Update 2024-08-18-RAG.md

_posts/2024-08-18-RAG.md

@@ -82,24 +82,6 @@ Introduction to RAG, LlamaIndex, examples.
 <tr><td>Perspective API</td><td>0.728</td><td>0.787</td><td>0.532</td><td>0.699</td></tr>
 </table>
 
----
-## GraphRAG
-**Blog:** [從 RAG 到 GraphRAG：透過圖譜節點關係增強回應精確度](https://idataagent.com/2024/05/06/from-rag-to-graphrag-enhance-response-accuracy-through-graph-node-relationships/)<br>
-![](https://miro.medium.com/v2/resize:fit:786/format:webp/0*22VVg9YOaqHLRJ0-)
-
-**Blog:** [GraphRAG: Unlocking LLM discovery on narrative private data)](https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/)<br>
-
-**Paper:** [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://arxiv.org/pdf/2404.16130)<br>
-**Code:** <br>
-* [GraphRAG Accelerator](https://github.com/Azure-Samples/graphrag-accelerator)
-* [GraphRAG Library](https://github.com/microsoft/graphrag)
-
----
-### HippoRAG
-**Paper:** [HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models](https://arxiv.org/abs/2405.14831)<br>
-**Code:** [https://github.com/OSU-NLP-Group/HippoRAG](https://github.com/OSU-NLP-Group/HippoRAG)<br>
-![](https://github.com/OSU-NLP-Group/HippoRAG/raw/main/images/hook_figure.png)
-
 ---
 ### [Contextual Retrieval RAG](https://www.anthropic.com/news/contextual-retrieval)
 [Anthropic Prompt Caching](https://github.com/anthropics/anthropic-cookbook/blob/main/misc/prompt_caching.ipynb)<br>
@@ -164,8 +146,6 @@ index.storage_context.persist()
 **Kaggle:** [https://www.kaggle.com/code/rkuo2000/llm-llamaindex](https://www.kaggle.com/code/rkuo2000/llm-llamaindex)
 
 ---
-## Applications
-
 ### [RAG using LlamaIndex framework to build a simple chatbot, to Q&A a bunch of documents](https://abvijaykumar.medium.com/prompt-engineering-retrieval-augmented-generation-rag-cd63cdc6b00)
 ![](https://miro.medium.com/v2/resize:fit:720/format:webp/1*PL-HZqYOdczK4PoZjEPlKQ.png)
 
@@ -197,8 +177,24 @@ Anyscale剛剛發布的一篇精彩好文，裡頭介紹了很多提升RAG成效
 ![](https://i.imgur.com/oWhiZHb.png)
 **Code:** [https://github.com/HuskyInSalt/CRAG](https://github.com/HuskyInSalt/CRAG)<br>
 
+---
+### GRAG
+**Paper:** [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://arxiv.org/pdf/2404.16130)<br>
+**Paper:** [GRAG: Graph Retrieval-Augmented Generation](https://arxiv.org/abs/2405.16506)<br>
+**Blog:** [GraphRAG: Unlocking LLM discovery on narrative private data](https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/)<br>
+**Blog:** [Knowledge Graph + RAG | Microsoft GraphRAG 實作與視覺化教學](https://medium.com/@cch.chichieh/knowledge-graph-rag-microsoft-graphrag-%E5%AF%A6%E4%BD%9C%E8%88%87%E8%A6%96%E8%A6%BA%E5%8C%96%E6%95%99%E5%AD%B8-ac07991855e6)
+**Code:** [https://github.com/microsoft/graphrag](https://github.com/microsoft/graphrag)<br>
+**Code:** [GraphRAG Accelerator](https://github.com/Azure-Samples/graphrag-accelerator)<br>
+
+---
+### HippoRAG
+**Paper:** [HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models](https://arxiv.org/abs/2405.14831)<br>
+**Code:** [https://github.com/OSU-NLP-Group/HippoRAG](https://github.com/OSU-NLP-Group/HippoRAG)<br>
+![](https://github.com/OSU-NLP-Group/HippoRAG/raw/main/images/hook_figure.png)
+
 ---
 ### TAG (Table-Augmented Generation)
+**Paper:** [Text2SQL is Not Enough: Unifying AI and Databases with TAG](https://arxiv.org/abs/2408.14717)<br>
 **Blog:** [Goodbye, Text2SQL: Why Table-Augmented Generation (TAG) is the Future of AI-Driven Data Queries!](https://ai.plainenglish.io/goodbye-text2sql-why-table-augmented-generation-tag-is-the-future-of-ai-driven-data-queries-892e24e06922)<br>
 **Code:** [https://github.com/TAG-Research/TAG-Bench](https://github.com/TAG-Research/TAG-Bench)<br>
 ![](https://github.com/TAG-Research/TAG-Bench/raw/main/assets/tag.png)