docs/technical/GRAPH_RAG_ENHANCEMENTS.md

🧠 UnifiedGraphRAG Enhancements - CgentCore Inspiration

Date: 2025-11-24
Status: ✅ ENHANCED - Based on CgentCore Architecture

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✅ IMPLEMENTED ENHANCEMENTS

1. LLM-Based Answer Synthesis ✅ IMPLEMENTED

Inspiration: CgentCore's L1 Director Agent response generation pattern

Implementation:

• Uses LlmService.generateContextualResponse() for natural language synthesis
• Builds comprehensive context from graph nodes
• Includes reasoning path explanation
• Provides confidence assessment and source citations

Before:

answer: "Reasoning complete. See nodes for details."

After:

answer: await this.synthesizeAnswer(query, topNodes, context)
// Returns comprehensive LLM-generated answer with:
// - Direct answer to query
// - Key insights from graph
// - Confidence assessment
// - Sources referenced

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2. CMA Graph Integration ✅ IMPLEMENTED

Inspiration: CgentCore's memory_relations table and CMA architecture

Implementation:

• Uses MemoryRepository.searchEntities() for direct memory relations
• Leverages memory_entities table as explicit graph edges
• Integrates with UnifiedMemorySystem for episodic memory
• Finds related memories based on keyword matching

New Expansion Strategies:

1. Pattern-based (existing): Uses widget patterns
2. CMA Relations (new): Direct memory entity connections
3. Episodic Memory (new): Related events from working memory

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3. Semantic Similarity ✅ IMPLEMENTED (Basic)

Inspiration: CgentCore's vector similarity approach (InMemoryVectorStoreAdapter)

Implementation:

• computeSemanticSimilarity() method using Jaccard similarity
• Keyword overlap + phrase matching
• Filters nodes by semantic relevance threshold

Current: Basic keyword-based similarity
Future: Replace with proper embeddings (Sentence Transformers → Pinecone)

Note: This is a simplified version. For production, integrate with:

• Sentence Transformers (MiniLM) for embeddings
• Pinecone/Weaviate for vector storage
• Cosine similarity for semantic matching

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4. MCP Tool Exposure ✅ PRIMARY INTERFACE

Status: Already implemented in previous work

• ✅ MCP Tool: autonomous.graphrag - PRIMARY INTERFACE (following WidgeTDC architecture)
• ✅ REST API: POST /api/mcp/autonomous/graphrag - Secondary/compatibility layer

Architecture: Following WidgeTDC pattern, all cognitive services are exposed via MCP tools first. REST endpoints exist for compatibility but MCP is the preferred interface for widget-to-service communication.

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⚠️ STILL MISSING (Future Enhancements)

1. Explicit Graph Database (Neo4j) ⚠️ PLANNED

Inspiration: CgentCore's structured data approach

Recommendation:

• Setup Neo4j for explicit graph storage
• Migrate from implicit patterns to explicit edges
• Use Cypher queries for graph traversal
• Store node properties and edge weights

Implementation Path:

// Future: Neo4j integration
import neo4j from 'neo4j-driver';

class Neo4jGraphStore {
  async createNode(node: GraphNode): Promise<void> { ... }
  async createEdge(from: string, to: string, relation: string): Promise<void> { ... }
  async expandNode(nodeId: string): Promise<GraphNode[]> { ... }
}

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2. Vector DB Integration (Pinecone) ⚠️ PLANNED

Inspiration: CgentCore's Pinecone/Weaviate integration specs

Current: Basic keyword similarity
Future: Proper vector embeddings

Implementation Path:

// Future: Pinecone integration
import { Pinecone } from '@pinecone-database/pinecone';

class VectorEmbeddingService {
  async embed(text: string): Promise<number[]> {
    // Use Sentence Transformers (MiniLM)
  }
  
  async findSimilar(queryEmbedding: number[], topK: number): Promise<GraphNode[]> {
    // Pinecone vector search
  }
}

Benefits:

• True semantic similarity (not just keywords)
• Better multi-hop expansion
• Context-aware node discovery

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3. Enhanced Semantic Similarity ⚠️ PLANNED

Current: Jaccard similarity (keyword-based)
Future: Embedding-based cosine similarity

Upgrade Path:

1. Integrate Sentence Transformers (MiniLM)
2. Generate embeddings for all nodes
3. Store in Pinecone/Weaviate
4. Use cosine similarity for expansion

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📊 COMPARISON: Before vs After

Feature	Before	After	Status
Answer Synthesis	Placeholder text	LLM-generated	✅
CMA Integration	Pattern-based only	+ Memory relations + Episodic	✅
Semantic Similarity	None	Basic keyword-based	✅
Graph Expansion	Single strategy	3 strategies	✅
Source Citations	None	Included in answer	✅
Confidence Scoring	Basic	Enhanced with semantic	✅

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🎯 NEXT STEPS (Priority Order)

Priority 1: Vector DB Integration

1. Setup Pinecone account/index
2. Integrate Sentence Transformers for embeddings
3. Replace keyword similarity with vector similarity
4. Store node embeddings in Pinecone

Priority 2: Neo4j Graph Database

1. Setup Neo4j instance
2. Migrate implicit patterns to explicit edges
3. Use Cypher for graph traversal
4. Store node properties and relations

Priority 3: Enhanced Semantic Search

1. Upgrade from keyword to embedding-based
2. Implement cosine similarity
3. Add query expansion
4. Multi-modal similarity (text + metadata)

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📚 REFERENCES

• CgentCore Architecture: https://github.com/Clauskraft/CgentCore
• L1 Director Agent: Response generation pattern
• CMA Spec: Memory relations and graph structure
• RAG Architecture: Vector DB integration patterns
• SRAG Spec: Hybrid search (BM25 + semantic)

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Status: ✅ ENHANCED - Core improvements implemented, infrastructure upgrades planned