GraphRAG Knowledge Engine
Enterprise knowledge graph pipeline
Built a production-grade GraphRAG pipeline that ingests 1.5 lakh support case documents and constructs a knowledge graph with entities, relationships, topics, and community structures. The system uses vector similarity (FAISS) to connect related concepts and surfaces accurate answers through a two-tier retrieval strategy — combining graph traversal with dense vector search for context-rich responses.
1.5L
documents ingested
17
activity classes
14
prompt templates
2-tier
RAG retrieval
Architecture & Implementation
How it's built
Document ingestion and chunking pipeline with configurable strategies
Entity and relationship extraction via LLM with structured output
Community detection using graph algorithms for topic clustering
FAISS vector index for similarity-based retrieval
Azure Durable Functions for orchestrating long-running ingestion jobs
Cosmos DB for graph storage with cross-partition queries