Graph-based RAG Research

This section serves as the central hub for all research, benchmarks, and exploratory work regarding Graph-Based Retrieval-Augmented Generation (GraphRAG) at Unique.

As we move beyond traditional vector-based search, we have investigated how structured knowledge, represented as networks of entities and relationships, can solve complex reasoning tasks that standard RAG often struggles to address. The pages document empirical testing of various graph architectures.

What is Graph-Based RAG?

While traditional RAG scans for text snippets that are semantically close to a query (vector similarity), Graph-Based RAG builds a map of knowledge. It extracts entities (people, companies, concepts) and their relationships (works at, located in, competitor of) to create a Knowledge Graph.

• The Advantage: It turns retrieval into a form of reasoning. Instead of just finding matching words, the system can follow "edges" in a graph to connect disparate pieces of information.

• The Goal: To improve performance on queries, such as document-wide aggregations ("Summarize all mentions of risk across 100 files") and multi-step logic.

Research & Implementation Pages

This folder contains detailed documentation of our specific experiments and findings:

• Beyond Traditional RAG: How GraphRAG Enhances Information Retrieval

An introduction to the "GraphRAG Manifesto." This page explores the theoretical shift from semantic retrieval of text to navigating an index of connections and the potential for reasoning-based retrieval.

• From GraphRAG to Surfacing the Structure: Lessons Learned and a New Path Forward

A deep dive into the practical challenges of graph construction. It covers our initial pipeline development, the difficulty of LLM-driven ontology extraction, and the "personalized recommendation" use case.

• LightRAG

An overview of our work with LightRAG, a hybrid framework designed to be more cost-efficient and flexible than heavy graph systems by combining dual-path retrieval (low-level facts + high-level themes).

• LightRAG Evaluation

Our primary benchmark report. This page details head-to-head testing between LightRAG and UniqueAI-Chat (Agentic RAG) on multi-step reasoning and document-wide information aggregation using our due diligence benchmarking dataset.

Key Research Takeaways

• Extraction is the Bottleneck: The quality of a GraphRAG system is only as good as the initial graph extraction. Our testing showed that missing links during the "build phase" often lead to lower recall compared to agentic search.

• Agentic vs. Graph: Currently, our agentic workflows (UniqueAI-Chat) outperform default LightRAG configurations in sequential logic and complex data mapping.

• Strategic Status: As of April 2026, we have paused the integration of graph-based retrieval in favor of optimizing agentic search.