local adapter = {} adapter.name = "opencode" adapter.version = "1.0.0" adapter.endpoint = "/chat/completions" -- opencode.ai zen 网关按 User-Agent 指纹识别官方客户端并把请求分到免费额度池; -- 非官方 UA(curl/Go 默认等)会被分到匿名池并触发 FreeUsageLimitError。 -- 因此固定发送 opencode 客户端的 UA;配合源配置 api_key: "public"(官方无 key -- 客户端实际发送 Bearer public)即可走免费池。 adapter.headers = { ["User-Agent"] = "opencode/0.1.0", } -- OpenAI /chat/completions format (pass-through, strip provider-specific fields) -- zen 上游 schema 只接受 text content part(无视觉/音频能力):多模态 part -- (image_url / input_audio / file 等)一律剥离;因此失去全部 content 的 -- 消息整条丢弃,避免上游 "unknown variant `image_url`, expected `text`"。 -- zen 上游角色白名单只有 system / user / assistant / tool / latest_reminder: -- OpenAI 的 developer(及 function 等)不在其中,直接透传会触发上游 -- "unknown variant `developer`, expected one of ..." 错误;统一归一化为 system。 local ROLE_WHITELIST = { system = true, user = true, assistant = true, tool = true, latest_reminder = true, } function adapter.transform_request(raw_body) local ok, req = pcall(json.decode, raw_body) if not ok then return raw_body end req.disable_thinking = nil req.extra_body = nil if req.messages then local kept = {} for _, msg in ipairs(req.messages) do if type(msg.role) == "string" and not ROLE_WHITELIST[msg.role] then msg.role = "system" end msg.reasoning_content = nil local drop = false if type(msg.content) == "table" then local parts = {} for _, part in ipairs(msg.content) do if type(part) == "table" and part.type ~= nil and part.type ~= "text" then -- multimodal part not supported by zen else table.insert(parts, part) end end if #parts == 0 then drop = true else msg.content = parts end end if not drop then table.insert(kept, msg) end end req.messages = kept end return json.encode(req) end function adapter.transform_response(raw_body) local ok, resp = pcall(json.decode, raw_body) if not ok or resp == nil then return raw_body end local unified = { content = "", finish_reason = "", token_usage = { prompt = 0, completion = 0, total = 0 } } if type(resp.usage) == "table" then unified.token_usage.prompt = resp.usage.prompt_tokens or 0 unified.token_usage.completion = resp.usage.completion_tokens or 0 unified.token_usage.total = resp.usage.total_tokens or 0 end if type(resp.choices) == "table" and #resp.choices > 0 then local ch = resp.choices[1] if type(ch.message) == "table" then unified.content = ch.message.content or "" if ch.message.reasoning_content then unified.reasoning_content = ch.message.reasoning_content end if type(ch.message.tool_calls) == "table" then local tcs = {} for _, tc in ipairs(ch.message.tool_calls) do local args_ok, args = pcall(json.decode, tc["function"].arguments) if not args_ok then args = {} end table.insert(tcs, { id = tc.id, type = tc.type or "function", name = tc["function"].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 then return "" end -- OpenAI-style streams may attach usage to a chunk with empty choices -- (the final usage chunk). Preserve it; the gateway emits it as the -- terminal usage chunk. Note: keys must match Go's TokenUsage json tags -- (prompt/completion/total); the gateway re-emits standard *_tokens. local uses = nil if type(chunk.usage) == "table" then uses = { prompt = chunk.usage.prompt_tokens or chunk.usage.prompt or 0, completion = chunk.usage.completion_tokens or chunk.usage.completion or 0, total = chunk.usage.total_tokens or chunk.usage.total or 0, } end if not chunk.choices or #chunk.choices == 0 then if uses ~= nil then return json.encode({ usage = uses, done = (chunk.usage ~= nil) }) end return "" end local delta = chunk.choices[1].delta or {} local fr = chunk.choices[1].finish_reason local unified = { content = delta.content or "", done = (fr ~= nil) } if uses ~= nil then unified.usage = uses end if delta.reasoning_content then unified.reasoning_content = delta.reasoning_content end if delta.tool_calls then -- pass raw streaming fragments through; OpenAI clients accumulate index+id+name+arguments unified.tool_calls = delta.tool_calls end return json.encode(unified) end return adapter