## Summary epic r04 - begin refactor ## Change Type - [x] Cowork feature - [ ] Bug fix - [ ] Core AI contribution - [ ] Test / hardening - [ ] Performance - [ ] Documentation ## Related Work Cowork Task: Core Repo: http://34.143.229.138/gitea-admin/fsg-ai-core-assets Core AI Issue: Core Task: Related PR: ## Scope What is intentionally included? What is intentionally NOT included? ## Validation - [ ] Unit tests - [ ] Integration tests - [ ] Manual verification - [ ] Regression check Commands / evidence: ## Security Impact Permission / credential / network / customer data impact: ## Compatibility - [ ] No breaking change - [ ] Breaking change documented ## Reviewer Notes Anything Cowork reviewers should pay attention to. --------- Co-authored-by: Anh Tran Nguyen Minh <anhtnm1@fpt.com> Co-authored-by: Huong Le Thi Thien <huongltt35@fpt.com> Co-authored-by: Nam Pham Dinh Thanh <nampdt@fpt.com> Co-authored-by: Vu Dam Tuan <vudt15@fpt.com> Co-authored-by: Hiep Ha Van <hiephv3@fpt.com> Co-authored-by: Lam Hoang Van <lamhv7@fpt.com> Reviewed-on: #7 Co-authored-by: Duy Le Huu <duylh19@fpt.com>
This commit was merged in pull request #7.
This commit is contained in:
Nam Pham Dinh Thanh
+29
-241
@@ -12,6 +12,17 @@ The turn's source/label is set by the caller ON THE WORKER THREAD via
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"""
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from __future__ import annotations
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# Giữ đường vào cũ: nhiều nơi import mấy tên này thẳng từ usage_tracker.
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from .usage_ai_report import build_ai_analysis_prompt # noqa: F401
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from .usage_cost import ( # noqa: F401
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DEFAULT_PRICING, SUPPORTED_CURRENCIES, cost_usd, cost_usd_events,
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format_cost, format_cost_compact,
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)
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from .usage_periods import ( # noqa: F401
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bucketed_series, period_bounds, period_breakdown, period_range_label,
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period_totals, period_window,
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)
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import json
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import threading
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from datetime import date, datetime
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@@ -19,6 +30,7 @@ from pathlib import Path
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from typing import Any, Dict, List, Optional
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from ..config import CONFIG_DIR
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from . import model_pricing as mp
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USAGE_DIR = CONFIG_DIR / "usage"
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@@ -49,6 +61,18 @@ def set_context(source: str, label: str = "") -> None:
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_local.label = label
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def current_context() -> tuple:
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"""The ``(source, label)`` currently tagged on THIS thread.
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Public counterpart to :func:`set_context`, added for
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``infrastructure/telemetry/usage_sink.py``: a subscriber that needs to
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attribute one event to a different surface must be able to save the
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caller's context and put it back afterwards, instead of leaving the worker
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thread permanently retagged.
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"""
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return getattr(_local, "source", "") or "", getattr(_local, "label", "") or ""
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# ---- per-thread usage accumulator -----------------------------------------
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# A step/run that wants to know its OWN token/cost (not the all-time file total)
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# calls begin_accumulation(), reads accumulated() before/after a unit of work,
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@@ -79,6 +103,11 @@ def end_accumulation() -> None:
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def estimate_tokens(text: str) -> int:
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"""Ước lượng số token của một đoạn văn bản theo tỉ lệ 4 ký tự ≈ 1 token.
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Ước lượng thô là đủ: con số này chỉ dùng để quyết định khi nào nén lịch sử,
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không dùng để tính tiền (tiền lấy từ số token thật provider trả về).
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"""
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return max(0, len(text or "") // 4)
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@@ -191,198 +220,30 @@ def summarize(events: List[Dict[str, Any]]) -> Dict[str, Any]:
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# ---- cost ------------------------------------------------------------------
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DEFAULT_PRICING = {
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"price_per_mtok_in_usd": 0.5, # USD per 1M input tokens (flat fallback rate)
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"price_per_mtok_out_usd": 1.5, # USD per 1M output tokens
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"price_per_mtok_cache_usd": 0.1, # USD per 1M cached tokens
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"currency": "USD", # display currency: USD | VND | JPY
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"usd_to_vnd": 25000.0,
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"usd_to_jpy": 150.0,
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# Per-model price table (USD / 1M tokens): {model: {"in","out","cache"}}.
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# Events whose model has an entry are costed with ITS rates; everything
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# else falls back to the flat price_per_mtok_* rates above. Edited in the
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# Monitoring Overview's pricing table.
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"model_prices": {},
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# Reference URL of the price list the table was filled from (set in
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# Settings; shown as a link beside the table — informational only, the
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# app never scrapes it).
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"pricing_url": "",
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}
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_CURRENCY_FMT = {"USD": ("$", 4), "VND": ("₫", 0), "JPY": ("¥", 1)}
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# Currencies the display picker offers — exactly the ones format_cost() can
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# actually convert to (symbol/precision above + a usd_to_* rate below).
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SUPPORTED_CURRENCIES = tuple(_CURRENCY_FMT)
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def cost_usd(summary: Dict[str, Any], pricing: Dict[str, Any]) -> Dict[str, float]:
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p = {**DEFAULT_PRICING, **(pricing or {})}
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return {
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"in": summary.get("in", 0) / 1e6 * float(p["price_per_mtok_in_usd"]),
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"out": summary.get("out", 0) / 1e6 * float(p["price_per_mtok_out_usd"]),
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"cache": summary.get("cache", 0) / 1e6 * float(p["price_per_mtok_cache_usd"]),
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}
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def cost_usd_events(events: List[Dict[str, Any]], pricing: Dict[str, Any]) -> Dict[str, float]:
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"""Per-bucket USD cost computed EVENT BY EVENT so the per-model price
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table applies: an event whose ``model`` has an entry in
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``pricing["model_prices"]`` is costed with that model's own rates; any
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other event uses the flat ``price_per_mtok_*`` rates. With an empty
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table this equals ``cost_usd(summarize(events), pricing)`` exactly."""
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p = {**DEFAULT_PRICING, **(pricing or {})}
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table = p.get("model_prices") or {}
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flat = {"in": float(p["price_per_mtok_in_usd"]),
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"out": float(p["price_per_mtok_out_usd"]),
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"cache": float(p["price_per_mtok_cache_usd"])}
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out = {"in": 0.0, "out": 0.0, "cache": 0.0}
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for e in events:
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rates = table.get(e.get("model", "")) or {}
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for bucket in ("in", "out", "cache"):
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try:
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rate = float(rates.get(bucket, flat[bucket]))
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except (TypeError, ValueError):
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rate = flat[bucket]
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out[bucket] += e.get(bucket, 0) / 1e6 * rate
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return out
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def bucketed_series(events: List[Dict[str, Any]], granularity: str = "day",
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pricing: Dict[str, Any] = None, last: int = None) -> List[tuple]:
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"""Group usage events into time buckets → ordered ``[(label, tokens, cost_usd)]``.
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``granularity``: ``day`` (YYYY-MM-DD) · ``month`` (YYYY-MM) · ``year`` (YYYY).
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``last`` keeps only the most recent N buckets (for the dashboard chart)."""
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from collections import OrderedDict
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pricing = pricing or {}
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def _key(ts: Any) -> str:
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s = str(ts or "")[:10]
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if granularity == "year":
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return s[:4]
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if granularity == "month":
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return s[:7]
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return s
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buckets: "OrderedDict[str, List[Dict[str, Any]]]" = OrderedDict()
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for e in sorted(events, key=lambda ev: str(ev.get("ts", ""))):
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k = _key(e.get("ts"))
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if k:
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buckets.setdefault(k, []).append(e)
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out = []
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for k, evs in buckets.items():
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tokens = sum(int(e.get("in", 0) or 0) + int(e.get("out", 0) or 0)
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+ int(e.get("cache", 0) or 0) for e in evs)
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cost = sum(cost_usd_events(evs, pricing).values())
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out.append((k, tokens, cost))
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if last and len(out) > last:
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out = out[-last:]
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return out
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def period_bounds(gran: str, offset: int, today: Optional[date] = None) -> tuple:
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"""[start, end) dates of the period ``offset`` periods from the current one
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(0 = current, -1 = the previous week/month/year). Weeks run Mon→Sun."""
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from datetime import timedelta
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today = today or date.today()
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if gran == "week":
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monday = today - timedelta(days=today.weekday()) # Monday of this week
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start = monday + timedelta(weeks=offset)
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return start, start + timedelta(days=7)
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if gran == "year":
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y = today.year + offset
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return date(y, 1, 1), date(y + 1, 1, 1)
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# month (default)
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base = today.year * 12 + (today.month - 1) + offset
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y, m = divmod(base, 12)
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y2, m2 = divmod(base + 1, 12)
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return date(y, m + 1, 1), date(y2, m2 + 1, 1)
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def _period_label(gran: str, start: date) -> str:
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if gran == "week":
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return start.isoformat() # the week's Monday (YYYY-MM-DD)
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if gran == "year":
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return str(start.year)
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return start.strftime("%Y-%m")
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def _sum_between(events: List[Dict[str, Any]], start: date, end: date,
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pricing: Dict[str, Any]) -> tuple:
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lo, hi = start.isoformat(), end.isoformat()
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evs = [e for e in events if lo <= str(e.get("ts", ""))[:10] < hi]
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tokens = sum(int(e.get("in", 0) or 0) + int(e.get("out", 0) or 0)
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+ int(e.get("cache", 0) or 0) for e in evs)
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cost = sum(cost_usd_events(evs, pricing).values()) if evs else 0.0
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return tokens, cost
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def period_totals(events: List[Dict[str, Any]], gran: str, pricing: Dict[str, Any],
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offset: int = 0, today: Optional[date] = None) -> tuple:
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"""(tokens, cost_usd) for the single period ``offset`` periods from now."""
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start, end = period_bounds(gran, offset, today)
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return _sum_between(events, start, end, pricing)
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def period_window(events: List[Dict[str, Any]], gran: str, pricing: Dict[str, Any],
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count: int, offset: int = 0, today: Optional[date] = None) -> List[tuple]:
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"""``count`` consecutive, ZERO-FILLED periods ending at (current + offset),
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ordered oldest→newest → ``[(label, tokens, cost_usd)]``. ``offset`` (≤ 0)
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pages the window into the past for the Dashboard's prev/next navigation."""
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out = []
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for i in range(count - 1, -1, -1):
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start, end = period_bounds(gran, offset - i, today)
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tok, cost = _sum_between(events, start, end, pricing)
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out.append((_period_label(gran, start), tok, cost))
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return out
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def period_breakdown(events: List[Dict[str, Any]], gran: str, pricing: Dict[str, Any],
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offset: int = 0, today: Optional[date] = None) -> List[tuple]:
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"""Break the SELECTED period (``offset`` periods from now) into its sub-parts
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→ ``[(label, tokens, cost_usd)]``:
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· week → 7 days Mon→Sun (label ``MM/DD``)
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· month → weeks W1…Wn (7-day chunks from the 1st)
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· year → 12 months (label ``01``…``12``)."""
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from datetime import timedelta
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start, end = period_bounds(gran, offset, today)
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out = []
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if gran == "week":
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for i in range(7):
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d = start + timedelta(days=i)
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tok, cost = _sum_between(events, d, d + timedelta(days=1), pricing)
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out.append((d.strftime("%m/%d"), tok, cost))
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elif gran == "year":
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for m in range(1, 13):
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ms = date(start.year, m, 1)
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me = date(start.year + 1, 1, 1) if m == 12 else date(start.year, m + 1, 1)
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tok, cost = _sum_between(events, ms, me, pricing)
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out.append((f"{m:02d}", tok, cost))
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else: # month → weeks W1..Wn
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ndays = (end - start).days
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wk, day = 1, 1
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while day <= ndays:
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ws = date(start.year, start.month, day)
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we = date(start.year, start.month, day + 7) if day + 7 <= ndays else end
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tok, cost = _sum_between(events, ws, we, pricing)
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out.append((f"W{wk}", tok, cost))
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wk += 1
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day += 7
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return out
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def period_range_label(gran: str, offset: int, today: Optional[date] = None) -> str:
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"""Human label for the selected period (shown in the Dashboard header) —
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week → MM/DD – MM/DD, month → YYYY/MM, year → YYYY."""
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from datetime import timedelta
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start, end = period_bounds(gran, offset, today)
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if gran == "week":
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last_day = end - timedelta(days=1)
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return f"{start.strftime('%m/%d')} – {last_day.strftime('%m/%d')}"
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if gran == "year":
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return str(start.year)
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return start.strftime("%Y/%m")
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def set_budget(config, amount: float, currency: Optional[str] = None) -> None:
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@@ -397,7 +258,6 @@ def set_budget(config, amount: float, currency: Optional[str] = None) -> None:
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order and ``budget_set_at`` only has 1-second resolution, so a timestamp
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cutoff could mis-include/exclude an event recorded in that same second —
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the count baseline is exact regardless of timing."""
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from . import model_pricing as mp
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usage = config.data.setdefault("usage", {})
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ccy = (currency or usage.get("currency") or "USD").upper()
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usage["budget_amount_usd"] = mp.convert(float(amount or 0), ccy, "USD", config)
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@@ -442,83 +302,11 @@ def budget_status(config) -> Optional[Dict[str, Any]]:
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}
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def format_cost(usd: float, pricing: Dict[str, Any], digits: Optional[int] = None) -> str:
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"""Format a USD amount in the display currency. ``digits`` caps the number
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of decimal places (e.g. ``digits=2`` for the Total cost / Budget cards, so
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USD shows $1.23 not the default up-to-4 $1.2345) — never ADDS decimals to a
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currency that uses fewer (VND stays whole, JPY one place)."""
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p = {**DEFAULT_PRICING, **(pricing or {})}
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cur = p.get("currency", "USD")
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rate = {"USD": 1.0, "VND": float(p["usd_to_vnd"]), "JPY": float(p["usd_to_jpy"])}.get(cur, 1.0)
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symbol, cur_digits = _CURRENCY_FMT.get(cur, ("$", 2))
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if digits is not None:
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cur_digits = min(cur_digits, digits)
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value = usd * rate
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return f"{symbol}{value:,.{cur_digits}f}"
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def format_cost_compact(usd: float, pricing: Dict[str, Any]) -> str:
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"""Compact cost format for the Dashboard chart's y-axis/endpoint labels —
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always 2 decimals (not format_cost's up-to-4 for USD) and abbreviated with
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K/M above 1,000/1,000,000, same convention as ``fmt_tokens``. The chart's
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y-axis label box is narrow; the longer full-precision string used to
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overflow it, visually clipping/obscuring the leading currency symbol."""
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p = {**DEFAULT_PRICING, **(pricing or {})}
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cur = p.get("currency", "USD")
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rate = {"USD": 1.0, "VND": float(p["usd_to_vnd"]), "JPY": float(p["usd_to_jpy"])}.get(cur, 1.0)
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symbol, _digits = _CURRENCY_FMT.get(cur, ("$", 2))
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value = usd * rate
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sign = "-" if value < 0 else ""
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value = abs(value)
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if value >= 1_000_000:
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body = f"{value / 1_000_000:,.2f}M"
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elif value >= 1_000:
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body = f"{value / 1_000:,.2f}K"
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else:
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body = f"{value:,.2f}"
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return f"{sign}{symbol}{body}"
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_AI_ANALYSIS_HEADERS = {
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"vi": ("Nhận xét thói quen", "Cách viết prompt tiết kiệm hơn", "Hành động giảm token"),
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"en": ("Usage habits", "Writing more efficient prompts", "Actions to cut token usage"),
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"ja": ("利用傾向", "より効率的なプロンプトの書き方", "トークン削減のためのアクション"),
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}
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def build_ai_analysis_prompt(summary: Dict[str, Any], language: str = "vi") -> str:
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"""The prompt sent to the model for '✨ AI analyze my usage': aggregated
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numbers only — never raw prompt contents — asking for concrete habits
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feedback and token-saving recommendations, in the CURRENTLY SELECTED
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display language (headers included — not just the model's free-text reply,
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which would otherwise leave the section titles in Vietnamese regardless of
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the app's language setting)."""
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lang_names = {"vi": "Vietnamese", "ja": "Japanese", "en": "English"}
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h1, h2, h3 = _AI_ANALYSIS_HEADERS.get(language, _AI_ANALYSIS_HEADERS["vi"])
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top = "\n".join(f"- {label}: {tok:,} tokens"
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for label, tok in summary.get("top_labels", []))
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by_source = ", ".join(f"{k}={v:,}" for k, v in summary.get("by_source", []))
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return (
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"You are a token-efficiency coach for an AI desktop app (chat tabs + "
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"scheduled agent tasks). Analyze this usage summary and give the user "
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"practical advice, replying in "
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f"{lang_names.get(language, 'Vietnamese')}.\n\n"
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f"Period stats: {summary.get('turns', 0)} turns, "
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f"input={summary.get('in', 0):,} tokens, output={summary.get('out', 0):,}, "
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f"cache={summary.get('cache', 0):,}, "
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f"avg per prompt={summary.get('avg_per_turn', 0):,}.\n"
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f"Top consumers:\n{top or '- (none)'}\n"
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f"By area: {by_source or '(none)'}\n"
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f"Busiest day: {summary.get('busiest_day')} · busiest hour: {summary.get('busiest_hour')}\n\n"
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"Reply with EXACTLY these 3 short sections, in markdown, using THESE "
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f"section headers verbatim (already in {lang_names.get(language, 'Vietnamese')}):\n"
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f"1. **{h1}** — 2-3 bullet points about the usage pattern.\n"
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f"2. **{h2}** — 3 concrete prompt-writing tips "
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"tailored to the numbers above (e.g. long inputs → attach less / summarize "
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"first; many small turns → batch questions).\n"
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f"3. **{h3}** — 2-3 app-level actions (compact history, "
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"smaller model for simple tasks, reuse task outputs instead of re-asking).\n"
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"Keep the whole reply under 250 words."
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)
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Reference in New Issue
Block a user