GraphRAG Configuration Guide - What is GraphRAG?

22/06/2026

GraphRAG version 1.2.0

Graph-based Retrieval-Augmented Generation (GraphRAG) represents the next evolution in AI retrieval architectures. While traditional RAG frameworks rely on simple text snippets, GraphRAG connects Large Language Models (LLMs) to a structured knowledge graph. This foundation enables multi-hop reasoning, minimizes AI hallucinations, and ensures the factual accuracy required for mission-critical enterprise decision-making.

Graphwise delivers a foundational, enterprise-ready engine that moves beyond "black box" AI. Its built-in workflow engine enables domain Subject Matter Experts (SMEs) to easily configure GraphRAG Agents, collaborating seamlessly with technical teams to deploy domain-aware solutions without maintaining complex, code-heavy frameworks.

Standard vector-only RAG applications encounter severe limitations when deployed in real-world enterprise environments whereas GraphRAG natively solves three recurring issues:

Reasoning vs. Retrieval: Vector search finds text that appears semantically similar but cannot navigate connections between entities. For example, asking which suppliers are affected by a specific regulation returns paragraphs containing the words "suppliers" and "regulation," rather than a calculated relational answer.
Auditability & Trust: Standard RAG frameworks lack clear audit trails. When an answer is generated without a direct link to its source, debugging retrieval, ranking, or generation errors becomes guesswork.
Hybrid Retrieval Demands: True enterprise questions require a unified approach. Relying on a single retrieval method leaves critical gaps. GraphRAG combines graph traversal, vector similarity, and full-text search into a single pipeline.

The core differentiator is the underlying data structure: knowledge is represented as a graph built on RDF and open semantic web standards. This provides explicit, machine-readable meanings and relationships governed by a shared schema, allowing the system to scale consistently.

GraphRAG builds on this foundation combining Graphwise's knowledge graph technologies such as Graph Modeling and GraphDB with Large Language Models (LLMs) in a governed and explainable pipeline.

All user queries flow through a transparent, four-stage pipeline executed in real time:

Guard (Input Guardrails):
Vets the incoming question before processing begins by running guardrails with additional checks and syncs. Out-of-scope or unsafe queries are rejected early to save tokens and mitigate risk.
Ground & Gather (Hybrid Retrieval):
Executes multiple retrieval methods in parallel. The initial query is expanded and enriched across knowledge graph lookups/traversals, SPARQL queries (via GraphDB MCP), full-text search, and vector search. Short-term conversation memory is prepared concurrently.
Contextualize & Answer (Grounded Generation):
Assembles retrieved data into a structured context package for LLM processing, resulting in the final generated answer.
Verify & Explain (Output Guardrails & Post-processing):
Evaluates the generated answer for quality, security, and compliance before it reaches the user. The final output is enriched with extracted concepts, sources, and follow-up questions.

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