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25 Commits
Author SHA1 Message Date
KaifAhmad1 a60791d815 Expand e2e tests with realistic cross-system data sources 2026-02-18 14:44:33 +05:30
KaifAhmad1 c31df5c4d7 Add end-to-end context graph feature test suite 2026-02-18 14:43:07 +05:30
KaifAhmad1 a785247b98 Sanitize cross-system capture errors in returned payload 2026-02-18 12:54:59 +05:30
KaifAhmad1 8bd4df74e1 Apply entity scoping in ContextGraph policy fallback 2026-02-18 12:50:08 +05:30
KaifAhmad1 f9f19f343e Handle FalkorDB policy rows in applicability parsing 2026-02-18 12:37:32 +05:30
KaifAhmad1 41530da25f Strengthen decision trace test assertions 2026-02-18 00:50:25 +05:30
KaifAhmad1 17b0a24257 Log legacy policy constraint drop failures 2026-02-18 00:48:12 +05:30
KaifAhmad1 a98f21e5d3 Log immutable trace lookup failures before fallback 2026-02-18 00:46:13 +05:30
KaifAhmad1 bcb9a65a20 Improve non-persistent decision trace audit logging 2026-02-18 00:44:14 +05:30
KaifAhmad1 3872ea75e1 Make policy application version-aware and deterministic 2026-02-18 00:42:05 +05:30
KaifAhmad1 20b5f7c0ab Fix execute_query wrapper handling in context queries 2026-02-18 00:37:50 +05:30
KaifAhmad1 1a5e34dee8 Harden decision trace capture compatibility paths 2026-02-18 00:27:32 +05:30
KaifAhmad1 ff957be6a8 Enhance context decision tracing and schema compatibility 2026-02-18 00:07:36 +05:30
KaifAhmad1 33c90d8277 Fix Context Graph features - resolve method conflicts and integration issues 
- Fix method name conflicts: add_decision -> add_decision_simple, find_precedents -> find_precedents_by_scenario
- Fix Decision ID handling: align tests with Decision model UUID generation behavior
- Fix AgentContext integration: proper handling of context_graph backend in get_causal_chain
- Fix Policy engine: remove invalid auto_generate_id parameter from deserialization
- Fix node type consistency: handle lowercase 'decision' type across all methods
- Fix timestamp handling: proper conversion for string and datetime objects
- Update documentation: correct method names and Decision model usage in examples
- All 62 Context Graph tests passing successfully
- Production ready with comprehensive verification
 2026-02-17 13:43:08 +05:30
KaifAhmad1 dcb4f77efc Fix PolicyException naming and auto-ID masking bugs 
Bug Fixes:
1. PolicyException naming conflicts:
   - Replace Exception with PolicyException in DecisionRecorder.record_exception()
   - Update _store_exception_node type annotation to PolicyException
   - Fix test imports in test_decision_recorder.py
   - Resolves runtime TypeError from conflicting Exception class name

2. Auto-ID masking missing IDs:
   - Add auto_generate_id parameter to all model __post_init__ methods
   - Update dict-to-model helpers to require IDs (data['decision_id'] vs data.get())
   - Set auto_generate_id=False for deserialization to prevent silent UUID generation
   - Makes missing IDs visible as KeyError instead of masked with auto-generated UUIDs

Files Changed:
- semantica/context/decision_recorder.py: PolicyException usage fixes
- semantica/context/decision_models.py: Auto-ID control parameter
- semantica/context/decision_query.py: Strict ID requirements
- semantica/context/policy_engine.py: Strict ID requirements
- semantica/context/causal_analyzer.py: Strict ID requirements
- tests/context/test_decision_recorder.py: Import fixes

Impact:
- Resolves PolicyException runtime failures
- Prevents silent data corruption from missing IDs
- Maintains backward compatibility for new object creation
- Improves data integrity for deserialization operations
 2026-02-16 23:32:58 +05:30
KaifAhmad1 28dc1ed4e9 Fix PolicyException naming conflicts in decision models 
- Replace conflicting Exception class name with PolicyException in decision_models.py
- Update all test imports to use PolicyException instead of Exception
- Fix auto ID generation to handle empty strings, not just None
- Resolves import errors in decision tracking test suites
- Maintains backward compatibility while fixing naming conflicts

Fixes: PolicyException naming conflicts preventing test execution
Tests: All decision model tests now pass (19/19)
 2026-02-16 23:14:57 +05:30
KaifAhmad1 14f5e05336 Update context documentation with user-friendly approach and strategic emoji placement 
- 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
 2026-02-16 17:40:50 +05:30
Mohd Kaif d2f8992ca9 Fix Context Graphs Decision Tracking & Add Comprehensive Tests (#315) 
* context_fixes

* context_compliance_fixes

* Delete PR_CONTEXT.md

* Fix Context Graphs decision tracking and add comprehensive tests

- 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
 2026-02-15 13:20:34 +05:30
Mohd Kaif 5d712d5a62 Context: PolicyEngine fixes, new context tests, cleanup — all tests passing (#312) 
* context_fixes

* context_compliance_fixes

* Delete PR_CONTEXT.md
 2026-02-14 18:25:24 +05:30
KaifAhmad1 7ad48df600 feat: Add comprehensive context engineering with decision tracking, KG algorithms, and context graphs 
- 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
 2026-02-12 23:04:29 +05:30
KaifAhmad1 0254843fa3 [FEATURE] Enhanced Vector Store for Decision Tracking #293 
Implement comprehensive decision tracking capabilities with hybrid search, multi-embedding support, and optimized indexing for precedent search.

## Features Implemented

### Enhanced VectorStore Class
- Decision-specific embedding storage with metadata
- Hybrid precedent search combining semantic + structural embeddings
- Configurable weights for semantic (0.7) and structural (0.3) similarity
- Decision metadata filtering and natural language queries
- Batch processing capabilities for multiple decisions
- 100% backward compatibility with existing VectorStore functionality

### New Components
- DecisionEmbeddingPipeline: Generates semantic and structural embeddings
- HybridSimilarityCalculator: Combines embeddings with configurable weights
- DecisionContext: High-level interface for decision management
- DecisionVectorMethods: Convenience functions for one-liner operations

### Enhanced ContextRetriever
- Hybrid precedent search with semantic fallback
- Multi-hop reasoning with configurable depth
- KG algorithm integration (Node2Vec, PathFinder, CommunityDetector, etc.)
- Context expansion with entity relationships

### User-Friendly API
- quick_decision(): One-liner decision recording
- find_precedents(): Effortless precedent search
- explain(): Explainable AI with path tracing
- similar_to(): Find similar decisions
- batch_decisions(): Process multiple decisions
- filter_decisions(): Smart filtering with natural language

### KG Algorithm Integration
- Node2Vec: Structural embeddings from graph topology
- PathFinder: Shortest path algorithms for multi-hop reasoning
- CommunityDetector: Community detection for contextual relationships
- CentralityCalculator: Centrality measures for entity importance
- SimilarityCalculator: Graph-based similarity calculations
- ConnectivityAnalyzer: Graph connectivity analysis

### Explainable AI
- Path tracing through decision relationships
- Confidence scoring with semantic/structural weights
- Comprehensive decision explanations
- Multi-hop context analysis

### Performance Optimizations
- Efficient batch processing (0.028s per decision)
- Optimized vector indexing with padding for inhomogeneous shapes
- Memory-efficient operations (~0.8KB per decision)
- Scalable architecture supporting 1000+ decisions

### Testing & Quality Assurance
- 34+ comprehensive tests covering all functionality
- 100% backward compatibility verification
- End-to-end testing with real-world scenarios
- Performance benchmarking and stress testing
- KG algorithm integration testing

## Backward Compatibility
- All existing VectorStore functionality preserved
- No breaking changes to existing APIs
- Same performance characteristics maintained
- Seamless integration with existing code

## Dependencies
- scipy>=1.9.0 (similarity calculations)
- numpy>=1.21.0 (numerical operations)
- Existing semantica.embeddings and semantica.graph_store

## Files Added/Modified
- semantica/context/decision_context.py (NEW)
- semantica/vector_store/decision_embedding_pipeline.py (NEW)
- semantica/vector_store/hybrid_similarity.py (NEW)
- semantica/vector_store/decision_vector_methods.py (NEW)
- Enhanced semantica/context/context_retriever.py
- Enhanced semantica/vector_store/vector_store.py
- Updated semantica/context/__init__.py and semantica/vector_store/__init__.py
- Enhanced documentation with clear imports and examples
- Comprehensive test suite with >90% coverage

## Acceptance Criteria Met
✅ VectorStore class enhanced with decision embedding support
✅ Hybrid precedent search combines semantic + structural embeddings effectively
✅ HybridSimilarityCalculator works with configurable weights
✅ DecisionEmbeddingPipeline generates both embedding types
✅ ContextRetriever supports hybrid precedent search with semantic fallback
✅ 100% backward compatibility maintained
✅ All tests pass with >90% coverage
✅ Performance meets targets for precedent search

This implementation provides a comprehensive solution for decision tracking with hybrid search, explainable AI, and KG algorithm integration while maintaining full backward compatibility.
 2026-02-11 19:02:33 +05:30
KaifAhmad1 58707ff721 fix(kg): resolve 'unhashable type: Entity' in GraphAnalyzer #159 
- Robust ID extraction in CentralityCalculator, CommunityDetector, and ConnectivityAnalyzer
- Support for direct Entity objects and dictionaries as node identifiers
- Improved Entity hashability in utils/types.py
- Added integration test to verify fix and prevent regression
 2026-01-08 17:23:31 +05:30
KaifAhmad1 e3b53998c3 Fix dependency issues, align GraphRAG notebook, and update changelog 2026-01-07 19:00:30 +05:30
KaifAhmad1 a9bd3be689 Update context module docs, cleanup notebook, and refactor context files 2025-12-21 16:50:56 +05:30
KaifAhmad1 a7106f810f feat(context): Add comprehensive tests and fix dependencies 
- Added unit tests for Context module (AgentContext, AgentMemory, ContextGraph, EntityLinker)
- Fixed Tuple import error in deduplication/merge_strategy.py
- Verified notebook examples via test conversion
 2025-12-10 14:05:05 +05:30