Examples and demos
Get inspired by GenAI with Memgraph
Welcome to the inspiration hub for GraphRAG and Generative AI (GenAI) applications powered by Memgraph. Explore demos, real-world examples, and community projects showcasing how Memgraph brings advanced graph-based knowledge retrieval to life. Whether you’re building LLM applications, tackling real-time analytics, or creating knowledge-driven solutions, these examples will help spark your next project.
How to Use This Page
- Explore demos - Try out fully functional examples built by the Memgraph Developer Experience team.
- Learn from real-world use cases - See how Memgraph users are solving complex challenges with GraphRAG and GenAI.
- Build your own - Use the provided GitHub links to start experimenting with Memgraph and customize your solutions and let us know about it in our Memgraph Discord community.
Featured demos
Agentic GraphRAG with Memgraph
An agentic GraphRAG application that automatically performs GraphRAG operations to answer questions. The application determines the best retrieval and reasoning strategy based on the query, making it completely dataset-agnostic. It automatically performs operations like vector search, deep path traversals, community detection and PageRank.
GraphRAG with Memgraph
Learn how to build a GraphRAG system from scratch on a simple example - start from the knowledge graph creation and embeddings calculations and hop onto the implementation of different retrieval strategies.
GenAI application template with Memgraph and LangChain
A practical demo and template to kickstart building GenAI applications with Memgraph, LangChain and GPT or Llama. This demo showcases how to integrate a knowledge graph with large language models (LLMs) to enhance retrieval and generate accurate, context-rich responses.
GraphRAG examples with Memgraph
Here’s how others are using Memgraph for GraphRAG and GenAI.
How Precina Health uses Memgraph and GraphRAG to revolutionize type 2 diabetes care with real-time insights
This example highlights real-time insights from dynamic patient data with multi-hop reasoning for personalized diabetes care.
Using Memgraph for knowledge-driven AutoML in Alzheimer’s research at Cedars-Sinai
This example highlights knowledge-driven machine learning models and retrieval-augmented generation for medical research.
How-to resources
How to extract entities and build a knowledge graph wtih Memgraph and spaCy
Building a knowledge graph from unstructured text becomes seamless with Memgraph and spaCy. This how-to guide walks through the process of entity extraction, relationship identification, and graph construction. It’s an excellent starting point for anyone working with text-heavy datasets and looking to visualize meaningful connections.
How to build a movie similarity search engine with vector search in Memgraph
Learn how to use Memgraph’s vector search capabilities to build a movie similarity search application. This guide demonstrates how to use graph-based retrieval for semantic similarity queries, making it perfect for applications like recommendation systems or content discovery.