[FEATURE] : Context Graphs 

## Overview

Implement comprehensive context graphs for AI agents with decision tracking, precedent search, causal chains, policy management, provenance tracking, and enhanced graph algorithms. This feature will transform how AI agents maintain context, track decisions, and learn from precedents.

**Key Libraries**: `scipy>=1.9.0` (similarity calculations), `numpy>=1.21.0` (numerical operations), `gensim` (Node2Vec embeddings), graph database drivers (Neo4j, Neptune), vector databases (FAISS, Qdrant, Weaviate)

---

## Problem Statement

Current AI agents lack comprehensive context tracking capabilities:

1. **No Decision Memory**: Agents can't track why decisions were made or learn from precedents
2. **Limited Context Understanding**: Basic context retrieval without causal relationships
3. **No Policy Tracking**: Policy applications and exceptions aren't tracked
4. **Missing Provenance**: No audit trail for decision-making processes
5. **Limited Graph Analysis**: Basic algorithms without advanced embeddings or similarity metrics

---

## Solution Overview

Implement a comprehensive context graph system with:

### Decision Tracking & Context
- **Decision Recording**: Capture complete decision context with entities, policies, and reasoning
- **Precedent Search**: Find similar past decisions using hybrid semantic + structural search
- **Causal Chains**: Trace decision influence and causality relationships
- **Policy Engine**: Track policy applications, versions, and compliance

### Advanced Graph Analytics
- **Node Embeddings**: Node2Vec for structural similarity analysis
- **Enhanced Centrality**: PageRank for influence identification
- **Similarity Algorithms**: Cosine similarity for embedding-based comparison
- **Path Finding**: Dijkstra for causal chain analysis
- **Link Prediction**: Preferential attachment for relationship discovery

### Enhanced Vector Operations
- **Multi-Embedding Support**: Store semantic, structural, and temporal embeddings
- **Hybrid Search**: Combine different embedding types with adaptive weighting
- **Version Tracking**: Maintain embedding evolution history
- **Optimized Indexing**: Efficient indexes for different embedding dimensions

### Provenance & Governance
- **W3C PROV-O Compliance**: Standard provenance tracking
- **Change Management**: Policy versioning and impact analysis
- **Audit Trails**: Complete decision lineage and source tracking
- **Compliance Reporting**: Policy compliance and exception tracking

---

---

## Key Features

### 🎯 Decision Intelligence
- **Smart Decision Recording**: Capture reasoning, entities, policies, and outcomes
- **Intelligent Precedent Search**: Hybrid semantic + structural similarity
- **Causal Chain Analysis**: Trace decision influence and impact
- **Policy Management**: Version tracking, compliance checking, exception handling

### 🔬 Advanced Analytics
- **Structural Embeddings**: Node2Vec for graph structure analysis
- **Influence Analysis**: PageRank for identifying key decision nodes
- **Similarity Discovery**: Multiple similarity metrics for different use cases
- **Path Analysis**: Shortest path algorithms for causal chain traversal

### 🚀 Performance & Scale
- **Optimized Indexing**: Different index types for various embedding dimensions
- **Hybrid Search**: Combine multiple embedding types with adaptive weighting
- **Version Management**: Track embedding evolution and enable rollback
- **Efficient Storage**: Multi-embedding support with compression

### 📖 Governance & Compliance
- **Provenance Tracking**: Complete W3C PROV-O compliant audit trails
- **Change Management**: Policy versioning with impact analysis
- **Compliance Reporting**: Automated compliance checks and reporting
- **Audit Readiness**: Complete decision lineage and source documentation

---

## Implementation Phases

### Phase 1: Foundation (Sub-Issue #291 )
- Decision data models and recording
- Basic query and causal analysis
- Policy engine with versioning
- Graph schema setup

### Phase 2: Analytics (Sub-Issue #292 )  
- Node2Vec embeddings
- Enhanced centrality and similarity
- Path finding and link prediction
- Algorithm registry and convenience functions

### Phase 3: Integration (Sub-Issue #293  )
- Enhanced vector store with multi-embeddings 
- Hybrid search and similarity calculation
- Index management and optimization
- Decision embedding pipeline

### Phase 4: System Integration
- Enhanced AgentContext integration
- ContextRetriever enhancements
- Provenance integration
- End-to-end testing and documentation

---

## Dependencies & Requirements

### Technical Dependencies
- **Graph Database**: Neo4j, Neptune, FalkorDB, or in-memory
- **Vector Database**: FAISS, Qdrant, Weaviate, Pinecone, Milvus
- **Python Libraries**: `scipy>=1.9.0`, `numpy>=1.21.0`
- **Optional**: `gensim` (for Node2Vec training)

### Integration Requirements
- **Existing Modules**: `semantica.context`, `semantica.kg`, `semantica.vector_store`
- **Provenance Integration**: W3C PROV-O compliance
- **Backward Compatibility**: 100% compatibility with existing APIs

---

## Success Metrics

### Functional Metrics
- **Decision Recall**: High recall rate for relevant precedents in search
- **Causal Accuracy**: Accurate reconstruction of causal chains
- **Policy Compliance**: Complete policy application tracking
- **Provenance Completeness**: Full decision lineage coverage

### Performance Metrics
- **Search Latency**: Fast response times for hybrid precedent search
- **Index Performance**: High throughput for similarity search
- **Storage Efficiency**: Minimal storage overhead for multi-embeddings
- **Scalability**: Support for large-scale decision volumes

### User Experience Metrics
- **API Simplicity**: Minimal code required for common operations
- **Discovery Time**: Quick access to relevant precedents
- **Compliance Time**: Efficient compliance reporting
- **Learning Curve**: Short developer onboarding time

---

## Business Impact

### Direct Benefits
- **Decision Quality**: Improved decision consistency through precedent learning
- **Compliance Efficiency**: Significant reduction in compliance reporting time
- **Audit Readiness**: Complete audit trail coverage with automated documentation
- **Knowledge Retention**: Reduced knowledge loss through decision tracking

### Strategic Benefits
- **Agent Intelligence**: Significant improvement in agent reasoning capabilities
- **Competitive Advantage**: Industry-leading context tracking and decision intelligence
- **Risk Reduction**: Improved risk management through comprehensive audit trails
- **Innovation Platform**: Foundation for advanced AI agent capabilities

---

## Dependencies & Requirements

### Technical Dependencies
- **Graph Database**: Neo4j, Neptune, FalkorDB, or in-memory
- **Vector Database**: FAISS, Qdrant, Weaviate, Pinecone, or Milvus
- **Python Libraries**: `scipy>=1.9.0`, `numpy>=1.21.0`, `gensim` (optional)

### System Requirements
- **Memory**: Minimum 8GB RAM for production workloads
- **Storage**: SSD storage recommended for optimal performance
- **Network**: Low-latency network for distributed deployments
- **CPU**: Multi-core processor for parallel embedding computation

### Integration Requirements
- **Existing Modules**: `semantica.context`, `semantica.kg`, `semantica.embeddings`
- **Provenance System**: `semantica.provenance.ProvenanceManager`
- **Graph Store**: `semantica.graph_store.GraphStore`
- **Vector Store**: `semantica.vector_store.VectorStore`

---

## Risks & Mitigations

### Technical Risks
- **Performance Bottlenecks**: Mitigated through optimized indexing and caching
- **Storage Growth**: Mitigated through compression and retention policies
- **Integration Complexity**: Mitigated through phased implementation and testing

### Business Risks
- **Adoption Barriers**: Mitigated through comprehensive documentation and examples
- **Maintenance Overhead**: Mitigated through automated testing and monitoring
- **Skill Requirements**: Mitigated through developer training and support materials

---

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## Example Use Cases

### Financial Services
```python
# Credit decision with precedent search
context = AgentContext(enable_decision_tracking=True)

decision_id = context.record_decision(
    category="credit_approval",
    scenario="High-risk credit limit increase",
    reasoning="Past fraud flag with velocity check failure",
    outcome="rejected",
    confidence=0.788,
    entities=["customer:jessica_norris"]
)

# Find similar precedents
precedents = context.find_precedents(
    scenario="High-risk customer credit increase",
    category="credit_approval",
    limit=5
)

# Analyze causal chain
causal_chain = context.get_causal_chain(decision_id, max_depth=5)
```

### Healthcare
```python
# Treatment decision with policy compliance
decision_id = context.record_decision(
    category="treatment_plan",
    scenario="Diabetic patient with comorbidities",
    reasoning="Standard protocol contraindicated due to renal function",
    outcome="modified_treatment",
    confidence=0.92
)

# Check policy compliance
policy_engine = context.get_policy_engine()
compliance = policy_engine.check_compliance(decision, policy_id="diabetes_protocol_v2")
```

### Legal & Compliance
```python
# Legal decision with precedent analysis
decision_id = context.record_decision(
    category="contract_review",
    scenario="Non-standard liability clause",
    reasoning="Precedent cases show similar clauses upheld",
    outcome="approved_with_modifications",
    confidence=0.85
)

# Find legal precedents
precedents = context.find_precedents(
    scenario="Liability limitation clauses",
    category="contract_review",
    limit=10
)
```

---

## References & Resources

### Context Graphs & AI Agents
- **AI's Trillion-Dollar Opportunity: Context Graphs** - Foundation Capital  
  https://foundationcapital.com/context-graphs-ais-trillion-dollar-opportunity  
  *Decision traces as first-class objects; policy/versioning; causal chains for agents.*

- **Context Graphs for AI Agents – Implementation Guide** - Cloudraft  
  https://www.cloudraft.io/blog/context-graph-for-ai-agents  
  *Provenance, entity linking, feedback loops, decision context capture.*

- **What Is a Context Graph?** - Squirro  
  https://squirro.com/squirro-blog/what-is-a-context-graph-agentic-ai-memory  
  *Living decision traces with auditability and reasoning history.*

### Decision Intelligence & Provenance
- **PROV-AGENT (arXiv)** - Academic Research  
  https://arxiv.org/html/2508.02866v1  
  *Extends W3C PROV for agent workflows and fine-grained decision provenance.*

- **PROV-O (W3C Standard)** - Official Standard  
  https://www.w3.org/TR/prov-o  
  *Canonical provenance model for entities, activities, agents.*

- **Governed Context Graphs for LLMs & Agents** - Medium  
  https://medium.com/@adnanmasood/context-graphs-a-practical-guide-to-governed-context-for-llms-agents-and-knowledge-systems-c49610c8ff27  
  *Policy enforcement, explanation packets, governance patterns.*

- **Capturing the "Why" with Context Graphs** - LinkedIn  
  https://www.linkedin.com/pulse/context-graphs-capturing-why-age-ai-dharmesh-shah-oyyze  
  *Queryable decision history: policies, exceptions, precedents.*

### Graph Algorithms & Analytics
- **Node2Vec: Scalable Feature Learning for Networks** - Stanford  
  https://snap.stanford.edu/node2vec/  
  *Structural similarity and node embeddings for graph analysis.*

- **PageRank Algorithm** - Google Research  
  https://ai.googleblog.com/2006/08/our-pagerank-story.html  
  *Influence identification and centrality analysis.*

- **Graph Neural Networks** - Nature Reviews  
  https://www.nature.com/articles/s42254-021-00327-2  
  *Advanced graph representation learning and analysis.*

### Vector Search & Embeddings
- **Hybrid Search: Combining Dense and Sparse Vectors** - Pinecone  
  https://www.pinecone.io/learn/hybrid-search/  
  *Multi-vector search and weighted similarity combinations.*

- **FAISS: A Library for Efficient Similarity Search** - Facebook Research  
  https://faiss.ai/  
  *Optimized indexing and similarity search for high-dimensional vectors.*

- **Decision Embedding for AI Agents** - Medium  
  https://medium.com/@adnanmasood/decision-embeddings-for-ai-agents  
  *Approaches for embedding decisions and reasoning for precedent search.*

### Enterprise Applications
- **Aviso Context Graphs** - Aviso  
  https://www.aviso.com/blog/aviso%E2%80%99s-context-graphs-turning-enterprise-judgment-into-intelligence  
  *Enterprise decision explainability and temporal tracking.*

- **Decision Management in Financial Services** - FICO  
  https://www.fico.com/en/decision-management  
  *Decision engines, policy management, and audit trails in financial contexts.*

### Technical Implementation
- **Neo4j Graph Data Science** - Neo4j Documentation  
  https://neo4j.com/docs/graph-data-science/current/  
  *Comprehensive graph algorithms and analytics library.*

*This feature represents a significant advancement in AI agent capabilities, enabling true context awareness, decision intelligence, and learning from precedents. The implementation will establish Semantica as a leader in context-aware AI systems.*


Uh oh!

[FEATURE] : Context Graphs #290

Description

Overview

Problem Statement

Solution Overview

Decision Tracking & Context

Advanced Graph Analytics

Enhanced Vector Operations

Provenance & Governance

Key Features

🎯 Decision Intelligence

🔬 Advanced Analytics

🚀 Performance & Scale

📖 Governance & Compliance

Implementation Phases

Phase 1: Foundation (Sub-Issue #291 )

Phase 2: Analytics (Sub-Issue #292 )

Phase 3: Integration (Sub-Issue #293 )

Phase 4: System Integration

Dependencies & Requirements

Technical Dependencies

Integration Requirements

Success Metrics

Functional Metrics

Performance Metrics

User Experience Metrics

Business Impact

Direct Benefits

Strategic Benefits

Dependencies & Requirements

Technical Dependencies

System Requirements

Integration Requirements

Risks & Mitigations

Technical Risks

Business Risks

Example Use Cases

Financial Services

Healthcare

Legal & Compliance

References & Resources

Context Graphs & AI Agents

Decision Intelligence & Provenance

Graph Algorithms & Analytics

Vector Search & Embeddings

Enterprise Applications

Technical Implementation

Sub-issues

Metadata

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

Issue actions