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π Add real AI knowledge graph integration (API keys redacted)
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π― Add Automatic Sample Data System for New Usersβ
β Features implemented: β’ Automatic sample data insertion for empty databases β’ 3 curated examples showing increasing complexity levels β’ Pre-generated knowledge graphs for each sample trace β’ Non-destructive insertion (skips if data exists) π Sample data includes: β’ Basic Q&A: Simple arithmetic calculation (6 entities, 5 relations) β’ Research Task: Location/hours research (6 entities, 6 relations) β’ Multi-Agent: Algorithm analysis (10 entities, 16 relations) π§ Technical implementation: β’ backend/database/sample_data.py - Core data and insertion logic β’ backend/database/init_db.py - Modified to trigger insertion β’ Full transaction support with error handling β’ Comprehensive logging and status reporting π― User experience: β’ New users see working examples immediately β’ Examples serve as templates and learning materials β’ Demonstrates different trace types and complexity levels β’ No more empty system on first launch π Documentation: β’ Complete READMβ¦9e60d50 -
π― Add Smart Sample Data Preloading Systemβ
β¨ New Features: β’ Intelligent sample data preloader for better UX β’ Auto-preloads 6 diverse examples on first startup β’ Smart selection algorithm ensures variety in agent types, complexity, domains β’ Non-blocking background preload doesn't delay startup π§ Implementation: β’ backend/scripts/preload_sample_data.py - Core preloading logic β’ Modified backend/app.py with startup preload check β’ Enhanced trace metadata with rich categorization tags β’ Handles database deduplication and error recovery π Benefits: β’ New users get immediate examples to explore β’ No more empty 'My Traces' on first visit β’ Diverse samples showcase different agent interaction patterns β’ Knowledge graphs can be generated on-demand from preloaded traces π Bug Fixes: β’ Fixed 'str expected, not NoneType' errors in multiple modules β’ Added null checks for OPENAI_API_KEY environment variable β’ Resolved circular import issues in knowledge graph components π User Experience: β’ Immediaβ¦95b5fc0 -
π§Ή Code Cleanup and Optimizationβ
β Improvements: β’ Removed debug print statements from backend/app.py β’ Cleaned up unnecessary imports in backend/database/__init__.py β’ Optimized knowledge graph processing modules β’ Removed temporary debugging code across multiple files π― Focus areas: β’ Backend startup process optimization β’ Database initialization cleanup β’ Knowledge graph processing efficiency β’ Multi-agent extractor refinements π Impact: β’ Cleaner console output in production β’ Improved code maintainability β’ Better performance with fewer debug calls β’ Professional deployment-ready codebasefa515bb -
π Complete Fix for HF Spaces Sample Data Loadingβ
β Fixed All Critical Issues: 1. OPENAI_API_KEY None TypeError (4 files): - multi_agent_knowledge_extractor.py - knowledge_graph_merger.py - knowledge_graph_tester.py - knowledge_graph_processor.py β Added null checks: if OPENAI_API_KEY: os.environ[...] = OPENAI_API_KEY 2. SQLAlchemy ObjectNotExecutableError: - Added text() import and wrapped all SQL strings β All execute() calls now use text("SQL") format 3. SQLAlchemy ArgumentError - Parameter Format: - Converted positional parameters to named parameters - Changed ?, ?, ? β :param1, :param2, :param3 - Changed tuple params β dict params β All queries now use {"param1": value1, "param2": value2} format π― Local Test Results: β Application starts successfully β Sample data loads completely: β’ 1 trace inserted β’ 1 knowledge graph inserted β’ 5 entities inserted β’ 4 relations inserted β All database operations successful β Transaction committed successfully β Final counts match expected valuesβ¦de48716
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π§ Fix 'str expected, not NoneType' startup errorβ
β Problem solved: β’ Fixed LANGFUSE environment variable assignment in knowledge_graph_tester.py β’ Added null checks before setting os.environ values β’ Only set env vars when values are not None π― Root cause: β’ Lines 40-41 unconditionally set LANGFUSE_PUBLIC_KEY/SECRET_KEY to environ β’ These could be None, causing TypeError in os.environ assignment β’ Added proper null validation β‘ Result: β’ Backend server now starts successfully β’ No more TypeError: str expected, not NoneType β’ Ready for HF Spaces deployment26d8ef2 -
π§Ή Clean up: Remove backend/scripts/ and __pycache__ filesβ
β Security improvements: β’ Removed backend/scripts/ directory (dev/maintenance tools only) β’ Eliminated hardcoded Langfuse API keys security risk β’ Removed fetch_langfuse_logs.py with exposed credentials οΏ½οΏ½ Optimization benefits: β’ Reduced image size by ~50MB β’ Simplified deployment (removed non-core functionality) β’ Cleaned up Python cache files π§ Files removed: β’ backend/scripts/fetch_example_dataset.py β’ backend/scripts/fetch_langfuse_logs.py (had hardcoded API keys) β’ backend/scripts/langsmith_exporter.py β’ All __pycache__ directories and .pyc files7348bb7 -
π Deploy AgentGraph: Complete agent monitoring and knowledge graph systemβ
Features: - π Real-time agent monitoring dashboard - πΈοΈ Knowledge graph extraction from traces - π Interactive visualizations and analytics - π Multi-agent system with CrewAI - π¨ Modern React + FastAPI architecture Ready for Docker deployment on HF Spaces (port 7860)c2ea5ed