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- Welcome to GraphRAG - GitHub Pages
GraphRAG is a structured, hierarchical approach to Retrieval Augmented Generation (RAG), as opposed to naive semantic-search approaches using plain text snippets The GraphRAG process involves extracting a knowledge graph out of raw text, building a community hierarchy, generating summaries for these communities, and then leveraging these
- GitHub - microsoft graphrag: A modular graph-based Retrieval-Augmented . . .
The GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs To learn more about GraphRAG and how it can be used to enhance your LLM's ability to reason about your private data, please visit the Microsoft Research Blog Post
- GraphRAG: Unlocking LLM discovery on narrative private data
Microsoft is transforming retrieval-augmented generation with GraphRAG, using LLM-generated knowledge graphs to significantly improve Q A when analyzing complex information and consistently outperforming baseline RAG Get the details
- What is GraphRAG?. Advanced RAG using Knowledge Graphs and . . . - Medium
Recently, a new advancement to improve naive RAG is introduced called GraphRAG which uses Knowledge Graphs over Vector DBs for finding relevant information from external documents when a user
- The Future of AI: GraphRAG – A better way to query interlinked . . .
GraphRAG is an advanced version of RAG that utilizes graph-based retrieval mechanisms, enhancing the generation process by capturing richer, more contextual information GraphRAG improves over vector RAG in the following ways
- What is GraphRAG? | GraphRAG. org
GraphRAG represents a novel approach to Retrieval-Augmented Generation (RAG) by integrating knowledge graphs with large language models (LLMs) This system addresses the limitations of traditional RAG implementations, offering a more sophisticated solution for information retrieval and generation
- What is GraphRAG? - IBM
GraphRAG is an advanced version of retrieval-augmented generation (RAG) that incorporates graph-structured data, such as knowledge graphs (KGs)
- Retrieval-Augmented Generation with Graphs (GraphRAG)
Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information, such as knowledge, skills, and tools from external sources
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