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

chapter3/agentic-rag-for-user-memory/demo_agent_logging.py

@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+"""Demonstration of agent tool logging with full content"""
+
+import os
+import json
+import logging
+from rich.console import Console
+from chunker import ConversationChunker, ConversationChunk, ConversationMessage
+from indexer import MemoryIndexer
+from agent import UserMemoryRAGAgent
+from config import Config
+
+# Set up logging to see agent logs
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(message)s'  # Simple format to show just the message
+)
+
+# Set dummy API key for demo
+os.environ["KIMI_API_KEY"] = "test_key"
+
+console = Console()
+
+
+def create_demo_conversation():
+    """Create a demo conversation with important information"""
+    messages = [
+        ConversationMessage(role="user", content="Hi, I'd like to open a checking account."),
+        ConversationMessage(role="assistant", content="I'd be happy to help you open a checking account. May I have your full name?"),
+        ConversationMessage(role="user", content="John Michael Smith"),
+        ConversationMessage(role="assistant", content="Thank you, Mr. Smith. What's your date of birth?"),
+        ConversationMessage(role="user", content="March 15, 1985"),
+        ConversationMessage(role="assistant", content="Perfect. And your Social Security Number?"),
+        ConversationMessage(role="user", content="123-45-6789"),
+        ConversationMessage(role="assistant", content="Thank you. What's your current address?"),
+        ConversationMessage(role="user", content="456 Oak Avenue, Springfield, IL 62701"),
+        ConversationMessage(role="assistant", content="Great. And a phone number where we can reach you?"),
+        ConversationMessage(role="user", content="555-0123"),
+        ConversationMessage(role="assistant", content="Excellent. I've set up your account. Your new account number is 4429853327."),
+        ConversationMessage(role="user", content="Great! What's the routing number?"),
+        ConversationMessage(role="assistant", content="The routing number for our Springfield branch is 071000013."),
+        ConversationMessage(role="user", content="Perfect, thank you for your help!"),
+        ConversationMessage(role="assistant", content="You're welcome! Your debit card will arrive in 7-10 business days."),
+    ]
+    
+    chunk = ConversationChunk(
+        chunk_id="demo_bank_account_001",
+        conversation_id="bank_conv_001",
+        test_id="demo_test",
+        chunk_index=0,
+        start_round=1,
+        end_round=8,
+        messages=messages,
+        metadata={
+            "business": "First National Bank",
+            "department": "New Accounts",
+            "date": "2024-11-15",
+            "agent": "Sarah Johnson"
+        }
+    )
+    
+    return [chunk]
+
+
+def demonstrate_agent_logging():
+    """Demonstrate how the agent logs tool calls and results"""
+    console.print("\n[bold cyan]Agent Tool Logging Demonstration[/bold cyan]")
+    console.print("="*80)
+    console.print("[yellow]Note: Watch for tool call parameters and full results in the logs[/yellow]\n")
+    
+    # Initialize configuration
+    config = Config.from_env()
+    config.agent.enable_reasoning = True  # Enable reasoning for more detailed logs
+    
+    # Initialize indexer and add demo conversation
+    indexer = MemoryIndexer(config.index)
+    chunks = create_demo_conversation()
+    
+    # Manually add chunks without pipeline indexing for demo
+    for chunk in chunks:
+        indexer.chunks[chunk.chunk_id] = chunk
+        indexer.chunk_texts[chunk.chunk_id] = chunk.to_text()
+    
+    # Initialize agent
+    agent = UserMemoryRAGAgent(indexer, config)
+    
+    # Test question that will trigger tool usage
+    test_question = "What is the customer's account number and when will they receive their debit card?"
+    
+    console.print(f"\n[bold]Test Question:[/bold] {test_question}")
+    console.print("\n[yellow]Agent processing (watch the tool logs below):[/yellow]\n")
+    console.print("="*80 + "\n")
+    
+    # This would normally call the LLM, but for demo we'll simulate tool calls
+    # In a real scenario, the agent.answer_question() method would be called
+    
+    # Simulate what the agent would do:
+    from tools import MemoryTools
+    tools = MemoryTools(indexer)
+    
+    # Simulate search_memory call (this is what the agent would do)
+    console.print("[dim]Agent would execute:[/dim]")
+    result1 = agent._execute_tool("search_memory", {
+        "query": "account number debit card",
+        "top_k": 3,
+        "filter_test_id": "demo_test"
+    })
+    
+    # The logging happens automatically in _execute_tool
+    
+    console.print("\n[dim]Agent might also execute:[/dim]")
+    result2 = agent._execute_tool("get_full_conversation", {
+        "conversation_id": "bank_conv_001",
+        "test_id": "demo_test"
+    })
+    
+    console.print("\n" + "="*80)
+    console.print("[green]Demonstration complete![/green]")
+    console.print("\nKey observations:")
+    console.print("  1. Tool call parameters are logged in full")
+    console.print("  2. Tool results show complete content (not truncated)")
+    console.print("  3. Each tool call is clearly separated with dividers")
+    console.print("  4. JSON formatting makes results easy to read")
+
+
+def main():
+    """Run the demonstration"""
+    console.print("\n[bold]Agentic RAG - Tool Logging Demonstration[/bold]")
+    console.print("This shows how tool calls and results are logged to the console\n")
+    
+    try:
+        demonstrate_agent_logging()
+        
+    except Exception as e:
+        console.print(f"\n[red]Error during demonstration: {e}[/red]")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    main()