add graphrag (#598)

README.md

@@ -645,14 +645,15 @@ Please review our [CONTRIBUTING.md](https://github.com/EthicalML/awesome-product
 * [Faiss](https://github.com/facebookresearch/faiss) ![](https://img.shields.io/github/stars/facebookresearch/faiss.svg?style=social) - Faiss is a library for efficient similarity search and clustering of dense vectors.
 * [fastRAG](https://github.com/IntelLabs/fastRAG) ![](https://img.shields.io/github/stars/IntelLabs/fastRAG.svg?style=social) - fastRAG is a research framework for efficient and optimized retrieval augmented generative pipelines, incorporating state-of-the-art LLMs and Information Retrieval.
 * [Finetuner](https://github.com/jina-ai/finetuner) ![](https://img.shields.io/github/stars/jina-ai/finetuner.svg?style=social) - Finetuner provides an effective way to improve performance on neural search tasks.
+* [GraphRAG](https://github.com/microsoft/graphrag) ![](https://img.shields.io/github/stars/microsoft/graphrag.svg?style=social) - GraphRAG is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs.
 * [llmware](https://github.com/llmware-ai/llmware) ![](https://img.shields.io/github/stars/llmware-ai/llmware.svg?style=social) - llmware provides a unified framework for building LLM-based applications (e.g, RAG, Agents), using small, specialized models that can be deployed privately, integrated with enterprise knowledge sources safely and securely, and cost-effectively tuned and adapted for any business process.
 * [Mem0](https://github.com/mem0ai/mem0) ![](https://img.shields.io/github/stars/mem0ai/mem0.svg?style=social) - Mem0 enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions.
 * [MindSQL](https://github.com/Mindinventory/MindSQL) ![](https://img.shields.io/github/stars/Mindinventory/MindSQL.svg?style=social) - MindSQL is a Python RAG library to streamline the interaction between users and their databases using just a few lines of code.
 * [NGT](https://github.com/yahoojapan/NGT) ![](https://img.shields.io/github/stars/yahoojapan/NGT.svg?style=social) - NGT provides commands and a library for performing high-speed approximate nearest neighbor searches against a large volume of data in high dimensional vector data space.
 * [NMSLIB](https://github.com/nmslib/nmslib) ![](https://img.shields.io/github/stars/nmslib/nmslib.svg?style=social) - Non-Metric Space Library (NMSLIB): An efficient similarity search library and a toolkit for evaluation of k-NN methods for generic non-metric spaces.
 * [Qdrant](https://github.com/qdrant/qdrant) ![](https://img.shields.io/github/stars/qdrant/qdrant.svg?style=social) - An open source vector similarity search engine with extended filtering support.
 * [RAGFlow](https://github.com/infiniflow/ragflow) ![](https://img.shields.io/github/stars/infiniflow/ragflow.svg?style=social) - RAGFlow is a RAG engine based on deep document understanding.
-* [RAGxplorer](https://github.com/gabrielchua/RAGxplorer) ![](https://img.shields.io/github/stars/gabrielchua/RAGxplorer.svg?style=social) - RAGxplorer is a tool to build Retrieval Augmented Generation (RAG) visualisations.
+* [RAGxplorer](https://github.com/gabrielchua/RAGxplorer) ![](https://img.shields.io/github/stars/gabrielchua/RAGxplorer.svg?style=social) - RAGxplorer is a tool to build RAG visualisations.
 * [Rule-based Retrieval](https://github.com/whyhow-ai/rule-based-retrieval) ![](https://img.shields.io/github/stars/whyhow-ai/rule-based-retrieval.svg?style=social) - Rule-based Retrieval enables users to create and manage RAG applications with advanced filtering capabilities.
 * [Vanna](https://github.com/vanna-ai/vanna) ![](https://img.shields.io/github/stars/vanna-ai/vanna.svg?style=social) - Vanna is a RAG framework for SQL generation and related functionality.