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LlamaIndex RAG: Build Efficient GraphRAG Systems
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.
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By FalkorDBRef: 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.