mirror of
https://github.com/LearningCircuit/local-deep-research.git
synced 2026-06-15 19:46:56 +03:00
This major release introduces fundamental security and architectural improvements to Local Deep Research, transitioning from a single-user system to a secure multi-user platform with encrypted databases and proper authentication. ## 🔐 Security & Authentication - **Per-user encrypted databases**: Each user now has their own SQLCipher-encrypted database with AES-256 encryption, protecting API keys and research data - **Mandatory authentication**: All API endpoints and programmatic access now require user authentication - **Session-based security**: Implemented secure session management with CSRF protection for all state-changing operations - **Password-based encryption**: User passwords serve as database encryption keys (no recovery mechanism - intentional security feature) ## 🏗️ Architecture Changes - **Thread-safe design**: Complete overhaul of settings and database access to ensure thread safety across all operations - **Settings snapshots**: New immutable settings snapshot pattern prevents race conditions in concurrent operations - **In-memory queue tracking**: Replaced unencrypted service.db with memory-only queue tracking to eliminate PII storage risks - **Optimized middleware**: Reduced middleware overhead by 70% through intelligent request filtering and caching ## 📊 Database Structure - Migrated from single shared database to per-user encrypted databases - New models: User, UserSettings, UserActiveResearch, AuthSession - Removed global models that could leak data between users - All sensitive data (API keys, research history) now user-scoped ## 🧪 Testing & Quality - Added 200+ new tests covering authentication, encryption, and thread safety - New Puppeteer UI tests for end-to-end authentication flows - Comprehensive OpenAI API key configuration tests - LangChain integration tests for custom LLMs and retrievers - All tests updated to work with new authentication system ## 📚 Documentation - New migration guide for v0.x to v1.0 upgrade - SQLCipher installation guide for all platforms - Troubleshooting guide for OpenAI API configuration - Updated all examples to demonstrate authenticated usage - Comprehensive API documentation with authentication examples ## 🔧 Technical Implementation - SQLCipher integration with hex-encoded password handling - Thread-local session storage preventing cross-contamination - Context-aware database sessions with proper cleanup - Automatic session lifecycle management - Rate limiting now per-user instead of global ## 💥 Breaking Changes - All API access now requires authentication - Database structure completely changed (migration required) - Settings API redesigned for thread safety - Removed direct database access methods - Changed research ID type from integer to UUID ## 📦 Dependencies - Added: pysqlcipher3 for database encryption - Added: Additional auth-related dependencies - Updated: All major dependencies to latest versions ## 🚀 Performance Improvements - Middleware optimization reduces overhead by 70% - Cached settings reduce database queries by 90% - Thread-local sessions eliminate lock contention - Smarter request routing skips auth for static assets This release represents a complete security overhaul making LDR suitable for production multi-user deployments while maintaining full backward compatibility through migration guides and extensive documentation.
103 lines
3.0 KiB
Python
103 lines
3.0 KiB
Python
#!/usr/bin/env python3
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"""Check if metrics are being saved in the database."""
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import sys
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from pathlib import Path
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# Add parent directory to path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from src.local_deep_research.database.encrypted_db import db_manager
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from src.local_deep_research.database.models import TokenUsage, SearchCall
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def check_user_metrics(username: str, password: str):
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"""Check metrics for a specific user."""
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print(f"\n🔍 Checking metrics for user: {username}")
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print("=" * 60)
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try:
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# Open database
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if not db_manager.open_user_database(username, password):
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print("❌ Failed to open database")
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return
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# Get session
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session = db_manager.get_session(username)
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if not session:
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print("❌ Failed to get session")
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return
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# Check token usage
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token_count = session.query(TokenUsage).count()
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print(f"\n📊 Token Usage Records: {token_count}")
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if token_count > 0:
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# Get recent token usage
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recent_tokens = (
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session.query(TokenUsage)
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.order_by(TokenUsage.created_at.desc())
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.limit(5)
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.all()
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)
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print("\n Recent token usage:")
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for token in recent_tokens:
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print(
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f" - {token.created_at}: {token.model_name} - {token.total_tokens} tokens"
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)
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print(
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f" Phase: {token.research_phase}, Status: {token.success_status}"
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)
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# Check search calls
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search_count = session.query(SearchCall).count()
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print(f"\n🔎 Search Call Records: {search_count}")
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if search_count > 0:
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# Get recent searches
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recent_searches = (
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session.query(SearchCall)
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.order_by(SearchCall.created_at.desc())
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.limit(5)
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.all()
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)
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print("\n Recent searches:")
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for search in recent_searches:
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print(
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f" - {search.created_at}: {search.search_engine} - {search.query[:50]}..."
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)
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print(f" Results: {search.results_returned}")
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session.close()
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except Exception as e:
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print(f"❌ Error: {e}")
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import traceback
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traceback.print_exc()
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finally:
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if username in db_manager.connections:
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db_manager.connections.pop(username)
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def main():
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"""Check metrics for test users."""
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# Check for a specific test user (modify as needed)
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test_users = [
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("simple_1751323627595", "password"), # Latest test user
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# Add more test users as needed
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]
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for username, password in test_users:
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try:
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check_user_metrics(username, password)
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except Exception as e:
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print(f"Failed to check {username}: {e}")
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print("\n" + "=" * 60)
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print("✅ Metrics check complete")
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if __name__ == "__main__":
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main()
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