I've been working on an implementation of graph RAG (GRAG) using Neo4j as the underlying store.
The overall DX is quite nice. The apoc-extended set of plugins[0] make it very seamless to work with embeddings and and LLMs during local dev/testing. The Graph Data Science package comes preloaded with a series of community detection algorithms[1] like Louvain and Leiden.
Performance has been very, very good as long as your strategy to enter the graph is sound and you've structured your graph in such a way that you can meaningfully traverse the adjacent properties/nodes.
We've currently deployed the Community edition to AWS ECS Fargate using AWS Copilot + EFS as a persistent volume. There were some kinks with respect to the docs, but it works great otherwise.
It's worth a look for any teams that are trying to improve their RAG or are exploring GRAG in general. It's not a silver bullet; you still need to have some "insight" into how to process your input data source for the graph to do its magic. But the combination of the built-in graph algorithms and the ergonomics of Cypher make it possible to perform certain types of queries and "explorations" that would otherwise be either harder to optimize or more expensive in a relational store.
The overall DX is quite nice. The apoc-extended set of plugins[0] make it very seamless to work with embeddings and and LLMs during local dev/testing. The Graph Data Science package comes preloaded with a series of community detection algorithms[1] like Louvain and Leiden.
Performance has been very, very good as long as your strategy to enter the graph is sound and you've structured your graph in such a way that you can meaningfully traverse the adjacent properties/nodes.
We've currently deployed the Community edition to AWS ECS Fargate using AWS Copilot + EFS as a persistent volume. There were some kinks with respect to the docs, but it works great otherwise.
It's worth a look for any teams that are trying to improve their RAG or are exploring GRAG in general. It's not a silver bullet; you still need to have some "insight" into how to process your input data source for the graph to do its magic. But the combination of the built-in graph algorithms and the ergonomics of Cypher make it possible to perform certain types of queries and "explorations" that would otherwise be either harder to optimize or more expensive in a relational store.
[0] https://neo4j.com/labs/apoc/5/ml/openai/
[1] https://neo4j.com/docs/graph-data-science/current/algorithms...