GraphRAG — Graph-backed Retrieval-Augmented Generation (Prototype → Industrialisation)

Concept proposed and prototyped during internship; later industrialised and maintained in full-time role.

One-line summary: GraphRAG augments retrieval-augmented generation pipelines with a knowledge-graph-centric retrieval and processing stage, aiming to improve context quality, relationship-aware retrieval and fact-consistency evaluation for production chatbots.

My role & timeline

• Intern (Feb–Aug 2024): Identified product need, proposed GraphRAG, implemented a working prototype within one week, and validated potential on selected use cases.
• Full-time (Oct 2024 – present): Responsible for driving GraphRAG from prototype to production: extending experiments, integrating with product pipelines, designing industrial data flows and coordinating evaluation.
• Core contributions: idea generation, prototype engineering, experimental design, KG construction & processing pipeline, fact-consistency evaluation integration, liaison between research and engineering teams.

Motivation

Traditional RAG approaches rely primarily on vector similarity and unstructured context; while effective, they sometimes miss relational structure or facts expressible only through multi-hop relationships. GraphRAG explores using extracted entities and relations to build a compact, high-quality graph as retrieval context, improving relevance, traceability and downstream fact-consistency checks.

Architecture (high-level)

Key pipeline stages:

1. Document ingestion: extract entities/relations from source documents and metadata.
2. Graph construction and post-processing: construct knowledge graph with entity normalization, relation extraction and provenance tracking.
3. Graph retrieval: subgraph-based; entity-hopping.
4. Context assembly: select graph elements and transform them into retrieval contexts by source attribute for LLM prompt construction.
5. Generation & evaluation: use LLMs for generation and apply graph-based checks for fact consistency / evaluation metrics.

Outcomes & impact

• Rapid feasibility checks: the prototype enabled quick feasibility checks on representative use cases, suggesting potential improvements in retrieved-context relevance and interpretability for downstream generation.
• Further development: the project proceeded to further experimentation and integration efforts within the company, with ongoing development and evaluation.
• Research ↔ Engineering bridge: GraphRAG served as a research-to-production testbed, demonstrating an ability to translate retrieval and verification ideas into production-oriented components.