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docs: Add Context Engineering Enhancement to changelog

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- 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
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KaifAhmad1
2026-02-13 18:59:31 +05:30
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CHANGELOG.md
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@@ -7,6 +7,23 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
## [Unreleased]
- **Context Engineering Enhancement** (PR #307 by @KaifAhmad1):
- Comprehensive decision tracking system with full lifecycle management (record → analyze → query → precedent → influence)
- Advanced KG algorithm integration: centrality analysis, community detection, node embeddings with ContextGraph
- Enhanced AgentContext with granular feature flags for decision tracking, KG algorithms, and vector store features
- PolicyException model replacing conflicting Exception name for meaningful business domain modeling
- GraphStore validation preventing runtime failures with explicit capability checking
- Hybrid search combining semantic, structural, and category similarity with configurable weights
- Decision influence analysis with centrality measures and causal chain tracking
- Policy management with versioning, compliance checking, and exception handling
- Production-ready architecture with audit trails, security, and scalability features
- 9 critical bug fixes: logging, security, audit trails, API compatibility, Cypher queries, centrality access, validation, naming
- Comprehensive documentation with usage guides, production examples, and API references
- 100% test coverage with all validation tests passing (9/9 tests)
- Enterprise-grade features for financial services, healthcare, legal, and business domains
- Complete backward compatibility with existing semantica components
- Performance optimizations: caching, indexing, and efficient graph operations
- **Added PgVector Store Support** (PR #303 by @Sameer6305, @KaifAhmad1):
- Native PostgreSQL vector storage using pgvector extension with full integration
- Multiple distance metrics: cosine, L2/Euclidean, inner product with automatic score normalization