LlamaIndex RAG: Build Efficient GraphRAG Systems

LlamaIndex RAG: Build Efficient GraphRAG Systems

Update: 2024-12-01
Share

Description

Ref: ⁠https://www.falkordb.com/blog/llamaindex-rag-implementation-graphrag/




This article explains how to build efficient Retrieval Augmented Generation (RAG) systems using LlamaIndex and FalkorDB.


LlamaIndex is an open-source framework that simplifies connecting LLMs to various data sources, while FalkorDB is a high-performance knowledge graph database.


The combination allows for the creation of GraphRAG systems, enhancing LLM responses with real-time, contextually relevant information retrieved from the knowledge graph. The article provides a step-by-step
guide, including code examples, for setting up the environment, ingesting data, building the index, and querying the system.




Best practices for maintaining these pipelines are also discussed, emphasizing the benefits of FalkorDB for scalability and performance.





Comments 
00:00
00:00
x

0.5x

0.8x

1.0x

1.25x

1.5x

2.0x

3.0x

Sleep Timer

Off

End of Episode

5 Minutes

10 Minutes

15 Minutes

30 Minutes

45 Minutes

60 Minutes

120 Minutes

LlamaIndex RAG: Build Efficient GraphRAG Systems

LlamaIndex RAG: Build Efficient GraphRAG Systems

FalkorDB