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ThearchyHelios
ba28dee1f0
- api/copilot.py：event_stream 原样搬成 async generator answer_ops(payload, *, catalog_view=None)，交出的 op dict 不变；开流前的准备（数据源、目录检查器、助手模型、历史、查看服务）用自己的短会话，在第一条操作之前做完。SSE 路由只包一层：先取第一条，开流前的拒绝（模型没配好、限定的数据源都不在）照旧是 HTTP 错误
- catalog_view 一路传给查看服务和自查的 SQL 检查器（目录视图接缝的「生成过程从参数读」）
- providers/usage.py：Meter 是一个 LangChain 回调，和 get_usage_metadata_callback 同一个机制（登记过的上下文变量），按模型累计 tokens、次数，金额按价格目录；估不出的记 unpriced_calls。没直接用 get_usage_metadata_callback：with 块跨 yield 会把变量留在调用方上下文里，调用方随后发起的运行会继承它、被重复计一遍；metered 只在每一步 __anext__ 期间挂上，嵌套时并进外层
- 生成过程读到 done / look / ask 就关模型的流（后面的闲话不要），流末尾才报的用量拿不到：这一次照样计次数和已报的 token，记成估不出，不悄悄少算（遗留：真实接入上建图那几次多半是下限）
- providers/budget.py：BudgetFallback.astream 用 aclosing 包住里面那条流——以前调用方关流时里面那条要等垃圾回收才关，模型那次调用的收尾（用量回调）落在算完账之后；测试去掉它回跑会少计一次
- answer_ops 末尾多一条 {op: usage, by_model, input_tokens, output_tokens, calls, cost_usd, unpriced_calls}；/copilot/review 拆出 review_of_run（问数据页、评测共用），结果带 usage（没调模型时全 0）和 retry_instruction（要不要带着原图重来、说什么，按 chat/turn.py 的决定算；ReviewIn 多一个 attempt）
- 新增 app/chat/turn.py（问数据一轮的决定，复核这边先用上；final.next、对照测试、前端照做在下一个提交）；新增 tests/test_copilot_answer_ops.py（八种结局的 SSE 输出和抽出来之前录下的 fixtures/copilot_answer_ops_baseline.json 逐条相同，末尾多一条 usage；直接迭代和 SSE 同一串；开流前的拒绝仍是 HTTP 错误）、tests/test_usage_meter.py；读 SSE 的十一个测试文件的收流帮手跳过末尾那条 usage
284 lines
13 KiB
Python
284 lines
13 KiB
Python
"""用量计量（providers/usage.py；细案 01 做法 3）：助手建图（含挑表、接着写、自查修正）和复核的用量也记。
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守的是：
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- 按模型累计 tokens 和调用次数，金额按价格目录算；目录外的模型、回复没带用量（而目录价又不是 0）的调用记 unpriced_calls，
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不当成 0 元；
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- metered 只计生成过程自己的调用（含它在一步里起的任务）；调用方在两次 yield 之间做的事（发起运行）不计——运行的用量
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另有 llm.end，包进来就重复了；嵌套时内层并进外层；
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- answer_ops 末尾的 usage 记下这一轮建图的每一次调用（起草、自查修正各一次）；复核的结果带 usage。
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模型都是剧本，不调真接口；价格用目录里登记过的模型 id，名字之外都是虚构的。
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"""
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from __future__ import annotations
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import asyncio
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import json
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from typing import Any
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import pytest
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from httpx import ASGITransport, AsyncClient
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.messages import AIMessage, AIMessageChunk
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from app.main import app
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from app.providers import usage
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from app.providers.catalog import estimate_cost
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PRICED = "claude-opus-5" # 价格目录里有
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UNPRICED = "made-up-model-x" # 价格目录里没有
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FREE = "mock-fast" # 目录价是 0
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class Scripted(BaseChatModel):
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"""真的 LangChain 模型：回调照常触发。每次调用吐 replies 里的下一份（用完重复最后一份），用量按 tokens 报；
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report=False 时回复不带用量（有的网关流式不给）。"""
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model: str = PRICED
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replies: list[list[str]] = [["好的"]]
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tokens: tuple[int, int] = (1000, 200)
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report: bool = True
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calls: int = 0
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@property
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def _llm_type(self) -> str:
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return "scripted"
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@property
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def _identifying_params(self) -> dict[str, Any]:
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return {"model": self.model}
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def _next(self) -> list[str]:
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lines = self.replies[min(self.calls, len(self.replies) - 1)]
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self.calls += 1
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return lines
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def _usage(self) -> dict[str, int] | None:
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if not self.report:
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return None
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inp, out = self.tokens
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return {"input_tokens": inp, "output_tokens": out, "total_tokens": inp + out}
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def _generate(self, messages, stop=None, run_manager=None, **kwargs) -> ChatResult:
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message = AIMessage(content="\n".join(self._next()))
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if (meta := self._usage()) is not None:
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message.usage_metadata = meta
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return ChatResult(generations=[ChatGeneration(message=message)])
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async def _astream(self, messages, stop=None, run_manager=None, **kwargs):
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"""像 Anthropic 那样报用量：第一段带输入 token，流末尾单独一段带输出 token。"""
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lines = self._next()
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inp, out = self.tokens
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for i, line in enumerate(lines):
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meta = {"input_tokens": inp, "output_tokens": 0, "total_tokens": inp} if self.report and i == 0 else None
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yield ChatGenerationChunk(message=AIMessageChunk(content=line + "\n", **({"usage_metadata": meta} if meta
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else {})))
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if self.report:
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yield ChatGenerationChunk(message=AIMessageChunk(content="", usage_metadata={
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"input_tokens": 0, "output_tokens": out, "total_tokens": out}))
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# --------------------------------------------------------------------------
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# 计量本身
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# --------------------------------------------------------------------------
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async def test_metering_counts_by_model_and_prices_from_the_catalog():
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m = usage.meter()
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with usage.metering(m):
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await Scripted(model=PRICED).ainvoke("你好")
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await Scripted(model=PRICED).ainvoke("再来")
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await Scripted(model=UNPRICED).ainvoke("你好")
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await Scripted(model=PRICED + "-silent", report=False).ainvoke("你好")
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await Scripted(model=FREE, report=False).ainvoke("你好")
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got = m.summary()
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assert got["by_model"][PRICED] == {"input_tokens": 2000, "output_tokens": 400, "calls": 2,
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"cost_usd": round(estimate_cost(PRICED, 2000, 400), 6), "unpriced_calls": 0}
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assert got["by_model"][UNPRICED]["unpriced_calls"] == 1 and got["by_model"][UNPRICED]["cost_usd"] == 0
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# 目录外的模型名（多了后缀）照样算估不出；目录价是 0 的本地模型没报用量也不算估不出
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assert got["by_model"][PRICED + "-silent"]["unpriced_calls"] == 1
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assert got["by_model"][FREE]["unpriced_calls"] == 0
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assert got["calls"] == 5 and got["unpriced_calls"] == 2
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assert got["cost_usd"] == pytest.approx(estimate_cost(PRICED, 2000, 400))
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async def test_outside_the_meter_nothing_is_counted():
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m = usage.meter()
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with usage.metering(m):
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pass
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await Scripted().ainvoke("你好")
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assert m.summary()["calls"] == 0
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assert usage.current() is None
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async def test_metered_counts_the_generator_and_its_tasks_but_not_the_caller():
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"""生成过程里的调用（含它起的任务）计进来；调用方在两次 yield 之间发起的「运行」不计。"""
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m = usage.meter()
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leaked: list[asyncio.Task] = []
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async def generation():
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await Scripted(model=PRICED).ainvoke("起草")
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# 心跳那一层每一步起一个任务：任务在步里创建，拷走的上下文带着计量
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await asyncio.ensure_future(Scripted(model=PRICED).ainvoke("挑表"))
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yield "first"
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await Scripted(model=PRICED).ainvoke("自查修正")
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yield "second"
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seen = []
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async for item in usage.metered(generation(), m):
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seen.append(item)
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# 调用方拿到一条操作后自己调模型（相当于发起运行）：它的上下文里没有计量
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await Scripted(model=UNPRICED).ainvoke("运行里的调用")
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leaked.append(asyncio.ensure_future(Scripted(model=UNPRICED).ainvoke("运行任务")))
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await asyncio.gather(*leaked)
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got = m.summary()
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assert seen == ["first", "second"]
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assert got["by_model"] == {PRICED: got["by_model"][PRICED]} and got["calls"] == 3
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assert usage.current() is None
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async def test_nested_meters_fold_into_the_outer_one():
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outer, inner = usage.meter(), usage.meter()
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async def generation():
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await Scripted(model=PRICED).ainvoke("起草")
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yield 1
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with usage.metering(outer):
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await Scripted(model=PRICED).ainvoke("外层自己的")
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async for _ in usage.metered(generation(), inner):
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pass
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with usage.metering(usage.meter()):
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await Scripted(model=PRICED).ainvoke("复核")
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assert inner.summary()["calls"] == 1
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assert outer.summary()["calls"] == 3
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# --------------------------------------------------------------------------
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# answer_ops 末尾的 usage、复核的 usage
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# --------------------------------------------------------------------------
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def _ops(*ops: dict) -> list[str]:
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return [json.dumps(o, ensure_ascii=False) for o in ops]
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GRAPH = _ops(
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{"op": "plan", "summary": "数一数"},
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{"op": "add_node", "node": {"id": "start", "type": "input", "label": "输入",
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"config": {"fields": [{"name": "question"}]}}},
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{"op": "add_node", "node": {"id": "lp", "type": "loop", "label": "循环",
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"config": {"mode": "while", "condition": "foo(vars.x)", "max_iterations": 2}}},
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{"op": "add_edge", "edge": {"source": "start", "target": "lp"}},
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{"op": "add_node", "node": {"id": "body", "type": "transform", "label": "体",
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"config": {"mode": "expression", "expression": "1"}}},
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{"op": "add_edge", "edge": {"source": "lp", "target": "body", "sourceHandle": "body"}},
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{"op": "add_edge", "edge": {"source": "body", "target": "lp"}},
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{"op": "add_node", "node": {"id": "out", "type": "output", "label": "成果",
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"config": {"fields": [{"name": "r", "value": "1"}]}}},
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{"op": "add_edge", "edge": {"source": "lp", "target": "out", "sourceHandle": "done"}},
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{"op": "done", "explanation": "数一数", "run": True},
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)
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FIX = _ops({"op": "update_node", "id": "lp", "config": {"mode": "while", "condition": "len(vars.x) > 1",
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"max_iterations": 2}},
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{"op": "done", "explanation": "改好了"})
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@pytest.fixture
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async def client():
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as c:
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yield c
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async def test_answer_ops_ends_with_the_usage_of_every_build_call(client, monkeypatch):
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"""起草、自查修正各一次。生成过程读到 done 就关流（后面的闲话不要），流末尾才报的输出 token 拿不到：这两次照样计次数、
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计已报的输入 token，记成估不出（unpriced_calls），金额只是估得出的那部分——不悄悄少算。"""
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import app.api.copilot as copilot
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model = Scripted(model=PRICED, replies=[GRAPH, FIX], tokens=(3000, 500))
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async def _model(*_a, **_k):
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return model, PRICED
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monkeypatch.setattr(copilot, "get_chat_model", _model)
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events = []
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async with client.stream("POST", "/api/copilot/generate-stream", json={"instruction": "数一数", "intent": "answer"}) as r:
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async for line in r.aiter_lines():
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if line.startswith("data: "):
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events.append(json.loads(line[6:]))
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assert [e["op"] for e in events if e["op"] == "check"] and events[-2]["op"] == "final"
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spent = events[-1]
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assert spent["op"] == "usage", spent
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assert model.calls == 2, spent # 起草一次、自查修正一次
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assert spent["by_model"][PRICED]["calls"] == 2, spent
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assert spent["by_model"][PRICED]["input_tokens"] == 6000 and spent["by_model"][PRICED]["output_tokens"] == 0
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assert spent["cost_usd"] == pytest.approx(estimate_cost(PRICED, 6000, 0), abs=1e-6)
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assert spent["unpriced_calls"] == 2, "关流前没报全的两次记成估不出"
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async def test_a_build_on_an_unpriced_model_says_it_cannot_be_priced(client, monkeypatch):
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import app.api.copilot as copilot
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model = Scripted(model=UNPRICED, replies=[_ops({"op": "reply", "text": "已经查过了"})])
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async def _model(*_a, **_k):
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return model, UNPRICED
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monkeypatch.setattr(copilot, "get_chat_model", _model)
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events = []
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async with client.stream("POST", "/api/copilot/generate-stream", json={"instruction": "刚才那个数是多少"}) as r:
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async for line in r.aiter_lines():
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if line.startswith("data: "):
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events.append(json.loads(line[6:]))
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assert [e["op"] for e in events] == ["model", "reply", "usage"]
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assert events[-1]["calls"] == 1 and events[-1]["unpriced_calls"] == 1 and events[-1]["cost_usd"] == 0
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async def test_a_stream_read_to_the_end_is_fully_priced():
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"""读完的流（直接回答那一行之后模型自己停了）：输入、输出 token 都报全了，金额估得出。"""
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m = usage.meter()
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async def generation():
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async for _ in Scripted(model=PRICED, tokens=(400, 60)).astream("问"):
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pass
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yield 1
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async for _ in usage.metered(generation(), m):
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pass
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row = m.summary()["by_model"][PRICED]
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assert (row["input_tokens"], row["output_tokens"], row["unpriced_calls"]) == (400, 60, 0)
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assert row["cost_usd"] == round(estimate_cost(PRICED, 400, 60), 6)
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async def test_the_review_carries_its_own_usage(client, monkeypatch):
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import app.api.copilot as copilot
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from app.core.events import EventType
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from app.db.base import SessionLocal
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from app.db.models import Run, RunEvent
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reply = json.dumps({"note": "agent 用满了步数，结论不完整。", "answer": "", "retry": True}, ensure_ascii=False)
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model = Scripted(model=PRICED, replies=[[reply]], tokens=(800, 100))
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async def _model(*_a, **_k):
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return model, PRICED
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monkeypatch.setattr(copilot, "get_chat_model", _model)
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async with SessionLocal() as session:
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run = Run(status="succeeded", output={"answer": "大概是 3 家"}, graph={})
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session.add(run)
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await session.flush()
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session.add(RunEvent(run_id=run.id, seq=1, type=EventType.LOG, node_id="a", ts=0.0,
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data={"level": "warn", "code": "step_limit", "message": "用满了步数"}))
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await session.commit()
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run_id = run.id
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body = (await client.post("/api/copilot/review", json={"run_id": run_id, "question": "有几家门店"})).json()
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assert body["usage"]["calls"] == 1 and body["usage"]["by_model"][PRICED]["input_tokens"] == 800
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# 干净的运行不调模型：usage 全 0
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async with SessionLocal() as session:
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clean = Run(status="succeeded", output={"answer": "3 家"}, graph={})
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session.add(clean)
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await session.commit()
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clean_id = clean.id
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body = (await client.post("/api/copilot/review", json={"run_id": clean_id, "question": "有几家门店"})).json()
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assert body["verdict"] == "ok" and body["usage"]["calls"] == 0 and body["usage"]["by_model"] == {}
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