ModelRouter/internal/lua/adapters/opencode.lua

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

    if not chunk.choices or #chunk.choices == 0 then 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 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