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Delete canvas_app.py and evaluation_service.py (#16614)
Follow on PR #13295
This commit is contained in:
Wang Qi
@@ -1,170 +0,0 @@
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#
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# Copyright 2026 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use it except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""Unit tests for token_usage tracking in EvaluationService._evaluate_single_case."""
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import pytest
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from types import SimpleNamespace
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from api.db.services.evaluation_service import EvaluationService
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@pytest.fixture
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def mock_dialog():
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"""Minimal dialog object for evaluation."""
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return SimpleNamespace(
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kb_ids=["kb-1"],
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prompt_config={"quote": True},
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llm_id="test-llm",
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tenant_id="tenant-1",
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)
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@pytest.fixture
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def minimal_case():
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"""Minimal test case for evaluation."""
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return {
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"id": "case-1",
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"question": "What is the capital of France?",
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"reference_answer": None,
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"relevant_chunk_ids": None,
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}
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@pytest.mark.p2
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def test_token_usage_structure_when_prompt_available(monkeypatch, mock_dialog, minimal_case):
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"""Verify token_usage dict has correct structure when ans contains 'prompt'."""
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captured_create = {}
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async def mock_async_chat(dialog, messages, stream, **kwargs):
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# Simulate async_chat yielding one result with full prompt
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yield {
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"answer": "Paris is the capital of France.",
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"reference": {"chunks": []},
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"prompt": "System instructions here.\n\nKnowledge: Some context.\n\nQuery: What is the capital of France?",
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}
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def capture_create(**kwargs):
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captured_create.update(kwargs)
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monkeypatch.setattr(
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"api.db.services.dialog_service.async_chat",
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mock_async_chat,
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)
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monkeypatch.setattr(
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"api.db.services.evaluation_service.EvaluationResult.create",
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capture_create,
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)
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result = EvaluationService._evaluate_single_case("run-1", minimal_case, mock_dialog)
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assert result is not None
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assert "token_usage" in captured_create
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token_usage = captured_create["token_usage"]
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assert "prompt_tokens" in token_usage
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assert "completion_tokens" in token_usage
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assert "total_tokens" in token_usage
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assert token_usage["total_tokens"] == token_usage["prompt_tokens"] + token_usage["completion_tokens"]
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assert token_usage["prompt_tokens"] > 0
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assert token_usage["completion_tokens"] > 0
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@pytest.mark.p2
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def test_token_usage_fallback_when_prompt_missing(monkeypatch, mock_dialog, minimal_case):
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"""Verify fallback to question-only count when ans has no 'prompt' key."""
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captured_create = {}
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async def mock_async_chat_no_prompt(dialog, messages, stream, **kwargs):
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# Simulate response without 'prompt' (e.g. async_chat_solo)
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yield {
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"answer": "Paris.",
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"reference": {"chunks": []},
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}
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def capture_create(**kwargs):
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captured_create.update(kwargs)
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monkeypatch.setattr(
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"api.db.services.dialog_service.async_chat",
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mock_async_chat_no_prompt,
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)
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monkeypatch.setattr(
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"api.db.services.evaluation_service.EvaluationResult.create",
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capture_create,
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)
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result = EvaluationService._evaluate_single_case("run-1", minimal_case, mock_dialog)
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assert result is not None
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assert "token_usage" in captured_create
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token_usage = captured_create["token_usage"]
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assert "prompt_tokens" in token_usage
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assert "completion_tokens" in token_usage
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assert "total_tokens" in token_usage
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assert token_usage["total_tokens"] == token_usage["prompt_tokens"] + token_usage["completion_tokens"]
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# With fallback, prompt_tokens should reflect question only (smaller than full prompt)
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assert token_usage["prompt_tokens"] >= 0
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assert token_usage["completion_tokens"] > 0
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@pytest.mark.p2
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def test_token_usage_no_answer_logs_warning(monkeypatch, mock_dialog, minimal_case, caplog):
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"""When chat yields no answers, we still record token_usage and log a warning."""
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captured_create = {}
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async def mock_async_chat_empty(dialog, messages, stream, **kwargs):
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# Simulate async_chat that yields no items at all
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if False:
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yield {}
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def capture_create(**kwargs):
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captured_create.update(kwargs)
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monkeypatch.setattr(
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"api.db.services.dialog_service.async_chat",
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mock_async_chat_empty,
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)
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monkeypatch.setattr(
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"api.db.services.evaluation_service.EvaluationResult.create",
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capture_create,
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)
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with caplog.at_level("WARNING"):
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result = EvaluationService._evaluate_single_case("run-1", minimal_case, mock_dialog)
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assert result is not None
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token_usage = captured_create["token_usage"]
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# No answer tokens in this case
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assert token_usage["completion_tokens"] == 0
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assert token_usage["prompt_tokens"] >= 0
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assert token_usage["total_tokens"] == token_usage["prompt_tokens"]
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assert any("produced no answer from chat" in msg for msg in caplog.messages)
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@pytest.mark.p2
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def test_compute_summary_metrics_aggregates_metrics():
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"""_compute_summary_metrics should average numeric metrics correctly."""
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results = [
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{"execution_time": 1.0, "metrics": {"precision": 0.5, "answer_length": 10}},
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{"execution_time": 3.0, "metrics": {"precision": 1.0, "answer_length": 20}},
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]
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summary = EvaluationService._compute_summary_metrics(results)
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assert summary["total_cases"] == 2
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assert summary["avg_execution_time"] == pytest.approx(2.0)
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assert summary["avg_precision"] == pytest.approx(0.75)
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assert summary["avg_answer_length"] == pytest.approx(15.0)
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