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add dof of MOne #236
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利用 MatrixOne 自带的向量化能力,我们推出了 AI 智能文档问答小助手 **MOne**。用户在云平台通过以文字的方式对小助手提问,可快速获取用户想了解的关于 MatrixOne 的信息,免去搜查资料的过程,这为用户提供了更加智能化、个性化的服务,使用户的体验感得到极大的提升。 | ||
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## 实现 | ||
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AI 框架我们采用的是 LangChain,Embedding 使用了 OpenAI Embedding 模型,OpenAI LLM 大模型使用的是 GPT-3.5-Turbo。OpenAI embedding 调用 MatrixOne 接口,基于向量化能力完成向量存储和相似度搜索,收集相似度最高的文本,调用 GPT 3.5 模型提炼优化文本,最终得到最为贴近的答案。 | ||
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实现的过程包括两部分: | ||
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- 加载文件 -> 读取文本 -> 文本分割 -> 文本向量化 -> 存储向量在 Matrixone 中 | ||
- 用户提问 -> 问题向量化 -> 在文本向量中匹配出与问题向量相似度最高的 -> 匹配出的文本作为上下文和问题一起添加到 prompt 中 -> 提交给 LLM 生成回答 | ||
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整体流程如下图所示: | ||
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![](https://community-shared-data-1308875761.cos.ap-beijing.myqcloud.com/artwork/mocdocs/overview/mone-1.png) | ||
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## 如何使用 MOne | ||
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登录实例管理平台或数据库管理平台之后,您可在右下角点击 MOne 图标唤醒它。 | ||
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![](https://community-shared-data-1308875761.cos.ap-beijing.myqcloud.com/artwork/mocdocs/overview/mone-2.png) | ||
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进入提问页面后,您可以点击具体某个推荐问题直接进入应用对话交互,也可提问其它您想了解的与 MatrixOne 有关的问题。我们还提供了答案一键复制的功能,当您获取到您想要的信息,只需点击答案右下角的复制图标即可进行一键复制。如果您对 MOne 助手提供的答案感到满意,也可以给我们点一个赞哦。 | ||
!!! note | ||
MOne 助手只能回答与 MatrixOne 相关的产品问题,且问题字数不能超过 200。 |
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“OpenAI embedding 调用 MatrixOne 接口” 这句话写的不对。是MOC调用的OpenAI embedding接口。