References

Every paper, book, and software citation in this book, sorted alphabetically by first-author surname. Click an entry’s anchor to share a deep link, or follow the arXiv / DOI / URL for the source.

  1. [angles2017foundations] Renzo Angles, Marcelo Arenas, Pablo Barceló, Aidan Hogan, Juan Reutter, and Domagoj Vrgoč (2017). Foundations of Modern Query Languages for Graph Databases. ACM Computing Surveys. Vol. 50, no. 5. pp. 1-40.
  2. [arize2024phoenix] Arize AI (2024). Arize Phoenix: Open-Source LLM Observability and Tracing. link.
  3. [bai2022constitutional] Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, and others (2022). Constitutional AI: Harmlessness from AI Feedback. link.
  4. [neo4j_cypher_ai_2025] Christoffer Bergman (2025). New Cypher AI Procedures. link.
  5. [brown2020language] Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, and others (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems. Vol. 33. pp. 1877-1901.
  6. [langchain2023] Harrison Chase (2023). LangChain: Building Applications with LLMs through Composability.
  7. [chen2021evaluating] Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, and others (2021). Evaluating Large Language Models Trained on Code. link.
  8. [christiano2017deep] Paul F Christiano, Jan Leike, Tom B Brown, Miljan Martic, Shane Legg, and Dario Amodei (2017). Deep Reinforcement Learning from Human Preferences. link.
  9. [deepseek2025r1] DeepSeek-AI (2025). DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. link.
  10. [dettmers2023qlora] Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer (2023). QLoRA: Efficient Finetuning of Quantized LLMs. Advances in Neural Information Processing Systems (NeurIPS). link.
  11. [edge2024local] Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, and Jonathan Larson (2024). From Local to Global: A Graph RAG Approach to Query-Focused Summarization. arXiv preprint arXiv:2404.16130.
  12. [ms_graphrag_2024] Darren Edge, Ha Trinh, Ning Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, and Jonathan Larson (2024). From Local to Global: A Graph RAG Approach to Query-Focused Summarization. arXiv preprint arXiv:2404.16130.
  13. [francis2018cypher] Nadime Francis, Alastair Green, Paolo Guagliardo, Leonid Libkin, Tobias Lindaaker, Victor Marsault, Stefan Plantikow, Mats Rydberg, Petra Selmer, and Andrés Taylor (2018). Cypher: An Evolving Query Language for Property Graphs. Proceedings of the 2018 International Conference on Management of Data. pp. 1433-1445.
  14. [gao2024retrieval] Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang (2024). Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv preprint arXiv:2312.10997.
  15. [hogan2021knowledge] Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, and others (2021). Knowledge Graphs. ACM Computing Surveys. Vol. 54, no. 4. pp. 1-37.
  16. [hu2021lora] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen (2021). LoRA: Low-Rank Adaptation of Large Language Models. link.
  17. [johnson2019billion] Jeff Johnson, Matthijs Douze, and Hervé Jégou (2019). Billion-Scale Similarity Search with GPUs. IEEE Transactions on Big Data. Vol. 7, no. 3. pp. 535-547.
  18. [kaplan2020scaling] Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei (2020). Scaling Laws for Neural Language Models. link.
  19. [lewis2020retrieval] Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, and others (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems. Vol. 33. pp. 9459-9474.
  20. [mihalcea2004textrank] Rada Mihalcea and Paul Tarau (2004). TextRank: Bringing Order into Texts. Proceedings of EMNLP. pp. 404-411.
  21. [neo4j_graphrag_python_2025] Neo4j (2025). neo4j-graphrag-python Releases. link.
  22. [llamaindex_propertygraph_2025] Neo4j and LlamaIndex (2025). Customizing PropertyGraphIndex in LlamaIndex. link.
  23. [neo4j2024vector] Neo4j Inc. (2024). Vector Search in Neo4j: Combining Graph and Vector for Better AI. Neo4j Documentation.
  24. [noy2019industry] Natasha Noy, Yuqing Gao, Anshu Jain, Anant Narang, Alan Patterson, and Jamie Taylor (2019). Industry-Scale Knowledge Graphs: Lessons and Challenges. Communications of the ACM. Vol. 62, no. 8. pp. 36-43.
  25. [opentelemetry2024spec] OpenTelemetry Authors (2024). OpenTelemetry Specification: Traces, Spans, and Semantic Conventions. link.
  26. [ouyang2022training] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, and others (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems (NeurIPS). link.
  27. [pan2024unifying] Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu (2024). Unifying Large Language Models and Knowledge Graphs: A Roadmap. IEEE Transactions on Knowledge and Data Engineering. Vol. 36, no. 7. pp. 3580-3599.
  28. [patil2023gorilla] Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez (2023). Gorilla: Large Language Model Connected with Massive APIs. link.
  29. [rafailov2023direct] Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea Finn (2023). Direct Preference Optimization: Your Language Model is Secretly a Reward Model. Advances in Neural Information Processing Systems (NeurIPS). link.
  30. [robinson2015graph] Ian Robinson, Jim Webber, and Emil Eifrem (2015). Graph Databases: New Opportunities for Connected Data. O'Reilly Media.
  31. [schulman2017proximal] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov (2017). Proximal Policy Optimization Algorithms. link.
  32. [shao2024deepseekmath] Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, and others (2024). DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models. link.
  33. [srivastava2024financial] Saurabh Srivastava, Sadid A. Hasan, Harish Yenala, and Suchitra Roychowdhury (2024). Financial Knowledge Graphs: A Survey of Methods, Applications, and Challenges. arXiv preprint arXiv:2401.02892.
  34. [sec2024edgar] U.S. Securities and Exchange Commission (2024). EDGAR Full-Text Search: Accessing SEC Filings. SEC.gov.
  35. [yao2022react] Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao (2022). ReAct: Synergizing Reasoning and Acting in Language Models. link.
  36. [zheng2023judging] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P Xing, and others (2023). Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. Advances in Neural Information Processing Systems (NeurIPS). link.