演講投影片：大型語言模型在工業領域的潛力 / Slide: The Potential of Large Language Models in Industrial Fields

大型語言模型(Large Language Model)成為AI浪潮之後下一個新的寵兒，它彷彿真人般的對談和創造力的發想對研究和教育上帶來了無數啟發。但是大型語言模型在要求精確的工業領域裡面，究竟可以扮演什麼角色呢？本次演講先講述工業5.0發展中對於大型語言模型的需求，再來講述工業領域應用大型語言模型的實例，最後介紹大型語言模型和檢索生成增強的相關技術作為結尾。如果你也想在產業應用大型語言模型的話，不妨先看看這份投影片，瞭解一下現況吧。

Large Language Models (LLMs) have become the next big thing in the wake of the AI wave, offering human-like conversation and creative brainstorming that have inspired countless research and educational endeavors.  But what role can LLMs play in demanding industrial fields that require precision? This presentation will first discuss the need for LLMs in the development of Industry 5.0, followed by examples of LLM applications in industrial settings. Finally, it will conclude with an introduction to related technologies like Retrieval-Augmented Generation. If you are also interested in applying LLMs in industry, take a look at this presentation to understand the current landscape.

Short URL: https://l.pulipuli.info/24/nkust  

RAG簡介投影片：現況、原理、發展 / RAG Introduction Slides: Current Status, Mechanisms, and Development

這份投影片對檢索生成增強(Retrieval-Augmented Generation, RAG)的觀念作一個容易理解的介紹，也是「資訊檢索的AI革新：從資訊檢索到檢索增強生成」這篇的簡化版本。一般說到AI大家都會想到創造力、彷彿真人的表現，但RAG本質上更接近資訊檢索的問題。把它當作資料庫就很容易理解RAG的用途了。

This presentation provides an accessible introduction to the concept of Retrieval Augmented Generation (RAG), and is a simplified version of "The AI Revolution in Information Retrieval: From Information Retrieval to Retrieval-Augmented Generation". When people talk about AI, they often think of creativity and human-like performance, but RAG is essentially closer to the problems of information retrieval. Thinking of it as a database makes it easier to understand the purpose of RAG.

Fixed Short URL: https://l.pulipuli.info/24/nccu/rag 

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

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。

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

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.

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

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。

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?

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。

雜談：什麼是RAG知識庫的必備條件？ / TALK: What Are the Prerequisites for a RAG Knowledge Base?

在陸陸續續地升級了Dify的各個元件後，赫然發現，目前作為RAG的知識庫，仍有許多不足之處。到底RAG知識庫應該具備什麼功能？這篇就來聊聊我的想法。

成為國家認可的圖書館員吧！一個人備考也能開的AI讀書會 / Become a Nationally Certified Librarian! An AI Study Group for Solo Learners

唸資圖系或圖資系，未來可以做什麼工作？雖然大家的說法眾說紛紜，不過「圖書館員」仍然是此領域人才訓練的主要目標。在台灣要成為圖書館員的各種途徑中，透過國家考試成為具有「圖書資訊管理」專業的公務人員，又是許多人理想中的圖書館員職位。那要怎麼透過公職考試考上館員？要讀什麼書？要怎麼準備？我們就用這份講座來跟大家說明吧！

What kind of jobs can you get with a degree in Library and Information Science? While there are many different opinions, "librarian" remains the primary career goal for individuals trained in this field. In Taiwan, among the various paths to becoming a librarian, securing a civil servant position specializing in "Library and Information Management" through national examinations is the ideal librarian role for many. So, how can one pass the civil service exam to become a librarian? What should one study? How should one prepare? Let's address these questions in this lecture.

Fixed Short URL: https://l.pulipuli.info/24/dils/moe 

如何用Felo AI搜尋特定網站的內容 / How to Search Specific Website Content Using Felo AI

用關鍵字檢索站內資訊時，你總是看到大量分散的網頁，不知道怎麽整合最需要的資料嗎？這篇教你用Felo Search來建立站內的「問答機器人」！

Are you tired of keyword searches returning tons of scattered web pages, making it difficult to consolidate the information you need? This post will teach you how to use Felo Search to create an internal "Q&A chatbot"!

檢索擴增生成(RAG)技術的發展現況與文本知識提取的應用 / Retrieval Augmented Generation (RAG) and Its Application in Text Knowledge Extraction

檢索擴增生成(RAG)現在已經是大型語言模型領域裡的新寵兒，而且是屬於易懂難經的技術。這篇文章內容是我在計劃書裡對於檢索擴增生成(RAG)技術的內涵和相關應用的介紹。在這個部落格也留存一份記錄吧。

Retrieval Augmented Generation (RAG) has become the new darling of the large language model domain, and it's one of those technologies that are easier to understand than to master. This post contains the introduction to RAG's core concepts and applications that I wrote for a proposal. I'm also keeping a copy here on the blog.

雜談：是時候該來處理一下Dify的問題了 / TALK: It's Time to Address the Issues with Dify

在「自行架設大型語言模式應用程式：Dify」這篇裡面，我用Dify在筆電架設了可客製化、具備RAG的大型語言模型應用程式。但這段期間用下來還是遭遇了很多問題。以下就稍微列舉一下我遭遇的狀況。

資訊檢索的AI革新：從資訊檢索到檢索增強生成 / The AI Revolution in Information Retrieval: From Information Retrieval to Retrieval-Augmented Generation

2024年7月我在中華民國圖書館學會於政治大學舉行的講習課程中，演講「資訊檢索的AI革新：從資訊檢索到檢索增強生成」。以下是投影片跟相關內容的連結。

In July 2024, I will give a lecture titled "AI Innovation in Information Retrieval: From Information Retrieval to Enhanced Generative Retrieval" during the seminar course held by the Library Association of the Republic of China at National Chengchi University. Below is the link to the slides and related content.

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