BobAi
5f42751e9a
🛡️ SMART MODEL SAFEGUARDS: - Implement runaway prevention with pattern detection (repetition, thinking loops, rambling) - Add context length management with optimal parameters per model size - Quality validation prevents problematic responses before reaching users - Helpful explanations when issues occur with recovery suggestions - Model-specific parameter optimization (qwen3:0.6b vs 1.7b vs 3b+) - Timeout protection and graceful degradation ⚡ OPTIMAL PERFORMANCE SETTINGS: - Context window: 32k tokens for good balance - Repeat penalty: 1.15 for 0.6b, 1.1 for 1.7b, 1.05 for larger models - Presence penalty: 1.5 for quantized models to prevent repetition - Smart output limits: 1500 tokens for 0.6b, 2000+ for larger models - Top-p/top-k tuning based on research best practices 🎬 DUAL-MODE DEMO SCRIPTS: - create_synthesis_demo.py: Shows fast search with AI synthesis workflow - create_exploration_demo.py: Interactive thinking mode with conversation memory - Realistic typing simulation and response timing for quality GIFs - Clear demonstration of when to use each mode Perfect for creating compelling demo videos showing both RAG experiences!
270 lines
10 KiB
Python
270 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Create demo GIF for Exploration Mode - Deep Thinking & Interactive Learning
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Shows the conversational workflow for understanding and debugging codebases.
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"""
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import time
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import sys
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import os
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from pathlib import Path
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class ExplorationDemoSimulator:
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def __init__(self):
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self.width = 100
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self.height = 35
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def clear_screen(self):
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print("\033[H\033[2J", end="")
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def type_command(self, command: str, delay: float = 0.05):
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"""Simulate typing a command."""
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print("$ ", end="", flush=True)
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for char in command:
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print(char, end="", flush=True)
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time.sleep(delay)
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print()
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time.sleep(0.5)
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def type_question(self, question: str, delay: float = 0.04):
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"""Simulate typing a question in exploration mode."""
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print("> ", end="", flush=True)
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for char in question:
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print(char, end="", flush=True)
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time.sleep(delay)
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print()
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time.sleep(0.5)
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def show_thinking(self, duration: float = 2.0):
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"""Show thinking animation."""
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print("🔍 Analyzing...", end="", flush=True)
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for _ in range(3):
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time.sleep(duration / 3)
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print(".", end="", flush=True)
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print()
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time.sleep(0.5)
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def show_response(self, lines: list, delay: float = 0.4):
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"""Show AI response with realistic timing."""
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for line in lines:
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print(line)
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time.sleep(delay)
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time.sleep(1.5)
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def run_exploration_demo(self):
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"""Run the exploration mode demonstration."""
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self.clear_screen()
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# Title
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print("🧠 FSS-Mini-RAG: Exploration Mode Demo")
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print("=" * 55)
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print("Deep thinking & interactive learning for complex codebases")
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print()
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time.sleep(2)
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# Step 1: Start exploration
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print("Step 1: Start interactive exploration session")
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print("-" * 45)
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self.type_command("rag-mini explore ./webapp-project")
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self.show_response([
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"🧠 EXPLORATION MODE STARTED",
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"=" * 50,
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"Project: webapp-project",
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"Session: explore_1702845632",
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"",
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"🎯 This mode uses thinking and remembers context.",
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" Perfect for debugging, learning, and deep exploration.",
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"",
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"💡 Tips:",
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" • Ask follow-up questions - I'll remember our conversation",
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" • Use 'why', 'how', 'explain' for detailed reasoning",
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" • Type 'quit' or 'exit' to end session",
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"",
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"=" * 50,
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"",
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"🤔 Ask your first question about the codebase:"
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])
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# Step 2: First question - How authentication works
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print("Step 2: Understanding authentication system")
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print("-" * 42)
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self.type_question("How does authentication work in this codebase?")
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self.show_thinking()
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self.show_response([
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"",
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"🧠 EXPLORATION ANALYSIS (Question #1)",
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"Session: 0.2m | Results: 8 | Time: 3.4s",
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"=" * 60,
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"",
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"📝 Analysis:",
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" This webapp implements a JWT-based authentication system with",
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" session management, role-based permissions, and OAuth2 integration.",
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"",
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"🔍 Key Insights:",
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" • JWT tokens generated in auth/jwt_handler.py with 24h expiry",
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" • User sessions stored in Redis for fast lookups and invalidation",
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" • Role-based access control through middleware/permissions.py",
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" • OAuth2 integration supports Google, GitHub, and Microsoft providers",
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" • Password hashing uses bcrypt with 12 rounds for security",
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"",
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"💡 Code Examples:",
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" JWT generation: jwt.encode(payload, SECRET_KEY, algorithm='HS256')",
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" Session storage: redis.setex(f'session:{user_id}', 86400, token)",
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" Permission check: @require_permission('admin') decorator",
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"",
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"🎯 Next Steps:",
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" • Review JWT secret rotation policy",
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" • Consider implementing refresh tokens",
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" • Audit OAuth2 scope permissions",
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"",
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"🟢 Confidence: 91%"
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])
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# Step 3: Follow-up question - Security concerns
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print("Step 3: Follow-up question (context remembered)")
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print("-" * 48)
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self.type_question("What security concerns should I be aware of?")
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self.show_thinking(1.8)
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self.show_response([
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"",
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"🧠 EXPLORATION ANALYSIS (Question #2)",
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"Session: 1.4m | Results: 6 | Time: 2.1s",
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"=" * 60,
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"",
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"📝 Analysis:",
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" Building on the JWT authentication system we just discussed,",
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" I've identified several security considerations that need attention.",
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"",
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"🔍 Key Insights:",
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" • JWT secret is hardcoded in config.py (CRITICAL RISK)",
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" • No rate limiting on login endpoints (brute force vulnerable)",
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" • Session invalidation only happens on logout, not on password change",
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" • OAuth2 state parameter validation could be strengthened",
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" • CORS settings allow all origins in development config",
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"",
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"💡 Code Examples:",
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" Vulnerable: SECRET_KEY = 'hardcoded-secret-123'",
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" Missing: @rate_limit('5 per minute') on /api/login",
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" Risk: CORS_ALLOW_ALL_ORIGINS = True",
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"",
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"🎯 Next Steps:",
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" • Move JWT secret to environment variables immediately",
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" • Implement rate limiting with Redis or in-memory store",
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" • Add session invalidation on password/email changes",
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" • Review and restrict CORS origins for production",
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" • Add security headers middleware",
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"",
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"🔴 Confidence: 94% | Context: 1 previous questions"
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])
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# Step 4: Third question - Performance issues
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print("Step 4: Exploring performance (conversation continues)")
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print("-" * 54)
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self.type_question("Why might the login process be slow?")
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self.show_thinking(2.2)
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self.show_response([
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"",
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"🧠 EXPLORATION ANALYSIS (Question #3)",
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"Session: 2.1m | Results: 7 | Time: 2.8s",
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"=" * 60,
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"",
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"📝 Analysis:",
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" Considering our previous discussion about JWT auth and security,",
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" the login slowness likely stems from several performance bottlenecks.",
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"",
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"🔍 Key Insights:",
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" • bcrypt rounds set to 15 (recommended: 10-12) causing CPU overhead",
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" • Database queries for user lookup aren't indexed on email field",
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" • Redis connection pool not configured, creating new connections",
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" • OAuth2 provider calls lack timeout settings (can hang)",
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" • Session cleanup runs synchronously during login flow",
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"",
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"💡 Code Examples:",
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" Slow: bcrypt.hashpw(password, bcrypt.gensalt(rounds=15))",
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" Missing: CREATE INDEX idx_users_email ON users(email)",
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" Blocking: cleanup_expired_sessions() # Should be async",
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"",
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"🎯 Next Steps:",
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" • Reduce bcrypt rounds to 12 for better balance",
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" • Add database index on users.email column",
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" • Configure Redis connection pooling",
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" • Move session cleanup to background task",
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" • Add OAuth2 request timeouts (5-10 seconds)",
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"",
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"🟡 Confidence: 89% | Context: 2 previous questions"
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])
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# Step 5: Session summary
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print("Step 5: Session summary and conclusion")
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print("-" * 38)
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self.type_question("summary")
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self.show_response([
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"",
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"🧠 EXPLORATION SESSION SUMMARY",
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"=" * 40,
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"Project: webapp-project",
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"Session ID: explore_1702845632",
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"Duration: 2.5 minutes",
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"Questions explored: 3",
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"",
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"📋 Topics explored:",
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" 1. How does authentication work in this codebase? (confidence: 91%)",
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" 2. What security concerns should I be aware of? (confidence: 94%)",
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" 3. Why might the login process be slow? (confidence: 89%)",
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"",
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"🎯 Key Discoveries:",
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" • JWT-based auth with session management",
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" • Critical security issues (hardcoded secrets, no rate limiting)",
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" • Performance bottlenecks (bcrypt settings, missing indexes)",
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"",
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"💡 Action Items Generated:",
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" • Immediate: Fix hardcoded JWT secret",
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" • High Priority: Add rate limiting and database indexes",
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" • Monitor: Review OAuth2 configurations"
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])
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# Step 6: Exit
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self.type_question("quit")
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self.show_response([
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"",
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"✅ Exploration session ended.",
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"",
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"🎬 This was Exploration Mode - perfect for learning and debugging!"
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])
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# Final summary
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print()
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print("💡 Exploration Mode Benefits:")
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print(" 🧠 Thinking-enabled AI for detailed reasoning")
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print(" 💭 Conversation memory across questions")
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print(" 🔍 Perfect for debugging and understanding")
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print(" 📚 Educational - learn how code really works")
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print(" 🎯 Context-aware follow-up responses")
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print()
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time.sleep(3)
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def main():
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"""Run the exploration mode demo."""
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demo = ExplorationDemoSimulator()
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print("Starting FSS-Mini-RAG Exploration Mode Demo...")
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print("Record with: asciinema rec exploration_demo.cast")
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print("Press Enter to start...")
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input()
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demo.run_exploration_demo()
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print("\n🎯 To create GIF:")
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print("agg exploration_demo.cast exploration_demo.gif")
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if __name__ == "__main__":
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main() |