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雜談:我可以只要RAG的「R」嗎? / TALK: Can I Just Have the “R” in RAG?

布丁布丁吃布丁

雜談:我可以只要RAG的「R」嗎? / TALK: Can I Just Have the “R” in RAG?

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很多人以為RAG可以取代搜尋引擎,但其實很多人要的功能只有「能用自然語言檢索」而已。

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RAG應用方案:Google NotebookLM / RAG Application Solutions: Google NotebookLM

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RAG應用方案:Google NotebookLM / RAG Application Solutions: Google NotebookLM

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Google NotebookLM已經成為使用RAG的最基本入門產品了。它大幅拉高了RAG的競爭門檻。沒有做的比Google NotebookLM好的話,都沒資格出來賣錢了。這篇是我在介紹Google NotebookLM的投影片,供大家參考。

Google NotebookLM has become the most basic entry-level product for using RAG. It has significantly raised the competitive bar for RAG.  If it's not better than Google NotebookLM, it's not worth selling. These are my slides introducing Google NotebookLM for your reference.

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公視地方新聞資料集 / PTS NEWS Local News Dataset

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公視地方新聞資料集 / PTS NEWS Local News Dataset

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最近我因為研究需求蒐集了公視新聞網地方新聞的一些內容,並把資料整理表格資料集,提供給有需要的人使用。

Recently, for my research, I collected local news content from the PTS News website and organized the data into a tabular dataset, making it available for anyone who needs it.

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只用CPU跑「小型」語言模型可行嗎? / Is Running "Small" Language Models on CPUs Only Feasible?

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只用CPU跑「小型」語言模型可行嗎? / Is Running "Small" Language Models on CPUs Only Feasible?

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很多人都說跑大型語言模型需要很高級的GPU,其實相對於門檻較高的大型語言模型,小型語言模型也一直在如火如荼地發展。最近我嘗試用12核CPU跟32GB的RAM來跑Gemma2:2B,意外地很順利呢。

Many people say that running large language models requires high-end GPUs. However, relative to the higher barrier to entry of large language models, small language models have also been developing rapidly. Recently, I experimented with running Gemma2:2B using a 12-core CPU and 32GB of RAM, and it went surprisingly smoothly.

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RAG應用方案:SeaSalt.AI的SeaMeet、SeaChat / RAG Application Solutions: SeaSalt.AI's SeaMeet and SeaChat

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RAG應用方案:SeaSalt.AI的SeaMeet、SeaChat / RAG Application Solutions: SeaSalt.AI's SeaMeet and SeaChat

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我最近試用了SeaSalt公司底下的SeaMeet跟SeaChat,前者可以自動記錄並摘要 Google Meet會議內容,後者則是簡單建立聊天機器人。這篇就是我報告投影片的彙整,供大家參考。

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量表Cronbach's Alpha內部信度分析計算器:以Colab實作 / Cronbach's Alpha Internal Consistency Reliability Calculator: Implementation Using Colab

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量表Cronbach's Alpha內部信度分析計算器:以Colab實作 / Cronbach's Alpha Internal Consistency Reliability Calculator: Implementation Using Colab

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我開發了一份Colab筆記本「內部一致性信度分析:Cronbach's Alpha計算器」,方便大家分析量表的內部一致信信度Cronbach's Alpha。這份筆記本能夠分析整份量表的Cronbach's Alpha,也可以各別計算量表中各個構面(subscaler)的Alpha係數。同時計算器還會用逐一移除題目的方式,藉由指出移除後能夠提高Cronbach's Alpha的題目,讓研究者仔細檢視量表的設計是否有改善空間。

I developed a Colab notebook titled "Internal Consistency Reliability Analysis: Cronbach's Alpha Calculator" to facilitate the analysis of a scale's internal consistency reliability using Cronbach's alpha. This notebook can analyze the Cronbach's alpha for the entire scale, as well as calculate the alpha coefficient for individual subscales. The calculator also performs item-level analysis, identifying items whose removal would increase Cronbach's alpha, allowing researchers to carefully examine and potentially improve the scale's design.

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雜談:怎麽讓AI能根據我的雲端硬碟回答問題 / TALK: How Can I Enable AI to Answer Questions Based on My Cloud Storage?

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雜談:怎麽讓AI能根據我的雲端硬碟回答問題 / TALK: How Can I Enable AI to Answer Questions Based on My Cloud Storage?

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Nextcloud的AI應用程式不能處理中文,所以我自己用Langflow整合到Nextcloud,讓大型語言模型能夠根據我在雲端硬碟裡面的內容來回答問題。這篇就講一下大致上的做法。

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