Files
AIRegulation-DocAnalysis/backend/tests/observability/test_tracked_client.py
T
wangweiandCopilot 7adc050968 feat: record streaming token usage in TrackedLLMClient.stream_chat
Implement manual generator driving using next()/StopIteration to capture
the return value (trailing usage dict) from inner stream_chat() implementations,
enabling token tracking for streaming LLM calls.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-07-23 13:42:33 +08:00

106 lines
4.1 KiB
Python

"""Unit tests for TrackedLLMClient — verifies transparent delegation + recording."""
from __future__ import annotations
from unittest.mock import MagicMock
from app.services.llm.base_client import LLMConfig, LLMProvider, LLMResponse
from app.services.llm.tracked_client import TrackedLLMClient
from app.shared.model_usage_tracker import ModelUsageTracker
def _make_inner(model: str = "deepseek-v4-flash") -> MagicMock:
"""Build a MagicMock standing in for a concrete BaseLLMClient subclass."""
# Use MagicMock to avoid requiring a real LLM provider implementation (e.g., DeepseekClient);
# tests focus on TrackedLLMClient's delegation and recording behavior, not provider logic.
inner = MagicMock()
inner.config = LLMConfig(
provider=LLMProvider.DEEPSEEK, model=model, api_key="test-key", base_url="http://example.test/v1",
)
return inner
def test_chat_delegates_and_returns_unchanged_response():
"""chat() must return exactly what the wrapped client returned."""
inner = _make_inner()
expected = LLMResponse(content="hello", model="deepseek-v4-flash", usage={"total_tokens": 12})
inner.chat.return_value = expected
tracker = ModelUsageTracker()
tracked = TrackedLLMClient(inner, tracker)
result = tracked.chat([{"role": "user", "content": "hi"}])
assert result is expected
inner.chat.assert_called_once_with([{"role": "user", "content": "hi"}], None, None, None)
def test_chat_records_success_and_tokens():
"""A successful chat() call must be recorded under 'deepseek:deepseek-v4-flash'."""
inner = _make_inner()
inner.chat.return_value = LLMResponse(content="hi", model="deepseek-v4-flash", usage={"total_tokens": 42})
tracker = ModelUsageTracker()
TrackedLLMClient(inner, tracker).chat([{"role": "user", "content": "hi"}])
entry = tracker.get("deepseek", "deepseek-v4-flash")
assert entry is not None
assert entry.total_tokens == 42
assert entry.status == "ok"
def test_chat_records_error_from_response():
"""A chat() call that returns an error-carrying LLMResponse is recorded as a failure."""
inner = _make_inner()
inner.chat.return_value = LLMResponse(content="", model="deepseek-v4-flash", error="API error: 500")
tracker = ModelUsageTracker()
TrackedLLMClient(inner, tracker).chat([{"role": "user", "content": "hi"}])
entry = tracker.get("deepseek", "deepseek-v4-flash")
assert entry.status == "error"
assert entry.last_error == "API error: 500"
def test_getattr_forwards_to_inner_client():
"""Attributes not defined on TrackedLLMClient must forward to the wrapped client."""
inner = _make_inner()
inner.get_available_models.return_value = ["deepseek-v4-flash"]
tracked = TrackedLLMClient(inner, ModelUsageTracker())
assert tracked.get_available_models() == ["deepseek-v4-flash"]
assert tracked.config is inner.config
def test_stream_chat_records_call_without_token_usage():
"""stream_chat() must record a call (latency/success) but not fabricate token counts."""
inner = _make_inner()
inner.stream_chat.return_value = iter(["chunk-1", "chunk-2"])
tracker = ModelUsageTracker()
chunks = list(TrackedLLMClient(inner, tracker).stream_chat([{"role": "user", "content": "hi"}]))
assert chunks == ["chunk-1", "chunk-2"]
entry = tracker.get("deepseek", "deepseek-v4-flash")
assert entry.call_count_ok == 1
assert entry.total_tokens == 0
def test_stream_chat_records_usage_from_generator_return_value():
"""stream_chat() must forward the inner generator's returned usage dict to record()."""
inner = _make_inner()
def fake_stream(*args, **kwargs):
yield "chunk-1"
yield "chunk-2"
return {"prompt_tokens": 6, "completion_tokens": 2, "total_tokens": 8}
inner.stream_chat.side_effect = fake_stream
tracker = ModelUsageTracker()
chunks = list(TrackedLLMClient(inner, tracker).stream_chat([{"role": "user", "content": "hi"}]))
assert chunks == ["chunk-1", "chunk-2"]
entry = tracker.get("deepseek", "deepseek-v4-flash")
assert entry.total_tokens == 8
assert entry.call_count_ok == 1