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Confidential Client
Agentic RAG System with Vector Search and Knowledge Graph Integration
A confidential enterprise client required a high-accuracy question-answering system combining vector retrieval and graph-based knowledge management to navigate complex, inter-related business data.
Business Challenge
Standard vector-only RAG approaches produced incomplete answers for the client's complex, relationship-rich enterprise data. The project required combining vector similarity search with graph traversal to capture explicit connections between entities, while Graphiti provided temporal knowledge management for data that evolves over time.
Key Features
AWS Bedrock Knowledge Bases as the primary managed RAG foundation.
Multi-agent system with specialised agents for retrieval, reasoning, and synthesis.
Hybrid retrieval: vector search (ChromaDB, PGVector/Supabase) combined with Neo4j graph traversal.
Graphiti integration for temporal knowledge management — tracking how entity relationships evolve.
Neo4j + APOC-Cypher for building and querying the enterprise knowledge graph.
LLM-powered semantic document chunking for improved retrieval precision.
AWS Bedrock Guardrails for PII protection and adversarial prompt injection prevention.
Results
Working Agentic RAG POC demonstrating significantly higher answer accuracy vs. vector-only baseline.
Temporal knowledge management enabled the system to reason about how facts change over time.
Graph-based retrieval surfaced multi-hop connections invisible to pure vector search.
Guardrails implementation passed client security and compliance review.
Tech Stack
AWS Bedrock
Graphiti
Neo4j
ChromaDB