feat(usage): 记账给前缀缓存命中折价 + 前端体现缓存命中/真实成本
排查"rust→PPT"task(flash,34 轮)发现累计 tokens_in 69.9 万里 88.6% 是缓存 命中,但 _fallback_chat_cost_cny 把命中段也按 input 全价算,记账虚高 2-3x。 - capabilities: 加 cache_hit_cny_per_mtoken(deepseek flash 0.1 / pro 0.2; 0=不区分按全价兜底,绝不少记) - usage: 成本公式拆三段「命中×缓存价 + (input−命中)×input价 + output×output价」; loop 把 cache_hit_tokens + 缓存单价透传进 record_chat_usage - web: 不加 DB 列。app.py 加 _usage_aggregates(单查询 GROUP BY usage_events, 复用列表 msg_counts 批量范式,无 N+1)on-the-fly 算每 task 真实成本 + 缓存命中, _task_dict 带出;dev.html 列表行显 ¥、顶栏 formatTaskUsage 显「tok·缓存命中%·¥」 - scripts: backfill_chat_cost_cache_discount.py 按 units 已存 token 重算历史 cost_cny(只改成本列,默认 dry-run,--apply 落库) 折价只对新 chat 事件即时生效;历史走 backfill 脚本(部署后跑)。 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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@ -2,7 +2,7 @@
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> 配合 `DESIGN.md`。本文件只记 phase 状态、决策偏差、文件量、下一步。每条 1-2 句:做了啥 + 关键判断;细节查 `git log` / `git diff` / `DESIGN §7.9`。
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最后更新:2026-06-04(sandbox 容器 env:shell 也注入 PYTHONPATH + HOME=/tmp)
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最后更新:2026-06-05(记账给前缀缓存命中折价 + 前端体现缓存命中/真实成本)
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---
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@ -21,6 +21,10 @@
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## 已完成关键能力
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### 2026-06-05
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- **记账给 DeepSeek 前缀缓存命中折价(修虚高 ~2-3x)+ 前端体现缓存命中/真实成本**:排查"rust 优势→PPT"那 task(flash,34 轮)发现 `tokens_in` 累计 69.9 万里 **88.6% 是缓存命中**,但 `usage.py::_fallback_chat_cost_cny` 把命中段也按 `input` 全价(1.0)算 → 记 ¥0.84,真实(命中按 0.1x)只 ~¥0.28,**越大的 task 虚高越多**(文献采集 53% 命中:¥33→~¥16)。修:① `ModelCapabilities` 加 `cache_hit_cny_per_mtoken`(deepseek flash 0.1 / pro 0.2;0=不区分按全价兜底,绝不少记);② 成本公式拆三段「命中×缓存价 + (input−命中)×input价 + output×output价」,`loop.py` 把 `cache_hit_tokens` + 缓存单价透传进 `record_chat_usage`;③ 前端不加 DB 列——`web/app.py` 加 `_usage_aggregates`(单查询 GROUP BY `usage_events`,复用列表 `msg_counts` 同款批量范式,无 N+1)on-the-fly 算每 task 真实成本 + 缓存命中 token,`_task_dict` 带出,列表行显 `¥`、顶栏 `formatTaskUsage` 显「总 tok · 缓存命中 N% · ¥真实花费」。**折价只对新 chat 事件生效**,历史 events 保留原记账(不回填,不删数据)。**注**:真正压低 token 体量的杠杆是减少轮数(高成本 task 全是 100+ 轮的逐步 write/run_python 循环),非本次范围。
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### 2026-06-04
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- **ppt skill 版式 helper 收进可 import 的模块 + 修中文字体没真生效**:逐页生成是「每页一个 run_python」,以前 ~150 行 helper(配色常量/`add_textbox`/`apply_brand` 等)要在每页里默写一遍 —— 烧 token 且长 deck 里坐标会漂(第 7 页 `apply_brand` 跟第 2 页对不上)。抽出 `skills/ppt/scripts/pptx_helpers.py`,每页 `import pptx_helpers as P` 调用;新增 `new_presentation`/`load`(按文件实际尺寸回填画布常量,逐页进程间同步)/`add_slide`/`set_palette`(默认商务红,`spec_path=` 自动取 spec 前 3 个 hex 作主/辅/强调)入口。**字体修复**:python-pptx `font.name` 只写 `<a:latin>`,中文字形走 `<a:ea>` 槽位没设 → 「指定微软雅黑却没真生效」的根因;`set_text` 改为同时写 latin=Arial + ea/cs=微软雅黑,中英混排各命中正确字体。改 `layouts.md`(helper 块换成 import 起手 + API 速查,9 个示例全改 `P.` 调用)、`icons.md` A5 示例、`SKILL.md` 资源/阶段二。冒烟测试过:`ea` 确写入、`set_palette` 覆盖生效、quality_check 正常解析。
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@ -185,7 +189,7 @@
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- **05-06 → 05-08**:Phase 6 部分(task + state.json + tokens 累计);TUI rich Markdown + spinner 实时耗时;`/resume [last|<id>]` + 懒创建 + `_cleanup_if_empty`。
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- **05-09 → 05-10**:DESIGN §7 初版(05-12 重写);`cli.py export` + `core/export_docx.py`。
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- **05-11**:`atomic_write_text` + `core/memory.py`(core.md 入 prompt,extended/* 索引);loop 事件流化 `sink.emit` 铺 SSE 路。
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- **06-04 token 优化启动**:`DESIGN.md §8.2` 写入上下文治理方案;chat usage 记录 cache hit/miss / reasoning tokens,LiteLLM cost=0 时按模型档案 CNY/Mtok 兜底;`run_python` 新增 `script_path` 模式(长代码先 write .py 再按路径执行,减少 tool_call arguments 进历史);`run_python` / `shell` 长输出只做上下文裁剪,不写 `.tool_logs`;`document_search` 默认召回量保持 6×1200 chars;`core/context.py` 先压缩旧 tool 消息和旧 `load_skill` 结果,不改持久化历史;`llm_start` SSE 输出 `context_*` 压缩统计,dev SPA 底部 hint 展示上下文压缩与 cache hit/miss。
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- **06-04 token 优化启动**:`DESIGN.md §8.2` 写入上下文治理方案;chat usage 记录 cache hit/miss / reasoning tokens,LiteLLM cost=0 时按模型档案 CNY/Mtok 兜底;`run_python` 新增 `script_path` 模式(长代码先 write .py 再按路径执行,减少 run_python code 入历史);`run_python` / `shell` 长输出只做上下文裁剪,不写 `.tool_logs`;`document_search` 默认召回量保持 6×1200 chars;`core/context.py` 先压缩旧 tool 消息、旧 `load_skill` 结果、旧 assistant tool_call arguments(`write(content=...)` 源码参数),不改持久化历史;`llm_start` SSE 输出 `context_*` 压缩统计,dev SPA 底部 hint 展示上下文压缩与 cache hit/miss。`rust介绍` 实测:task 列表 `70条 / 711k tok` 是历史累计,最近单轮 22k input 且 cache hit 高;新增 arguments 压缩后 sent_chars 估算 `49,166 → 34,415`。
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---
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@ -24,6 +24,7 @@ variants:
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extended_thinking: false
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input_cny_per_mtoken: 1.0
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output_cny_per_mtoken: 12.0
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cache_hit_cny_per_mtoken: 0.1 # DeepSeek 前缀缓存命中价(input 的 ~0.1x)
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pro:
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display_name: DeepSeek V4 Pro
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extended_thinking: false
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input_cny_per_mtoken: 2.0
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output_cny_per_mtoken: 20.0
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cache_hit_cny_per_mtoken: 0.2 # DeepSeek 前缀缓存命中价(input 的 ~0.1x)
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@ -44,6 +44,9 @@ class ModelCapabilities:
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# 计费兜底(CNY / million tokens)。provider / LiteLLM cost map 缺失时使用。
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input_cny_per_mtoken: float = 0.0
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output_cny_per_mtoken: float = 0.0
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# 前缀缓存命中价(DeepSeek 等自动缓存 prompt 前缀,命中部分按此价,通常 ~0.1x input)。
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# 0 = 不区分,缓存命中按 input 全价记(安全兜底,不会少记)。
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cache_hit_cny_per_mtoken: float = 0.0
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# API 接入
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api_base: str = ""
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@ -177,6 +177,8 @@ class AgentLoop:
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completion_tokens=ct,
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input_cny_per_mtoken=self.caps.input_cny_per_mtoken,
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output_cny_per_mtoken=self.caps.output_cny_per_mtoken,
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cache_hit_tokens=usage_details["cache_hit_tokens"],
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cache_hit_cny_per_mtoken=self.caps.cache_hit_cny_per_mtoken,
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extra_units={
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k: v for k, v in usage_details.items()
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if k not in ("tokens_in", "tokens_out") and v
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@ -47,15 +47,31 @@ def _fallback_chat_cost_cny(
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completion_tokens: int,
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input_cny_per_mtoken: float,
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output_cny_per_mtoken: float,
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cache_hit_tokens: int = 0,
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cache_hit_cny_per_mtoken: float = 0.0,
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) -> Decimal:
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"""按本地模型档案价格兜底计算 chat 成本(CNY)。"""
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"""按本地模型档案价格兜底计算 chat 成本(CNY)。
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`prompt_tokens` = 全部输入(含缓存命中部分)。DeepSeek 等对前缀缓存命中按更低
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单价计费,这里把输入拆成「命中 / 未命中」两段:命中段按 `cache_hit_cny_per_mtoken`,
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其余按 `input_cny_per_mtoken`。`cache_hit_cny_per_mtoken<=0` 时不区分,全按 input 价
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(老行为,绝不少记)。
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"""
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input_price = Decimal(str(input_cny_per_mtoken or 0))
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output_price = Decimal(str(output_cny_per_mtoken or 0))
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pt = Decimal(str(int(prompt_tokens)))
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ct = Decimal(str(int(completion_tokens)))
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hit_price = Decimal(str(cache_hit_cny_per_mtoken or 0))
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if hit_price <= 0:
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hit_price = input_price # 未配缓存价 → 命中段按 input 全价(安全兜底)
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pt = int(prompt_tokens)
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hit = max(0, min(int(cache_hit_tokens), pt)) # clamp:命中数不超过总输入
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miss = pt - hit
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ct = int(completion_tokens)
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cost = (
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pt * input_price / Decimal("1000000")
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+ ct * output_price / Decimal("1000000")
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Decimal(str(miss)) * input_price / Decimal("1000000")
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+ Decimal(str(hit)) * hit_price / Decimal("1000000")
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+ Decimal(str(ct)) * output_price / Decimal("1000000")
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)
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return cost.quantize(Decimal("0.000001"))
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completion_tokens: int,
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input_cny_per_mtoken: float = 0.0,
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output_cny_per_mtoken: float = 0.0,
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cache_hit_tokens: int = 0,
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cache_hit_cny_per_mtoken: float = 0.0,
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extra_units: Optional[Mapping[str, Any]] = None,
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response: Any = None,
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) -> Decimal:
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completion_tokens=completion_tokens,
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input_cny_per_mtoken=input_cny_per_mtoken,
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output_cny_per_mtoken=output_cny_per_mtoken,
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cache_hit_tokens=cache_hit_tokens,
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cache_hit_cny_per_mtoken=cache_hit_cny_per_mtoken,
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)
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units = {
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"tokens_in": int(prompt_tokens),
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@ -0,0 +1,140 @@
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"""Backfill 历史 chat usage_events 的 cost_cny —— 给前缀缓存命中折价 + 修 ¥0 旧账。
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背景:`usage.py::_fallback_chat_cost_cny` 早期(a)对未知模型 litellm 返 0 又无兜底 →
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大量 chat 事件 cost_cny 记成 ¥0;(b)后来加了兜底但把缓存命中段也按 input 全价算 →
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命中率高的 task 虚高 2-3x。本脚本按每条事件 units 里已存的 token 数 + 模型档案价
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**重算 cost_cny**,只改成本列,**不动任何 token 数 / units**。
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价格来源:`ModelCapabilities.load(model_profile)`(config 当前价,含 cache_hit 折价)。
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config 里没有该 profile 或无 input/output 价(如 glm 未配价)→ 跳过,保留原值
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(不臆造价格)。缓存命中数取 units.cache_hit_tokens(缺 → 0,即按全价,绝不少记)。
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跑法: .venv/Scripts/python.exe scripts/backfill_chat_cost_cache_discount.py
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默认 dry-run 只打印汇总,加 --apply 真写。
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幂等:重算是确定性的;再跑一遍 0 改动。前端任务成本是现算 SUM(usage_events.cost_cny),
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改完即时反映,无需动 tasks 表。
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"""
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from __future__ import annotations
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import argparse
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import os
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import sys
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from collections import defaultdict
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from decimal import Decimal
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from pathlib import Path
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ROOT = Path(__file__).resolve().parent.parent
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sys.path.insert(0, str(ROOT))
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env_file = ROOT / ".env"
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if env_file.exists():
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for line in env_file.read_text(encoding="utf-8").splitlines():
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line = line.strip()
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if not line or line.startswith("#") or "=" not in line:
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continue
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k, _, v = line.partition("=")
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os.environ.setdefault(k.strip(), v.strip())
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from sqlalchemy import select
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from core.agent_builder import ROOT as AB_ROOT, load_config
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from core.capabilities import ModelCapabilities
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from core.storage import session_scope
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from core.storage.models import UsageEvent
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from core.storage.usage import _fallback_chat_cost_cny
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def main() -> int:
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ap = argparse.ArgumentParser()
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ap.add_argument("--apply", action="store_true", help="真写;默认 dry-run 只打印")
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args = ap.parse_args()
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cfg = load_config()
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models_dir = AB_ROOT / cfg["models_dir"]
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# ModelCapabilities.load 按 profile 缓存(避免每行重读 yaml);None = 无法定价
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caps_cache: dict[str, ModelCapabilities | None] = {}
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def get_caps(profile: str) -> ModelCapabilities | None:
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if profile not in caps_cache:
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try:
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caps_cache[profile] = ModelCapabilities.load(profile, models_dir)
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except Exception:
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caps_cache[profile] = None
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return caps_cache[profile]
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# per-profile 统计:事件数 / 改动数 / 跳过数 / 旧总额 / 新总额
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stat_n: dict[str, int] = defaultdict(int)
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stat_changed: dict[str, int] = defaultdict(int)
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stat_skipped: dict[str, int] = defaultdict(int)
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old_sum: dict[str, Decimal] = defaultdict(lambda: Decimal("0"))
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new_sum: dict[str, Decimal] = defaultdict(lambda: Decimal("0"))
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with session_scope() as s:
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rows = s.execute(
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select(UsageEvent).where(UsageEvent.kind == "chat")
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).scalars().all()
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for e in rows:
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profile = e.model_profile or "?"
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stat_n[profile] += 1
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u = e.units or {}
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caps = get_caps(profile)
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if caps and (caps.input_cny_per_mtoken or caps.output_cny_per_mtoken):
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inp = caps.input_cny_per_mtoken
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outp = caps.output_cny_per_mtoken
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chp = caps.cache_hit_cny_per_mtoken
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else:
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# config 无价 → 退 units 价格快照(老事件多半也没有);仍无 → 跳过
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inp = float(u.get("input_cny_per_mtoken") or 0)
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outp = float(u.get("output_cny_per_mtoken") or 0)
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chp = float(u.get("cache_hit_cny_per_mtoken") or 0)
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if not (inp or outp):
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stat_skipped[profile] += 1
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old_sum[profile] += Decimal(str(e.cost_cny))
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new_sum[profile] += Decimal(str(e.cost_cny)) # 无价不变
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continue
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new_cost = _fallback_chat_cost_cny(
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prompt_tokens=int(u.get("tokens_in") or 0),
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completion_tokens=int(u.get("tokens_out") or 0),
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input_cny_per_mtoken=inp,
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output_cny_per_mtoken=outp,
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cache_hit_tokens=int(u.get("cache_hit_tokens") or 0),
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cache_hit_cny_per_mtoken=chp,
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)
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old_cost = Decimal(str(e.cost_cny))
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old_sum[profile] += old_cost
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new_sum[profile] += new_cost
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if new_cost != old_cost:
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e.cost_cny = new_cost
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stat_changed[profile] += 1
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if args.apply:
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s.commit()
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else:
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s.rollback()
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print()
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print(f"{'model_profile':<22}{'events':>8}{'changed':>9}{'skipped':>9}"
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f"{'old_¥':>12}{'new_¥':>12}")
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tot_old = Decimal("0")
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tot_new = Decimal("0")
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for profile in sorted(stat_n):
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o, n = old_sum[profile], new_sum[profile]
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tot_old += o
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tot_new += n
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print(f"{profile:<22}{stat_n[profile]:>8}{stat_changed[profile]:>9}"
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f"{stat_skipped[profile]:>9}{float(o):>12.4f}{float(n):>12.4f}")
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print(f"{'TOTAL':<22}{sum(stat_n.values()):>8}"
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f"{sum(stat_changed.values()):>9}{sum(stat_skipped.values()):>9}"
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f"{float(tot_old):>12.4f}{float(tot_new):>12.4f}")
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print()
|
||||
print(f"[mode] {'APPLIED (committed)' if args.apply else 'DRY-RUN (no commit, rerun with --apply)'}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
|
|
@ -33,6 +33,31 @@ class UsageAccountingTests(unittest.TestCase):
|
|||
|
||||
self.assertEqual(cost, Decimal("6.000000"))
|
||||
|
||||
def test_fallback_chat_cost_discounts_cache_hits(self) -> None:
|
||||
# 100 万输入里 80 万命中缓存(0.1 价),20 万未命中(1.0 价),50 万输出(10 价)
|
||||
cost = _fallback_chat_cost_cny(
|
||||
prompt_tokens=1_000_000,
|
||||
completion_tokens=500_000,
|
||||
input_cny_per_mtoken=1.0,
|
||||
output_cny_per_mtoken=10.0,
|
||||
cache_hit_tokens=800_000,
|
||||
cache_hit_cny_per_mtoken=0.1,
|
||||
)
|
||||
# 0.2(miss) + 0.08(hit) + 5.0(out) = 5.28
|
||||
self.assertEqual(cost, Decimal("5.280000"))
|
||||
|
||||
def test_fallback_chat_cost_no_cache_price_charges_full(self) -> None:
|
||||
# 未配缓存价(0)→ 命中段不打折,按 input 全价(老行为,绝不少记)
|
||||
cost = _fallback_chat_cost_cny(
|
||||
prompt_tokens=1_000_000,
|
||||
completion_tokens=0,
|
||||
input_cny_per_mtoken=1.0,
|
||||
output_cny_per_mtoken=10.0,
|
||||
cache_hit_tokens=900_000,
|
||||
cache_hit_cny_per_mtoken=0.0,
|
||||
)
|
||||
self.assertEqual(cost, Decimal("1.000000"))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
|
|
|||
64
web/app.py
64
web/app.py
|
|
@ -26,7 +26,7 @@ from fastapi.middleware.cors import CORSMiddleware
|
|||
from fastapi.responses import FileResponse, RedirectResponse, StreamingResponse
|
||||
from fastapi.staticfiles import StaticFiles
|
||||
from pydantic import BaseModel
|
||||
from sqlalchemy import func, select, update
|
||||
from sqlalchemy import BigInteger, cast, func, select, update
|
||||
from starlette.background import BackgroundTask
|
||||
|
||||
from core.paths import to_db_path
|
||||
|
|
@ -35,7 +35,7 @@ from core.storage import (
|
|||
check_no_subtask,
|
||||
session_scope,
|
||||
)
|
||||
from core.storage.models import Message, Task
|
||||
from core.storage.models import Message, Task, UsageEvent
|
||||
from core.storage.utils import ensure_local_task_row
|
||||
|
||||
from .auth import (
|
||||
|
|
@ -93,8 +93,47 @@ def _parse_ordering(s: Optional[str]) -> list:
|
|||
return cols
|
||||
|
||||
|
||||
def _task_dict(row: Any, *, n_messages: Optional[int] = None) -> dict:
|
||||
"""Task ORM row → API JSON dict。"""
|
||||
def _usage_aggregates(s: Any, tids: list) -> dict:
|
||||
"""按 task_id 批量聚合 usage_events:真实成本 + 缓存命中 token。
|
||||
|
||||
单查询 GROUP BY(复用列表接口 msg_counts 同款批量范式,无 N+1)。on-the-fly 现算,
|
||||
不落 tasks 列 —— 对所有历史 task 即时准确,免回填。
|
||||
- cost_cny:全 kind(chat+image+video)合计 = task 真实花费
|
||||
- cache_hit:仅 chat,units.cache_hit_tokens 之和(DeepSeek 等前缀缓存命中部分)
|
||||
返回 {task_id: {"cost_cny": float, "tokens_cache_hit": int}}。
|
||||
"""
|
||||
if not tids:
|
||||
return {}
|
||||
cache_hit_col = cast(UsageEvent.units["cache_hit_tokens"].astext, BigInteger)
|
||||
rows = s.execute(
|
||||
select(
|
||||
UsageEvent.task_id,
|
||||
func.coalesce(func.sum(UsageEvent.cost_cny), 0),
|
||||
func.coalesce(
|
||||
func.sum(cache_hit_col).filter(UsageEvent.kind == "chat"), 0
|
||||
),
|
||||
)
|
||||
.where(UsageEvent.task_id.in_(tids))
|
||||
.group_by(UsageEvent.task_id)
|
||||
).all()
|
||||
return {
|
||||
tid: {"cost_cny": float(cost or 0), "tokens_cache_hit": int(hit or 0)}
|
||||
for tid, cost, hit in rows
|
||||
}
|
||||
|
||||
|
||||
def _task_dict(
|
||||
row: Any,
|
||||
*,
|
||||
n_messages: Optional[int] = None,
|
||||
usage: Optional[dict] = None,
|
||||
) -> dict:
|
||||
"""Task ORM row → API JSON dict。
|
||||
|
||||
`usage`(可选)= `_usage_aggregates` 算出的本 task 概要,带真实成本与缓存命中;
|
||||
缺省回退到 tasks.cost_cny 列(多为 0)与 0 命中,前端据此显 ¥ / 缓存命中率。
|
||||
"""
|
||||
u = usage or {}
|
||||
d = {
|
||||
"task_id": str(row.task_id),
|
||||
"name": row.name or "",
|
||||
|
|
@ -107,6 +146,10 @@ def _task_dict(row: Any, *, n_messages: Optional[int] = None) -> dict:
|
|||
"tokens_prompt": row.tokens_prompt or 0,
|
||||
"tokens_completion": row.tokens_completion or 0,
|
||||
"tokens": (row.tokens_prompt or 0) + (row.tokens_completion or 0),
|
||||
# 缓存命中 token(chat 前缀缓存)+ 真实成本(已按缓存折价,见 usage.py)。
|
||||
# on-the-fly 聚合;未传 usage 时回退列/0。
|
||||
"tokens_cache_hit": int(u.get("tokens_cache_hit", 0)),
|
||||
"cost_cny": float(u["cost_cny"]) if "cost_cny" in u else float(row.cost_cny or 0),
|
||||
# 当前 run 状态(0004 schema 简化:原 runs 表合并入 task)
|
||||
"run_status": row.run_status or "idle",
|
||||
"run_error": row.run_error or None,
|
||||
|
|
@ -914,13 +957,18 @@ def create_app() -> FastAPI:
|
|||
).all())
|
||||
if tids else {}
|
||||
)
|
||||
usage = _usage_aggregates(s, tids)
|
||||
|
||||
return {
|
||||
"page": page,
|
||||
"page_size": page_size,
|
||||
"count": int(cnt),
|
||||
"results": [
|
||||
_task_dict(r, n_messages=msg_counts.get(r.task_id, 0))
|
||||
_task_dict(
|
||||
r,
|
||||
n_messages=msg_counts.get(r.task_id, 0),
|
||||
usage=usage.get(r.task_id),
|
||||
)
|
||||
for r in rows
|
||||
],
|
||||
}
|
||||
|
|
@ -941,7 +989,8 @@ def create_app() -> FastAPI:
|
|||
n = s.execute(
|
||||
select(func.count()).select_from(Message).where(Message.task_id == tid)
|
||||
).scalar_one()
|
||||
return _task_dict(row, n_messages=n)
|
||||
usage = _usage_aggregates(s, [tid])
|
||||
return _task_dict(row, n_messages=n, usage=usage.get(tid))
|
||||
|
||||
@app.get("/v1/folders", tags=["folders"])
|
||||
def list_folders(user_id: UUID = Depends(require_user)):
|
||||
|
|
@ -1090,7 +1139,8 @@ def create_app() -> FastAPI:
|
|||
n = s.execute(
|
||||
select(func.count()).select_from(Message).where(Message.task_id == tid)
|
||||
).scalar_one()
|
||||
return _task_dict(row, n_messages=n)
|
||||
usage = _usage_aggregates(s, [tid])
|
||||
return _task_dict(row, n_messages=n, usage=usage.get(tid))
|
||||
|
||||
# ───────────── Messages ─────────────
|
||||
|
||||
|
|
|
|||
|
|
@ -1218,6 +1218,33 @@ function fmtTokens(n) {
|
|||
return (n / 1000000).toFixed(1) + "M";
|
||||
}
|
||||
|
||||
// 紧凑成本显示(¥,已按缓存折价的真实花费):0 不显;<0.01 三位小数;否则两位
|
||||
function fmtCost(n) {
|
||||
n = n || 0;
|
||||
if (n <= 0) return "";
|
||||
if (n < 0.01) return "¥" + n.toFixed(3);
|
||||
return "¥" + n.toFixed(2);
|
||||
}
|
||||
|
||||
// 任务级累计用量(顶栏):总 token · 缓存命中率 · 真实花费。
|
||||
// 缓存命中率 = cache_hit / 总输入(tokens_prompt);命中越高说明前缀复用越好、越省钱。
|
||||
function formatTaskUsage(t) {
|
||||
const tok = t.tokens || 0;
|
||||
if (!tok) return "";
|
||||
const hit = t.tokens_cache_hit || 0;
|
||||
const pin = t.tokens_prompt || 0;
|
||||
const bits = [`${fmtTokens(tok)} tok`];
|
||||
if (pin > 0 && hit > 0) {
|
||||
bits.push(`缓存命中 ${Math.round(hit / pin * 100)}%`);
|
||||
}
|
||||
const cost = fmtCost(t.cost_cny);
|
||||
if (cost) bits.push(cost);
|
||||
const title = `累计:输入 ${pin.toLocaleString()} / 输出 ${(t.tokens_completion || 0).toLocaleString()} tok`
|
||||
+ (hit > 0 ? `\n前缀缓存命中 ${hit.toLocaleString()} tok（命中部分按低价计费）` : "")
|
||||
+ (t.cost_cny > 0 ? `\n真实花费 ¥${(t.cost_cny).toFixed(4)}（已按缓存命中折价）` : "");
|
||||
return `<span class="muted" title="${escapeHtml(title)}" style="white-space:nowrap;">${bits.join(" · ")}</span>`;
|
||||
}
|
||||
|
||||
function formatContextStats(d) {
|
||||
d = d || {};
|
||||
const orig = d.context_original_chars || 0;
|
||||
|
|
@ -1729,7 +1756,8 @@ function renderTaskList(tasks, append = false) {
|
|||
<span class="badge ${t.status}">${statusLabel}</span>
|
||||
${t.skill ? `<span class="muted" title="${escapeHtml(t.skill)}">${escapeHtml(t.skill)}</span>` : ""}
|
||||
<span class="num right-group">${t.n_messages || 0} 条</span>
|
||||
<span class="num" title="${(t.tokens || 0).toLocaleString()} tokens">${fmtTokens(t.tokens)} tok</span>
|
||||
<span class="num" title="${(t.tokens || 0).toLocaleString()} tokens${t.tokens_cache_hit ? `（其中缓存命中 ${(t.tokens_cache_hit).toLocaleString()}）` : ""}">${fmtTokens(t.tokens)} tok</span>
|
||||
${t.cost_cny > 0 ? `<span class="num" title="真实花费(已按缓存命中折价)">${fmtCost(t.cost_cny)}</span>` : ""}
|
||||
<span class="muted time-ago" title="${escapeHtml(fmtTime(t.updated_at))}">${escapeHtml(fmtTimeAgo(t.updated_at))}</span>
|
||||
</div>
|
||||
</div>
|
||||
|
|
@ -1889,6 +1917,7 @@ function renderChatMeta() {
|
|||
${wdBadge}
|
||||
${t.skill ? `<span class="muted">${escapeHtml(t.skill)}</span>` : ""}
|
||||
<span class="tid">${t.task_id.slice(0, 8)}</span>
|
||||
${formatTaskUsage(t)}
|
||||
${t.description ? `<span class="muted desc">${escapeHtml(t.description)}</span>` : ""}
|
||||
<span class="spacer"></span>
|
||||
${renderModelDropdown(t)}
|
||||
|
|
|
|||
Loading…
Reference in New Issue