[Feature Request]: Can support memgraph for graph indexing

[kingle-zhuang]

Feature Description

memgraph is high performance then others, so maybe we can support it

Reason

high performance low cost, compatible with neo4j

Value of Feature

No response

[dosubot]

🤖

Hello @kingle-zhuang! Welcome to the LlamaIndex repository. I'm Dosu, a bot here to assist you with any queries, bugs, or contributions you'd like to make. While we're waiting for a human maintainer, feel free to ask me anything. Let's make your experience here as smooth as possible.

Thank you for your feature request. It's indeed possible to enhance LlamaIndex to support Memgraph for graph indexing. The current implementation of LlamaIndex uses a GraphStore object for storing the graph data, as seen in the graph_store property in the KnowledgeGraphIndex class:

@property
def graph_store(self) -> GraphStore:
    return self._graph_store

To integrate Memgraph, a new class (e.g., MemgraphStore) that implements the GraphStore interface and uses Memgraph for the underlying storage would need to be created. This new class should implement all the methods required by the GraphStore interface, such as upsert_triplet, get_rel_map, and others, using Memgraph's API.

Once the MemgraphStore class is implemented, an instance of it can be passed to the KnowledgeGraphIndex constructor via the graph_store parameter:

kg_index = KnowledgeGraphIndex(
    graph_store=MemgraphStore(),
    # other parameters...
)

This way, LlamaIndex would use Memgraph for storing and retrieving graph data. However, please note that this would require a good understanding of both LlamaIndex and Memgraph, as well as the ability to write Python code that interacts with Memgraph's API.

As for the current graph indexing solution used in LlamaIndex, it's the Knowledge Graph Index. This system is designed to handle large amounts of data efficiently by processing it in chunks. The cost aspect is not directly mentioned, but the system can be configured to balance between performance and resource usage. In terms of compatibility, the Knowledge Graph Index seems to be designed with flexibility in mind, suggesting that it can be integrated with various systems and configurations.

Please note that the actual performance, cost, and compatibility would depend on the specific implementation details and usage scenarios.

For more details, you can refer to the KnowledgeGraphIndex class in the LlamaIndex repository.

I hope this information is helpful. If you have any further questions or need more clarification, feel free to ask.

Best regards,
Dosu

Sources

• llama_index/indices/knowledge_graph/base.py

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[matea16]

Hi @kingle-zhuang, just saw your comment here and wanted to let you know that Memgraph integration has recently been added to LlamaIndex.
There's also an usage example available in the docs. Are you working on something with Memgraph and Llama?