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feat(mma): Integrate Tier 4 QA analysis across all providers and conductor

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This commit is contained in:
Edward R. Gonzalez
2026-02-26 20:29:34 -05:00
parent 80a10f4d12
commit fb3da4de36
5 changed files with 122 additions and 19 deletions
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34
ai_client.py
View File

@@ -21,6 +21,7 @@ import difflib
import threading
import requests
from pathlib import Path
from typing import Optional, Callable
import os
import project_manager
import file_cache
@@ -522,10 +523,10 @@ def _gemini_tool_declaration():
return types.Tool(function_declarations=declarations) if declarations else None
def _run_script(script: str, base_dir: str) -> str:
def _run_script(script: str, base_dir: str, qa_callback: Optional[Callable[[str], str]] = None) -> str:
if confirm_and_run_callback is None:
return "ERROR: no confirmation handler registered"
result = confirm_and_run_callback(script, base_dir)
result = confirm_and_run_callback(script, base_dir, qa_callback)
if result is None:
output = "USER REJECTED: command was not executed"
else:
@@ -669,7 +670,8 @@ def _get_gemini_history_list(chat):
def _send_gemini(md_content: str, user_message: str, base_dir: str,
file_items: list[dict] | None = None,
discussion_history: str = "",
pre_tool_callback = None) -> str:
pre_tool_callback = None,
qa_callback: Optional[Callable[[str], str]] = None) -> str:
global _gemini_chat, _gemini_cache, _gemini_cache_md_hash, _gemini_cache_created_at
try:
@@ -848,7 +850,7 @@ def _send_gemini(md_content: str, user_message: str, base_dir: str,
elif name == TOOL_NAME:
scr = args.get("script", "")
_append_comms("OUT", "tool_call", {"name": TOOL_NAME, "script": scr})
out = _run_script(scr, base_dir)
out = _run_script(scr, base_dir, qa_callback)
else: out = f"ERROR: unknown tool '{name}'"
if i == len(calls) - 1:
@@ -880,7 +882,8 @@ def _send_gemini(md_content: str, user_message: str, base_dir: str,
def _send_gemini_cli(md_content: str, user_message: str, base_dir: str,
file_items: list[dict] | None = None,
discussion_history: str = "",
pre_tool_callback = None) -> str:
pre_tool_callback = None,
qa_callback: Optional[Callable[[str], str]] = None) -> str:
global _gemini_cli_adapter
try:
if _gemini_cli_adapter is None:
@@ -984,7 +987,7 @@ def _send_gemini_cli(md_content: str, user_message: str, base_dir: str,
elif name == TOOL_NAME:
scr = args.get("script", "")
_append_comms("OUT", "tool_call", {"name": TOOL_NAME, "id": call_id, "script": scr})
out = _run_script(scr, base_dir)
out = _run_script(scr, base_dir, qa_callback)
else:
out = f"ERROR: unknown tool '{name}'"
@@ -1277,7 +1280,7 @@ def _repair_anthropic_history(history: list[dict]):
})
def _send_anthropic(md_content: str, user_message: str, base_dir: str, file_items: list[dict] | None = None, discussion_history: str = "", pre_tool_callback = None) -> str:
def _send_anthropic(md_content: str, user_message: str, base_dir: str, file_items: list[dict] | None = None, discussion_history: str = "", pre_tool_callback = None, qa_callback: Optional[Callable[[str], str]] = None) -> str:
try:
_ensure_anthropic_client()
mcp_client.configure(file_items or [], [base_dir])
@@ -1441,7 +1444,7 @@ def _send_anthropic(md_content: str, user_message: str, base_dir: str, file_item
"id": b_id,
"script": script,
})
output = _run_script(script, base_dir)
output = _run_script(script, base_dir, qa_callback)
_append_comms("IN", "tool_result", {
"name": TOOL_NAME,
"id": b_id,
@@ -1520,7 +1523,8 @@ def _send_deepseek(md_content: str, user_message: str, base_dir: str,
file_items: list[dict] | None = None,
discussion_history: str = "",
stream: bool = False,
pre_tool_callback = None) -> str:
pre_tool_callback = None,
qa_callback: Optional[Callable[[str], str]] = None) -> str:
"""
Sends a message to the DeepSeek API, handling tool calls and history.
Supports streaming responses.
@@ -1713,7 +1717,7 @@ def _send_deepseek(md_content: str, user_message: str, base_dir: str,
elif tool_name == TOOL_NAME:
script = tool_args.get("script", "")
_append_comms("OUT", "tool_call", {"name": TOOL_NAME, "id": tool_id, "script": script})
tool_output = _run_script(script, base_dir)
tool_output = _run_script(script, base_dir, qa_callback)
else:
tool_output = f"ERROR: unknown tool '{tool_name}'"
@@ -1811,6 +1815,7 @@ def send(
discussion_history: str = "",
stream: bool = False,
pre_tool_callback = None,
qa_callback: Optional[Callable[[str], str]] = None,
) -> str:
"""
Send a message to the active provider.
@@ -1825,16 +1830,17 @@ def send(
conversation message instead of caching it)
stream : Whether to use streaming (supported by DeepSeek)
pre_tool_callback : Optional callback (payload: str) -> bool called before tool execution
qa_callback : Optional callback (stderr: str) -> str called for Tier 4 error analysis
"""
with _send_lock:
if _provider == "gemini":
return _send_gemini(md_content, user_message, base_dir, file_items, discussion_history, pre_tool_callback)
return _send_gemini(md_content, user_message, base_dir, file_items, discussion_history, pre_tool_callback, qa_callback)
elif _provider == "gemini_cli":
return _send_gemini_cli(md_content, user_message, base_dir, file_items, discussion_history, pre_tool_callback)
return _send_gemini_cli(md_content, user_message, base_dir, file_items, discussion_history, pre_tool_callback, qa_callback)
elif _provider == "anthropic":
return _send_anthropic(md_content, user_message, base_dir, file_items, discussion_history, pre_tool_callback)
return _send_anthropic(md_content, user_message, base_dir, file_items, discussion_history, pre_tool_callback, qa_callback)
elif _provider == "deepseek":
return _send_deepseek(md_content, user_message, base_dir, file_items, discussion_history, stream=stream, pre_tool_callback=pre_tool_callback)
return _send_deepseek(md_content, user_message, base_dir, file_items, discussion_history, stream=stream, pre_tool_callback=pre_tool_callback, qa_callback=qa_callback)
raise ValueError(f"unknown provider: {_provider}")
def get_history_bleed_stats(md_content: str | None = None) -> dict:

4
gui_2.py
View File

@@ -1066,7 +1066,7 @@ class App:
self.is_viewing_prior_session = True
self.ai_status = f"viewing prior session: {Path(path).name} ({len(entries)} entries)"
def _confirm_and_run(self, script: str, base_dir: str) -> str | None:
def _confirm_and_run(self, script: str, base_dir: str, qa_callback: Optional[Callable[[str], str]] = None) -> str | None:
print(f"[DEBUG] _confirm_and_run triggered for script length: {len(script)}")
dialog = ConfirmDialog(script, base_dir)
@@ -1106,7 +1106,7 @@ class App:
self.ai_status = "running powershell..."
print(f"[DEBUG] Running powershell in {base_dir}")
output = shell_runner.run_powershell(final_script, base_dir)
output = shell_runner.run_powershell(final_script, base_dir, qa_callback=qa_callback)
self._append_tool_log(final_script, output)
self.ai_status = "powershell done, awaiting AI..."
return output

4
gui_legacy.py
View File

@@ -910,7 +910,7 @@ class App:
# ---------------------------------------------------------------- tool execution
def _confirm_and_run(self, script: str, base_dir: str) -> str | None:
def _confirm_and_run(self, script: str, base_dir: str, qa_callback: Optional[Callable[[str], str]] = None) -> str | None:
dialog = ConfirmDialog(script, base_dir)
with self._pending_dialog_lock:
@@ -923,7 +923,7 @@ class App:
return None
self._update_status("running powershell...")
output = shell_runner.run_powershell(final_script, base_dir)
output = shell_runner.run_powershell(final_script, base_dir, qa_callback=qa_callback)
self._append_tool_log(final_script, output)
self._update_status("powershell done, awaiting AI...")
return output

3
multi_agent_conductor.py
View File

@@ -82,7 +82,8 @@ def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files:
md_content="",
user_message=user_message,
base_dir=".",
pre_tool_callback=confirm_execution if ticket.step_mode else None
pre_tool_callback=confirm_execution if ticket.step_mode else None,
qa_callback=ai_client.run_tier4_analysis
)
if "BLOCKED" in response.upper():

96
tests/test_tier4_interceptor.py
View File

@@ -126,3 +126,99 @@ def test_end_to_end_tier4_integration():
mock_analysis.assert_called_once_with(stderr_content)
assert f"QA ANALYSIS:\n{expected_analysis}" in output
def test_ai_client_passes_qa_callback():
"""
Verifies that ai_client.send passes the qa_callback down to the provider function.
"""
import ai_client
# Mocking a provider function to avoid actual API calls
mock_send_gemini = MagicMock(return_value="AI Response")
qa_callback = MagicMock(return_value="QA Analysis")
# Force provider to gemini and mock its send function
with patch("ai_client._provider", "gemini"), \
patch("ai_client._send_gemini", mock_send_gemini):
ai_client.send(
md_content="Context",
user_message="Hello",
qa_callback=qa_callback
)
# Verify provider received the qa_callback
mock_send_gemini.assert_called_once()
args, kwargs = mock_send_gemini.call_args
# qa_callback is the 7th positional argument in _send_gemini
assert args[6] == qa_callback
def test_gemini_provider_passes_qa_callback_to_run_script():
"""
Verifies that _send_gemini passes the qa_callback to _run_script.
"""
import ai_client
# Mock Gemini chat and client
mock_client = MagicMock()
mock_chat = MagicMock()
# Simulate a tool call response
mock_part = MagicMock()
mock_part.text = ""
mock_part.function_call = MagicMock()
mock_part.function_call.name = "run_powershell"
mock_part.function_call.args = {"script": "dir"}
mock_candidate = MagicMock()
mock_candidate.content.parts = [mock_part]
mock_candidate.finish_reason.name = "STOP"
mock_response = MagicMock()
mock_response.candidates = [mock_candidate]
mock_response.usage_metadata.prompt_token_count = 10
mock_response.usage_metadata.candidates_token_count = 5
# Second call returns a stop response to break the loop
mock_stop_part = MagicMock()
mock_stop_part.text = "Done"
mock_stop_part.function_call = None
mock_stop_candidate = MagicMock()
mock_stop_candidate.content.parts = [mock_stop_part]
mock_stop_candidate.finish_reason.name = "STOP"
mock_stop_response = MagicMock()
mock_stop_response.candidates = [mock_stop_candidate]
mock_stop_response.usage_metadata.prompt_token_count = 5
mock_stop_response.usage_metadata.candidates_token_count = 2
mock_chat.send_message.side_effect = [mock_response, mock_stop_response]
# Mock count_tokens to avoid chat creation failure
mock_count_resp = MagicMock()
mock_count_resp.total_tokens = 100
mock_client.models.count_tokens.return_value = mock_count_resp
qa_callback = MagicMock()
# Set global state for the test
with patch("ai_client._gemini_client", mock_client), \
patch("ai_client._gemini_chat", None), \
patch("ai_client._ensure_gemini_client"), \
patch("ai_client._run_script", return_value="output") as mock_run_script, \
patch("ai_client._get_gemini_history_list", return_value=[]):
# Ensure chats.create returns our mock_chat
mock_client.chats.create.return_value = mock_chat
ai_client._send_gemini(
md_content="Context",
user_message="Run dir",
base_dir=".",
qa_callback=qa_callback
)
# Verify _run_script received the qa_callback
mock_run_script.assert_called_once_with("dir", ".", qa_callback)