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manual_slop/tests/test_ai_client_tool_loop_send_func.py
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ed 81d8bce419 refactor(ai_client): merge vendor_capabilities into ai_client; git rm src/vendor_capabilities.py
Per spec FR2 + Phase 2.1: VendorCapabilities + register + get_capabilities +
list_models_for_vendor + the ~40 vendor registrations move into ai_client.py
as a region block. Renamed internal _REGISTRY to _VENDOR_REGISTRY to avoid
collision with mcp_tool_specs._REGISTRY.

Importers (in src/) updated:
- src/ai_client.py: removed top-level import; removed 4 local imports of
  list_models_for_vendor/get_capabilities (symbol now in module namespace)
- src/app_controller.py: 2 sites updated to 'from src.ai_client import get_capabilities'
- src/gui_2.py: 1 site updated to 'from src.ai_client import VendorCapabilities, get_capabilities'

Tests updated:
- 8 test_*.py files: changed 'from src.vendor_capabilities import' to
  'from src.ai_client import'
- tests/test_vendor_capabilities.py: _clean_registry fixture updated to
  reference src.ai_client._VENDOR_REGISTRY (was src.vendor_capabilities._REGISTRY)

Verification: 157 tests pass across the affected files (vendor_capabilities,
ai_client_tool_loop variants, openai_compatible, command_palette,
diff_viewer, patch_modal, app_controller_result, app_controller_sigint,
handle_reset_session, ai_loop_regressions, grok/llama/minimax provider tests).
2026-06-26 07:07:12 -04:00

48 lines
1.9 KiB
Python

"""Verify run_with_tool_loop supports a custom send_func for vendors
that don't use send_openai_compatible (gemini_cli, gemini, anthropic,
deepseek). The vendor provides a send_func that returns a
NormalizedResponse, and the helper handles history + dispatch.
"""
from __future__ import annotations
from typing import Any
from unittest.mock import MagicMock, patch
from src.openai_compatible import NormalizedResponse
from src.openai_schemas import UsageStats
from src.ai_client import run_with_tool_loop
from src.ai_client import VendorCapabilities
def _make_normalized_response(text: str = "ok", tool_calls: list[dict[str, Any]] | None = None) -> NormalizedResponse:
return NormalizedResponse(
text=text, tool_calls=tool_calls or [],
usage=UsageStats(input_tokens=10, output_tokens=5, cache_read_tokens=0, cache_creation_tokens=0),
raw_response=None,
)
def test_run_with_tool_loop_uses_send_func_when_provided() -> None:
client = MagicMock()
def send_func(_round_idx: int) -> NormalizedResponse:
return _make_normalized_response(f"from-send-func-{_round_idx}")
result = run_with_tool_loop(
client, request=lambda _i: MagicMock(), # should be IGNORED
base_dir=".", vendor_name="custom",
send_func=send_func,
)
assert result == "from-send-func-0"
def test_run_with_tool_loop_dispatches_via_send_func() -> None:
client = MagicMock()
tool_resp = _make_normalized_response(
"first", tool_calls=[{"id": "c1", "type": "function", "function": {"name": "t", "arguments": "{}"}}]
)
final = _make_normalized_response("done")
def send_func(round_idx: int) -> NormalizedResponse:
return [tool_resp, final][round_idx]
with patch("src.ai_client._execute_tool_calls_concurrently", return_value=[("t", "c1", "r", "")]) as dispatch:
result = run_with_tool_loop(
client, request=lambda _i: MagicMock(),
base_dir=".", vendor_name="custom",
send_func=send_func,
)
assert result == "done"
assert dispatch.call_count == 1