openspg

Version 0.7.1 (2025-04-25)

Dear Open Source Community,We are excited to officially announce the release of version 0.7.1 for OpenSPG/KAG! This release represents the collaborative efforts of both our core technical team and contributors from the global open source community. The update focuses on improving user experience, optimizing system performance, and addressing key issues based on your feedback. Below are the highlights of this release:

🛠️ Fixes & Optimization Highlights:

1. Resolved source code compilation errors: Fixed NoSuchBeanDefinitionException issues during openspg compilation to ensure a smooth development experience.
2. Enhanced error diagnostics: Resolved the issue where timeout errors for vectorizer calls couldn't be traced, enabling quicker troubleshooting.
3. Improved task performance: Optimized small task construction times by adjusting the scheduling interval, allowing asynchronous tasks to trigger immediately without waiting.
4. Enhanced container stability: Fixed the issue where container restarts caused scheduling states to hang or crash.
5. Reasoning Q&A Stream output optimization: Improved the responsiveness and fluidity of streaming outputs for Reasoning Q&A scenarios.
6. Task details page upgrades: Added auto-refresh capabilities for logs and time cost statistics, allowing users to monitor system and task performance in real time.
7. Simplified task splitting configuration: Disable the semantic segmentation module to avoid excessive token consumption caused by misoperations.
8. Website updates: Added a new partner showcase section to the homepage and included a redirect to the OpenKG official website to foster collaborative opportunities.
9. Bug fixes and product optimization: Implemented multiple fixes and refinements to address reported issues, enhancing product stability.

🌟 Join Our Community

The OpenSPG community thrives on collective wisdom and the shared mission of advancing open-source technology. We warmly invite you to contribute, share your insights, and collaborate on exciting projects. For more details about this release and user guides, please visit our official website.

Thank you for your continued support and enthusiasm for OpenSPG! If you have questions or suggestions, feel free to reach out through our community channels or via email.

Version 0.7.1 (2025-04-25)

亲爱的开源社区伙伴们，非常高兴地宣布 OpenSPG/KAG 发布了最新版本 0.7.1！这一版本是我们的技术团队与社区开发者们共同努力的成果，其目标旨在提升用户体验、优化性能，并解决用户反馈的若干问题。以下是本次版本更新的核心内容概要：

🛠️ 问题修复 & 功能优化

1. 修复源码编译问题：解决 openspg 编译时可能遇到的 NoSuchBeanDefinitionException 报错问题，确保开发者可以顺利构建项目。
2. 诊断优化：解决 vectorizer 调用超时异常信息无法透出的问题，帮助用户快速定位故障原因。
3. 任务性能提升：优化小任务构建耗时，将任务调度间隔调整为更高效的频率，同时确保异步任务在无需等待的条件下立即触发。
4. 镜像稳定性提升：解决镜像重启时调度状态异常卡死的问题，增强运行环境的稳定性。
5. 推理问答流式输出优化：显著改善推理问答场景中流式输出的慢速和卡顿问题，提供更流畅的用户体验。
6. 任务详情页面增强：新增日志自动刷新功能和任务耗时统计，帮助用户实时监控系统状态和任务性能。
7. 构建任务切分调整：下线语义化切分的选项，避免误操作带来的大量tokens 消耗
8. 官网功能更新：为官网主页新增合作伙伴信息板块，同时添加 OpenKG 官网跳转链接以促进资源共享。
9. Bug修复和产品优化：针对社区反馈的问题做出多项功能修复与细节优化，提高整体产品的稳定性。

🌟 加入我们的社区

我们的开源社区凝聚了多方智慧，推动了开源技术的发展。我们诚挚邀请您参与项目的共建，共同探索更优秀的解决方案。有关本次更新的更多信息和操作指南，请访问 官网。

感谢您对 OpenSPG 的关注和支持！如果有任何问题或建议，欢迎通过社区或邮件与我们联系。

Version 0.7 (2025-04-17)

1、Overview

We are very pleased to announce the release of KAG 0.7. This update continues our commitment to increasing the consistency, rigor, and precision of large language models leveraging external knowledge bases, while introducing several important new features.

Firstly, we have completely refactored the framework. The update adds support for both static and iterative task planning modes, along with a more rigorous hierarchical knowledge mechanism during the reasoning phase. Additionally, the new multi-executor extension mechanism and MCP protocol integration enable horizontal scaling of various symbolic solvers (such as math-executor and cypher-executor). These improvements not only help users quickly build knowledge-augmented applications to validate innovative ideas or domain-specific solutions, but also support continuous optimization of KAG Solver's capabilities, thereby further enhancing reasoning rigor in vertical applications.

Secondly, we have comprehensively optimized the product experience: during the reasoning phase, we introduced dual modes "Simple Mode" and "Deep Reasoning" and added support for streaming reasoning output, significantly reducing user wait times. Particularly noteworthy is the introduction of the "Lightweight Construction" mode to better facilitate the large-scale business application of KAG and address the community's most pressing concern about high knowledge construction costs. As shown in the KAG-V0.7LC column of Figure 1, we tested a hybrid approach where a 7B model handles knowledge construction and a 72B model handles knowledge-based question answering. The results on the two_wiki, hotpotqa, and musique benchmarks showed only minor declines of 1.20%, 1.90%, and 3.11%, respectively. However, the token cost（Refer to Aliyun Bailian pricing）for constructing a 100,000-character document was reduced from 4.63￥ to 0.479￥, a 89% reduction, which substantially saves users both time and financial costs. Additionally, we will release a KAG-specific extraction model and a distributed offline batch construction version, continuously compressing model size and improving construction throughput to achieve daily construction capabilities for millions or even tens of millions of documents in a single scenario.

Finally, to better promote business applications, technological advancement, and community exchange for knowledge-augmented LLMs, we have added an open_benchmark directory at the root level of the KAG repository. This directory includes reproduction methods for various datasets to help users replicate and improve KAG's performance across different tasks. Moving forward, we will continue to expand with more vertical scenario task datasets to provide users with richer resources.

Beyond these framework and product optimizations, we've fixed several bugs in both reasoning and construction phases. This update uses Qwen2.5-72B as the base model, completing effect alignment across various RAG frameworks and partial KG datasets. For overall benchmark results, please refer to Figures 1 and 2, with detailed rankings available in the open_benchmark section.

Figure1. Performance of KAG V0.7 and baselines on Multi-hop QA benchmarks

_Figure2. Performance of KAG V0.7 and baselines(from OpenKG OneEval) on _Knowledge based QA benchmarks

2、Framework Enhancements

2.1、Hybrid Static-Dynamic Task Planning

This release introduces optimizations to the KAG-Solver framework implementation, providing more flexible architectural support for: "Retrieval during reasoning" workflows, Multi-scenario algorithm experimentation, LLM-symbolic engine integration (via MCP protocol).

The fr

Version 0.6 (2025-01-07)

On January 7, 2025, OpenSPG officially released version 0.6, bringing updates across multiple areas, including domain knowledge mounting, vertical domain schema management, visual knowledge exploration, and support for summary generation tasks. In terms of user experience, it offers a mechanism for resuming knowledge base tasks from breakpoints, introduces a user login and permission system, and optimizes task scheduling for building processes. In developer mode, it supports configuring different models for different stages and enables schema-constraint mode for extraction, significantly enhancing the system's flexibility, usability, performance, and security. This release provides users with a more powerful knowledge management platform that adapts to diverse application scenarios.

🌟 New Features

1. Support for Summary Generation Tasks

   • Native support for abstractive summarization tasks without sacrificing multi-hop factual reasoning accuracy. On the CSQA dataset, while comprehensiveness, diversity, and empowerment metrics are slightly lower than LightRAG (-1.2/10), the factual accuracy metric is better than LightRAG (+0.1/10). On multi-hop question answering datasets such as HotpotQA, TwoWiki, and MuSiQue, since LightRAG and GraphRAG do not provide a factual QA evaluation entry, the EM metric using the default entry is close to 0. For quantitative evaluation results, please refer to the KAG code repository under examples/csqa/README.md and follow the steps to reproduce.

2. Domain Schema Management

   • The product provides SPG schema management capabilities, allowing users to optimize knowledge base construction and inference Q&A performance by customizing schemas.

3. Knowledge Exploration

   • Added a knowledge exploration feature to enable visual query and analysis of knowledge base data, and provided an HTTP API for integration with other systems.

4. Support for Mounting Domain Knowledge in KAG-Builder(Developer Mode)

   • In developer mode, the system supports injecting domain knowledge (domain vocabulary, relationships between terms) into the knowledge base, which can significantly improve knowledge base construction and inference Q&A performance (with a 10%+ improvement in the medical domain).

5. Adding Knowledge Alignment Component to the KAG-Builder Pipeline

   • Kag-Builder provides a default knowledge alignment component that includes features such as filtering out invalid data and linking similar entities. This optimizes the structure and data quality of the graph.

⚙️ User Experience Optimizations

1. Resumable Tasks

   • Provide resumable capabilities for knowledge base construction tasks at the file level and chunk level in both product mode and developer mode, to reduce the time and token consumption caused by full re-runs after task failures.

2. User Login & Permission System

   • Implement a user login and permission system to prevent unauthorized access and operations on the knowledge base data.

3. Optimized Knowledge Base Construction Task Scheduling

   • Provide database-based knowledge base construction task scheduling to avoid task anomalies or interruptions after container restarts.

4. Support for Configuring Different Models at Different Stages (Developer Mode)

   • The system provides a component management mechanism based on a registry, allowing users to instantiate component objects via configuration files. This supports users in developing and embedding custom components into the KAG-Builder and KAG-Solver workflows. Additionally, it enables the configuration of different-sized models at different stages of the workflow, thereby enhancing the overall reasoning and question-answering performance.

5. Optimization of Layout Analysis for Markdown, PDF, and Word Files

   • For Markdown, PDF, and Word files, the system prioritizes dividing the content into chunk

Version 0.5.1 (2024-11-21)

OpenSPG released version v0.5.1 on November 21, 2024. This version focuses on addressing user feedback and introduces a series of new features and user experience optimizations.

🌟 New Features

1. Support for Word Documents

   • Users can now directly upload .doc or .docx files to streamline the knowledge base construction process.

2. New Project Deletion API

   • Quickly clear and delete projects and related data through an API, compatible with the latest Neo4j image version.

3. Model Call Concurrency Setting

   • Added the builder.model.execute.num parameter, with a default concurrency of 5, to improve efficiency in large-scale knowledge base construction.

4. Improved Logging

   • Added a startup success marker in the logs to help users quickly verify if the service is running correctly.

⚙️ User Experience Optimizations

1. Neo4j Memory Overflow Issues
   • Addressed memory overflow problems in Neo4j during large-scale data processing, ensuring stable operation for extensive datasets.
2. Concurrent Neo4j Query Execution Issues
   • Optimized execution strategies to resolve Graph Data Science (GDS) library conflicts or failures in high-concurrency scenarios.
3. Schema Preview Prefix Issue
   • Fixed issues where extracted schema preview entities lacked necessary prefixes, ensuring consistency between extracted entities and predefined schemas.
4. Default Neo4j Password for Project Creation/Modification
   • Automatically fills a secure default password if none is specified during project creation or modification, simplifying the configuration process.
5. Frontend Bug Fixes
   • Resolved issues with JS dependencies relying on external addresses and embedded all frontend files into the image. Improved the knowledge base management interface for a smoother user experience.
6. Empty Node/Edge Type in Neo4j Writes
   • Enhanced writing logic to handle empty node or edge types during knowledge graph construction, preventing errors or data loss in such scenarios.

Version 0.5.1 (2024-11-21)

OpenSPG 在 2024 年 11 月 21 日发布了 v0.5.1 版本。此版本重点解决了用户反馈的问题，并带来了一系列新功能和用户体验的优化。

🌟 新增功能

1. 支持 word 文档的构建

   • 用户现可通过知识库管理页面直接上传 .doc 或 .docx 后缀的文件，进行知识库的构建流程。这一更新使得知识内容的导入更加便捷，提高效率。

2. 提供项目删除接口

   • 为了帮助用户更高效地管理项目，我们新增了一个项目删除接口。用户可以通过访问 http://127.0.0.1:8887/project/api/delete?projectId=xx 完成项目的快速清空与删除操作。该接口会同步清理项目下的所有schema、知识库任务、知识库问答任务以及关联的 Neo4j 数据库。 Tips：使用此功能前，需确保已将 openspg-neo4j 镜像更新至最新版本

3. 支持模型调用并发度设置

   • 在大规模知识库构建过程中，为了提高构建效率，我们引入了模型调用的并发控制机制。用户可以通过设置 builder.model.execute.num 参数来调整并发数量，默认值设定为5。这有助于避免因模型服务性能瓶颈而导致的任务失败或系统卡顿。

4. 日志中添加启动成功标识

   • 为了让用户能够更直观地判断 OpenSPG 服务是否启动成功，我们在日志输出中加入了明确的启动成功标识。openspg-server 成功启动后，会输出这一标识。

⚙️ 用户体验优化

1. 解决大规模数据构建下 Neo4j 调用内存超限问题
   • 针对在处理大规模数据集时出现的 Neo4j 内存溢出问题，我们进行了深入分析并实施了有效的解决方案。现在，面对大规模数据集Neo4j 能保持稳定运行，有效防止了因内存不足而导致的服务中断。
2. 解决多并发下执行 Neo4j 查询导致的 GDS 加载问题
   • 在多并发场景下执行 Neo4j 查询时，图数据科学 (GDS) 库的加载会出现冲突或失败的情况。为此，我们优化了查询执行策略，确保了在高并发环境下的查询性能和稳定性。
3. 解决抽取结果 Schema 预览实体无前缀问题
   • 在之前版本中，部分用户反馈在查看抽取结果的 Schema 预览时，实体名称缺少必要的前缀信息导致抽取的实体和预定义的Schema不一致。此次更新修正了这一问题，保证了所有实体名称的完整性和准确性。
4. 创建修改项目时 Neo4j 无密码时填充默认值
   • 当用户在创建或修改项目时，如果未指定