Files
LangBot/tests/unit_tests/provider/test_localagent_no_duplicate.py
T
DongXiaoming 76c5003c21 fix(localagent): stop re-seeding stream accumulator with previous round content (#2329)
When the LLM (e.g. MiniMax-M3) returns multiple rounds after tool calls, the _StreamAccumulator was initialized with initial_content=first_content, causing every subsequent round to repeat the entire first message. Remove the re-seeding so each round starts with a clean accumulator.

Add regression test verifying multi-round tool call content is not duplicated.
2026-07-20 16:38:11 +08:00

218 lines
7.1 KiB
Python

from __future__ import annotations
import json
from types import SimpleNamespace
from unittest.mock import AsyncMock, Mock
import pytest
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
import langbot_plugin.api.entities.builtin.provider.message as provider_message
import langbot_plugin.api.entities.builtin.provider.session as provider_session
from langbot.pkg.provider.runners.localagent import LocalAgentRunner
class RecordingProvider:
"""Non-streaming provider that returns a tool-call on round 1 and plain text on round 2."""
def __init__(self):
self.requests: list[dict] = []
async def invoke_llm(self, query, model, messages, funcs, extra_args=None, remove_think=None):
self.requests.append(
{
'messages': list(messages),
'funcs': list(funcs),
'remove_think': remove_think,
}
)
if len(self.requests) == 1:
return provider_message.Message(
role='assistant',
content='Let me check that.',
tool_calls=[
provider_message.ToolCall(
id='call-1',
type='function',
function=provider_message.FunctionCall(
name='exec',
arguments=json.dumps({'command': "python -c 'print(42)'"}),
),
)
],
)
return provider_message.Message(
role='assistant',
content='The result is 42.',
)
class RecordingStreamProvider:
"""Streaming provider that returns a tool-call on round 1 and plain text on round 2."""
def __init__(self):
self.stream_requests: list[dict] = []
def invoke_llm_stream(self, query, model, messages, funcs, extra_args=None, remove_think=None):
self.stream_requests.append(
{
'messages': list(messages),
'funcs': list(funcs),
'remove_think': remove_think,
}
)
async def _stream():
if len(self.stream_requests) == 1:
yield provider_message.MessageChunk(
role='assistant',
content='Let me check that.',
tool_calls=[
provider_message.ToolCall(
id='call-1',
type='function',
function=provider_message.FunctionCall(
name='exec',
arguments=json.dumps({'command': "python -c 'print(42)'"}),
),
)
],
is_final=True,
)
return
yield provider_message.MessageChunk(
role='assistant',
content='The result is 42.',
is_final=True,
)
return _stream()
def make_query() -> pipeline_query.Query:
adapter = AsyncMock()
adapter.is_stream_output_supported = AsyncMock(return_value=False)
return pipeline_query.Query.model_construct(
query_id='no-dup-query',
launcher_type=provider_session.LauncherTypes.PERSON,
launcher_id=12345,
sender_id=12345,
message_chain=[],
message_event=None,
adapter=adapter,
pipeline_uuid='pipeline-uuid',
bot_uuid='bot-uuid',
pipeline_config={
'ai': {
'runner': {'runner': 'local-agent'},
'local-agent': {'model': {'primary': 'test-model-uuid', 'fallbacks': []}, 'prompt': 'test-prompt'},
},
'output': {'misc': {'remove-think': False}},
},
prompt=SimpleNamespace(messages=[]),
messages=[],
user_message=provider_message.Message(
role='user',
content='What is the answer?',
),
use_funcs=[SimpleNamespace(name='exec')],
use_llm_model_uuid='test-model-uuid',
variables={},
)
def _make_app(provider) -> SimpleNamespace:
model = SimpleNamespace(
provider=provider,
model_entity=SimpleNamespace(
uuid='test-model-uuid',
name='test-model',
abilities=['func_call'],
extra_args={},
),
)
return SimpleNamespace(
logger=Mock(),
model_mgr=SimpleNamespace(get_model_by_uuid=AsyncMock(return_value=model)),
tool_mgr=SimpleNamespace(
execute_func_call=AsyncMock(
return_value={
'session_id': 'no-dup-query',
'backend': 'podman',
'status': 'completed',
'ok': True,
'exit_code': 0,
'stdout': '42',
'stderr': '',
'duration_ms': 10,
}
)
),
rag_mgr=SimpleNamespace(),
box_service=SimpleNamespace(
get_system_guidance=Mock(return_value='sandbox guidance'),
),
skill_mgr=SimpleNamespace(
get_skills_for_pipeline=AsyncMock(return_value=[]),
detect_skill_activation=AsyncMock(return_value=None),
build_activation_prompt=Mock(return_value=None),
),
)
@pytest.mark.asyncio
async def test_localagent_non_streaming_no_duplicate():
"""Non-streaming: round-2 content must not contain round-1 text."""
provider = RecordingProvider()
app = _make_app(provider)
runner = LocalAgentRunner(app, pipeline_config={})
query = make_query()
results = [message async for message in runner.run(query)]
# Expect: assistant (tool call) -> tool -> assistant (final answer)
assert [message.role for message in results] == ['assistant', 'tool', 'assistant']
final_message = results[-1]
assert final_message.content == 'The result is 42.'
assert 'Let me check that.' not in final_message.content
@pytest.mark.asyncio
async def test_localagent_streaming_no_duplicate():
"""Streaming: round-2 content must not be re-seeded with round-1 text.
Regression test for the bug where _StreamAccumulator was initialized with
initial_content=first_content, causing every subsequent round to repeat
the entire opening line.
"""
provider = RecordingStreamProvider()
app = _make_app(provider)
adapter = AsyncMock()
adapter.is_stream_output_supported = AsyncMock(return_value=True)
query = make_query()
query.adapter = adapter
runner = LocalAgentRunner(app, pipeline_config={})
results = [message async for message in runner.run(query)]
# All yielded messages should be MessageChunk in streaming mode
assert all(isinstance(message, provider_message.MessageChunk) for message in results)
# The last assistant chunk is the final answer for round 2
assistant_chunks = [m for m in results if m.role == 'assistant']
assert len(assistant_chunks) >= 2
final_chunk = assistant_chunks[-1]
assert final_chunk.content == 'The result is 42.'
assert 'Let me check that.' not in final_chunk.content