mirror of
https://gitcode.com/JianFeeeee/HomeAgent.git
synced 2026-09-27 21:03:16 +00:00
## 缺陷
deepseek.lua 的 tool_calls 处理只存在于 `transform_response`(**非流式**路径),
而 `transform_stream_chunk` 只透 content/done:
return json.encode({ content = delta.content or "", done = (fr ~= nil) })
于是 deepseek 源在**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**,只是"工具好像不听话"。
## 为什么难发现
- 非流式路径是好的 ⇒ 端到端手工测试也过
- 内核的 tool call 循环默认走**流式**(provider.go 的 stream 分支)⇒ 实际不可用
- 功能判据(core 包的批内测试)直接构造 `agentAPI.StreamChunk{}`,
**绕过适配器** ⇒ 测不到这一层
配置里 `deepseek` 源预设指向 `adapters/deepseek.lua`,所以任何按预设配置
的用户都会踩到(生产当前未启用该源,配置里 deepseek 相关键为 0)。
## 修法
照 openai.lua 的做法在流式路径补上:OpenAI 兼容格式
`{function:{name,arguments}, id, type, index}` → homed 扁平结构
`{id, type, name, raw_arguments, stream_index}`,含 reasoning_content 透传。
两个容易踩的点也写进注释:
- **不能按 name 过滤**:流式续传片 name 为空但携带 arguments,
内核按 stream_index 分桶累积
- **必须透传 stream_index**:否则多个分片并到槽 0、argsRaw 混拼
## 判据
新增 TestDeepSeekAdapterHandlesStreamToolCalls:喂两个含 tool_call 的分片,
断言 tool_calls 未被丢弃且 stream_index 正确。修前两条分片全被丢弃。
## 体检分类随之变化
修前: ✓ [openai] ✗ [deepseek ...]
修后: ✓ [deepseek openai] ✗ [anthropic gemini github groq mistral ollama]
137 lines
5.2 KiB
Lua
137 lines
5.2 KiB
Lua
local adapter = {}
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adapter.name = "deepseek"
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adapter.version = "2.1.0"
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adapter.endpoint = "/chat/completions"
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adapter.headers = {}
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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 "deepseek-chat"
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req.stream = req.stream or false
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if req.disable_thinking then
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req.extra_body = req.extra_body or {}
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req.extra_body.thinking = { type = "disabled" }
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end
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req.disable_thinking = 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 ch.message.reasoning_content then
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unified.reasoning_content = ch.message.reasoning_content
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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 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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