ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter3/contextual-retrieval-for-user-memory/test_contextual_system.py

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+#!/usr/bin/env python3
+"""Test script for the Contextual Retrieval + Advanced Memory Cards System"""
+
+import logging
+from config import Config
+from contextual_evaluator import ContextualMemoryEvaluator
+from contextual_indexer import ContextualMemoryIndexer
+from contextual_agent import ContextualUserMemoryAgent
+from advanced_memory_manager import create_sample_cards
+from chunker import ConversationChunk, ConversationMessage
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+
+def test_dual_memory_system():
+    """Test the dual memory system with a sample scenario"""
+    
+    print("\n" + "="*60)
+    print("Testing Contextual Retrieval + Advanced Memory Cards")
+    print("="*60)
+    
+    # Initialize components
+    config = Config.from_env()
+    user_id = "test_user_contextual"
+    
+    # Create indexer with contextual chunking
+    print("\n1. Initializing Contextual Memory Indexer...")
+    indexer = ContextualMemoryIndexer(
+        user_id=user_id,
+        use_contextual=True
+    )
+    print(f"   ✓ Indexer initialized")
+    
+    # Add sample memory cards
+    print("\n2. Adding Advanced Memory Cards...")
+    sample_cards = create_sample_cards()
+    for card in sample_cards:
+        indexer.memory_manager.add_card(card)
+    print(f"   ✓ Added {len(sample_cards)} memory cards")
+    
+    # Create sample conversation chunks
+    print("\n3. Creating Sample Conversation Chunks...")
+    chunks = []
+    
+    # Conversation about travel
+    messages1 = [
+        ConversationMessage("user", "我想订一张去东京的机票", 1),
+        ConversationMessage("assistant", "好的，请问您什么时候出发？", 2),
+        ConversationMessage("user", "1月25日出发，2月1日返回", 3),
+        ConversationMessage("assistant", "让我为您查询1月25日到2月1日的东京往返机票", 4),
+    ]
+    
+    chunk1 = ConversationChunk(
+        chunk_id="test_chunk_001",
+        conversation_id="test_conv",
+        test_id="test",
+        chunk_index=0,
+        start_round=1,
+        end_round=2,
+        messages=messages1,
+        metadata={"topic": "travel"}
+    )
+    chunks.append(chunk1)
+    
+    # Conversation about passport
+    messages2 = [
+        ConversationMessage("user", "我的护照快过期了，什么时候需要续签？", 5),
+        ConversationMessage("assistant", "您的护照将于2025年2月18日过期，建议提前3-6个月办理续签", 6),
+        ConversationMessage("user", "好的，我会尽快去办理", 7),
+        ConversationMessage("assistant", "建议您在出国前确保护照有效期至少6个月", 8),
+    ]
+    
+    chunk2 = ConversationChunk(
+        chunk_id="test_chunk_002",
+        conversation_id="test_conv",
+        test_id="test",
+        chunk_index=1,
+        start_round=3,
+        end_round=4,
+        messages=messages2,
+        metadata={"topic": "passport"}
+    )
+    chunks.append(chunk2)
+    
+    print(f"   ✓ Created {len(chunks)} conversation chunks")
+    
+    # Process with contextual chunking
+    print("\n4. Processing with Contextual Chunking...")
+    result = indexer.process_conversation_history(
+        chunks=chunks,
+        conversation_id="test_conv",
+        generate_summary_cards=False
+    )
+    print(f"   ✓ Generated {result['contextual_chunks']} contextual chunks")
+    print(f"   ✓ Processing time: {result['processing_time']:.2f}s")
+    
+    # Initialize agent
+    print("\n5. Initializing Contextual Agent...")
+    agent = ContextualUserMemoryAgent(
+        indexer=indexer,
+        config=config
+    )
+    print(f"   ✓ Agent initialized with {sum(len(cards) for cards in indexer.memory_manager.categories.values())} memory cards")
+    
+    # Test queries
+    print("\n6. Testing Queries...")
+    test_queries = [
+        ("我的护照什么时候过期？", "Should find passport expiration date from memory cards"),
+        ("我一月份的东京之行需要准备什么？", "Should combine travel and passport info"),
+        ("我的银行账户信息是什么？", "Should find bank account from memory cards"),
+    ]
+    
+    for i, (query, expected) in enumerate(test_queries, 1):
+        print(f"\n   Query {i}: {query}")
+        print(f"   Expected: {expected}")
+        
+        trajectory = agent.answer_question(
+            question=query,
+            test_id=f"test_{i}",
+            stream=False
+        )
+        
+        if trajectory.final_answer:
+            print(f"   Answer: {trajectory.final_answer[:200]}...")
+            print(f"   ✓ Memory cards used: {len(trajectory.memory_cards_used)}")
+            print(f"   ✓ Chunks retrieved: {len(trajectory.chunks_retrieved)}")
+        else:
+            print(f"   ✗ No answer generated")
+    
+    # Show statistics
+    print("\n7. System Statistics:")
+    stats = indexer.get_statistics()
+    print(f"   • Chunks indexed: {stats.get('chunks_indexed', 0)}")
+    print(f"   • Memory cards: {stats.get('memory_cards', 0)}")
+    
+    if 'chunker_stats' in stats:
+        cs = stats['chunker_stats']
+        print(f"   • Context generation tokens: {cs.get('total_context_tokens', 0)}")
+        print(f"   • Estimated cost: ${cs.get('estimated_cost', 0):.3f}")
+    
+    print("\n" + "="*60)
+    print("Test Complete! The dual memory system is working correctly.")
+    print("="*60)
+
+
+def test_evaluation_system():
+    """Test the evaluation system with Layer 1 test cases"""
+    
+    print("\n" + "="*60)
+    print("Testing Evaluation System")
+    print("="*60)
+    
+    config = Config.from_env()
+    evaluator = ContextualMemoryEvaluator(config)
+    
+    # Load Layer 1 test cases
+    print("\n1. Loading Test Cases...")
+    test_cases = evaluator.load_test_cases("layer1")
+    print(f"   ✓ Loaded {len(test_cases)} test cases")
+    
+    if test_cases:
+        # Test the first case
+        first_test = test_cases[0]
+        print(f"\n2. Testing First Case: {first_test}")
+        
+        test_case = evaluator.test_cases[first_test]
+        print(f"   Title: {test_case.title}")
+        print(f"   Category: {test_case.category}")
+        print(f"   Conversations: {len(test_case.conversation_histories)}")
+        
+        # Run evaluation
+        print("\n3. Running Evaluation...")
+        try:
+            result = evaluator.evaluate_test_case(first_test)
+            print(f"   ✓ Evaluation complete")
+            print(f"   Success: {result.success}")
+            print(f"   Iterations: {result.iterations}")
+            print(f"   Tool calls: {result.tool_calls}")
+            print(f"   Processing time: {result.processing_time:.2f}s")
+            
+            if result.agent_answer:
+                print(f"   Answer preview: {result.agent_answer[:100]}...")
+        except Exception as e:
+            print(f"   ✗ Evaluation failed: {e}")
+    
+    print("\n" + "="*60)
+    print("Evaluation System Test Complete!")
+    print("="*60)
+
+
+if __name__ == "__main__":
+    import sys
+    
+    if len(sys.argv) > 1 and sys.argv[1] == "eval":
+        test_evaluation_system()
+    else:
+        test_dual_memory_system()