✨ Inspired by “Knowledge Graphs for RAG” by DeepLearningAI. ✨
✨ Collaborate with NTU’s Civil Engineering AI Ph.D. Colleague. ✨
What are knowledge graphs?Knowledge Graphs are comprised of nodes and edges, which respectively represent entities or concepts, as well as the relationships, facts, attributes, or categories between them.This graph describes data in the form of nodes, as well as the associations (relationships) between the nodes.

• Nodes are data records
• Both nodes and relationships can have properties.
• Nodes can be given labels to group them together.
• Relationships always have a type and a direction.
• A knowledge graph is a database that stores information in nodes and relationships.

Knowledge Graphs’ main components
A knowledge graph is a directed labeled graph that comprises three elements:

近期大型語言模型(Large Language Models, LLMs)崛起，其自然語言處理能力在理解、翻譯、總結、擬人口吻方面有著令人耳目一新的表現，使用者主要透過提問(Query)或下達指令方式，與大型語言模型交流，不過在問答過程中，使用者或許會發現模型所給予的答覆可能存在資料偏誤、時效性、資料幻覺、字數限制等問題，以至於獲得答非所問的結果。為了解決大型語言模型在理解上的缺陷，檢索增強生成(Retrieval-Augmented Generation, RAG)的引入，可使模型從外部資料庫提取特定專業知識，利用額外知識生成合適的回覆或完成特定任務。
本文將主要說明 RAG 之原理及其重要性、作業流程，並簡介 “Building and Evaluating Advanced RAG” 課程中所提及之 RAG 的評估指標(Triad metric)，試比較 LlamaIndex 與 LangChain 之差異，最後說明 RAG 的兩個衍生模型 — — Sentence window retrieval 與 Auto-merging retrieval。
✨ Inspired by “Building and Evaluating Advanced RAG” of DeepLearningAI.
✨ 團隊成員來自國震中心與台大土木合設AI研究中心(NCREE — NTUCE Joint Artificial Intelligence Research Center)

RAG 基礎作業流程下圖說明 RAG 介入 LLMs 前後的流程差異：