SETTLEMENT TELEMETRY

Inference Log

MID: gtgHTlWj49J0XryG

Log MIDgtgHTlWj49J0XryG
Typechat_completion
Aliasdeepseek-v4-flash-g2 / gtNPvZSEVNq72rnw
Hostinforaxi-server:3300:v1.1.3736-1-gf63106d5c
Mode / Protoreq-v1-chatcmplopenai-chatcmpl
Vendor/LLMvolcengine-bizplan-dtx/deepseek-v4-flash-ga-260731
Statussuccess
User Pricing
138 × 1.8 = 0.000248
0 × 0.06 = 0.000000
383 × 5.4 = 0.002068
CNY 0.002317
Vendor Cost
138 × 1.2 = 0.000166
0 × 0.04 = 0.000000
383 × 3.6 = 0.001379
CNY 0.001544
Latency4.52914927s
Summary
{
  "system_prefix": null,
  "history_count": 1,
  "user_summary": {
    "text": "请详细回答:解释为什么递归可能导致栈溢出。图的深度优先搜索使用递归或显式栈实现,用于遍历或检测连通性。例如计算阶乘时,n的阶乘等于n乘以n-1的阶乘,这就是典型的递归定义。",
    "has_image": false,
    "has_tool_result": false
  },
  "assistant_summary": {
    "text": "递归是一种函数调用自身的编程技巧。要理解为什么递归可能导致栈溢出,需要先理解程序运行时**函数调用栈(Call Stack)**的工作方式。\n\n## 1. 函数调用栈与栈帧\n\n程序在运行时,操作系统会为每个线程分配一段内存作为**调用栈**。每次调用一个函数,系统会在栈上压入一个**栈帧(Stack",
    "tool_calls": [],
    "stop_reason": "length"
  }
}
Stop Reasonlength
Vendor Request
Method: POST

URL: https://ark.cn-beijing.volces.com/api/v3/chat/completions

Headers:
{
  "authorization": "Bearer ark-f137c261-ce2c-4ac5-b065-fafb2270c3d7-1801e",
  "content-type": "application/json"
}

Body:
{
  "max_tokens": 300,
  "messages": [
    {
      "content": "请详细回答:解释为什么递归可能导致栈溢出。图的深度优先搜索使用递归或显式栈实现,用于遍历或检测连通性。例如计算阶乘时,n的阶乘等于n乘以n-1的阶乘,这就是典型的递归定义。",
      "role": "user"
    }
  ],
  "model": "deepseek-v4-flash-ga-260731",
  "stream": false
}
Vendor Response
Headers:
{
  "content-length": "2101",
  "content-type": "application/json; charset=utf-8",
  "date": "Sun, 30 Aug 2026 16:32:43 GMT",
  "server": "istio-envoy",
  "vary": "Accept-Encoding",
  "x-client-request-id": "unknown-20260831003239-fZbsQFZi",
  "x-envoy-upstream-service-time": "4494",
  "x-request-id": "021788107559864de824baea9702e17bffa39e9c3bdc4f65ce204"
}

Body:
{
  "choices": [
    {
      "finish_reason": "length",
      "index": 0,
      "logprobs": null,
      "message": {
        "content": "递归是一种函数调用自身的编程技巧。要理解为什么递归可能导致栈溢出,需要先理解程序运行时**函数调用栈(Call Stack)**的工作方式。\n\n## 1. 函数调用栈与栈帧\n\n程序在运行时,操作系统会为每个线程分配一段内存作为**调用栈**。每次调用一个函数,系统会在栈上压入一个**栈帧(Stack Frame)**,其中保存:\n\n- 函数的局部变量\n- 参数\n- 返回地址\n- 保存的寄存器状态等\n\n当函数返回时,其栈帧被弹出,控制权交还给调用者。\n\n```c\nint factorial(int n) {\n    if (n <= 1) return 1;          // 基准情况\n    return n * factorial(n - 1);   // 递归调用\n}\n```\n\n调用 `factorial(5)` 时,调用栈的变化如下:\n\n```\nfactorial(1)  -> 返回 1\nfactorial(2)  -> 等待 factorial(1) 的结果\nfactorial(3)  -> 等待 factorial(2) 的结果\nfactorial(4)  -> 等待 factorial(3) 的结果\nfactorial(5)  -> 等待 factorial(4) 的结果\n```\n\n每一层递归都未返回,因此每一层的栈帧都同时存在。递归深度越大,占用的栈内存越多。\n\n## 2. 为什么会导致栈",
        "reasoning_content": "我们 need answer in Chinese. Need explain recursion stack overflow, DFS recursion or explicit stack, example factorial. Need detailed. We can structure. Need mention call stack, activation records, base case, depth limit, tail recursion? Let's craft thorough but not too long. Need include factorial code, recursion process, stack overflow. Also DFS recursion vs explicit stack, detection connectivity. Need answer.\n\nLet's produce.",
        "role": "assistant"
      }
    }
  ],
  "created": 1788107564,
  "id": "021788107559864de824baea9702e17bffa39e9c3bdc4f65ce204",
  "model": "deepseek-v4-flash-ga-260731",
  "service_tier": "default",
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 383,
    "prompt_tokens": 138,
    "total_tokens": 521,
    "prompt_tokens_details": {
      "cached_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 82
    }
  }
}
Token Usage
{
  "completion_tokens": 383,
  "completion_tokens_details": {
    "reasoning_tokens": 82
  },
  "prompt_tokens": 138,
  "prompt_tokens_details": {
    "cached_tokens": 0
  },
  "total_tokens": 521
}
Fixes
User Request
Method: POST

URL: /v1/chat/completions

Headers:
{
  "accept": "*/*",
  "accept-encoding": "gzip",
  "authorization": "Bearer sk_r4gAXloJQt74Cp4prb19OItKzoG77lLhjmWT4ei20Ua",
  "content-length": "354",
  "content-type": "application/json",
  "host": "api.tokendiet.cn",
  "user-agent": "Go-http-client/2.0",
  "via": "2.0 Caddy",
  "x-forwarded-for": "13.212.110.206",
  "x-forwarded-host": "api.tokendiet.cn",
  "x-forwarded-proto": "https"
}

Body:
{
  "model": "deepseek-v4-flash-g2",
  "messages": [
    {
      "role": "user",
      "content": "请详细回答:解释为什么递归可能导致栈溢出。图的深度优先搜索使用递归或显式栈实现,用于遍历或检测连通性。例如计算阶乘时,n的阶乘等于n乘以n-1的阶乘,这就是典型的递归定义。"
    }
  ],
  "stream": false,
  "max_tokens": 300
}
User Response
Body:
{
  "choices": [
    {
      "finish_reason": "length",
      "index": 0,
      "logprobs": null,
      "message": {
        "content": "递归是一种函数调用自身的编程技巧。要理解为什么递归可能导致栈溢出,需要先理解程序运行时**函数调用栈(Call Stack)**的工作方式。\n\n## 1. 函数调用栈与栈帧\n\n程序在运行时,操作系统会为每个线程分配一段内存作为**调用栈**。每次调用一个函数,系统会在栈上压入一个**栈帧(Stack Frame)**,其中保存:\n\n- 函数的局部变量\n- 参数\n- 返回地址\n- 保存的寄存器状态等\n\n当函数返回时,其栈帧被弹出,控制权交还给调用者。\n\n```c\nint factorial(int n) {\n    if (n <= 1) return 1;          // 基准情况\n    return n * factorial(n - 1);   // 递归调用\n}\n```\n\n调用 `factorial(5)` 时,调用栈的变化如下:\n\n```\nfactorial(1)  -> 返回 1\nfactorial(2)  -> 等待 factorial(1) 的结果\nfactorial(3)  -> 等待 factorial(2) 的结果\nfactorial(4)  -> 等待 factorial(3) 的结果\nfactorial(5)  -> 等待 factorial(4) 的结果\n```\n\n每一层递归都未返回,因此每一层的栈帧都同时存在。递归深度越大,占用的栈内存越多。\n\n## 2. 为什么会导致栈",
        "reasoning_content": "我们 need answer in Chinese. Need explain recursion stack overflow, DFS recursion or explicit stack, example factorial. Need detailed. We can structure. Need mention call stack, activation records, base case, depth limit, tail recursion? Let's craft thorough but not too long. Need include factorial code, recursion process, stack overflow. Also DFS recursion vs explicit stack, detection connectivity. Need answer.\n\nLet's produce.",
        "role": "assistant"
      }
    }
  ],
  "created": 1788107564,
  "id": "021788107559864de824baea9702e17bffa39e9c3bdc4f65ce204",
  "model": "deepseek-v4-flash-g2",
  "object": "chat.completion",
  "service_tier": "default",
  "usage": {
    "completion_tokens": 383,
    "completion_tokens_details": {
      "reasoning_tokens": 82
    },
    "prompt_tokens": 138,
    "prompt_tokens_details": {
      "cached_tokens": 0
    },
    "total_tokens": 521
  }
}
Created At8/31/2026, 12:32:39 AM