If you’re doing GraphRAG in the style of Microsoft’s original model , where the graph is inferred dynamically by the LLM and used primarily for retrieval or reasoning , then Neo4j is often overkill in my opinion. Not to mention its performance issues and toxic attitude to open source.
Lightweight, in-memory alternatives like NetworkX or even plain Python dictionaries are typically faster to set up, easier to integrate, and better suited to ephemeral, prompt driven graph reasoning.
Here are some other options :
NetworkX
LlamaIndex’s KnowledgeGraphIndex
LangChain with in-memory Python structures, Lets you model entities and relationships as dictionaries or lists, making it easy to traverse or query on demand.
john-mark|9 months ago
Lightweight, in-memory alternatives like NetworkX or even plain Python dictionaries are typically faster to set up, easier to integrate, and better suited to ephemeral, prompt driven graph reasoning.
Here are some other options : NetworkX
LlamaIndex’s KnowledgeGraphIndex
LangChain with in-memory Python structures, Lets you model entities and relationships as dictionaries or lists, making it easy to traverse or query on demand.
https://docs.llamaindex.ai/en/stable/examples/index_structs/...
https://www.microsoft.com/en-us/research/blog/moving-to-grap...
https://github.com/microsoft/graphrag