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@andylau-55 andylau-55 released this 04 Nov 07:14
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v0.5
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Version 0.5 (2024-10-25)

retrieval Augmentation Generation (RAG) technology promotes the integration of domain applications with large models. However, RAG has problems such as a large gap between vector similarity and knowledge reasoning correlation, and insensitivity to knowledge logic (such as numerical values, time relationships, expert rules, etc.), which hinder the implementation of professional knowledge services. On October 25, OpenSPG released version V0.5, officially releasing the professional domain knowledge Service Framework for knowledge enhancement generation (KAG) .

Highlights of the Release Version:

1. KAG: Knowledge Augmented Generation

KAG aims to make full use of the advantages of Knowledge Graph and vector retrieval, and bi-directionally enhance large language models and knowledge graphs through four aspects to solve RAG challenges
(1) LLM-friendly semantic knowledge management
(2) Mutual indexing between the knowledge map and the original snippet.
(3) Logical symbol-guided hybrid inference engine
(4) Knowledge alignment based on semantic reasoning
KAG is significantly better than NaiveRAG, HippoRAG and other methods in multi-hop question and answer tasks. The F1 score on hotpotQA is relatively improved by 19.6, and the F1 score on 2wiki is relatively improved by 33.5

2. Knowledge base management

OpenSPG also provides a user-friendly product interface for KAG, allowing users to upload and manage documents, preview extraction results, and quiz through the visual interface after local deployment. In the knowledge question and answer session, the system not only shows the final answer, but also presents the reasoning process, thus enhancing the transparency and interpretability of the whole question and answer process. Through this product interface, users can use KAG more intuitively and easily

3. Continuous Optimization and Bug Fixes

  • feat(schema): support maintenance of simplified DSL in #335
  • feat(reasoner): support thinker in knext in #344
  • feat(reasoner): support ProntoQA and ProofWriter. in #352
  • feat(reasoner): thinker support deduction expression in #369
  • feat(openspg): support kag in #372
  • feat(reasoner): add udf split_part in #378
  • fix(reasoner): support triple in thinker context in #341
  • fix(reasoner): bugfix in graph store. in #346
  • fix(reasoner): fix pattern schema extra in #351
  • fix(knext): add remote client addr in #376
  • fix(knext): reasoner command add default cfg config in #377

Version 0.5 (2024-10-25)

检索增强生成（RAG）技术推动了领域应用与大模型结合。然而，RAG 存在着向量相似度与知识推理相关性差距大、对知识逻辑（如数值、时间关系、专家规则等）不敏感等问题，这些都阻碍了专业知识服务的落地。10 月 25 日，OpenSPG 发布 V0.5 版本，正式发布了知识增强生成（KAG）的专业领域知识服务框架

版本亮点

1. KAG 专业领域知识服务框架

KAG 旨在充分利用知识图谱和向量检索的优势，并通过四个方面双向增强大型语言模型和知识图谱，以解决 RAG 挑战
(1) 对 LLM 友好的语义化知识管理
(2) 知识图谱与原文片段之间的互索引
(3) 逻辑符号引导的混合推理引擎
(4) 基于语义推理的知识对齐
KAG 在多跳问答任务中显著优于 NaiveRAG、HippoRAG 等方法，在 hotpotQA 上的 F1 分数相对提高了 19.6%，在 2wiki 上的 F1 分数相对提高了33.5%

2. 知识库管理

OpenSPG针对KAG 还提供了一个用户友好的产品界面，支持用户在本地部署后，通过可视化界面进行文档上传和管理、预览抽取结果、以及知识问答。在知识问答环节，系统不仅展示最终答案，还会呈现推理过程，从而增强了整个问答流程的透明度和可解释性。通过这个产品界面，用户能够更直观、更轻松地上手使用 KAG

3. 持续优化与问题修复

  • feat(schema): support maintenance of simplified DSL in #335
  • feat(reasoner): support thinker in knext in #344
  • feat(reasoner): support ProntoQA and ProofWriter. in #352
  • feat(reasoner): thinker support deduction expression in #369
  • feat(openspg): support kag in #372
  • feat(reasoner): add udf split_part in #378
  • fix(reasoner): support triple in thinker context in #341
  • fix(reasoner): bugfix in graph store. in #346
  • fix(reasoner): fix pattern schema extra in #351
  • fix(knext): add remote client addr in #376
  • fix(knext): reasoner command add default cfg config in #377
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