#!/usr/bin/env python3 """Main entry point for Contextual Retrieval + Advanced Memory Cards System This demonstrates the dual-layer memory system combining: 1. Contextual chunking for conversation history 2. Advanced JSON cards for structured facts """ import argparse import json import logging import sys from pathlib import Path from typing import Optional, List from datetime import datetime from rich.console import Console from rich.prompt import Prompt, Confirm from rich.table import Table from rich.panel import Panel from rich.progress import Progress, SpinnerColumn, TextColumn from config import Config from contextual_evaluator import ContextualMemoryEvaluator from contextual_indexer import ContextualMemoryIndexer from contextual_agent import ContextualUserMemoryAgent from advanced_memory_manager import AdvancedMemoryCard, create_sample_cards from chunker import ConversationChunker # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) logger = logging.getLogger(__name__) # Rich console for better output console = Console() class InteractiveContextualRAG: """Interactive interface for the contextual RAG system""" def __init__(self, config: Optional[Config] = None): """Initialize the interactive system""" self.config = config or Config.from_env() self.evaluator = ContextualMemoryEvaluator(self.config) self.current_user = "demo_user" self.indexer = None self.agent = None def run(self): """Run the interactive session""" console.print(Panel.fit( "[bold cyan]Contextual RAG + Advanced Memory Cards System[/bold cyan]\n" "双层记忆系统：上下文感知检索 + 结构化记忆卡片\n" "[dim]LLM Judge enabled for automatic evaluation[/dim]", border_style="cyan" )) while True: self.show_menu() choice = Prompt.ask( "Select an option", choices=["1", "2", "3", "4", "5", "6", "7", "8", "0"], default="1" ) if choice == "1": self.demo_mode() elif choice == "2": self.load_and_index_conversations() elif choice == "3": self.manage_memory_cards() elif choice == "4": self.test_query() elif choice == "5": self.evaluate_test_cases() elif choice == "6": self.evaluate_specific_test_case() elif choice == "7": self.show_statistics() elif choice == "8": self.configure_settings() elif choice == "0": if Confirm.ask("Are you sure you want to exit?"): console.print("[yellow]Goodbye![/yellow]") break def show_menu(self): """Display the main menu""" console.print("\n[bold]Main Menu:[/bold]") console.print("1. 🚀 Demo Mode (Quick Start)") console.print("2. 📚 Load & Index Conversations") console.print("3. 🎴 Manage Memory Cards") console.print("4. 🔍 Test Query") console.print("5. 📊 Evaluate All Test Cases (by Category) [LLM Judge]") console.print("6. 🎯 Evaluate Specific Test Case [LLM Judge]") console.print("7. 📈 Show Statistics") console.print("8. ⚙️ Configure Settings") console.print("0. Exit") def demo_mode(self): """Run a quick demo with sample data""" console.print("\n[cyan]Demo Mode - Quick Start[/cyan]") # Initialize components user_id = "demo_user" self.indexer = ContextualMemoryIndexer( user_id=user_id, use_contextual=True ) # Create sample memory cards console.print("\n[yellow]Creating sample memory cards...[/yellow]") sample_cards = create_sample_cards() for card in sample_cards: self.indexer.memory_manager.add_card(card) console.print(f"[green]✓ Added {len(sample_cards)} memory cards[/green]") # Create sample conversation chunks console.print("\n[yellow]Creating sample conversation chunks...[/yellow]") sample_chunks = self._create_sample_chunks() # Process with contextual chunking with Progress( SpinnerColumn(), TextColumn("[progress.description]{task.description}"), console=console ) as progress: task = progress.add_task("Processing conversations...", total=None) result = self.indexer.process_conversation_history( chunks=sample_chunks, conversation_id="demo_conv", generate_summary_cards=False ) progress.update(task, completed=True) console.print(f"[green]✓ Indexed {result['contextual_chunks']} contextual chunks[/green]") # Initialize agent self.agent = ContextualUserMemoryAgent( indexer=self.indexer, config=self.config ) # Show memory status console.print("\n[bold]Memory System Status:[/bold]") console.print(f" Memory Cards: {sum(len(cards) for cards in self.indexer.memory_manager.categories.values())}") console.print(f" Contextual Chunks: {len(self.indexer.contextual_chunks)}") # Test queries test_queries = [ "我的护照什么时候过期？", "我一月份的东京之行需要准备什么？", "我的医疗信息有哪些？" ] console.print("\n[bold]Test Queries:[/bold]") for i, query in enumerate(test_queries, 1): console.print(f"\n[cyan]Query {i}:[/cyan] {query}") if Confirm.ask("Run this query?", default=True): trajectory = self.agent.answer_question( question=query, test_id=f"demo_{i}", stream=False ) console.print(Panel( trajectory.final_answer or "No answer generated", title="Answer", border_style="green" )) if trajectory.memory_cards_used: console.print(f" Memory cards used: {', '.join(trajectory.memory_cards_used)}") if trajectory.chunks_retrieved: console.print(f" Chunks retrieved: {len(trajectory.chunks_retrieved)}") def _create_sample_chunks(self): """Create sample conversation chunks for demo""" from chunker import ConversationChunk, ConversationMessage chunks = [] # Sample conversation about travel messages = [ 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), ] chunk = ConversationChunk( chunk_id="demo_chunk_001", conversation_id="demo_conv", test_id="demo", chunk_index=0, start_round=1, end_round=2, messages=messages, metadata={"topic": "travel"} ) chunks.append(chunk) # Sample 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="demo_chunk_002", conversation_id="demo_conv", test_id="demo", chunk_index=1, start_round=3, end_round=4, messages=messages2, metadata={"topic": "passport"} ) chunks.append(chunk2) return chunks def load_and_index_conversations(self): """Load and index conversation histories""" console.print("\n[cyan]Load & Index Conversations[/cyan]") # Get user ID user_id = Prompt.ask("Enter user ID", default=self.current_user) self.current_user = user_id # Initialize indexer self.indexer = ContextualMemoryIndexer( user_id=user_id, use_contextual=Confirm.ask("Enable contextual chunking?", default=True) ) # Load conversation files conv_dir = Prompt.ask( "Enter conversation directory path", default="../../week2/user-memory-evaluation/conversations" ) conv_path = Path(conv_dir) if not conv_path.exists(): console.print(f"[red]Directory not found: {conv_path}[/red]") return # Process conversation files json_files = list(conv_path.glob("*.json")) console.print(f"Found {len(json_files)} conversation files") if not json_files: console.print("[yellow]No JSON files found[/yellow]") return # Process each file chunker = ConversationChunker(self.config.chunking) all_chunks = [] with Progress(console=console) as progress: task = progress.add_task("Processing files...", total=len(json_files)) for json_file in json_files: try: with open(json_file, 'r', encoding='utf-8') as f: data = json.load(f) # Extract conversations conversations = data if isinstance(data, dict) else {"conv": data} for conv_id, messages in conversations.items(): chunks = chunker.chunk_conversation( messages=messages, conversation_id=conv_id, test_id=json_file.stem ) all_chunks.extend(chunks) progress.advance(task) except Exception as e: console.print(f"[red]Error processing {json_file}: {e}[/red]") console.print(f"[green]Created {len(all_chunks)} chunks[/green]") # Index with contextual processing if all_chunks: result = self.indexer.process_conversation_history( chunks=all_chunks, conversation_id="batch_index", generate_summary_cards=Confirm.ask("Generate summary cards?", default=True) ) console.print(f"[green]✓ Indexed {result['contextual_chunks']} contextual chunks[/green]") console.print(f"[green]✓ Total memory cards: {result['memory_cards_after']}[/green]") def manage_memory_cards(self): """Manage advanced memory cards""" if not self.indexer: console.print("[yellow]Please initialize the system first (option 1 or 2)[/yellow]") return console.print("\n[cyan]Memory Card Management[/cyan]") # Show current cards stats = self.indexer.memory_manager.get_statistics() console.print(f"\nCurrent cards: {stats['total_cards']}") for category, info in stats['categories'].items(): console.print(f" {category}: {info['count']} cards") # Options console.print("\n1. View all cards") console.print("2. Add new card") console.print("3. Search cards") console.print("4. Delete card") console.print("5. Back") choice = Prompt.ask("Select option", choices=["1", "2", "3", "4", "5"]) if choice == "1": # View all cards context = self.indexer.memory_manager.get_context_string() console.print(Panel(context, title="Memory Cards", border_style="cyan")) elif choice == "2": # Add new card category = Prompt.ask("Category") card_key = Prompt.ask("Card key") backstory = Prompt.ask("Backstory") person = Prompt.ask("Person", default="User") relationship = Prompt.ask("Relationship", default="primary") # Get additional data fields data = {} while True: field = Prompt.ask("Add data field (empty to finish)") if not field: break value = Prompt.ask(f"Value for {field}") data[field] = value # Create and add card card = AdvancedMemoryCard( category=category, card_key=card_key, backstory=backstory, date_created=datetime.now().strftime('%Y-%m-%d %H:%M:%S'), person=person, relationship=relationship, data=data ) memory_id = self.indexer.memory_manager.add_card(card) console.print(f"[green]✓ Added card: {memory_id}[/green]") elif choice == "3": # Search cards query = Prompt.ask("Search query") results = self.indexer.memory_manager.search_cards(query) if results: console.print(f"\n[green]Found {len(results)} cards:[/green]") for memory_id, card in results: console.print(f"\n{memory_id}:") console.print(f" Backstory: {card.backstory}") console.print(f" Person: {card.person}") else: console.print("[yellow]No cards found[/yellow]") elif choice == "4": # Delete card category = Prompt.ask("Category") card_key = Prompt.ask("Card key") if Confirm.ask(f"Delete {category}.{card_key}?"): if self.indexer.memory_manager.delete_card(category, card_key): console.print("[green]✓ Card deleted[/green]") else: console.print("[red]Card not found[/red]") def test_query(self): """Test a query against the system""" if not self.indexer: console.print("[yellow]Please initialize the system first (option 1 or 2)[/yellow]") return if not self.agent: self.agent = ContextualUserMemoryAgent( indexer=self.indexer, config=self.config ) console.print("\n[cyan]Test Query[/cyan]") # Show current memory status console.print(f"\nMemory Status:") console.print(f" Cards: {sum(len(cards) for cards in self.indexer.memory_manager.categories.values())}") console.print(f" Chunks: {len(self.indexer.contextual_chunks)}") # Get query query = Prompt.ask("\nEnter your question") # Process query with Progress( SpinnerColumn(), TextColumn("[progress.description]{task.description}"), console=console ) as progress: task = progress.add_task("Processing...", total=None) trajectory = self.agent.answer_question( question=query, test_id="interactive", stream=False ) progress.update(task, completed=True) # Display results console.print(Panel( trajectory.final_answer or "No answer generated", title="Answer", border_style="green" )) # Show details console.print(f"\n[bold]Query Details:[/bold]") console.print(f" Iterations: {len(trajectory.iterations)}") console.print(f" Tool calls: {len(trajectory.tool_calls)}") if trajectory.memory_cards_used: console.print(f"\n[bold]Memory Cards Used:[/bold]") for card_id in trajectory.memory_cards_used: console.print(f" • {card_id}") if trajectory.chunks_retrieved: console.print(f"\n[bold]Chunks Retrieved:[/bold] {len(trajectory.chunks_retrieved)}") if Confirm.ask("Show chunk details?"): for chunk_id in trajectory.chunks_retrieved[:3]: if chunk_id in self.indexer.contextual_chunks: chunk = self.indexer.contextual_chunks[chunk_id] console.print(f"\n Chunk: {chunk_id}") console.print(f" Context: {chunk.context[:200]}...") def evaluate_specific_test_case(self): """Evaluate a specific test case selected by the user""" console.print("\n[cyan]Evaluate Specific Test Case[/cyan]") # First, load all test cases to show to the user console.print("\nLoading available test cases...") # Load all categories all_test_cases = [] categories = ["layer1", "layer2", "layer3"] for category in categories: test_cases = self.evaluator.load_test_cases(category) for test_id in test_cases: test_case = self.evaluator.test_cases[test_id] all_test_cases.append({ "id": test_id, "category": category, "title": test_case.title, "conversations": len(test_case.conversation_histories) }) if not all_test_cases: console.print("[yellow]No test cases found[/yellow]") return # Sort test cases by test ID (name) all_test_cases.sort(key=lambda x: x["id"]) console.print(f"\n[green]Found {len(all_test_cases)} test cases[/green]") # Create a table to display test cases table = Table(title="Available Test Cases (Sorted by Name)", show_lines=True) table.add_column("#", style="dim", width=4) table.add_column("Test ID", style="cyan", width=25) table.add_column("Category", style="magenta", width=8) table.add_column("Title", style="green", width=50) table.add_column("Conv.", justify="right", width=5) for idx, test_info in enumerate(all_test_cases, 1): title = test_info["title"][:47] + "..." if len(test_info["title"]) > 50 else test_info["title"] table.add_row( str(idx), test_info["id"], test_info["category"], title, str(test_info["conversations"]) ) console.print(table) # Let user select a test case console.print("\n[bold]Select a test case to evaluate:[/bold]") console.print("Enter the number (#) or the Test ID directly") user_input = Prompt.ask("Your choice") # Find the selected test case selected_test_id = None # Check if user entered a number if user_input.isdigit(): idx = int(user_input) - 1 if 0 <= idx < len(all_test_cases): selected_test_id = all_test_cases[idx]["id"] else: console.print(f"[red]Invalid number: {user_input}[/red]") return else: # Check if user entered a test ID for test_info in all_test_cases: if test_info["id"] == user_input: selected_test_id = user_input break if not selected_test_id: console.print(f"[red]Test case not found: {user_input}[/red]") return # Get the test case details test_case = self.evaluator.test_cases[selected_test_id] # Show test case details console.print(Panel( f"[bold]{test_case.title}[/bold]\n\n" f"Category: {test_case.category}\n" f"Description: {test_case.description}\n\n" f"[yellow]User Question:[/yellow]\n{test_case.user_question}\n\n" f"[green]Evaluation Criteria:[/green]\n{test_case.evaluation_criteria[:200]}...\n\n" f"Conversations: {len(test_case.conversation_histories)}", title=selected_test_id, border_style="cyan" )) # Run evaluation console.print(f"\n[cyan]Evaluating {selected_test_id}...[/cyan]") console.print(f"[dim]Using LLM Judge for automatic evaluation[/dim]\n") with Progress( SpinnerColumn(), TextColumn("[progress.description]{task.description}"), console=console ) as progress: task = progress.add_task("Processing...", total=None) try: result = self.evaluator.evaluate_test_case(selected_test_id) progress.update(task, completed=True) # Display result status = "✓ Success" if result.success else "✗ Failed" console.print(f"\n[{'green' if result.success else 'red'}]{status}[/{'green' if result.success else 'red'}]") console.print("\n[bold]Agent Answer:[/bold]") console.print(Panel(result.agent_answer or "No answer generated", border_style="cyan")) console.print("\n[bold]Evaluation Criteria:[/bold]") console.print(Panel(result.evaluation_criteria, border_style="green")) # Display LLM evaluation if available if result.llm_evaluation: console.print("\n[bold cyan]LLM Judge Evaluation:[/bold cyan]") llm_eval = result.llm_evaluation reward = llm_eval.get('reward', 0) passed = llm_eval.get('passed', False) # Format reward with color based on score if reward >= 0.8: reward_color = "green" elif reward >= 0.6: reward_color = "yellow" else: reward_color = "red" console.print(f" Reward Score: [{reward_color}]{reward:.3f}/1.000[/{reward_color}]") console.print(f" Passed: [{'green' if passed else 'red'}]{'Yes' if passed else 'No'}[/{'green' if passed else 'red'}]") if 'reasoning' in llm_eval: console.print(f"\n[bold]Reasoning:[/bold]") console.print(Panel(llm_eval['reasoning'], border_style="cyan")) if 'required_info_found' in llm_eval and llm_eval['required_info_found']: console.print(f"\n[bold]Required Information Found:[/bold]") for key, found in llm_eval['required_info_found'].items(): status = "✓" if found else "✗" color = "green" if found else "red" console.print(f" [{color}]{status}[/{color}] {key}") console.print(f"\n[bold]Statistics:[/bold]") console.print(f" Iterations: {result.iterations}") console.print(f" Tool Calls: {result.tool_calls}") console.print(f" Memory Cards Used: {len(result.memory_cards_used)}") console.print(f" Chunks Retrieved: {len(result.chunks_retrieved)}") console.print(f" Contextual Chunks: {result.contextual_chunks_count}") console.print(f" Processing Time: {result.processing_time:.2f}s") console.print(f" Context Generation Time: {result.context_generation_time:.2f}s") if result.error: console.print(f"\n[red]Error: {result.error}[/red]") except Exception as e: progress.update(task, completed=True) console.print(f"[red]Error evaluating test case: {e}[/red]") def evaluate_test_cases(self): """Run evaluation on all test cases in a category""" console.print("\n[cyan]Evaluate All Test Cases (by Category)[/cyan]") # Load test cases category = Prompt.ask( "Select category", choices=["all", "layer1", "layer2", "layer3"], default="layer1" ) test_cases = self.evaluator.load_test_cases( category=None if category == "all" else category ) # Sort test cases by ID test_cases = sorted(test_cases) console.print(f"[green]Loaded {len(test_cases)} test cases (sorted by name)[/green]") console.print(f"[dim]Using LLM Judge for automatic evaluation[/dim]\n") # Run evaluation if Confirm.ask("Run evaluation?"): with Progress(console=console) as progress: task = progress.add_task("Evaluating...", total=len(test_cases)) for test_id in test_cases: try: result = self.evaluator.evaluate_test_case(test_id) progress.advance(task) except Exception as e: console.print(f"[red]Error evaluating {test_id}: {e}[/red]") progress.advance(task) # Show results report = self.evaluator.generate_report() console.print("\n" + report) # Save results if Confirm.ask("Save results to file?"): timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") output_file = f"results/evaluation_{timestamp}.json" self.evaluator.save_results(output_file) console.print(f"[green]✓ Results saved to {output_file}[/green]") def show_statistics(self): """Show system statistics""" console.print("\n[cyan]System Statistics[/cyan]") if self.indexer: stats = self.indexer.get_statistics() # Create statistics table table = Table(title="Contextual Memory Statistics") table.add_column("Metric", style="cyan") table.add_column("Value", justify="right") # Indexer stats table.add_row("Indexed Chunks", str(stats.get("chunks_indexed", 0))) table.add_row("Memory Cards", str(stats.get("memory_cards", 0))) table.add_row("Indexing Time", f"{stats.get('indexing_time', 0):.2f}s") # Chunker stats if "chunker_stats" in stats: cs = stats["chunker_stats"] table.add_row("Contextual Chunks", str(cs.get("contextual_chunks", 0))) table.add_row("Context Tokens", str(cs.get("total_context_tokens", 0))) table.add_row("Cache Hit Rate", f"{cs.get('cache_hit_rate', 0):.1%}") table.add_row("Est. Cost", f"${cs.get('estimated_cost', 0):.3f}") # Memory stats if "memory_stats" in stats: ms = stats["memory_stats"] table.add_row("Total Cards", str(ms.get("total_cards", 0))) for cat, info in ms.get("categories", {}).items(): table.add_row(f" {cat}", str(info.get("count", 0))) console.print(table) else: console.print("[yellow]System not initialized[/yellow]") def configure_settings(self): """Configure system settings""" console.print("\n[cyan]Configuration Settings[/cyan]") # Show current settings console.print(f"\nCurrent Settings:") console.print(f" LLM Provider: {self.config.llm.provider}") console.print(f" LLM Model: {self.config.llm.model}") console.print(f" Chunking: {self.config.chunking.rounds_per_chunk} rounds/chunk") console.print(f" Index Mode: {self.config.index.mode}") if Confirm.ask("\nModify settings?"): # LLM settings if Confirm.ask("Change LLM provider?"): provider = Prompt.ask( "Provider", choices=["dashscope", "qwen", "bailian", "kimi", "doubao", "siliconflow", "openai"], default=self.config.llm.provider ) self.config.llm.provider = provider # Chunking settings if Confirm.ask("Change chunking settings?"): rounds = Prompt.ask( "Rounds per chunk", default=str(self.config.chunking.rounds_per_chunk) ) self.config.chunking.rounds_per_chunk = int(rounds) console.print("[green]✓ Settings updated[/green]") def main(): """主入口：实验 3-11 上下文感知检索增强用户记忆""" parser = argparse.ArgumentParser( description=( "实验 3-11：利用上下文感知检索增强用户记忆。\n" "在把对话记忆块送入嵌入/索引前先生成『上下文前缀』，" "提升脱离上下文的孤立片段（如『好的，就订这个吧』）的召回。" ), formatter_class=argparse.RawDescriptionHelpFormatter, epilog=( "示例：\n" " python main.py --mode compare # 离线对比上下文化 vs 原始块（无需 API）\n" " python main.py --mode compare --query '我的护照什么时候过期？' # 单条查询离线检索对比\n" " python main.py --mode compare --output results/compare.json # 保存对比结果\n" " python main.py --mode evaluate --category layer1 # 端到端评估（需 API/检索服务）\n" " python main.py --mode interactive # 交互式界面（默认，需 API）\n" ), ) parser.add_argument( "--mode", choices=["interactive", "evaluate", "demo", "compare"], default="interactive", help="运行模式：interactive 交互式(默认) / evaluate 端到端评估 / demo 演示 / compare 离线对比(无需 API)", ) parser.add_argument( "--category", choices=["layer1", "layer2", "layer3"], help="评估的测试分类（layer1 基础回忆 / layer2 多会话检索 / layer3 主动服务）", ) parser.add_argument( "--config", type=str, help="配置文件（JSON）路径", ) # 离线对比（compare 模式）相关参数 parser.add_argument( "--dataset", type=str, default=None, help="compare 模式使用的记忆问答对照集 JSON（默认：memory_qa_eval.json）", ) parser.add_argument( "--query", type=str, default=None, help="compare 模式下对单条查询做离线检索对比（plain vs contextual 的 Top-K）", ) parser.add_argument( "--output", type=str, default=None, help="将 compare / evaluate 的结果保存为 JSON 的路径", ) # 配置覆盖项（可选，覆盖环境变量/配置文件；不改变默认行为） parser.add_argument( "--user-id", type=str, default=None, help="用户标识（写入输出结果作为标签，便于区分多用户记忆）", ) parser.add_argument( "--model", type=str, default=None, help="覆盖 LLM 模型名（默认取环境变量/提供商默认值）", ) parser.add_argument( "--provider", type=str, default=None, help="覆盖 LLM 提供商（kimi / doubao / siliconflow / openai 等）", ) parser.add_argument( "--store-path", type=str, default=None, help="记忆块存储（chunk_store）路径，覆盖默认 data/chunk_store.json", ) contextual_group = parser.add_mutually_exclusive_group() contextual_group.add_argument( "--contextual", dest="contextual", action="store_true", default=None, help="启用上下文化（索引前为每块生成上下文前缀，默认开启）", ) contextual_group.add_argument( "--no-contextual", dest="contextual", action="store_false", help="关闭上下文化（直接索引原始对话块，用于对照）", ) args = parser.parse_args() # compare 模式：完全离线，无需加载 LLM / 检索服务配置 if args.mode == "compare": from contextual_compare import ( run_comparison, single_query, DEFAULT_DATASET, ) dataset = args.dataset or DEFAULT_DATASET if args.query: single_query(dataset, args.query) else: run_comparison(dataset, output_path=args.output) return # Load configuration if args.config: config = Config.load(args.config) else: config = Config.from_env() # 应用命令行覆盖项 if args.provider: config.llm.provider = args.provider if args.model: config.llm.model = args.model if args.store_path: config.index.chunk_store_path = args.store_path if args.contextual is not None: config.index.enable_contextual = args.contextual if args.mode == "interactive": # Interactive mode app = InteractiveContextualRAG(config) app.run() elif args.mode == "evaluate": # Evaluation mode evaluator = ContextualMemoryEvaluator(config) test_cases = evaluator.load_test_cases(args.category) console.print(f"[cyan]Evaluating {len(test_cases)} test cases[/cyan]") for test_id in test_cases: try: result = evaluator.evaluate_test_case(test_id) status = "✓" if result.success else "✗" console.print(f"{status} {test_id}: {result.processing_time:.2f}s") except Exception as e: console.print(f"✗ {test_id}: Error - {e}") # Generate report report = evaluator.generate_report() console.print("\n" + report) # Save results timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") output_file = f"results/evaluation_{timestamp}.json" Path("results").mkdir(exist_ok=True) evaluator.save_results(output_file) console.print(f"[green]Results saved to {output_file}[/green]") elif args.mode == "demo": # Demo mode app = InteractiveContextualRAG(config) app.demo_mode() if __name__ == "__main__": main()