- Document PR #307 with comprehensive decision tracking system
- Include KG algorithm integration, PolicyException naming fix, and 9 bug fixes
- Note production-ready architecture with enterprise features
- Record 100% test coverage and comprehensive documentation
- Highlight backward compatibility and performance optimizations
- Remove broken link from reference/context.md that was causing CI failure
- Decision tracking functionality is now integrated into the context module
- Fix mkdocs build strict mode warning about missing target file
- Ensure documentation builds successfully in CI pipeline
- Add PolicyException to imports and examples
- Add comprehensive section on enhanced AgentContext with decision tracking and KG algorithms
- Add enhanced ContextGraph section with KG algorithm examples (centrality, community detection, embeddings)
- Add PolicyException management section with creation, storage, and retrieval examples
- Update table of contents to include new sections
- Include GraphStore requirement notes for decision tracking
- Add production-ready examples with all advanced features enabled
- Ensure documentation reflects all recent context engineering enhancements
- Rename Exception dataclass to PolicyException to avoid shadowing Python's built-in Exception
- Update all imports across decision tracking modules to use PolicyException
- Update type hints and method signatures to use PolicyException
- Update __init__.py exports to include PolicyException instead of Exception
- Update documentation examples to use PolicyException
- Ensure compliance with PR Compliance ID 2 for meaningful naming
- Prevent confusion between business model exceptions and Python exceptions
- Add explicit capability check for execute_query method before initializing decision tracking
- Prevent runtime failures when ContextGraph is used with decision tracking enabled
- Provide clear error message guiding users to use GraphStore or disable decision tracking
- Ensure compatibility between knowledge graph type and decision tracking requirements
- Validate GraphStore interface during AgentContext initialization
- Fix centrality access to properly read nested 'centrality' dictionary structure
- Update calculate_degree_centrality result access from centrality.get(decision_id) to centrality.get('centrality', {}).get(decision_id)
- Fix calculate_all_centrality result access to extract measures from nested wrapper structure
- Correct influence score calculation to use proper centrality measure keys
- Ensure centrality boosts and influence values are calculated correctly
- Fix undefined path variable by properly binding path in MATCH clause
- Change MATCH (start)-[*1..{max_hops}]-(d:Decision) to MATCH path = (start)-[*1..{max_hops}]-(d:Decision)
- Ensure length(path) function works correctly in multi-hop reasoning queries
- Prevent runtime undefined variable errors in Cypher execution
- Maintain proper hop count calculation for decision relevance ranking
- Convert query strings to f-strings to properly substitute max_depth parameter
- Fix Cypher syntax for variable-length paths from *1..{max_depth} to *1..{max_depth}
- Remove max_depth from query parameters since it's now embedded in the query
- Ensure proper Neo4j/FalkorDB compatibility for influence analysis queries
- Prevent runtime query failures in analyze_decision_influence method
- Fix method name from calculate_all_centralities to calculate_all_centrality
- Update _to_kg_format() to return relationships key expected by CentralityCalculator
- Ensure proper graph format conversion for KG algorithms
- Fix centrality analysis in both analyze_graph_with_kg() and get_node_centrality()
- Prevent AttributeError and ensure correct analytics results
- Fix audit logging to include actor, timestamp, outcome, and category
- Ensure compliance with PR Compliance ID 1 for comprehensive audit trails
- Add decision_maker, timestamp, and outcome to decision recording logs
- Enable proper reconstruction of who did what and when for auditing
- Maintain structured log format for easy parsing and analysis
- Fix security issue where raw exception messages were exposed to callers
- Replace str(e) with generic error message for user-facing responses
- Keep detailed error information in secure internal logs only
- Ensure compliance with PR Compliance ID 4 for secure error handling
- Prevent potential exposure of internal implementation details and sensitive backend errors
- Fix bug where exceptions were swallowed without logging in context_retriever.py
- Restore warning log for policy search failures with sanitized category
- Ensure compliance with PR Compliance ID 3 for robust error handling
- Prevent silent failures that hinder debugging and mask missing policy coverage
- Add decision tracking system with DecisionRecorder, DecisionQuery, CausalChainAnalyzer, PolicyEngine
- Implement KG algorithm integration with centrality, community detection, embeddings, path finding
- Add vector store integration with hybrid search and custom similarity weights
- Enhance context graphs with advanced analytics and decision support
- Update documentation with comprehensive context module reference
- Add production examples for banking and healthcare use cases
- Update README to highlight context graph framework capabilities
- Add comprehensive test suite for all new features
- Document complete pgvector integration with all features
- Include security, performance, and CI/CD improvements
- Reference PR #303 and contributors @Sameer6305 and @KaifAhmad1
- Fix test_vector_storage_manager_overhead to work with backend stores
- Handle both in-memory vectors and backend store vector_ids
- Ensure benchmark works with FAISS backend and other vector stores
- Fix delegation logic for store_vectors() to handle add() vs add_vectors()
- Fix delegation logic for search_vectors() to handle search() vs search_similar()
- Add proper error handling for unsupported method names
- Resolve CI benchmark failure with FAISSStore integration
- Keep pgvector backend integration with _init_backend_store method
- Preserve decision-specific components from main branch
- Maintain both VectorStore backend support and decision pipeline functionality
- Fix duplicate initialization and proper component placement
- Add 'pgvector' to SUPPORTED_BACKENDS
- Implement _init_backend_store() method for backend-specific initialization
- Add delegation logic for store_vectors() and search_vectors() methods
- Provide proper error handling for missing connection_string
- Enable VectorStore(backend='pgvector') usage pattern
Resolves integration gap in PgVectorStore implementation
## Critical Fixes Applied
### 1. Sensitive Data Logging (Security)
- Sanitize scenario text in decision_context.py (truncate to 30 chars)
- Sanitize entity names in context_retriever.py (truncate to 20 chars)
- Sanitize category names in context_retriever.py (truncate to 20 chars)
- Replace raw exception details with exception type names
- Prevents PII/PHI leakage into application logs
### 2. Random Embedding Fallback (Reliability)
- Remove random embedding fallback in semantic embedding generation
- Remove random embedding fallback in structural embedding generation
- Replace with clear RuntimeError exceptions with actionable messages
- Prevents silent degradation and misleading similarity results
### 3. Filter Decisions kwargs TypeError (API Compatibility)
- Add **kwargs parameter to VectorStore.filter_decisions()
- Process kwargs ending with '_min'/'_max' as range filters
- Process other kwargs as exact match filters
- Maintains backward compatibility with existing API
### 4. Entities Filter Never Matches (Core Functionality)
- Fix list-to-list comparison in _filter_by_metadata()
- Handle both scalar and list metadata values correctly
- Use set intersection for list-to-list matching
- Fixes search_by_entities() and filter_decisions(entities=...)
## Testing Verification
- All critical fixes tested and verified working
- Sensitive data properly truncated in logs
- Embedding failures raise clear errors
- kwargs API works with loan_amount_min filters
- Entities filter correctly matches decisions
- Context retriever logging sanitized
## Impact
- Security: Prevents sensitive data exposure in logs
- Reliability: Clear error messages instead of silent failures
- Compatibility: Full backward API compatibility maintained
- Functionality: Core filtering features now work correctly
- Add gensim>=4.3.0 to core dependencies
- Required for Node2Vec embeddings in enhanced vector store
- Fixes ImportError in benchmark tests
- Ensures Node2Vec functionality works out of the box
- CRUD unit tests
- Similarity search tests with filters
- Index creation tests (HNSW, IVFFlat)
- Docker-based PostgreSQL + pgvector support
- Tests skip if DB unavailable
- Implement PgVectorStore with psycopg3/psycopg2 support
- Support cosine, L2, and inner_product distance metrics
- Support IVFFlat and HNSW index types
- JSONB metadata storage with filtering
- Connection pooling and batch operations
- Idempotent index creation
- Added comprehensive KG algorithms overview to README
- Updated Knowledge Graph Construction section with new algorithms
- Added examples for NodeEmbedder, SimilarityCalculator, CentralityCalculator
- Listed all 8 algorithm categories with descriptions
- Added provenance tracking mention
- Updated cookbook links to include advanced graph analytics
Follow-up commit for PR #292
KaifAhmad1