zen 上游 schema 只接受 text content part（无视觉/音频能力）：多模态 part
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-- （image_url / input_audio / file 等）一律剥离。剥离后 content 变空的消息
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-- 若不再携带 tool_calls / tool_call_id 才整条丢弃（避免上游
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-- "unknown variant `image_url`, expected `text`"）；带工具调用的必须保留，
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-- 否则会把紧随其后的 tool 结果变成孤儿，模型会反复重发同一个调用。
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-- zen 上游角色白名单只有 system / user / assistant / tool / latest_reminder：
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-- OpenAI 的 developer（及 function 等）不在其中，直接透传会触发上游
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-- "unknown variant `developer`, expected one of ..." 错误；统一归一化为 system。
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local ROLE_WHITELIST = {
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system = true,
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user = true,
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assistant = true,
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tool = true,
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latest_reminder = true,
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}
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function adapter.transform_request(raw_body)
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local ok, req = pcall(json.decode, raw_body)
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if not ok then return raw_body end
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req.disable_thinking = nil
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req.extra_body = nil
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-- stream_options is only valid alongside stream:true; sending it on a
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-- non-streaming request is rejected by strict upstreams.
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if req.stream then
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if type(req.stream_options) ~= "table" then req.stream_options = {} end
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req.stream_options.include_usage = true
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end
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if req.messages then
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local kept = {}
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for _, msg in ipairs(req.messages) do
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if type(msg.role) == "string" and not ROLE_WHITELIST[msg.role] then
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msg.role = "system"
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end
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-- Go 的 thinking 模式**必须**回传 reasoning_content：剥掉它会让
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-- 上游直接 400（"The `reasoning_content` in the thinking mode must
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-- be passed back to the API"），agent 的每一轮都会失败。
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-- 注意：与 opencodezen.lua 的行为**相反**，不要在这里剥。
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--
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-- 但绝大多数客户端（pi 等）根本不保存也不回传 reasoning，只保留
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-- 工具调用本身。上游只在“带 tool_calls 的助手轮”上校验这个字段，
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-- 实测**空串即可通过校验**，所以缺省时补空串：既满足上游的
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-- 一致性要求，又不伪造任何推理内容（用户看到的 reasoning 仍然是
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-- 上游本轮真实返回的）。
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if msg.role == "assistant"
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and type(msg.tool_calls) == "table" and #msg.tool_calls > 0
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and msg.reasoning_content == nil then
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msg.reasoning_content = ""
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end
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local drop = false
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if type(msg.content) == "table" then
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local parts = {}
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for _, part in ipairs(msg.content) do
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if type(part) == "table" and part.type ~= nil and part.type ~= "text" then
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-- multimodal part not supported by zen
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else
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table.insert(parts, part)
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end
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end
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if #parts == 0 then
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-- Content collapsed to nothing after stripping unsupported
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-- parts. A message that still carries a tool call must
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-- NEVER be dropped: the very next message is its tool
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-- result, and dropping the call orphans that result. The
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-- model then sees a result for a call it never made and
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-- re-issues the same tool call on every turn (observed as
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-- an infinite "repeated tool call" loop).
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if (type(msg.tool_calls) == "table" and #msg.tool_calls > 0)
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or msg.tool_call_id ~= nil then
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msg.content = ""
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else
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drop = true
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end
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else
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msg.content = parts
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end
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end
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if not drop then
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table.insert(kept, msg)
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end
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end
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req.messages = kept
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end
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return json.encode(req)
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end
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function adapter.transform_response(raw_body)
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local ok, resp = pcall(json.decode, raw_body)
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if not ok or resp == nil then return raw_body end
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local unified = {
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content = "",
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finish_reason = "",
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token_usage = { prompt = 0, completion = 0, total = 0 }
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}
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if type(resp.usage) == "table" then
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unified.token_usage.prompt = resp.usage.prompt_tokens or 0
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unified.token_usage.completion = resp.usage.completion_tokens or 0
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unified.token_usage.total = resp.usage.total_tokens or 0
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local hit = 0
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if type(resp.usage.prompt_tokens_details) == "table" and resp.usage.prompt_tokens_details.cached_tokens ~= nil then
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hit = resp.usage.prompt_tokens_details.cached_tokens
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unified.token_usage.prompt_tokens_details = { cached_tokens = hit }
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end
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if (resp.usage.prompt_cache_hit_tokens or 0) > 0 then
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unified.token_usage.prompt_cache_hit_tokens = resp.usage.prompt_cache_hit_tokens
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unified.token_usage.prompt_cache_miss_tokens = resp.usage.prompt_cache_miss_tokens or 0
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if hit == 0 then
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unified.token_usage.prompt_tokens_details = { cached_tokens = resp.usage.prompt_cache_hit_tokens }
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end
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end
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end
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if type(resp.choices) == "table" and #resp.choices > 0 then
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local ch = resp.choices[1]
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if type(ch.message) == "table" then
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unified.content = ch.message.content or ""
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local reasoning = ch.message.reasoning_content or ch.message.reasoning
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if reasoning then
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unified.reasoning_content = reasoning
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end
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if type(ch.message.tool_calls) == "table" then
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local tcs = {}
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for _, tc in ipairs(ch.message.tool_calls) do
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local args_ok, args = pcall(json.decode, tc["function"].arguments)
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if not args_ok then args = {} end
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table.insert(tcs, {
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id = tc.id,
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type = tc.type or "function",
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name = tc["function"].name,
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arguments = args
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})
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end
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unified.tool_calls = tcs
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end
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end
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unified.finish_reason = ch.finish_reason or ""
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end
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return json.encode(unified)
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end
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function adapter.transform_stream_chunk(raw_chunk)
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local ok, chunk = pcall(json.decode, raw_chunk)
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if not ok then return "" end
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-- OpenAI-style streams may attach usage to a chunk with empty choices
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-- (the final usage chunk). Preserve it; the gateway emits it as the
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-- terminal usage chunk. Note: keys must match Go's TokenUsage json tags
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-- (prompt/completion/total); the gateway re-emits standard *_tokens.
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local uses = nil
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if type(chunk.usage) == "table" then
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uses = {
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prompt = chunk.usage.prompt_tokens or chunk.usage.prompt or 0,
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completion = chunk.usage.completion_tokens or chunk.usage.completion or 0,
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total = chunk.usage.total_tokens or chunk.usage.total or 0,
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}
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if type(chunk.usage.prompt_tokens_details) == "table" and chunk.usage.prompt_tokens_details.cached_tokens ~= nil then
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uses.prompt_tokens_details = { cached_tokens = chunk.usage.prompt_tokens_details.cached_tokens }
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elseif (chunk.usage.prompt_cache_hit_tokens or 0) > 0 then
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uses.prompt_cache_hit_tokens = chunk.usage.prompt_cache_hit_tokens
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uses.prompt_cache_miss_tokens = chunk.usage.prompt_cache_miss_tokens or 0
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uses.prompt_tokens_details = { cached_tokens = chunk.usage.prompt_cache_hit_tokens }
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end
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end
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if not chunk.choices or #chunk.choices == 0 then
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if uses ~= nil then
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-- usage-only chunk is not a content/finish signal; the gateway
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-- emits its own terminal stop chunk and merges this usage.
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return json.encode({ usage = uses, done = false })
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end
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return ""
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end
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local delta = chunk.choices[1].delta or {}
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local fr = chunk.choices[1].finish_reason
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local finish = (type(fr) == "string" and fr ~= "") and fr or nil
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local unified = {
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content = delta.content or "",
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done = (finish ~= nil)
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}
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if finish then
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unified.finish_reason = finish
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end
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if uses ~= nil then
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unified.usage = uses
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end
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-- zen 用 reasoning 字段承载推理文本（OpenAI 惯例是 reasoning_content）
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local reasoning = delta.reasoning_content or delta.reasoning
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if reasoning then
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unified.reasoning_content = reasoning
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end
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if delta.tool_calls then
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-- pass raw streaming fragments through; OpenAI clients accumulate index+id+name+arguments
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unified.tool_calls = delta.tool_calls
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end
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return json.encode(unified)
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end
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-- 错误收敛（可选钩子）：zen 错误信封固定为 {error={type,message}}；
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-- 免费池限流 FreeUsageLimitError 单独标注。返回 nil 走通用兜底。
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function adapter.transform_error(status, body)
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local ok, resp = pcall(json.decode, body)
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if not ok or type(resp) ~= "table" then return nil end
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local e = resp.error
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if type(e) ~= "table" then return nil end
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if e.type == "FreeUsageLimitError" then
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return "zen free pool quota exhausted"
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end
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return e.message
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end
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return adapter
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