- Renamed decision-specific method to _calculate_decision_content_similarity
- Preserves node-based _calculate_content_similarity for find_similar_nodes()
- Updates method call to use renamed method
- Fixes core node-similarity functionality that was broken
- Ensures both node similarity and decision similarity work correctly
- Prevents find_similar_nodes() from calling wrong method signature
- Maintains backward compatibility for all similarity features
- Added validation for all required fields (category, scenario, reasoning, outcome)
- Added confidence range validation (0.0 to 1.0)
- Added type checking for all parameters
- Added length limits to prevent data corruption
- Added entity list validation with individual item checks
- Added metadata dictionary validation
- Added kwargs validation for additional fields
- Added input sanitization (trimming, type conversion)
- Ensures compliance with security-first input validation requirements
- Prevents malicious/corrupted data from affecting graph operations and analytics
- Fixed agent_context.py: Use logger.exception() instead of raw exception in logs
- Fixed context_graph.py: Use logger.exception() for secure structured logging
- Fixed policy_engine.py: Replaced 10 instances of raw exception logging with structured logging
- Fixed decision_recorder.py: Replaced 8 instances of raw exception logging with structured logging
- Ensures compliance with secure logging practices (Rule 5: Generic Secure Logging Practices)
- Maintains detailed exception information in internal logs while protecting user-facing outputs
- Prevents potential sensitive data leakage through log messages
- Enhanced README.md with strategic emojis for better visual appeal
- Updated context_usage.md with detailed, user-friendly examples
- Improved docs/reference/context.md with accessible language
- Added AgentContext sections with progressive learning approach
- Maintained professional appearance while improving readability
- Consistent documentation across all context module files
Documented fixes and enhancements related to Context Graphs and PolicyEngine, including comprehensive test coverage and improvements in decision handling.
Documented fixes and enhancements related to Context Graphs and PolicyEngine, including comprehensive test coverage and improvements in decision handling.
- Fix empty/None decision ID handling in ContextGraph.add_decision()
- Fix None metadata handling to prevent TypeError
- Fix causal chain depth logic and node exclusion
- Fix nonexistent node handling in add_causal_relationship()
- Add missing properties field in to_dict serialization
- Add missing from_dict method for graph deserialization
- Fix precedent search direction in find_precedents()
- Fix UUID generation logic in all decision models
- Add comprehensive test suite with 9 tests covering all features
- Test coverage: decision tracking, graph analytics, use cases, performance
- All 71 context tests now passing (100% success rate)
Resolves critical bugs in Context Graphs feature (#290) implementation
- 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
KaifAhmad1