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- 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
- Welcome - GraphRAG
Retrieval-Augmented Generation (RAG) is a technique to improve LLM outputs using real-world information This technique is an important part of most LLM-based tools and the majority of RAG approaches use vector similarity as the search technique, which we call Baseline RAG
- 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
- 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? - IBM
GraphRAG identifies unusual patterns that deviate from expected behavior For example, in financial services, it can detect suspicious transaction patterns to prevent fraud or uncover cross-selling opportunities by analyzing customer behavior
- 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?. 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
- 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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