Power GenAI Apps with Your Own Data using GraphRAG

Embed domain-specific knowledge from your graphs to build GenAI apps. Reduce AI hallucinations. Improve accuracy. Deliver answers that matter.

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Intelligence with Your Context

LLMs often hallucinate because they lack access to your domain-specific data. They're working in isolation from your proprietary knowledge.

With GraphRAG, you combine Memgraph's in-memory graph database, LLMs, and Retrieval-Augmented Generation (RAG) to deliver accurate, context-rich answers—grounded in your own proprietary data.

Search, Retrieve, and Discover

With GraphRAG, your search queries go beyond surface-level results. By tapping into knowledge graphs, it not only retrieves precise, context-driven answers quickly but also uncovers hidden insights by analyzing real-world relationships in your data. This combination empowers you to make smarter, faster decisions while gaining a deeper understanding of the connections within your data.

Why GraphRAG with Memgraph?

Fewer Hallucinations

Memgraph anchors AI models to real, structured data, reducing the risk of hallucinations or irrelevant responses. Your models stop guessing and start knowing.

Better Accuracy

Memgraph's community detection and centrality algorithms—including Louvain, LabelRankT, and PageRank—ensure higher response accuracy by uncovering deeper relationships in your knowledge graph.

Contextual Upgrade

Basic RAG systems pull data from flat, relational models, missing the bigger picture. GraphRAG represents real relationships between entities, capturing connections and context to give LLMs the deeper structure needed for smarter, more accurate answers.

Real-Time, Relevant Information

With up-to-date schema tracking, real-time processing, and data persistence, Memgraph keeps your knowledge graph synced with the latest data, ensuring your LLM-powered apps deliver fresh, relevant information while securely storing critical knowledge.

Memgraph Tools to build GraphRAG

Memgraph as Your GraphDB

Use Memgraph as the backbone for your GraphRAG apps. It's built for scale, performance, and can handle complex queries across large knowledge graphs—whether you're working with millions of nodes or performing real-time calculations.

Integrated Algorithms

Memgraph's powerful algorithms—Community Detection (Louvain, Leiden), PageRank, and Graph Traversals—ensure your AI isn't just making things up. It responds based on real, structured relationships embedded within your data.

GraphChat

Query your graph with plain English. GraphChat is your direct line to your graph database inside Memgraph Lab. Forget about writing complex queries—just ask. GraphChat translates your natural language question into a Cypher query, runs it on Memgraph, and provides you with the best possible answer in human language.

This two-phase generative AI app gives you answers grounded in the context of your knowledge graph.

LangChain and LlamaIndex Integrations

Memgraph integrates with Langchain and LLamaIndex, allowing for multi-hop retrieval to answer complex questions by connecting data from different sources. You can easily integrate Memgraph with your existing LLM workflows to power advanced knowledge extraction.

Upcoming Features

Vector Search

Typically used in the first step of finding and extracting relevant information (pivot search), vector search enables similarity search alongside relevance-based graph search in a single, high-performance system.

GraphChat Updates

The upcoming release will bring major improvements to GraphChat in Memgraph Lab, making it even easier to query your graph data with natural language and receive detailed, context-rich responses.

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