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一、目前完全依赖于Postgresql的存储方案可能存在性能上的问题
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https://github.com/HKUDS/LightRAG/issues/1277
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PostgreSQL AGE performance issues have been resolved since v1.3.3. You can pull the latest version from the main branch and give it a try.
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However, for large-scale deployments, however, we recommend using Neo4j.
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翻译:PostgreSQL AGE的性能问题已经在V1.3.3中解决(我们使用的是V1.3.9>V1.3.3)
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但是,对于大规模的数据,我们推荐使用Neo4j!
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这就意味着,我们需要继续研究向Neo4j进行迁移的问题。
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https://github.com/HKUDS/LightRAG/issues/1363
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此外,LightRAG 的 PostgreSQL 存储实现目前存在一些尚未解决的性能问题,尤其是在 AGE 图存储方面。我们建议使用 MonogoDB 来处理 KV 和 Vector 数据,并使用 Neo4j 作为图存储解决方案。
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LightRAG 与 Neo4j 社区版无缝运行。
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Lightrag mongo 示例不存储数据 #843
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https://github.com/HKUDS/LightRAG/issues/843
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修复 mongo 文档状态 #1160
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https://github.com/HKUDS/LightRAG/pull/1160
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二、深入研究Postgresql中向量库+图数据库的使用
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1、向量库的使用
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PostgreSQL - pgvector 插件构建向量数据库并进行相似度查询
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https://blog.csdn.net/fenglingguitar/article/details/142357092
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2、Python中使用PostgreSQL和Apache AGE扩展来绘制和显示图表
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https://blog.csdn.net/u014158430/article/details/144942197
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三、深入学习LightRAG的维护
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https://github.com/HKUDS/LightRAG/blob/main/README-zh.md
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(1)编辑实体和关系
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(2)删除功能
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(3)实体合并
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(4) 尝试用python+postgresql+age 绘制出指定主题的知识图谱
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四、需要为文档入库提供两个工具:
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1、音频转文字(视频转文字)
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教程 | 用 openai 开源模型 whisper 实现音频转文字
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https://www.53ai.com/news/OpenSourceLLM/2024071482650.html
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2、PDF转office
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Python简单使用MinerU
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https://blog.csdn.net/make_progress/article/details/145802697
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