[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"product:semantica":3},{"id":4,"name":5,"slug":6,"slogan":7,"description":8,"organization":5,"images":9,"avatar_url":10,"image_url":11,"website_url":12,"access_links":13,"status":18,"build_by":19,"categories":20,"stats":30,"upload_relationship":36,"create_at":37,"update_at":38,"owner_id":33,"upload_user_id":39,"reviewer_id":40,"video_info":41,"prerelease_cover_url":42,"scheduled_publish_at":41,"user_scheduled":43,"emoji_reacts":44,"tag":46,"pass_review":41,"user_star":43},3385,"Semantica","semantica","增加 AI 结构化上下文的开源智能决策框架","{\"type\": \"doc\", \"content\": [{\"type\": \"paragraph\", \"content\": [{\"text\": \"Semantica 是一个开源的 Python AI 上下文图与决策智能框架，主要面向 AI Agent、GraphRAG 和企业级 LLM 应用开发者。它解决普通向量检索或对话记忆难以表达实体关系、时间变化、因果链和决策依据的问题，为 AI 应用增加结构化上下文、可解释推理和完整审计记录。项目支持 Python 3.8+，采用 MIT 许可证，可免费使用。\", \"type\": \"text\"}]}, {\"type\": \"paragraph\", \"content\": [{\"text\": \"开发者可以通过 pip 安装 Semantica，创建上下文图并加入实体、关系、来源和决策记录，再将其接入 LangGraph、CrewAI、LlamaIndex、Claude Code 等 AI 工具。相比只负责流程编排或文档检索的框架，Semantica 更强调让 AI 的记忆、推理过程和最终决策能够被追踪、查询与复核。\", \"type\": \"text\"}, {\"text\": \"能力说明\", \"type\": \"text\", \"marks\": [{\"type\": \"link\", \"attrs\": {\"rel\": \"noopener noreferrer nofollow\", \"href\": \"https:\u002F\u002Fgetsemantica.ai\u002Fcapabilities\", \"class\": null, \"target\": \"_blank\"}}]}]}, {\"type\": \"paragraph\", \"content\": [{\"text\": \"核心功能\", \"type\": \"text\", \"marks\": [{\"type\": \"bold\"}]}]}, {\"type\": \"paragraph\", \"content\": [{\"text\": \"上下文图与结构化记忆：\", \"type\": \"text\", \"marks\": [{\"type\": \"bold\"}]}, {\"text\": \"将实体、关系、属性和时间有效期保存为可查询的图结构，帮助 Agent 理解知识之间的关联，而不只是匹配相似文本。\", \"type\": \"text\"}]}, {\"type\": \"paragraph\", \"content\": [{\"text\": \"决策追踪与因果推理：\", \"type\": \"text\", \"marks\": [{\"type\": \"bold\"}]}, {\"text\": \"记录 AI 决策的输入、理由、结果和置信度，并追踪因果链、历史先例及后续影响，方便解释“为什么这样决定”。\", \"type\": \"text\"}]}, {\"type\": \"paragraph\", \"content\": [{\"text\": \"知识图谱与 GraphRAG：\", \"type\": \"text\", \"marks\": [{\"type\": \"bold\"}]}, {\"text\": \"从文档或业务数据构建知识图谱，支持路径查询、社区发现、中心性分析和链接预测，也可与向量数据库结合进行混合检索。\", \"type\": \"text\"}]}, {\"type\": \"paragraph\", \"content\": [{\"text\": \"来源追踪与审计导出：\", \"type\": \"text\", \"marks\": [{\"type\": \"bold\"}]}, {\"text\": \"为实体、关系和结论记录来源、页面及置信度，并支持以 W3C PROV-O、JSON-LD、CSV 等格式导出审计信息。\", \"type\": \"text\"}]}, {\"type\": \"paragraph\", \"content\": [{\"text\": \"时间与规则推理：\", \"type\": \"text\", \"marks\": [{\"type\": \"bold\"}]}, {\"text\": \"查询知识图谱在过去某个时间点的状态，结合规则引擎、Datalog、SPARQL 或政策检查，对合规和业务判断进行验证。\", \"type\": \"text\"}]}, {\"type\": \"paragraph\", \"content\": [{\"text\": \"多框架与数据源接入：\", \"type\": \"text\", \"marks\": [{\"type\": \"bold\"}]}, {\"text\": \"支持 LangGraph、CrewAI、LlamaIndex、AutoGen 等 Agent 框架，并可连接 Snowflake、Databricks、Neo4j、Qdrant、Pinecone 等数据与存储系统。\", \"type\": \"text\"}]}, {\"type\": \"paragraph\", \"content\": [{\"text\": \"适合谁\", \"type\": \"text\", \"marks\": [{\"type\": \"bold\"}]}]}, {\"type\": \"bulletList\", \"content\": [{\"type\": \"listItem\", \"content\": [{\"type\": \"paragraph\", \"content\": [{\"text\": \"需要为 AI Agent 增加长期记忆和结构化上下文的开发者。\", \"type\": \"text\"}]}]}, {\"type\": \"listItem\", \"content\": [{\"type\": \"paragraph\", \"content\": [{\"text\": \"正在构建 GraphRAG、知识图谱或企业搜索系统的团队。\", \"type\": \"text\"}]}]}, {\"type\": \"listItem\", \"content\": [{\"type\": \"paragraph\", \"content\": [{\"text\": \"需要解释 AI 决策过程、保留来源和审计记录的金融、医疗、法律等场景。\", \"type\": \"text\"}]}]}, {\"type\": \"listItem\", \"content\": [{\"type\": \"paragraph\", \"content\": [{\"text\": \"使用 LangGraph、CrewAI、LlamaIndex 等框架开发智能体应用的工程师。\", \"type\": \"text\"}]}]}]}, {\"type\": \"paragraph\", \"content\": [{\"text\": \"使用方式\", \"type\": \"text\", \"marks\": [{\"type\": \"bold\"}]}]}, {\"type\": \"orderedList\", \"attrs\": {\"type\": null, \"start\": 1}, \"content\": [{\"type\": \"listItem\", \"content\": [{\"type\": \"paragraph\", \"content\": [{\"text\": \"在 Python 3.8+ 环境中执行 \", \"type\": \"text\"}, {\"text\": \"pip install semantica\", \"type\": \"text\", \"marks\": [{\"type\": \"code\"}]}, {\"text\": \"。\", \"type\": \"text\"}]}]}, {\"type\": \"listItem\", \"content\": [{\"type\": \"paragraph\", \"content\": [{\"text\": \"创建 \", \"type\": \"text\"}, {\"text\": \"ContextGraph\", \"type\": \"text\", \"marks\": [{\"type\": \"code\"}]}, {\"text\": \"，添加实体、关系及其时间和来源信息。\", \"type\": \"text\"}]}]}, {\"type\": \"listItem\", \"content\": [{\"type\": \"paragraph\", \"content\": [{\"text\": \"导入文档、数据库或企业数据，构建知识图谱和结构化上下文。\", \"type\": \"text\"}]}]}, {\"type\": \"listItem\", \"content\": [{\"type\": \"paragraph\", \"content\": [{\"text\": \"使用决策记录、因果链、规则检查和历史查询分析 AI 的推理过程。\", \"type\": \"text\"}]}]}, {\"type\": \"listItem\", \"content\": [{\"type\": \"paragraph\", \"content\": [{\"text\": \"将 Semantica 接入现有 Agent、向量数据库、MCP 服务或 REST 应用中。\", \"type\": \"text\"}]}]}]}, {\"type\": 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