Memgraph's AI Ecosystem

To learn about Memgraph's key features to build AI apps, explore the following pages:

• GraphRAG
• Machine learning

AI spans multiple areas like machine learning (ML), natural language processing (NLP), and knowledge representation and reasoning (KRR), often overlapping to create advanced systems. A key example is Generative AI (GenAI), which generates new content like text or images. Large Language Models (LLMs) power many GenAI apps, but getting them to work with your custom data can be challenging.

Fine-tuning LLMs to incorporate custom data is often complex, slow, and costly. Plus, frequent updates make it inefficient.

Retrieval-Augmented Generation (RAG) solves this by enhancing LLMs with external data sources, enabling dynamic, scalable knowledge updates. Traditional RAG is based on vector structure with vector databases, and it has proven to be a great solution in many use cases. Still, it often falls short when retrieving crucial knowledge from complex datasets. That is where GraphRAG excels.

GraphRAG improves on this by using knowledge graphs and graph features (e.g., community detection, neighborhood analysis) for more accurate retrieval and data-rich insights. This hybrid approach provides better context and performance for GenAI applications.

Memgraph has been a popular choice in AI, especially for cases that utilize machine learning. It also proves to be a great choice to build a GraphRAG.