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cowork-local/tests/fakes/turn_runtime_fakes.py
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Feature/delta team/epic r04 (#7)
## 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>
2026-08-31 05:15:13 +00:00

142 lines
5.5 KiB
Python

"""Offline test doubles for the R04 turn runtime seams.
Sits beside ``fake_provider.py``/``fake_tool_executor.py`` (R01-T02) and plays
the same role one level up: those fake a *provider*, these fake the ports
``ConversationApplicationService`` is driven through
(``application/conversations/turn_runtime.py``).
Deliberately dumb — they record what they were asked and return canned answers.
A failing test then points at the service under test rather than at a mock
framework's configuration.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Tuple
from cowork_local.domain.agents.agent_event import ToolPreview
from cowork_local.domain.agents.conversation_execution_request import (
ConversationExecutionRequest,
)
class FakeSpec:
"""An advertised tool. The service only ever reads ``.name`` off a spec."""
def __init__(self, name: str) -> None:
self.name = name
class FakeReply:
"""One programmed provider answer."""
def __init__(self, content: str = "", tool_calls=None, chunks=None, reasoning: str = ""):
self.content = content
self.tool_calls = tool_calls or []
# Default to streaming the whole content as a single chunk, which is what
# a non-streaming gateway effectively does.
self.chunks = chunks if chunks is not None else ([content] if content else [])
self.reasoning = reasoning
class FakeModelCall:
""":class:`ModelCallPort` returning programmed replies in order.
A programmed entry may be an exception instead of a reply, which is how a
test simulates the gateway dying mid-turn.
"""
def __init__(self, replies: List[Any]) -> None:
self.replies = list(replies)
self.calls: List[Dict[str, Any]] = []
def call(self, messages, tools, on_text=None, on_reasoning=None, cancel=None):
# Snapshot the messages: the service keeps mutating its own list, so
# storing it by reference would make every recorded call look identical.
self.calls.append({"messages": [dict(m) for m in messages],
"tool_names": [getattr(t, "name", "") for t in tools]})
reply = self.replies.pop(0) if self.replies else FakeReply(content="(default)")
if isinstance(reply, BaseException):
raise reply
if reply.reasoning and on_reasoning:
on_reasoning(reply.reasoning)
for chunk in reply.chunks:
if on_text and chunk:
on_text(chunk)
assistant: Dict[str, Any] = {"role": "assistant", "content": reply.content}
if reply.tool_calls:
assistant["tool_calls"] = reply.tool_calls
return assistant
class FakeToolRuntime:
""":class:`ToolRuntimePort` over an imaginary output folder."""
def __init__(self, specs=("save_file", "run_command", "update_plan"),
results: Optional[Dict[str, Dict[str, Any]]] = None,
removed: Tuple[str, ...] = (), added: Tuple[str, ...] = ()) -> None:
self._specs = [FakeSpec(n) for n in specs]
self._results = results or {}
self._removed, self._added = removed, added
self.executed: List[Tuple[str, Dict[str, Any]]] = []
self.finalize_calls: List[Dict[str, Any]] = []
# When set, every executed tool streams this string through ``on_output``.
self.emit_output: Optional[str] = None
def specs(self, allowed_tools=None):
if allowed_tools is None:
return list(self._specs)
return [s for s in self._specs if s.name in allowed_tools]
def preview(self, name, args):
return ToolPreview(kind="info", title=name, text=str(args))
def execute(self, name, args, on_output=None, cancel=None):
self.executed.append((name, dict(args)))
if self.emit_output and on_output:
on_output(self.emit_output)
return dict(self._results.get(name, {"ok": True, "output": f"{name} ok"}))
def snapshot(self):
return "before"
def finalize(self, before, cancelled=False):
self.finalize_calls.append({"before": before, "cancelled": cancelled})
return list(self._removed), list(self._added)
# --------------------------------------------------------------------------- #
# Small helpers shared by the turn tests.
# --------------------------------------------------------------------------- #
def make_request(**overrides) -> ConversationExecutionRequest:
"""A minimal valid request; each test overrides only what it exercises."""
base: Dict[str, Any] = {"turn_id": "t1", "session_id": "s1", "prompt": "do it"}
base.update(overrides)
return ConversationExecutionRequest(**base)
def run_turn(service, request=None, cancel=None):
"""Execute a turn and return ``(result, events)``."""
events: List[Any] = []
result = service.execute(request or make_request(), events.append, cancel=cancel)
return result, events
def events_of_type(events, cls):
"""Every emitted event of one type, in order."""
return [e for e in events if isinstance(e, cls)]
def tool_turn(tool_name: str = "save_file", args=None, **tool_kwargs):
"""A turn that calls one tool and then answers — ``(model, tools)``."""
calls = [{"id": "c1", "name": tool_name, "arguments": args or {"filename": "a.md"}}]
model = FakeModelCall([FakeReply(content="working", tool_calls=calls),
FakeReply(content="done")])
return model, FakeToolRuntime(**tool_kwargs)
__all__ = [
"FakeSpec", "FakeReply", "FakeModelCall", "FakeToolRuntime",
"make_request", "run_turn", "events_of_type", "tool_turn",
]