mirror of
https://gitcode.com/JianFeeeee/ModelRouter.git
synced 2026-09-20 00:48:00 +00:00
bb3af3bdb3
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.
187 lines
7.2 KiB
Lua
187 lines
7.2 KiB
Lua
local adapter = {}
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adapter.name = "opencode"
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adapter.version = "1.0.0"
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adapter.endpoint = "/chat/completions"
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-- opencode.ai zen 网关按客户端指纹（UA + x-opencode-* 头）路由请求池：
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-- 缺少 x-opencode-client/session/request/project 身份头的请求会被判为匿名
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-- 客户端，x-preview-f-free 等模型在带 tools 时上游直接失败——流式返回单
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-- chunk "finish_reason":"network_error" 空 content，非流式返回 503
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-- "Endpoint is unavailable"，表现为"空回复"。因此除 UA 外必须附带
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-- x-opencode-* 身份头；session/request 每请求派生唯一值（见 build_headers）。
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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；因此失去全部 content 的
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-- 消息整条丢弃，避免上游 "unknown variant `image_url`, expected `text`"。
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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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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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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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msg.reasoning_content = nil
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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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drop = true
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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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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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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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return adapter
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