MemoryBear/api/app/version_info.json

{
  "v0.2.2": {
    "introduction": {
      "codeName": "淬锋（Temper）",
      "releaseDate": "2026-1-31",
      "upgradePosition": "本次发布聚焦平台稳定性和性能优化。正如\"淬锋\"之名——千锤百炼，淬火成锋，我们通过严格测试和修复打磨系统品质。引入 Agent 工作流的代码执行能力、改进模型并发管理，并修复了记忆系统的多个关键问题。",
      "coreUpgrades": [
        "1. Agent平台增强<br>* 模型并发管理：优化模型广场的并发请求处理和资源分配能力。",
        "2. 记忆系统优化<br>* Celery 队列修复：解决任务队列问题，提升异步记忆处理的可靠性<br>* 记忆 Agent 优化：提升记忆 Agent 的性能和效率<br>* 接口响应速度优化：优化记忆接口响应时间，加快操作速度。",
        "3. 情绪记忆与识别升级<br>* 情绪记忆角色识别修复：解决情绪记忆上下文中的角色/人物识别问题<br>* 角色识别增强：提升对话记忆中的角色/人物识别准确性。",
        "<br>",
        "MemoryBear 持续致力于为 AI 应用提供类人记忆能力。本次以稳定性为核心的发布，进一步夯实了「感知→精炼→关联→遗忘」范式的基础。",
        "未来版本将在此坚实基础上，扩展 Agent 能力并深化记忆智能特性。"
      ]
    },
    "introduction_en": {
      "codeName": "Temper (淬锋)",
      "releaseDate": "2026-1-31",
      "upgradePosition": "This release focuses on platform stability and performance optimization — true to its codename \"淬锋\" (tempered blade), we've refined the system through rigorous testing and fixes. Introducing Python code execution for Agent workflows, improved model concurrency management, and critical fixes across the memory system.",
      "coreUpgrades": [
        "1. Agent Platform Enhancements<br>* Model Concurrency Management: Enhanced Model Plaza with improved concurrent model request handling and resource allocation.",
        "2. Memory System Improvements<br>* Celery Queue Fix: Resolved task queue issues for more reliable asynchronous memory processing<br>* Memory Agent Optimization: Improved memory Agent performance and efficiency<br>* API Response Speed: Optimized memory interface response times for faster operations.",
        "3. Emotional Memory & Recognition Upgrades<br>* Emotion Memory Role Recognition Fix: Resolved issues with role/character identification in emotional memory contexts<br>* Role Recognition Enhancement: Improved character/role identification accuracy in conversation memory.",
        "<br>",
        "MemoryBear continues advancing toward human-like memory capabilities for AI applications. This stability-focused release strengthens the foundation for our Perception → Refinement → Association → Forgetting paradigm.",
        "Future releases will build on this solid base with expanded Agent capabilities and deeper memory intelligence features."
      ]
    }
  },
  "v0.2.1": {
    "introduction": {
      "codeName": "启知",
      "releaseDate": "2026-1-23",
      "upgradePosition": "\uD83D\uDC3B 本次更新主要优化使用体验和修复已知问题，让系统更稳定、更好用。",
      "coreUpgrades": [
        "1. 工作流更好用了<br>* 界面更清晰，一眼看懂怎么配置<br>* 新增节点输出变量展示，方便其他节点引用<br>* 修复了几个影响体验的bug",
        "2. 智能体配置更简单<br>* 提示词和变量联动更顺畅<br>* 配置界面重新整理，找功能更方便",
        "3. 记忆系统更稳定<br>* 优化了情绪记忆和隐性记忆的缓存更新<br>* 修复了记忆配置页面的报错问题<br>* 现在能自动识别用户和AI的身份了",
        "4. 知识库体验提升<br>* 修复了文档解析异常的问题<br>* 上传文档时能看到处理进度了<br>* 取消了操作也不会报错了",
        "5. 系统整体更可靠<br>* 修复了新用户访问跳转问题<br>* 流式接口更稳定，长对话不断线<br>* 调整了菜单顺序，操作更顺手",
        "<br>",
        "这次更新虽然不大，但让记忆熊的基础更扎实、体验更流畅。我们继续努力，让AI记忆更好用！",
        "记忆熊，记得更牢，用得更好。\uD83D\uDC3B✨"
      ]
    },
    "introduction_en": {
      "codeName": "Qizhi",
      "releaseDate": "2026-1-23",
      "upgradePosition": "\uD83D\uDC3B This update focuses on improving usability and fixing known issues, making the system more stable and easier to use overall.",
      "coreUpgrades": [
        "1. Improved Workflow Experience<br>* Cleaner, more intuitive UI for easier configuration at a glance<br>* Added visibility of node output variables, making them easier to reference in downstream nodes<br>* Fixed several usability-related bugs that affected the workflow experience",
        "2. Simpler Agent Configuration<br>* Smoother linkage between prompts and variables<br>* Reorganized configuration layout for easier navigation and better clarity",
        "3. More Stable Memory System<br>* Optimized cache refresh for emotional memory and implicit memory<br>* Fixed error issues on the memory configuration page<br>* The system can now automatically distinguish between user and AI roles",
        "4. Enhanced Knowledge Base Experience<br>* Fixed issues with document parsing failures<br>* Upload progress is now displayed during document processing<br>* Canceling an upload no longer triggers errors",
        "5. Overall System Reliability Improvements<br>* Fixed redirect issues affecting new users<br>* Improved stability of streaming APIs to prevent interruptions during long conversations<br>* Adjusted menu ordering for a smoother and more intuitive workflow",
        "<br>",
        "Although this is a relatively small update, it strengthens MemoryBear’s foundation and delivers a noticeably smoother experience. We’ll keep refining the system to make AI memory more powerful and easier to use.",
        "MemoryBear — remember better, work smarter. \uD83D\uDC3B✨"
      ]
    }
  },
  "v0.2.0": {
    "introduction": {
      "codeName": "启知",
      "releaseDate": "2026-1-16",
      "upgradePosition": "本次为架构升级，核心目标是把\"被动存储\"升级为\"主动认知\"，让系统具备情绪感知、情景理解与类人记忆机制，为后续多智能体协作与专业场景落地奠定底座。",
      "coreUpgrades": [
        "1. 记忆详情：拟人记忆——情绪引擎、情景记忆、短期记忆、工作记忆、感知记忆、显性记忆、隐性记忆，并配套类脑遗忘机制，实现从感知→情绪→情景→长期沉淀的完整人类记忆闭环",
        "2. 可视化工作流：拖拽式节点编排（LLM、知识库、逻辑、工具），业务落地周期由天缩至小时。",
        "3. 多模态知识处理：PDF、PPT、MP3、MP4 一键解析，时间感知检索准确率 94.3%，问答对数据即插即用。",
        "4. Agent集群内置\"记忆-知识-工具-审核\"四类角色模板，用户一键生成；主控Agent把复杂任务拆为子任务并行分发，再靠情景记忆统一消解冲突、校验一致性，输出完整报告。"
      ]
    },
    "introduction_en": {
      "codeName": "Qizhi",
      "releaseDate": "2026-1-16",
      "upgradePosition": "This release marks a foundational upgrade to the system’s cognitive architecture. The core objective is to evolve the platform from passive information storage into active cognitive intelligence—enabling emotional awareness, situational understanding, and human-like memory mechanisms. This upgrade lays the groundwork for future multi-agent collaboration and domain-specific, production-grade AI applications.",
      "coreUpgrades": [
        "1. Human-Like Memory Architecture: A comprehensive, human-inspired memory system is introduced, encompassing emotional processing, situational memory, short-term and working memory, perceptual memory, as well as explicit and implicit memory. Combined with brain-inspired forgetting mechanisms, the system now supports a complete cognitive loop—from perception → emotion → context → long-term consolidation, closely mirroring human memory formation.",
        "2. Visual Workflow Orchestration: A fully visual, drag-and-drop workflow enables modular composition of LLMs, knowledge bases, logic, and tools. This dramatically reduces the time required to move from experimentation to production—from days to hours.",
        "3. Multimodal Knowledge Processing: The system now supports one-click parsing and ingestion of PDF, PPT, MP3, and MP4 content. With time-aware retrieval accuracy reaching 94.3%, structured Q&A data becomes instantly usable for downstream reasoning and generation.",
        "4. Built-in Agent Clusters: Predefined role templates across four categories—Memory, Knowledge, Tools, and Review—can be generated with a single click. A Coordinator Agent decomposes complex tasks into parallel subtasks, while situational memory is used to resolve conflicts, validate consistency, and synthesize outputs into a coherent, end-to-end report."
      ]
    }
  },
  "v0.1.0": {
    "introduction": {
      "codeName": "初心",
      "releaseDate": "2025-12-01",
      "upgradePosition": "这是一款专注于管理和利用AI记忆的工具，支持RAG和知识图谱两种主流存储方式，旨在为AI应用提供持久化、结构化的\"记忆\"能力。",
      "coreUpgrades": [
        "1. 记忆空间：用户可以创建独立的空间来隔离不同记忆，并灵活选择存储方式。",
        "2. 记忆配置：简化了配置流程，内置自动提取关键信息的\"记忆萃取\"和管理生命周期的\"遗忘\"引擎。",
        "3. 知识检索：提供语义、分词和混合三种检索模式，并支持多种参数微调和结果重排序，以提升召回效果。",
        "4. 全局管理：支持统一设置默认检索参数，并可一键应用到所有知识库。",
        "5. 测试与调试：内置\"召回测试\"功能，方便用户实时验证检索效果并调整参数，支持通过分享码与他人协作。",
        "6. 记忆洞察：可查看详细的对话记录、用户画像和分析报告，帮助理解AI的\"记忆\"内容。",
        "7. 集成与管理：提供API Key用于系统集成，并包含基本的用户管理功能。",
        "8. 界面与体验：采用现代化的卡片式布局和渐变色设计，注重交互的流畅性和视觉美感。",
        "9. 起步与使用：文档中提供了清晰的基础使用流程，引导用户从创建空间、配置记忆到测试检索快速上手。",
        "10. 版本说明与限制: 记忆熊 v0.1.0 版本\"初心\"囊括智能记忆管理的核心思路和基础能力，为后续开发奠定了基础。",
        "<br>",
        "文档资源：用户手册、API文档、FAQ",
        "问题反馈：GitHub Issues、邮件支持",
        "致谢：感谢所有参与测试和提供反馈的用户！"
      ]
    },
    "introduction_en": {
      "codeName": "Original Intent",
      "releaseDate": "2025-12-01",
      "upgradePosition": "A tool focused on managing and utilizing AI memory, supporting both RAG and knowledge graph storage methods, aiming to provide persistent and structured 'memory' capabilities for AI applications.",
      "coreUpgrades": [
        "1. Memory Space: Users can create independent spaces to isolate different memories and flexibly choose storage methods.",
        "2. Memory Configuration: Simplified configuration process with built-in 'memory extraction' for automatic key information extraction and 'forgetting' engine for lifecycle management.",
        "3. Knowledge Retrieval: Provides semantic, tokenization, and hybrid retrieval modes with various parameter tuning and result reranking to improve recall.",
        "4. Global Management: Supports unified default retrieval parameter settings with one-click application to all knowledge bases.",
        "5. Testing & Debugging: Built-in 'recall testing' for real-time verification of retrieval effects and parameter adjustment, with sharing code support for collaboration.",
        "6. Memory Insights: View detailed conversation records, user profiles, and analysis reports to understand AI 'memory' content.",
        "7. Integration & Management: Provides API Key for system integration with basic user management features.",
        "8. Interface & Experience: Modern card-based layout with gradient design, focusing on interaction fluidity and visual aesthetics.",
        "9. Getting Started: Documentation provides clear basic usage flow, guiding users from creating spaces, configuring memory to testing retrieval.",
        "10. Version Notes: MemoryBear v0.1.0 'Original Intent' encompasses core concepts and basic capabilities of intelligent memory management, laying foundation for future development.",
        "<br>",
        "Documentation: User Manual, API Documentation, FAQ",
        "Feedback: GitHub Issues, Email Support",
        "Acknowledgments: Thanks to all users who participated in testing and provided feedback!"
      ]
    }
  }
}