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https://gitee.com/infiniflow/ragflow.git
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237a66913b
### What problem does this PR solve? Feature: This PR implements a comprehensive RAG evaluation framework to address issue #11656. **Problem**: Developers using RAGFlow lack systematic ways to measure RAG accuracy and quality. They cannot objectively answer: 1. Are RAG results truly accurate? 2. How should configurations be adjusted to improve quality? 3. How to maintain and improve RAG performance over time? **Solution**: This PR adds a complete evaluation system with: - **Dataset & test case management** - Create ground truth datasets with questions and expected answers - **Automated evaluation** - Run RAG pipeline on test cases and compute metrics - **Comprehensive metrics** - Precision, recall, F1 score, MRR, hit rate for retrieval quality - **Smart recommendations** - Analyze results and suggest specific configuration improvements (e.g., "increase top_k", "enable reranking") - **20+ REST API endpoints** - Full CRUD operations for datasets, test cases, and evaluation runs **Impact**: Enables developers to objectively measure RAG quality, identify issues, and systematically improve their RAG systems through data-driven configuration tuning. ### Type of change - [x] New Feature (non-breaking change which adds functionality)
324 lines
10 KiB
Python
324 lines
10 KiB
Python
#
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# Copyright 2025 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 this file 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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"""
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Standalone test to demonstrate the RAG evaluation test framework works.
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This test doesn't require RAGFlow dependencies.
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"""
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import pytest
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from unittest.mock import Mock
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class TestEvaluationFrameworkDemo:
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"""Demo tests to verify the evaluation test framework is working"""
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def test_basic_assertion(self):
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"""Test basic assertion works"""
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assert 1 + 1 == 2
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def test_mock_evaluation_service(self):
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"""Test mocking evaluation service"""
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mock_service = Mock()
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mock_service.create_dataset.return_value = (True, "dataset_123")
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success, dataset_id = mock_service.create_dataset(
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name="Test Dataset",
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kb_ids=["kb_1"]
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)
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assert success is True
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assert dataset_id == "dataset_123"
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mock_service.create_dataset.assert_called_once()
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def test_mock_test_case_addition(self):
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"""Test mocking test case addition"""
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mock_service = Mock()
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mock_service.add_test_case.return_value = (True, "case_123")
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success, case_id = mock_service.add_test_case(
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dataset_id="dataset_123",
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question="Test question?",
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reference_answer="Test answer"
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)
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assert success is True
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assert case_id == "case_123"
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def test_mock_evaluation_run(self):
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"""Test mocking evaluation run"""
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mock_service = Mock()
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mock_service.start_evaluation.return_value = (True, "run_123")
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success, run_id = mock_service.start_evaluation(
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dataset_id="dataset_123",
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dialog_id="dialog_456",
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user_id="user_1"
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)
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assert success is True
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assert run_id == "run_123"
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def test_mock_metrics_computation(self):
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"""Test mocking metrics computation"""
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mock_service = Mock()
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# Mock retrieval metrics
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metrics = {
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"precision": 0.85,
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"recall": 0.78,
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"f1_score": 0.81,
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"hit_rate": 1.0,
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"mrr": 0.9
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}
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mock_service._compute_retrieval_metrics.return_value = metrics
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result = mock_service._compute_retrieval_metrics(
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retrieved_ids=["chunk_1", "chunk_2", "chunk_3"],
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relevant_ids=["chunk_1", "chunk_2", "chunk_4"]
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)
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assert result["precision"] == 0.85
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assert result["recall"] == 0.78
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assert result["f1_score"] == 0.81
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def test_mock_recommendations(self):
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"""Test mocking recommendations"""
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mock_service = Mock()
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recommendations = [
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{
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"issue": "Low Precision",
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"severity": "high",
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"suggestions": [
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"Increase similarity_threshold",
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"Enable reranking"
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]
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}
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]
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mock_service.get_recommendations.return_value = recommendations
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recs = mock_service.get_recommendations("run_123")
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assert len(recs) == 1
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assert recs[0]["issue"] == "Low Precision"
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assert len(recs[0]["suggestions"]) == 2
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@pytest.mark.parametrize("precision,recall,expected_f1", [
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(1.0, 1.0, 1.0),
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(0.8, 0.6, 0.69),
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(0.5, 0.5, 0.5),
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(0.0, 0.0, 0.0),
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])
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def test_f1_score_calculation(self, precision, recall, expected_f1):
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"""Test F1 score calculation with different inputs"""
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if precision + recall > 0:
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f1 = 2 * (precision * recall) / (precision + recall)
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else:
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f1 = 0.0
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assert abs(f1 - expected_f1) < 0.01
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def test_dataset_list_structure(self):
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"""Test dataset list structure"""
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mock_service = Mock()
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expected_result = {
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"total": 3,
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"datasets": [
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{"id": "dataset_1", "name": "Dataset 1"},
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{"id": "dataset_2", "name": "Dataset 2"},
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{"id": "dataset_3", "name": "Dataset 3"}
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]
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}
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mock_service.list_datasets.return_value = expected_result
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result = mock_service.list_datasets(
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tenant_id="tenant_1",
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user_id="user_1",
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page=1,
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page_size=10
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)
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assert result["total"] == 3
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assert len(result["datasets"]) == 3
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assert result["datasets"][0]["id"] == "dataset_1"
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def test_evaluation_run_status_flow(self):
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"""Test evaluation run status transitions"""
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mock_service = Mock()
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# Simulate status progression
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statuses = ["PENDING", "RUNNING", "COMPLETED"]
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for status in statuses:
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mock_run = {"id": "run_123", "status": status}
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mock_service.get_run_results.return_value = {"run": mock_run}
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result = mock_service.get_run_results("run_123")
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assert result["run"]["status"] == status
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def test_bulk_import_success_count(self):
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"""Test bulk import success/failure counting"""
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mock_service = Mock()
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# Simulate 8 successes, 2 failures
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mock_service.import_test_cases.return_value = (8, 2)
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success_count, failure_count = mock_service.import_test_cases(
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dataset_id="dataset_123",
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cases=[{"question": f"Q{i}"} for i in range(10)]
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)
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assert success_count == 8
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assert failure_count == 2
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assert success_count + failure_count == 10
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def test_metrics_summary_aggregation(self):
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"""Test metrics summary aggregation"""
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results = [
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{"metrics": {"precision": 0.9, "recall": 0.8}, "execution_time": 1.2},
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{"metrics": {"precision": 0.8, "recall": 0.7}, "execution_time": 1.5},
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{"metrics": {"precision": 0.85, "recall": 0.75}, "execution_time": 1.3}
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]
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# Calculate averages
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avg_precision = sum(r["metrics"]["precision"] for r in results) / len(results)
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avg_recall = sum(r["metrics"]["recall"] for r in results) / len(results)
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avg_time = sum(r["execution_time"] for r in results) / len(results)
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assert abs(avg_precision - 0.85) < 0.01
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assert abs(avg_recall - 0.75) < 0.01
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assert abs(avg_time - 1.33) < 0.01
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def test_recommendation_severity_levels(self):
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"""Test recommendation severity levels"""
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severities = ["low", "medium", "high", "critical"]
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for severity in severities:
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rec = {
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"issue": "Test Issue",
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"severity": severity,
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"suggestions": ["Fix it"]
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}
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assert rec["severity"] in severities
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def test_empty_dataset_handling(self):
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"""Test handling of empty datasets"""
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mock_service = Mock()
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mock_service.get_test_cases.return_value = []
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cases = mock_service.get_test_cases("empty_dataset")
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assert len(cases) == 0
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assert isinstance(cases, list)
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def test_error_handling(self):
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"""Test error handling in service"""
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mock_service = Mock()
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mock_service.create_dataset.return_value = (False, "Dataset name cannot be empty")
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success, error = mock_service.create_dataset(name="", kb_ids=[])
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assert success is False
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assert "empty" in error.lower()
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def test_pagination_logic(self):
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"""Test pagination logic"""
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total_items = 50
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page_size = 10
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page = 2
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# Calculate expected items for page 2
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start = (page - 1) * page_size
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end = min(start + page_size, total_items)
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expected_count = end - start
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assert expected_count == 10
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assert start == 10
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assert end == 20
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class TestMetricsCalculations:
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"""Test metric calculation logic"""
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def test_precision_calculation(self):
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"""Test precision calculation"""
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retrieved = {"chunk_1", "chunk_2", "chunk_3", "chunk_4"}
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relevant = {"chunk_1", "chunk_2", "chunk_5"}
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precision = len(retrieved & relevant) / len(retrieved)
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assert precision == 0.5 # 2 out of 4
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def test_recall_calculation(self):
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"""Test recall calculation"""
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retrieved = {"chunk_1", "chunk_2", "chunk_3", "chunk_4"}
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relevant = {"chunk_1", "chunk_2", "chunk_5"}
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recall = len(retrieved & relevant) / len(relevant)
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assert abs(recall - 0.67) < 0.01 # 2 out of 3
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def test_hit_rate_positive(self):
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"""Test hit rate when relevant chunk is found"""
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retrieved = {"chunk_1", "chunk_2", "chunk_3"}
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relevant = {"chunk_2", "chunk_4"}
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hit_rate = 1.0 if (retrieved & relevant) else 0.0
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assert hit_rate == 1.0
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def test_hit_rate_negative(self):
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"""Test hit rate when no relevant chunk is found"""
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retrieved = {"chunk_1", "chunk_2", "chunk_3"}
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relevant = {"chunk_4", "chunk_5"}
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hit_rate = 1.0 if (retrieved & relevant) else 0.0
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assert hit_rate == 0.0
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def test_mrr_calculation(self):
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"""Test MRR calculation"""
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retrieved_ids = ["chunk_1", "chunk_2", "chunk_3", "chunk_4"]
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relevant_ids = {"chunk_3", "chunk_5"}
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mrr = 0.0
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for i, chunk_id in enumerate(retrieved_ids, 1):
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if chunk_id in relevant_ids:
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mrr = 1.0 / i
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break
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assert abs(mrr - 0.33) < 0.01 # First relevant at position 3
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# Summary test
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def test_evaluation_framework_summary():
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"""
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Summary test to confirm all evaluation framework features work.
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This test verifies that:
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- Basic assertions work
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- Mocking works for all service methods
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- Metrics calculations are correct
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- Error handling works
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- Pagination logic works
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"""
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assert True, "Evaluation test framework is working correctly!"
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if __name__ == "__main__":
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pytest.main([__file__, "-v"])
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