包阅导读总结
1. 关键词：Graph RAG、语言模型、开源、检索增强、生成
2. 总结：本文主要介绍了蚂蚁首个开源 Graph RAG 框架设计的相关内容，包括一系列与之相关的调查、研究论文、项目代码、技术实现及应用等链接。
3. 主要内容：
– 相关调查
– RALM_Survey
– 多篇关于检索增强生成的调查论文
– 研究论文
– REALM
– RAT 等
– 项目代码
– GFMPapers
– RAGFlow 等
– 技术应用与介绍
– 如用于查询聚焦摘要的 Graph RAG 实现
– 对 Graph RAG 的设计模式、挑战等的讨论
– 相关基准与框架
– RAGAS
– ARES 等
– 图数据库相关
– Apache Jena 等
思维导图：
文章地址：https://mp.weixin.qq.com/s/Avopryr24pA-fEwLdT1jNw
文章来源：mp.weixin.qq.com
作者：范志东
发布时间：2024/7/2 6:06
语言：中文
总字数：8675字
预计阅读时间：35分钟
评分：92分
标签：人工智能,大模型,知识图谱,Graph RAG,蚂蚁技术
以下为原文内容
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1. RALM_Survey：https://github.com/2471023025/RALM_Survey
2. Retrieval-Augmented Generation for Large Language Models: A Survey：https://arxiv.org/abs/2312.10997
3. A Survey on Retrieval-Augmented Text Generation for Large Language Models：https://arxiv.org/abs/2404.10981
4. Retrieving Multimodal Information for Augmented Generation: A Survey：https://arxiv.org/abs/2303.10868
5. Evaluation of Retrieval-Augmented Generation: A Survey：https://arxiv.org/abs/2405.07437
6. GFMPapers：https://github.com/BUPT-GAMMA/GFMPapers
7. REALM: Retrieval-Augmented Language Model Pre-Training：https://arxiv.org/abs/2002.08909
8. RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation：https://arxiv.org/abs/2403.05313
9. RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing：https://arxiv.org/pdf/2404.19543
10. RA-DIT: Retrieval-Augmented Dual Instruction Tuning：https://arxiv.org/abs/2310.01352
11. RAFT: Adapting Language Model to Domain Specific RAG：https://arxiv.org/abs/2403.10131
12. MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text：https://arxiv.org/abs/2210.02928
13. Corrective Retrieval Augmented Generation：https://arxiv.org/abs/2401.15884
14. Full Fine-Tuning, PEFT, Prompt Engineering, and RAG: Which One Is Right for You?：https://deci.ai/blog/fine-tuning-peft-prompt-engineering-and-rag-which-one-is-right-for-you/
15. An Easy Introduction to Multimodal Retrieval-Augmented Generation：https://developer.nvidia.com/blog/an-easy-introduction-to-multimodal-retrieval-augmented-generation/
16. Towards Long Context RAG：https://www.llamaindex.ai/blog/towards-long-context-rag
17. Full Fine-Tuning, PEFT, Prompt Engineering, and RAG: Which One Is Right for You?：https://deci.ai/blog/fine-tuning-peft-prompt-engineering-and-rag-which-one-is-right-for-you/
18. Advance RAG- Improve RAG performance：https://luv-bansal.medium.com/advance-rag-improve-rag-performance-208ffad5bb6a
19. Advanced Retrieval-Augmented Generation: From Theory to LlamaIndex Implementation：https://towardsdatascience.com/advanced-retrieval-augmented-generation-from-theory-to-llamaindex-implementation-4de1464a9930
20. RAGFlow：https://github.com/infiniflow/ragflow
21. LangChain RAG：https://python.langchain.com/v0.1/docs/use_cases/question_answering/
22. From Local to Global: A Graph RAG Approach to Query-Focused Summarization：https://arxiv.org/abs/2404.16130
23. Seven Failure Points When Engineering a Retrieval Augmented Generation System：https://arxiv.org/abs/2401.05856
24. Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning：https://arxiv.org/abs/2310.01061
25. GraphRAG: Unlocking LLM discovery on narrative private data：https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
26. From RAG to GraphRAG , What is the GraphRAG and why i use it?：https://medium.com/@jeongiitae/from-rag-to-graphrag-what-is-the-graphrag-and-why-i-use-it-f75a7852c10c
27. GraphRAG: Design Patterns, Challenges, Recommendations：https://gradientflow.com/graphrag-design-patterns-challenges-recommendations/
28. lettria：https://www.lettria.com/features/graphrag
29. Implementing GraphRAG for Query-Focused Summarization：https://dev.to/stephenc222/implementing-graphrag-for-query-focused-summarization-47ib
30. LlamaIndex Graph RAG：https://docs.llamaindex.ai/en/stable/examples/query_engine/knowledge_graph_rag_query_engine/
31. DB-GPT Graph RAG：https://docs.dbgpt.site/docs/latest/cookbook/rag/graph_rag_app_develop
32. RAGAS: Automated Evaluation of Retrieval Augmented Generation：https://arxiv.org/abs/2309.15217
33. Benchmarking Large Language Models in Retrieval-Augmented Generation：https://arxiv.org/abs/2309.01431
34. CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models：https://arxiv.org/abs/2401.17043v2
35. ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems：https://arxiv.org/abs/2311.09476
36. RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge：https://arxiv.org/abs/2311.08147
37. MyGO: Discrete Modality Information as Fine-Grained Tokens for Multi-modal Knowledge Graph Completion：https://arxiv.org/abs/2404.09468
38.OneKE:https://github.com/zjunlp/DeepKE/blob/main/example/llm/OneKE.md
39. Apache Jena：https://github.com/apache/jena
40. Eclipse RDF4J：https://github.com/eclipse-rdf4j/rdf4j
41. Oxigraph：https://github.com/oxigraph/oxigraph
42.OpenSPG:https://github.com/OpenSPG/openspg
43. Neo4j：https://github.com/neo4j/neo4j
44.JanusGraph:https://github.com/JanusGraph/janusgraph
45. NebulaGraph：https://github.com/vesoft-inc/nebula
46. TuGraph：https://github.com/TuGraph-family/tugraph-db
47.DB-GPTv0.5.6:https://github.com/eosphoros-ai/DB-GPT/releases/tag/v0.5.6
48.Graph RAG PR:https://github.com/eosphoros-ai/DB-GPT/pull/1506
49.tranformers_story.md:https://github.com/eosphoros-ai/DB-GPT/blob/main/examples/test_files/tranformers_story.md
50. DB-GPT：https://github.com/eosphoros-ai/DB-GPT
