feat: OpenMesh 基础平台与 MD/PDF 转换技能
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- 后端: coworker 智能体框架, WS API, 文件上传, 附件处理
- 前端: Open WebUI, 文件全量走 upload API (含 MD/TXT/JSON 等文本类)
- 技能: md-to-office (pandoc + wkhtmltopdf)
- 修复: 上传文件路径丢失, Agent 搜索浪费, 输出文件跑到 uploads/
- 打包: PyInstaller one-dir, 预打包 pandoc/wkhtmltopdf/chromium
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2026-09-13 23:41:04 +08:00
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"""Mangled tool calls (`{"_raw": …}` fallback args) get a real diagnosis, not execution.
The field failure (2026-08-15, report-page generation): a large write_file call blew the
output-token limit, the truncated JSON became `{"_raw": …}`, and the tool's bare
parameter error sent the model into a "wrong parameter" retry loop. Worse, each stored
`_raw` call read as a worked example — after a few, the model began emitting `_raw` as
if it were a real parameter, and every replay re-sent thousands of junk tokens.
"""
from __future__ import annotations
import json
import pytest
from coworker.engine import EventType, TurnEngine, _MANGLED_PREVIEW_CHARS
from coworker.permissions import Mode, PermissionEngine
from coworker.providers import AssistantTurn, ModelCapabilities, ProviderClient, ToolCall
from coworker.tools import ToolRegistry
class MangledProvider(ProviderClient):
"""One turn with unparseable write_file args, then a plain reply."""
def __init__(self, finish_reason: str, raw: str = "x" * 5000):
self.calls = 0
self.finish_reason = finish_reason
self.raw = raw
def complete(self, *, model, messages, tools=None, **settings):
self.calls += 1
if self.calls == 1:
return AssistantTurn(
text="",
tool_calls=[
ToolCall(id="t1", name="write_file", arguments={"_raw": self.raw})
],
finish_reason=self.finish_reason,
)
return AssistantTurn(text="done", tool_calls=[])
def capabilities(self, model):
return ModelCapabilities(tools=True)
def _engine(tmp_path, provider) -> TurnEngine:
return TurnEngine(
provider=provider,
registry=ToolRegistry(),
permissions=PermissionEngine(workspace_root=tmp_path, mode=Mode.INTERACTIVE),
model="m",
)
async def _run(engine) -> list:
return [e async for e in engine.run("build the report")]
def _last_tool_message(engine) -> str:
return str([m for m in engine.messages if m.get("role") == "tool"][-1]["content"])
@pytest.mark.asyncio
async def test_truncated_call_is_answered_with_the_truncation_diagnosis(tmp_path):
"""finish_reason "length" + unparseable args = the call was cut off. The model's cure
is smaller pieces — the error must say so instead of a bare parameter complaint."""
engine = _engine(tmp_path, MangledProvider("length"))
events = await _run(engine)
finished = [e for e in events if e.type is EventType.TOOL_FINISHED]
assert finished[0].data["status"] == "error"
body = _last_tool_message(engine)
assert "output-token limit" in body
assert "smaller pieces" in body
# The turn continues — no retry loop, the model answers after the diagnosis.
assert [e for e in events if e.type is EventType.TURN_END]
@pytest.mark.asyncio
async def test_plain_bad_json_is_answered_with_the_parameter_diagnosis(tmp_path):
"""No truncation → the model wrote bad JSON (or imitated `_raw`). Tell it `_raw` is
not a parameter and to re-issue with the declared ones."""
engine = _engine(tmp_path, MangledProvider("stop"))
await _run(engine)
body = _last_tool_message(engine)
assert "_raw" in body and "not a parameter" in body
assert "declared parameters" in body
@pytest.mark.asyncio
async def test_raw_junk_is_shrunk_before_it_enters_history(tmp_path):
"""The unparsed text must not be replayed at full size: it costs tokens every turn
and teaches the model that `_raw` is a shape to imitate."""
engine = _engine(tmp_path, MangledProvider("length", raw="y" * 5000))
await _run(engine)
assistant = [m for m in engine.messages if m.get("role") == "assistant" and m.get("tool_calls")][0]
stored = json.loads(assistant["tool_calls"][0]["function"]["arguments"])
assert len(stored["_raw"]) < _MANGLED_PREVIEW_CHARS + 100
assert "truncated in history" in stored["_raw"]
@pytest.mark.asyncio
async def test_short_raw_args_are_kept_verbatim(tmp_path):
"""Below the preview cap there is nothing to shrink — storage stays faithful."""
engine = _engine(tmp_path, MangledProvider("stop", raw='{"path": "repo'))
await _run(engine)
assistant = [m for m in engine.messages if m.get("role") == "assistant" and m.get("tool_calls")][0]
stored = json.loads(assistant["tool_calls"][0]["function"]["arguments"])
assert stored == {"_raw": '{"path": "repo'}