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

chapter3/contextual-retrieval/quickstart.py

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+#!/usr/bin/env python3
+"""Quick start script to test the Contextual Retrieval System
+
+This script provides a quick way to test contextual retrieval
+with a sample document and see the improvements.
+"""
+
+import logging
+from pathlib import Path
+from config import Config
+from contextual_chunking import ContextualChunker
+from contextual_tools import ContextualKnowledgeBaseTools
+
+# Simple logging for quickstart
+logging.basicConfig(level=logging.INFO, format='%(message)s')
+logger = logging.getLogger(__name__)
+
+
+def main():
+    """Quick demonstration of contextual retrieval"""
+    
+    print("\n" + "="*60)
+    print("CONTEXTUAL RETRIEVAL - QUICK START")
+    print("="*60 + "\n")
+    
+    # Sample document about multiple companies
+    document = """
+    2023 Technology Sector Report
+    
+    Apple Inc. Performance:
+    Apple reported exceptional results in 2023. The company's revenue reached 
+    $394 billion, with iPhone sales contributing 52% of total revenue. The 
+    services division showed strong growth of 16% year-over-year. Tim Cook 
+    emphasized the company's commitment to innovation and sustainability.
+    
+    Microsoft Corporation Update:
+    Microsoft achieved record cloud revenue in 2023. Azure revenue grew by 27% 
+    as enterprises accelerated digital transformation. The company's total 
+    revenue was $211 billion. CEO Satya Nadella highlighted AI integration 
+    across all product lines as a key strategic priority.
+    
+    Google (Alphabet) Highlights:
+    Google's parent company Alphabet reported $283 billion in revenue for 2023. 
+    Search advertising remained the largest revenue driver at $175 billion. 
+    The company increased AI research spending by 30% to maintain competitive 
+    advantage. YouTube advertising revenue exceeded $40 billion.
+    
+    Market Analysis:
+    The technology sector showed resilience despite economic headwinds. Companies 
+    that invested heavily in AI and cloud infrastructure outperformed the market. 
+    The sector's average growth rate was 12%, with cloud services growing at 25% 
+    and traditional hardware declining by 3%.
+    """
+    
+    print("Step 1: Initializing systems...")
+    config = Config.from_env()
+    
+    # Create both contextual and non-contextual systems
+    contextual_chunker = ContextualChunker(use_contextual=True)
+    non_contextual_chunker = ContextualChunker(use_contextual=False)
+    
+    contextual_kb = ContextualKnowledgeBaseTools(use_contextual=True)
+    non_contextual_kb = ContextualKnowledgeBaseTools(use_contextual=False)
+    
+    print("\nStep 2: Processing document...")
+    print("-" * 40)
+    
+    # Process with contextual system
+    print("Creating contextual chunks...")
+    contextual_chunks = contextual_chunker.chunk_document(
+        text=document,
+        doc_id="tech_report_2023"
+    )
+    contextual_kb.index_contextual_chunks(contextual_chunks)
+    print(f"✓ Created {len(contextual_chunks)} contextual chunks")
+    
+    # Process with non-contextual system
+    print("Creating non-contextual chunks...")
+    non_contextual_chunks = non_contextual_chunker.chunk_document(
+        text=document,
+        doc_id="tech_report_2023"
+    )
+    non_contextual_kb.index_contextual_chunks(non_contextual_chunks)
+    print(f"✓ Created {len(non_contextual_chunks)} non-contextual chunks")
+    
+    # Show example contextual chunk
+    if contextual_chunks:
+        print("\nExample Contextual Chunk:")
+        print("-" * 40)
+        chunk = contextual_chunks[0]
+        print(f"Original text: {chunk.text[:100]}...")
+        print(f"Added context: {chunk.context}")
+    
+    print("\n" + "="*60)
+    print("Step 3: Testing Search Queries")
+    print("="*60)
+    
+    # Test queries
+    queries = [
+        "What was the company's revenue?",
+        "Which company emphasized AI?",
+        "What was the growth rate?"
+    ]
+    
+    for query in queries:
+        print(f"\nQuery: '{query}'")
+        print("-" * 40)
+        
+        # Contextual search
+        contextual_results = contextual_kb.contextual_search(query, top_k=1)
+        
+        # Non-contextual search
+        non_contextual_results = non_contextual_kb.contextual_search(query, top_k=1)
+        
+        print("\nContextual Result:")
+        if contextual_results:
+            result = contextual_results[0]
+            print(f"  Score: {result.score:.4f}")
+            if result.context_text:
+                print(f"  Context: {result.context_text[:80]}...")
+            print(f"  Match: {result.text[:100]}...")
+        else:
+            print("  No results")
+        
+        print("\nNon-Contextual Result:")
+        if non_contextual_results:
+            result = non_contextual_results[0]
+            print(f"  Score: {result.score:.4f}")
+            print(f"  Match: {result.text[:100]}...")
+        else:
+            print("  No results")
+        
+        # Compare scores
+        if contextual_results and non_contextual_results:
+            improvement = ((contextual_results[0].score - non_contextual_results[0].score) 
+                         / non_contextual_results[0].score * 100)
+            print(f"\n📊 Improvement: {improvement:+.1f}%")
+    
+    print("\n" + "="*60)
+    print("SUMMARY")
+    print("="*60)
+    
+    # Get statistics
+    stats = contextual_chunker.get_statistics()
+    
+    print(f"\nContextual Chunking Statistics:")
+    print(f"  Chunks processed: {stats['total_chunks']}")
+    print(f"  Context tokens used: {stats['total_context_tokens']}")
+    print(f"  Estimated cost: ${stats['estimated_cost']:.4f}")
+    
+    print("\nKey Insights:")
+    print("✓ Contextual chunks preserve company-specific information")
+    print("✓ Ambiguous queries ('the company') are resolved correctly")
+    print("✓ Search accuracy improves significantly with context")
+    
+    print("\n" + "="*60)
+    print("Quick start complete! Try with your own documents:")
+    print("  python contextual_main.py --mode index --document your_file.txt")
+    print("="*60 + "\n")
+
+
+if __name__ == "__main__":
+    main()