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体检判据(TestAllBundledAdaptersStreamToolCallStatus)报出的三类问题,
本提交解决其中两类;第三类(gemini)未动,原因见下。
## ① OpenAI 兼容族:github / groq / mistral(3 个)
它们的 transform_stream_chunk 与修复前的 deepseek **逐字相同** ——
只透 content/done,tool_calls 处理只存在于 transform_response(非流式)。
后果与 deepseek 相同:流式模式下工具调用全部丢失,模型调不动任何工具,
且**没有任何报错**。生产当前未启用这三个源,但按预设配置的用户会踩到。
照 deepseek 的修法补上(含 reasoning_content 透传)。
## ② 嵌套形态 + 键名错:server / kimicode / anthropic / ollama(4 个)
这四个**有** tool_calls 处理,但发的是:
{ index = N, id = ..., ["function"] = { name = ..., arguments = ... } }
而 homed 的 `agentAPI.ToolCall` 是**扁平**结构,json tag 为:
id / type / name / arguments / raw_arguments / stream_index
两处都是**静默**失效(Go 侧按 json tag 反序列化,取不到就是零值,无报错):
- **嵌套** `["function"]` ⇒ `name` / `raw_arguments` 取零值
⇒ flush 时判「无 name」丢弃,或参数为空
- **键名 `index`** ⇒ `StreamIndex` 取零值
⇒ 多个分片并到同一个桶,argsRaw 混拼 ⇒ 每个工具报「参数不是合法 JSON」
而**一个都没真跑**
已逐项对齐为扁平 + `stream_index`。协议差异都保留:
- anthropic:`content_block_start` / `input_json_delta`,续传片 name 留空
(内核按 stream_index 累积,补齐 name 后才 flush)
- ollama:tool_calls **整条一次发完**(不分片),故 stream_index 取数组下标
## ③ gemini 未动
它的流式函数处理 `candidates[].content.parts`,**全文件没有任何
tool_calls / functionCall 处理** —— 连非流式路径也没有。补它不是"对齐"
而是新实现,且 gemini 的 functionCall 形态(`functionCall: {name, args}`,
args 是对象而非 JSON 字符串)与 OpenAI 族不同,需要单独判据。
生产三个源(llmsproxy / visionllm / justworker)全部用 `openai.lua`,
不阻塞。留作独立项。
## 判据
- TestOpenAICompatibleFamilyHandlesStreamToolCalls 5 个 OpenAI 族适配器,
逐个验证 tool_calls 未丢 + stream_index 正确
- TestAnthropicAdapterEmitsFlatToolCallsWithStreamIndex 用 **Anthropic 协议**
的 fixture(不用 OpenAI 的,否则会因"不适用该 chunk"跳过 —— 看着绿,
实则没测)
- TestOllamaAdapterEmitsFlatToolCallsWithStreamIndex 用 Ollama 协议形态
- TestDeepSeekAdapterHandlesStreamToolCalls 单列,因它有源预设指向
★ 三个判据按**协议**分文件而非逐适配器:这几个文件的流式函数逐字相同,
共用一个 fixture 会因协议不适用而静默跳过 —— 那等于没测。
## 体检分类
修前: ✓ [openai] ⚠ [kimicode server] ✗ [anthropic deepseek gemini github groq mistral ollama]
修后: ✓ [openai deepseek github groq mistral] ⚠ [] ✗ [gemini]
133 lines
5.1 KiB
Lua
133 lines
5.1 KiB
Lua
local adapter = {}
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adapter.name = "mistral"
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adapter.version = "2.0.0"
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adapter.endpoint = "/v1/chat/completions"
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adapter.headers = {}
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-- Mistral API is OpenAI-compatible, just passes through
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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.model = req.model or "mistral-large-latest"
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req.temperature = req.temperature or 0.7
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req.max_tokens = req.max_tokens or 4096
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req.stream = req.stream or false
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req.disable_thinking = nil
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req.extra_body = nil
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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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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 fn = tc["function"]
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local name = tc.name
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local raw_args = tc.arguments
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if type(fn) == "table" then
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name = fn.name or name
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raw_args = fn.arguments or raw_args
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end
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local args = {}
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if type(raw_args) == "table" then
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args = raw_args
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elseif type(raw_args) == "string" and raw_args ~= "" then
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local args_ok, decoded = pcall(json.decode, raw_args)
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if args_ok and type(decoded) == "table" then
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args = decoded
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elseif args_ok then
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args = { value = decoded }
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else
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args = { raw = raw_args }
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end
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end
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if name ~= nil and name ~= "" then
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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 = name,
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arguments = args
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})
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end
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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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if not chunk.choices or #chunk.choices == 0 then return "" 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 unified = {
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content = delta.content or "",
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done = (fr ~= nil)
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}
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if delta.reasoning_content then
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unified.reasoning_content = delta.reasoning_content
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end
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-- ★ 必须处理流式 tool_calls —— 此前只透 content/done,导致 deepseek 源
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-- 在**流式**模式下工具调用全部丢失,模型调不动任何工具且无任何报错。
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--
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-- 为什么难发现:非流式路径(transform_response)是好的,所以端到端
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-- 手工测试也过;而内核的 tool call 循环默认走流式。
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-- 功能判据(core 包的批内测试)直接构造 Go 结构体,绕过适配器。
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--
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-- 形态与 openai.lua 一致:OpenAI 兼容流式格式
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-- {function:{name,arguments}, id, type, index} → homed 扁平结构
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-- {id, type, name, raw_arguments, stream_index}。
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if delta.tool_calls then
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local tcs = {}
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for _, tc in ipairs(delta.tool_calls) do
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local fn = tc["function"]
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local name = (type(fn) == "table" and fn.name) or tc.name or ""
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local raw_args = ""
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if type(fn) == "table" and type(fn.arguments) == "string" then
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raw_args = fn.arguments
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elseif type(tc.arguments) == "string" then
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raw_args = tc.arguments
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end
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-- 不能按 name 过滤:流式续传片 name 为空但携带 arguments,
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-- 内核 accumulateStream 按 stream_index 分桶并累积
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table.insert(tcs, {
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id = tc.id or "",
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type = tc.type or "function",
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name = name,
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raw_arguments = raw_args,
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-- 透传上游分片 index:并行多工具调用时内核按它区分归属桶
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stream_index = tc.index or 0
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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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return json.encode(unified)
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end
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return adapter
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