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* (1) Langchain. ''https://github.com/langchain-ai''.
* (2) Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. GPT-4 technical report. ''arXiv preprint arXiv:2303.08774'' (2023).
* (3) Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., and Abdelrazek, M. Seven failure points when engineering a retrieval augmented generation system. ''arXiv preprint arXiv:2401.05856'' (2024).
* (4) Es, S., James, J., Espinosa-Anke, L., and Schockaert, S. Ragas: Automated evaluation of retrieval augmented generation. ''arXiv preprint arXiv:2309.15217'' (2023).
* (5) Galitsky, B. ''Developing enterprise chatbots''. Springer, 2019.
* (6) Glantz, W. 12 rag pain points and proposed solutions.
* (7) Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., and Neubig, G. Active retrieval augmented generation. ''arXiv preprint arXiv:2305.06983'' (2023).
* (8) Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. ''Advances in Neural Information Processing Systems 33'' (2020), 9459–9474.
* (9) Liu, J. LlamaIndex. ''https://github.com/jerryjliu/llama_index''(2022).
* (10) Liu, M., Ene, T.-D., Kirby, R., Cheng, C., Pinckney, N., Liang, R., Alben, J., Anand, H., Banerjee, S., Bayraktaroglu, I., et al. Chipnemo: Domain-adapted llms for chip design. ''arXiv preprint arXiv:2311.00176'' (2023).
* (11) Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. Self-refine: Iterative refinement with self-feedback. ''Advances in Neural Information Processing Systems 36'' (2024).
* (12) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al. Augmented language models: a survey. ''arXiv preprint arXiv:2302.07842'' (2023).
* (13) Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., and Cohen, J. Nemo guardrails: A toolkit for controllable and safe llm applications with programmable rails. ''arXiv preprint arXiv:2310.10501'' (2023).
* (14) Saad-Falcon, J., Khattab, O., Potts, C., and Zaharia, M. Ares: An automated evaluation framework for retrieval-augmented generation systems. ''arXiv preprint arXiv:2311.09476'' (2023).
* (15) Setty, S., Jijo, K., Chung, E., and Vidra, N. Improving retrieval for rag based question answering models on financial documents. ''arXiv preprint arXiv:2404.07221'' (2024).
* (16) Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. React: Synergizing reasoning and acting in language models. ''arXiv preprint arXiv:2210.03629'' (2022).
* (17) Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Dou, Z., and Wen, J.-R. Large language models for information retrieval: A survey. ''arXiv preprint arXiv:2308.07107'' (2023).

(1) Langchain. https://github.com/langchain-ai。
(2) Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., 等人。GPT-4 技术报告。arXiv preprint arXiv:2303.08774 (2023)。
(3) Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., 和 Abdelrazek, M. 工程化检索增强生成系统的七个失败点。arXiv preprint arXiv:2401.05856 (2024)。
(4) Es, S., James, J., Espinosa-Anke, L., 和 Schockaert, S. Ragas: 自动化评估检索增强生成。arXiv preprint arXiv:2309.15217 (2023)。
(5) Galitsky, B. 开发企业聊天机器人。Springer, 2019。
(6) Glantz, W. 12 个 RAG 痛点及建议解决方案。
(7) Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., 和 Neubig, G. 主动检索增强生成。arXiv preprint arXiv:2305.06983 (2023)。
(8) Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., 等人。用于知识密集型 NLP 任务的检索增强生成。Advances in Neural Information Processing Systems 33 (2020), 9459–9474。
(9) Liu, J. LlamaIndex. https://github.com/jerryjliu/llama_index(2022)。
(10) Liu, M., Ene, T.-D., Kirby, R., Cheng, C., Pinckney, N., Liang, R., Alben, J., Anand, H., Banerjee, S., Bayraktaroglu, I., 等人。Chipnemo: 为芯片设计领域适应的 LLMs。arXiv preprint arXiv:2311.00176 (2023)。
(11) Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., 等人。自我精炼：通过自我反馈的迭代精炼。Advances in Neural Information Processing Systems 36 (2024)。
(12) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., 等人。增强语言模型：一项调查。arXiv preprint arXiv:2302.07842 (2023)。
(13) Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., 和 Cohen, J. Nemo Guardrails: 一种用于可控和安全 LLM 应用的可编程轨道工具包。arXiv preprint arXiv:2310.10501 (2023)。
(14) Saad-Falcon, J., Khattab, O., Potts, C., 和 Zaharia, M. Ares: 一种用于检索增强生成系统的自动化评估框架。arXiv preprint arXiv:2311.09476 (2023)。
(15) Setty, S., Jijo, K., Chung, E., 和 Vidra, N. 改进基于 RAG 的金融文档问答模型的检索。arXiv preprint arXiv:2404.07221 (2024)。
(16) Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., 和 Cao, Y. React: 在语言模型中协同推理和行动。arXiv preprint arXiv:2210.03629 (2022)。
(17) Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Dou, Z., 和 Wen, J.-R. 大型语言模型用于信息检索：一项调查。arXiv preprint arXiv:2308.07107 (2023)。