ModelRouter/internal/lua/adapters/anthropic.lua

local adapter = {}
adapter.name = "anthropic"
adapter.version = "3.0.0"
adapter.endpoint = "/v1/messages"
adapter.headers = {
    ["anthropic-version"] = "2023-06-01",
}

-- ============================================================
-- build_headers  (鉴权头)
-- ============================================================
-- Anthropic 协议要 **x-api-key**，不是 `Authorization: Bearer`。
-- 此前本基座只有一个静态 `anthropic-version`、**没有** build_headers ⇒ 内核
-- 走兜底 `Authorization: Bearer <api_key>` ⇒ 上游一律 401/403：
--   `{"type":"error","error":{"type":"authentication_error",
--     "message":"invalid x-api-key"}}`
-- 继承本基座的渠道源（channels-zcode 等）一有账号就会立刻暴露 —— 与 trae
-- 修复前完全同型（静态头 + 内核 Bearer，缺协议要的那一个头）。
--
-- 三条纪律：
--   ① 值取自内核注入的 `meta.api_key`（源的凭据），**绝不**写日志、不额外
--      放进返回值以外的任何地方；本函数只把它放进请求头。
--   ② **不自己发 Authorization** —— 源级 headers 若已显式配置 Authorization，
--      重复发两种鉴权方案会让部分 Anthropic 兼容上游直接拒（歧义）。
--   ③ `anthropic-version` 与静态头同值，保证「装了 hook」与「没装 hook」两种
--      路径产出同一份协议头，不会因为加了 hook 而少一个版本头。
function adapter.build_headers(meta)
    local h = {
        ["anthropic-version"] = "2023-06-01",
    }
    local key = ""
    if type(meta) == "table" then
        if type(meta.api_key) == "string" then key = meta.api_key end
        -- 兜底：个别调用方只给 source.meta.api_key（与 kimicode 同一约定）
        if key == "" and type(meta.source) == "table"
            and type(meta.source.meta) == "table"
            and type(meta.source.meta.api_key) == "string" then
            key = meta.source.meta.api_key
        end
    end
    if key ~= "" then
        h["x-api-key"] = key
    end
    return h
end

-- INGRESS CLAIM (inbound dual of serialize_*): this adapter owns parsing of the
-- client-facing POST /v1/messages request body. The kernel keeps a built-in
-- Anthropic parser as its fallback (R5) — that is what an unconfigured
-- deployment uses — but once this adapter is loaded the kernel YIELDS, and the
-- deserialize_request hook below is the single place Anthropic's REQUEST shape
-- is understood. Iterating on that shape therefore means editing this file,
-- never the kernel.
adapter.ingress = "/v1/messages"

-- ============================================================
-- helpers
-- ============================================================

local ROLE_WHITELIST = {
    system = true, user = true, assistant = true, tool = true,
}

-- Anthropic requires tool_use.id / tool_result.tool_use_id to match
-- ^[a-zA-Z0-9_-]{1,64}$ and rejects the WHOLE request otherwise with
-- REQUEST_BODY_INVALID / "Invalid tool use format". OpenAI imposes no such
-- rule, so an OpenAI-compatible upstream can hand a client an id like
-- "bash:0" (observed from moonshotai/kimi-k3); once that lands in the
-- client's history, every later replay kills every Claude slot at once and
-- an AUTO chain falls through all tiers.
--
-- safe_tool_id is pure and deterministic, so a tool_use block and its
-- matching tool_result are rewritten identically within one request. A
-- rewritten id keeps an 8-hex digest of the ORIGINAL id: without it two
-- distinct ids could collapse into one ("a:b" and "a_b"), which Anthropic
-- rejects as a duplicate/unpaired tool_use. Already-legal ids pass through
-- untouched so upstreams that round-trip their own ids are unaffected.
local TOOL_ID_MAX = 64

local function safe_tool_id(id)
    if type(id) ~= "string" or id == "" then return "" end
    local clean = string.gsub(id, "[^A-Za-z0-9_-]", "_")
    if clean == id and #clean <= TOOL_ID_MAX then
        return clean
    end
    local digest = string.sub(sha256_hex(id), 1, 8)
    local keep = TOOL_ID_MAX - #digest - 1
    if #clean > keep then clean = string.sub(clean, 1, keep) end
    return clean .. "_" .. digest
end

-- map_usage converts one Anthropic usage object to the gateway's TokenUsage
-- shape. The two APIs disagree on what the prompt count MEANS:
--
--   Anthropic: input_tokens EXCLUDES cached blocks. cache_read_input_tokens and
--              cache_creation_input_tokens are separate, additive, billed input.
--   OpenAI:    prompt_tokens INCLUDES its cached_tokens subset.
--
-- Mapping input_tokens straight onto prompt therefore did two wrong things at
-- once: it undercounted the billed prompt by the entire cache portion, and it
-- could report cached_tokens > prompt_tokens — a cache hit rate above 100%.
-- cache_creation_input_tokens was not read at all, so a cache-write turn lost
-- those billed tokens outright. Summing all three restores OpenAI semantics,
-- which is what clients (and the gateway's own audit trail) assume.
--
-- Returns nil when the object carries no counts, so callers can keep treating
-- "no usage in this chunk" as "say nothing" rather than reporting zeros.
local function map_usage(u)
    if type(u) ~= "table" then return nil end
    local fresh  = u.input_tokens or 0
    local read   = u.cache_read_input_tokens or 0
    local create = u.cache_creation_input_tokens or 0
    local p = fresh + read + create
    local c = u.output_tokens or 0
    if p <= 0 and c <= 0 then return nil end
    local uses = { prompt = p, completion = c, total = p + c }
    -- Emit the split whenever Anthropic reports either cache field, even at 0:
    -- a reported zero-hit ("cache missed") must stay distinguishable from "this
    -- upstream never reports cache info at all".
    if u.cache_read_input_tokens ~= nil or u.cache_creation_input_tokens ~= nil then
        uses.prompt_tokens_details = { cached_tokens = read }
        uses.prompt_cache_hit_tokens = read
        uses.prompt_cache_miss_tokens = fresh + create
    end
    return uses
end

local function collect_blocks(content)
    if type(content) == "string" then
        if content == "" then return {} end
        return { { type = "text", text = content } }
    end
    if type(content) ~= "table" then return {} end
    local blocks = {}
    for _, p in ipairs(content) do
        if type(p) == "string" then
            table.insert(blocks, { type = "text", text = p })
        elseif type(p) == "table" then
            if p.type == "text" then
                table.insert(blocks, { type = "text", text = p.text or "" })
            elseif p.type == "image_url" and type(p.image_url) == "table" and p.image_url.url then
                local url = p.image_url.url
                local mt, b64 = string.match(url, "^data:([^,]+);base64,(.+)$")
                if b64 then
                    table.insert(blocks, { type = "image", source = { type = "base64", media_type = mt or "image/png", data = b64 } })
                else
                    table.insert(blocks, { type = "image", source = { type = "url", url = url } })
                end
            else
                -- Unknown content type: preserve for forward compatibility
                table.insert(blocks, p)
            end
        end
    end
    return blocks
end

local function text_of(content)
    if type(content) == "string" then return content end
    local t = ""
    for _, p in ipairs(content or {}) do
        if type(p) == "string" then
            t = t .. p
        elseif type(p) == "table" and p.type == "text" and p.text then
            t = t .. p.text
        end
    end
    return t
end

--- Append blocks to the last user message (merge) or create a new one.
local function append_user(msgs, blocks)
    if #msgs > 0 and msgs[#msgs].role == "user" then
        for _, b in ipairs(blocks) do
            table.insert(msgs[#msgs].content, b)
        end
    else
        table.insert(msgs, { role = "user", content = blocks })
    end
end

-- ============================================================
-- deserialize_request  (Anthropic → unified)   [INGRESS]
-- ============================================================
-- The inbound dual of serialize_response. The kernel hands over the raw client
-- body and receives the unified request; from there scheduling is identical to
-- /v1/chat/completions. Ported field-for-field from the kernel's former
-- built-in anthropicRequest.toUnified — which remains as the kernel fallback
-- for an unconfigured deployment.
function adapter.deserialize_request(raw_body)
    local ok, req = pcall(json.decode, raw_body)
    if not ok or type(req) ~= "table" then
        error("invalid json")
    end

    -- system accepts Anthropic's two spellings: a plain string, or an array of
    -- { type = "text", text = ... } blocks. Only text blocks contribute.
    local system = ""
    local sys = req.system
    if type(sys) == "string" then
        system = sys
    elseif type(sys) == "table" then
        for _, b in ipairs(sys) do
            if type(b) == "table" and b.type == "text" and type(b.text) == "string" then
                system = system .. b.text
            end
        end
    end

    local msgs = {}
    local in_msgs = req.messages
    if type(in_msgs) ~= "table" or #in_msgs == 0 then
        error("messages is required")
    end
    for i, m in ipairs(in_msgs) do
        if type(m) ~= "table" or type(m.role) ~= "string" or m.role == "" then
            error("messages[" .. (i - 1) .. "].role is required")
        end
        -- Content is carried VERBATIM: a string stays a string, a block array
        -- stays an array. The unified format keeps multimodal/tool blocks
        -- untouched because only an adapter knows which block types the upstream
        -- accepts. An absent/null content becomes the empty string.
        local content = m.content
        if content == nil or content == "" then
            content = ""
        end
        table.insert(msgs, { role = m.role, content = content })
    end

    -- Anthropic's default is thinking OFF; emit that explicitly so the unified
    -- request carries the client's intent rather than leaving it absent.
    local thinking = req.thinking
    if thinking == nil then
        thinking = { enabled = false }
    end

    local out = {
        model          = req.model or "",
        messages       = msgs,
        system         = system,
        max_tokens     = req.max_tokens or 0,
        stream         = req.stream == true,
        temperature    = req.temperature,
        top_p          = req.top_p,
        top_k          = req.top_k or 0,
        stop_sequences = req.stop_sequences,
        tools          = req.tools,
        tool_choice    = req.tool_choice,
        thinking       = thinking,
        extra_body     = {},
    }
    return json.encode(out)
end

-- ============================================================
-- transform_request  (OpenAI → Anthropic)
-- ============================================================

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

    local msgs = {}
    local system = ""
    local pending_tool = {}  -- accumulated tool_result blocks

    for _, m in ipairs(req.messages or {}) do
        local role = m.role or "user"

        if role == "system" then
            system = system .. text_of(m.content) .. "\n"

        elseif role == "assistant" then
            -- Build content array: text/image blocks from content + tool_use from tool_calls
            local blocks = collect_blocks(m.content)

            if type(m.tool_calls) == "table" then
                for _, tc in ipairs(m.tool_calls) do
                    if type(tc) == "table" then
                        local fn = tc["function"] or {}
                        local args = fn.arguments
                        local input = {}
                        if type(args) == "string" and args ~= "" then
                            local ok2, parsed = pcall(json.decode, args)
                            if ok2 and type(parsed) == "table" then input = parsed end
                        elseif type(args) == "table" then
                            input = args
                        end
                        table.insert(blocks, {
                            type  = "tool_use",
                            id    = safe_tool_id(tc.id),
                            name  = fn.name or "",
                            input = input,
                        })
                    end
                end
            end

            table.insert(msgs, { role = "assistant", content = blocks })

        elseif role == "tool" then
            -- Accumulate consecutive tool results; will be flushed as one
            -- user message with tool_result content blocks.
            table.insert(pending_tool, {
                type        = "tool_result",
                tool_use_id = safe_tool_id(m.tool_call_id),
                content     = text_of(m.content),
            })

        else
            -- user / any other role: flush pending tool results first
            if #pending_tool > 0 then
                append_user(msgs, pending_tool)
                pending_tool = {}
            end
            append_user(msgs, collect_blocks(m.content))
        end
    end

    -- Flush any trailing tool results
    if #pending_tool > 0 then
        append_user(msgs, pending_tool)
    end

    -- ── Build Anthropic request ──────────────────────────────
    local anthropic_req = {
        model      = req.model or "claude-sonnet-4-20250514",
        max_tokens = req.max_tokens or 4096,
        messages   = msgs,
        stream     = req.stream or false,
    }

    -- ── tools ────────────────────────────────────────────────
    local has_tools = false
    if req.tools and type(req.tools) == "table" then
        local tools = {}
        for _, t in ipairs(req.tools) do
            if type(t) == "table" and t.type == "function"
                and type(t["function"]) == "table" then
                local fn = t["function"]
                table.insert(tools, {
                    name         = fn.name or "",
                    description  = fn.description or "",
                    input_schema = fn.parameters or { type = "object", properties = {} },
                })
            end
        end
        if #tools > 0 then
            anthropic_req.tools = tools
            has_tools = true
        end
    end

    -- ── tool_choice ──────────────────────────────────────────
    -- OpenAI → Anthropic mapping:
    --   "auto"     → {type:"auto"}
    --   "none"     → remove tools entirely (Anthropic has no "none")
    --   "required" → {type:"any"}
    --   {type:"function", function:{name:"X"}} → {type:"tool", name:"X"}
    if has_tools and req.tool_choice then
        local tc = req.tool_choice
        if type(tc) == "string" then
            if tc == "auto" then
                anthropic_req.tool_choice = { type = "auto" }
            elseif tc == "none" then
                anthropic_req.tools = nil
                anthropic_req.tool_choice = nil
            elseif tc == "required" then
                anthropic_req.tool_choice = { type = "any" }
            end
        elseif type(tc) == "table" and tc.type == "function" then
            local fn = tc["function"] or {}
            anthropic_req.tool_choice = { type = "tool", name = fn.name or "" }
        end
    end

    -- ── thinking ─────────────────────────────────────────────
    -- Anthropic 的原生字段就是 {type, budget_tokens}，所以中立语义几乎直译。
    --
    -- 中立格式（v2）：{ enabled = true/false, budget_tokens = N }
    --   enabled=false → 不传该字段（Anthropic 默认关闭思考，传 type=disabled
    --                  是非法的，这也是过去只能「不传」的原因）
    --   enabled=true  → { type="enabled", budget_tokens=N 或默认 }
    --
    -- 仍接受 extra_body.thinking 的原生写法：已有部署的配置不因此失效。
    local th = req.thinking
    if type(th) == "table" and th.enabled ~= false then
        local budget = th.budget_tokens
        if budget == nil or budget <= 0 then budget = 4096 end  -- Anthropic 必填
        anthropic_req.thinking = { type = "enabled", budget_tokens = budget }
    elseif type(req.extra_body) == "table" and type(req.extra_body.thinking) == "table" then
        anthropic_req.thinking = req.extra_body.thinking
    end
    req.thinking = nil

    -- ── system ───────────────────────────────────────────────
    if system ~= "" then
        -- Strip trailing newline from accumulation
        anthropic_req.system = string.match(system, "^(.-)\n*$")
    end

    return json.encode(anthropic_req)
end

-- ============================================================
-- transform_response  (Anthropic → OpenAI, non-streaming)
-- ============================================================

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 },
    }

    local mapped = map_usage(resp.usage)
    if mapped ~= nil then
        unified.token_usage = mapped
    end

    if resp.content and #resp.content > 0 then
        local tcs = {}
        for _, block in ipairs(resp.content) do
            if block.type == "text" then
                unified.content = unified.content .. (block.text or "")
            elseif block.type == "thinking" and block.thinking then
                unified.reasoning_content = (unified.reasoning_content or "") .. block.thinking
            elseif block.type == "tool_use" then
                table.insert(tcs, {
                    id        = safe_tool_id(block.id),
                    type      = "function",
                    name      = block.name or "",
                    arguments = block.input or {},
                })
            end
        end
        if #tcs > 0 then unified.tool_calls = tcs end
    end

    unified.finish_reason = resp.stop_reason or ""
    if unified.finish_reason == "tool_use" then
        unified.finish_reason = "tool_calls"
    elseif unified.finish_reason == "max_tokens" then
        unified.finish_reason = "length"
    elseif unified.finish_reason == "end_turn"
        or unified.finish_reason == "stop_sequence" then
        unified.finish_reason = "stop"
    end

    return json.encode(unified)
end

-- ============================================================
-- transform_stream_chunk  (Anthropic SSE → OpenAI SSE delta)
-- ============================================================

function adapter.transform_stream_chunk(raw_chunk)
    local ok, chunk = pcall(json.decode, raw_chunk)
    if not ok then return "" end

    -- ── message_start: initial usage ─────────────────────────
    if chunk.type == "message_start" then
        if chunk.message then
            -- Some Anthropic-compatible upstreams report a placeholder here and
            -- only send the true prompt count in message_delta (justwoker:
            -- 160 at message_start vs 6931 at message_delta). The gateway's
            -- mergeUsage lets a later non-zero value win, so both are emitted.
            local uses = map_usage(chunk.message.usage)
            if uses ~= nil then
                return json.encode({ usage = uses, done = false })
            end
        end
        return ""
    end

    -- ── message_delta: stop_reason + final usage ─────────────
    if chunk.type == "message_delta" then
        local finish = nil
        if chunk.delta and chunk.delta.stop_reason ~= nil then
            local sr = chunk.delta.stop_reason
            if sr == "max_tokens" then
                finish = "length"
            elseif sr == "tool_use" then
                finish = "tool_calls"
            else
                finish = "stop"
            end
        end
        -- message_delta carries the FINAL usage, so it must map the cache
        -- fields too; the old code dropped them here, which silently lost the
        -- whole cache split on every streamed response.
        local uses = map_usage(chunk.usage)
        if uses ~= nil then
            return json.encode({ content = "", done = (finish ~= nil), finish_reason = finish, usage = uses })
        end
        return json.encode({ content = "", done = (finish ~= nil), finish_reason = finish })
    end

    -- ── content_block_start: begin text / thinking / tool_use ─
    if chunk.type == "content_block_start" and chunk.content_block then
        local cb = chunk.content_block
        if cb.type == "tool_use" then
            -- Pass Anthropic content_block index through; Go-side rewrites
            -- to sequential OpenAI tool_call ordinal for multi-tool streams.
            return json.encode({
                content = "", done = false,
                tool_calls = { {
                    index = chunk.index or 0,
                    id    = safe_tool_id(cb.id),
                    type  = "function",
                    ["function"] = { name = cb.name or "", arguments = "" },
                } },
            })
        end
        return ""  -- text / thinking block start: no OpenAI equivalent
    end

    -- ── content_block_delta: incremental content ──────────────
    if chunk.type == "content_block_delta" and chunk.delta then
        local d = chunk.delta
        if d.type == "input_json_delta" then
            -- Tool call argument fragment; Go-side rewrites index.
            return json.encode({
                content = "", done = false,
                tool_calls = { {
                    index = chunk.index or 0,
                    id    = "",
                    type  = "function",
                    ["function"] = { name = "", arguments = d.partial_json or "" },
                } },
            })
        end
        if d.type == "thinking_delta" and d.thinking then
            return json.encode({ content = "", done = false, reasoning_content = d.thinking })
        end
        if d.type == "text_delta" and d.text then
            return json.encode({ content = d.text, done = false })
        end
    end

    -- ── content_block_stop / message_stop ─────────────────────
    if chunk.type == "message_stop" then
        -- The message_delta event already emitted the true finish_reason.
        -- Do NOT emit done=true here: an empty finish_reason would
        -- overwrite the real one (tool_calls) in the Go gateway's
        -- lastFinish tracker, causing the final SSE chunk to say
        -- finish_reason=stop instead of tool_calls.
        return ""
    end

    return ""
end

-- ============================================================
-- transform_error  (Anthropic error → human-readable string)
-- ============================================================

function adapter.transform_error(status, body)
    local ok, resp = pcall(json.decode, body)
    if not ok or type(resp) ~= "table" then return nil end

    -- Anthropic envelope: {type:"error", error:{type, message}}
    if resp.type == "error" and type(resp.error) == "table"
        and type(resp.error.message) == "string" then
        return resp.error.message
    end

    -- Flat envelope: {error: {message: "..."}}
    if type(resp.error) == "table" and type(resp.error.message) == "string" then
        return resp.error.message
    end

    return nil
end

-- ============================================================
-- 对外序列化（统一格式 → Anthropic 形状）
-- ============================================================
--
-- 内核只持统一格式；对外发什么形状由适配器决定（Phase 2）。
-- 这几个钩子让以 anthropic 适配器为服务端的源，直接对 Anthropic 协议客户端
-- 提供 /v1/messages 语义，而内核里不再有任何 Anthropic 字段名。
--
-- 与入向的 transform_* 互为逆运算：那里的映射（end_turn→stop）在这里反着
-- 做回去（stop→end_turn），且默认 stop_sequence 是最保守的选择——它语义上
-- 最接近"正常结束"。

-- 统一 finish_reason → Anthropic stop_reason
local function to_stop_reason(finish)
    if finish == "tool_calls" then return "tool_use" end
    if finish == "length" then return "max_tokens" end
    if finish == "content_filter" then return "refusal" end
    -- stop / 空 / 未知 → end_turn（Anthropic 的"正常说完"）
    return "end_turn"
end

-- usage 数字化：内部 TokenUsage 的字段名与 Anthropic 的 input_tokens/output_tokens
-- 不同名，这里做最后一次翻译。
local function to_anthropic_usage(u)
    if type(u) ~= "table" then
        return { input_tokens = 0, output_tokens = 0 }
    end
    return {
        input_tokens  = u.prompt or 0,
        output_tokens = u.completion or 0,
    }
end

-- 非流式响应：统一结果 → Anthropic Messages 响应对象
function adapter.serialize_response(payload)
    local p = json.decode(payload)
    local blocks = {}
    -- thinking 放最前，与 Anthropic 自己的块顺序一致
    if p.reasoning ~= nil and p.reasoning ~= "" then
        blocks[#blocks + 1] = { type = "thinking", thinking = p.reasoning }
    end
    blocks[#blocks + 1] = { type = "text", text = p.content or "" }
    if type(p.tool_calls) == "table" and #p.tool_calls > 0 then
        for _, tc in ipairs(p.tool_calls) do
            blocks[#blocks + 1] = {
                type = "tool_use",
                id = tc.id or "",
                name = tc.name or "",
                input = tc.arguments or {},
            }
        end
    end
    return json.encode({
        id = p.id or "",
        type = "message",
        role = "assistant",
        model = p.model or "",
        content = blocks,
        stop_reason = to_stop_reason(p.finish_reason),
        stop_sequence = nil,
        usage = to_anthropic_usage(p.token_usage),
    })
end

-- 角色前导：Anthropic 的流以 message_start 开头（不是 role delta）
function adapter.serialize_open_preamble(payload)
    local p = json.decode(payload)
    return json.encode({
        type = "message_start",
        message = {
            id = p.id or "",
            type = "message",
            role = "assistant",
            model = p.model or "",
            content = {},
            stop_reason = nil,
            stop_sequence = nil,
            usage = { input_tokens = 0, output_tokens = 0 },
        },
    })
end

-- 流式增量：统一 chunk → Anthropic content_block_delta
--
-- 真实 Anthropic 流需要 content_block_start（开块）→ delta → stop（关块）
-- 的三段式。这里用与多数 Anthropic 客户端兼容的简化形式：首个文本增量
-- 带 index=0 的 delta，后续同 index；关闭由 serialize_close 负责。
function adapter.serialize_chunk(payload)
    local p = json.decode(payload)
    if p.done then
        -- 终止由 serialize_close 统一发，这里不重复
        return ""
    end
    if p.reasoning ~= nil and p.reasoning ~= "" then
        return json.encode({
            type = "content_block_delta",
            index = 0,
            delta = { type = "thinking_delta", thinking = p.reasoning },
        })
    end
    return json.encode({
        type = "content_block_delta",
        index = 0,
        delta = { type = "text_delta", text = p.content or "" },
    })
end

-- 收尾：message_delta（stop_reason + usage）→ message_stop
function adapter.serialize_close(payload)
    local p = json.decode(payload)
    return json.encode({
        type = "message_delta",
        delta = {
            stop_reason = to_stop_reason(p.finish_reason),
            stop_sequence = nil,
        },
        usage = { output_tokens = 0 },
    }) .. "\n" .. json.encode({ type = "message_stop" })
end

return adapter