The Best GenAI Applications Combine Fresh Data with Top Language Models
The best GenAI applications combine the freshest, most pertinent customer data with top language models, but getting that data into the model’s context window isn’t easy. That’s where the new GraphRAG capability announced today by in-memory graph database Memgraph comes into play.
Memgraph: The Open-Source In-Memory Graph Database
Memgraph develops an in-memory graph database that excels at real-time use cases that are a mix of transactional and analytical workloads, such as fraud detection and supply chain planning. It was launched as an open-source offering in 2016 by Dominik Tomicevic and Marcko Budiselić, who found that traditional graph databases couldn’t handle the demands of this particular type of application.
Graph Databases: The Problem with Traditional Graph Databases
Traditional graph databases, such as Neo4j, are batch-oriented and store data on disk. This works well when you want to ask a wide range of graph questions on large amounts of slow-moving data, but it doesn’t work well when you need quick answers on faster-moving but smaller data sets, Tomicevic says.
Memgraph: The Solution
Instead of trying to fit analytic use cases into a batch graph database, Tomicevic and Budiselić decided to build a graph database from scratch that caters to this particular type of workload. Memgraph stores all data in RAM, providing not only fast data ingest but also the capability to run analytics and data science algorithms on the entirety of the graph.
GraphRAG in Memgraph 3.0
With today’s launch of Memgraph 3.0, the company is taking its real-time analytics investment into the world of generative AI. It is launching a pair of new features with Memgraph 3.0 that position the database to be more useful for emerging GenAI workloads, such as serving chatbots or AI agents.
Vector Search and GraphRAG
The first new feature in Memgraph 3.0 is the addition of vector search. By storing graph data as vector embeddings, users will be able to serve explicit relationships (as defined by the graph nodes and edges) into the context windows of language models to get a better result as part of a RAG pipeline, or GraphRAG.
Conclusion
Memgraph’s GraphRAG capability, along with its in-memory graph database, provides a powerful solution for GenAI applications. By leveraging vector search and GraphRAG, developers can create better language models that can handle complex queries and provide more accurate results.
FAQs
Q: What is GraphRAG?
A: GraphRAG (Graph-based Reasoning and Generation) is a new capability in Memgraph 3.0 that allows users to serve explicit relationships (as defined by the graph nodes and edges) into the context windows of language models to get a better result.
Q: What is the purpose of GraphRAG in Memgraph 3.0?
A: The purpose of GraphRAG in Memgraph 3.0 is to position the database to be more useful for emerging GenAI workloads, such as serving chatbots or AI agents.
Q: How does GraphRAG work?
A: GraphRAG works by storing graph data as vector embeddings and serving explicit relationships (as defined by the graph nodes and edges) into the context windows of language models to get a better result.
Q: What are the benefits of using GraphRAG?
A: The benefits of using GraphRAG include providing more accurate results, reducing the need for manual data preprocessing, and enabling developers to create better language models.
