Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
37 tokens/sec
GPT-4o
11 tokens/sec
Gemini 2.5 Pro Pro
37 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
10 tokens/sec
DeepSeek R1 via Azure Pro
33 tokens/sec
2000 character limit reached

DB-GPT: Empowering Database Interactions with Private Large Language Models (2312.17449v2)

Published 29 Dec 2023 in cs.DB

Abstract: The recent breakthroughs in LLMs are positioned to transition many areas of software. Database technologies particularly have an important entanglement with LLMs as efficient and intuitive database interactions are paramount. In this paper, we present DB-GPT, a revolutionary and production-ready project that integrates LLMs with traditional database systems to enhance user experience and accessibility. DB-GPT is designed to understand natural language queries, provide context-aware responses, and generate complex SQL queries with high accuracy, making it an indispensable tool for users ranging from novice to expert. The core innovation in DB-GPT lies in its private LLM technology, which is fine-tuned on domain-specific corpora to maintain user privacy and ensure data security while offering the benefits of state-of-the-art LLMs. We detail the architecture of DB-GPT, which includes a novel retrieval augmented generation (RAG) knowledge system, an adaptive learning mechanism to continuously improve performance based on user feedback and a service-oriented multi-model framework (SMMF) with powerful data-driven agents. Our extensive experiments and user studies confirm that DB-GPT represents a paradigm shift in database interactions, offering a more natural, efficient, and secure way to engage with data repositories. The paper concludes with a discussion of the implications of DB-GPT framework on the future of human-database interaction and outlines potential avenues for further enhancements and applications in the field. The project code is available at https://github.com/eosphoros-ai/DB-GPT. Experience DB-GPT for yourself by installing it with the instructions https://github.com/eosphoros-ai/DB-GPT#install and view a concise 10-minute video at https://www.youtube.com/watch?v=KYs4nTDzEhk.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (54)
  1. Palm 2 technical report. arXiv preprint arXiv:2305.10403, 2023.
  2. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023.
  3. Baichuan. Baichuan 2: Open large-scale language models. arXiv preprint arXiv:2309.10305, 2023.
  4. Language models are few-shot learners. Advances in Neural Information Processing Systems (NeurIPS), 2020.
  5. KQA pro: A dataset with explicit compositional programs for complex question answering over knowledge base. In Muresan, S., Nakov, P., and Villavicencio, A. (eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.  6101–6119, Dublin, Ireland, May 2022. Association for Computational Linguistics.
  6. Chase, H. LangChain, 2022.
  7. Evaluating large language models trained on code, 2021.
  8. Palm: Scaling language modeling with pathways, 2022.
  9. Leveraging large language models for pre-trained recommender systems. arXiv preprint arXiv:2308.10837, 2023.
  10. A survey on in-context learning. 2022.
  11. Group, A. OceanBase, 2021.
  12. Textbooks are all you need. arXiv preprint arXiv:2306.11644, 2023.
  13. H2O.ai. H2OGPT, May 2023.
  14. Metagpt: Meta programming for multi-agent collaborative framework, 2023.
  15. Chatdb: Augmenting llms with databases as their symbolic memory, 2023.
  16. Huggingface. Text Generation Inference, May 2021.
  17. Towards anytime fine-tuning: Continually pre-trained language models with hypernetwork prompt. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.
  18. Large models for time series and spatio-temporal data: A survey and outlook, 2023.
  19. Efficient memory management for large language model serving with pagedattention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles, 2023.
  20. Complex knowledge base question answering: A survey, 2022.
  21. Retrieval-augmented generation for knowledge-intensive nlp tasks. ArXiv, abs/2005.11401, 2020.
  22. A comprehensive evaluation of chatgpt’s zero-shot text-to-sql capability, 2023.
  23. Liu, J. LlamaIndex, 11 2022.
  24. PrivateGPT, May 2023.
  25. MongoDB. MongoDB.
  26. MySQL. MySQL.
  27. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332, 2021.
  28. NVIDIA. TensorRT, May 2021.
  29. OpenAI. GPT-4 technical report. arXiv preprint arXiv:2303.08774, 2023.
  30. Deep optimal timing strategies for time series. In ICDM, 2023.
  31. Bellman meets hawkes: Model-based reinforcement learning via temporal point processes. In Proceedings of the AAAI Conference on Artificial Intelligence, 2023.
  32. Richards, T. B. Autogpt, 2022.
  33. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761, 2023.
  34. Language models can improve event prediction by few-shot abductive reasoning. In Advances in Neural Information Processing Systems, 2023.
  35. skypilot org. Skypilot, 2022.
  36. Sql-palm: Improved large language model adaptation for text-to-sql, 2023.
  37. Llama 2: Open foundation and fine-tuned chat models, 2023.
  38. Identity-based proxy-oriented data uploading and remote data integrity checking in public cloud. IEEE Transactions on Information Forensics and Security, 11(6):1165–1176, 2016.
  39. Text embeddings by weakly-supervised contrastive pre-training. arXiv preprint arXiv:2212.03533, 2022a.
  40. Enhancing recommender systems with large language model reasoning graphs. arXiv preprint arXiv:2308.10835, 2023.
  41. Learning to prompt for continual learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  139–149, 2022b.
  42. Emergent abilities of large language models, 2022.
  43. A graph regularized point process model for event propagation sequence. In IJCNN, pp.  1–7, 2021.
  44. A meta reinforcement learning approach for predictive autoscaling in the cloud. In Zhang, A. and Rangwala, H. (eds.), KDD ’22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022, pp.  4290–4299. ACM, 2022a.
  45. Hypro: A hybridly normalized probabilistic model for long-horizon prediction of event sequences. In Advances in Neural Information Processing Systems, 2022b.
  46. Easytpp: Towards open benchmarking the temporal point processes. 2023a.
  47. Prompt-augmented temporal point process for streaming event sequence. In NeurIPS, 2023b.
  48. Weaverbird: Empowering financial decision-making with large language model, knowledge base, and search engine, 2023c.
  49. ReAct: Synergizing reasoning and acting in language models. In International Conference on Learning Representations (ICLR), 2023.
  50. Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018. Association for Computational Linguistics.
  51. Glm-130b: An open bilingual pre-trained model. arXiv preprint arXiv:2210.02414, 2022.
  52. Calibrate before use: Improving few-shot performance of language models. arXiv preprint arXiv:2102.09690, 2021.
  53. Judging llm-as-a-judge with mt-bench and chatbot arena, 2023.
  54. Llm as dba, 2023.
Citations (18)
View on Semantic Scholar

Summary

  • The paper demonstrates a novel integration of private, domain-specific LLMs with databases to improve SQL query generation and overall interaction efficiency.
  • It introduces a service-oriented, multi-model framework that leverages Retrieval-Augmented Generation for dynamic, context-aware processing of natural language queries.
  • The evaluation highlights significant privacy, performance, and multilingual advantages over traditional systems, reducing technical barriers and ensuring data security.

Integrating LLMs into Database Systems: A Review of DB-GPT

The paper "DB-GPT: Empowering Database Interactions with Private LLMs" introduces an advanced system that integrates LLMs with traditional database technologies, aiming to enhance user interactions with data repositories. The core of DB-GPT lies in its private LLM technology, which is fine-tuned to domain-specific corpora to ensure user privacy and data security, thus promoting efficient and intuitive database interaction.

Architectural Overview

DB-GPT is built upon a service-oriented multi-model framework (SMMF) that leverages a multi-source Retrieval-Augmented Generation (RAG) knowledge system. This architecture facilitates a dynamic, context-aware interface capable of processing natural language queries, generating SQL queries, and improving continuously through user feedback. The inclusion of a RAG system enables DB-GPT to incorporate external knowledge data sources, processed via a robust indexing mechanism, ensuring relevance in the retrieval and democratization of database access irrespective of user expertise levels.

Distinct Features and Capabilities

  • Privacy-Centric Design: Privacy and data security are emphasized through local deployment capabilities and de-identification techniques. DB-GPT operates on user-end devices or local servers, eliminating data leakage risks.
  • Text-to-SQL Capabilities: By fine-tuning commonly used LLMs like Qwen and Baichuan for Text-to-SQL tasks, DB-GPT lowers barriers for those unfamiliar with SQL, optimizing LLMs for structured data interactions. This approach is framed in the context of improving query accuracy as measured by execution accuracy (EX) metrics, with significant improvement shown over baseline LLM performance.
  • Multi-Agent Framework: DB-GPT incorporates advanced agents that extend beyond simple database interactions, offering roles like data analysts and database architects. These agents leverage adaptive decision-making and reasoning capabilities to enhance performance, driven by a fine-tuning of models tailored to general reasoning frameworks.

Comparative Advantage and Evaluation

In comparison with existing frameworks, such as LangChain and LlamaIndex, DB-GPT demonstrates notable advantages in multilingual support and the ability to handle complex generative analytics tasks. The paper emphasizes the system’s flexibility in dealing with unstructured data incorporating techniques like natural language processing to SQL generation, underscoring scalability and general applicability in diverse database scenarios.

Evaluations over varied datasets, including DatabaseQA and FinancialQA, reveal that while commercial LLMs like ChatGPT-3.5 excel in specific contexts, DB-GPT's integration of various LLMs offers a versatile solution tailored to specific requirements, ensuring broad applicability. Furthermore, SMMF evaluation highlights DB-GPT's efficiency improvements in first token latency and inference throughput, indicating the framework’s robustness in handling concurrent user interactions.

Implications and Future Directions

The implications of the DB-GPT framework extend across both practical and theoretical domains. Practically, it sets a new standard for database interaction by reducing the technical knowledge required for complex data queries and ensuring data security. Theoretically, it prompts further investigation into the confluence of privacy, performance, and user-friendliness in AI applications. Moreover, future work could explore enhanced agent capabilities with predictive decision-making, integration of advanced model training techniques, and user-friendly enhancements like data visualizations.

This examination of DB-GPT aligns with broader efforts to integrate AI more deeply into data management, illustrating a promising trajectory for future innovations in human-database interaction.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube